Learning and Behavior

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This book reviews how people and animals learn and how their behaviors are changed as a
result of learning. It describes the most important principles, theories, controversies, and
experiments that pertain to learning and behavior that are applicable to diverse species and
different learning situations. Both classic studies and recent trends and developments are
explored, providing a comprehensive survey of the field. Although the behavioral approach
is emphasized, many cognitive theories are covered as well, along with a chapter on compara-
tive cognition. Real-world examples and analogies make the concepts and theories more
concrete and relevant to students. In addition, most chapters provide examples of how the
principles covered have been applied in behavior modification and therapy. Thoroughly
updated, each chapter features many new studies and references that reflect recent develop-
ments in the field. Learning objectives, bold-faced key terms, practice quizzes, a chapter
summary, review questions, and a glossary are included.

The volume is intended for undergraduate or graduate courses in psychology of learning,
(human) learning, introduction to learning, learning processes, animal behavior, (principles
of) learning and behavior, conditioning and learning, learning and motivation, experimental
analysis of behavior, behaviorism, and behavior analysis.

Highlights of the new edition include:

• A new text design with more illustrations, photos, and tables;
• In the Media , Spotlight on Research , and Applying the Research boxes that highlight recent

applications of learning principles in psychology, education, sports, and the workplace;
• Discussions of recent developments in the growing field of neuroscience;
• Coverage of various theoretical perspectives to the study of learning—behavioral,

cognitive, and physiological;
• Expanded coverage of emerging topics such as the behavioral economics of addic-

tions, disordered gambling, and impulsivity;
• New examples, references, and research studies to ensure students are introduced to

the latest developments in the field;
• A website at where instructors will find a test bank,

PowerPoint slides, and Internet links. Students will find practice quizzes, definitions of
key terms, chapter outlines, and Internet sources for additional information.

James E. Mazur is Emeritus Professor of Psychology at Southern Connecticut State
University, USA.

Learning and Behavior

Eighth Edition

James E. Mazur

Eighth edition published 2017
by Routledge
711 Third Avenue, New York, NY 10017

and by Routledge
2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN

Routledge is an imprint of the Taylor & Francis Group, an informa business

© 2017 Taylor & Francis

The right of James E. Mazur to be identified as the author of this work has been
asserted by him in accordance with sections 77 and 78 of the Copyright, Designs
and Patents Act 1988.

All rights reserved. No part of this book may be reprinted or reproduced or
utilised in any form or by any electronic, mechanical, or other means, now
known or hereafter invented, including photocopying and recording, or in any
information storage or retrieval system, without permission in writing from the

Trademark notice: Product or corporate names may be trademarks or registered
trademarks, and are used only for identification and explanation without intent to

First edition published 1990 by Prentice-Hall
Seventh edition published 2012 by Taylor & Francis

Library of Congress Cataloging in Publication Data
Names: Mazur, James E., 1951– author.
Title: Learning and behavior / James E. Mazur.
Description: Eighth edition. | New York, NY : Routledge, 2017. |
Includes bibliographical references and index.
Identifiers: LCCN 2016026434 | ISBN 9781138689947 (hardback : alk. paper)
Subjects: LCSH: Learning, Psychology of. | Conditioned response. |
Behavior modification. | Psychology, Comparative.
Classification: LCC BF318 .M38 2017 | DDC 153.1/5—dc23
LC record available at

ISBN: 978-1-138-68994-7 (hbk)
ISBN: 978-1-315-45028-5 (ebk)

Typeset in Bembo and Helvetica Neue
by Apex CoVantage, LLC

In memory of my parents, Ann and Lou Mazur, who responded to
my early interests in science with encouragement, understanding,
and patience.

Preface xvii
About the Author xix

1 History, Background, and Basic Concepts 1

2 Innate Behavior Patterns and Habituation 29

3 Basic Principles of Classical Conditioning 56

4 Theories and Research on Classical Conditioning 84

5 Basic Principles of Operant Conditioning 113

6 Reinforcement Schedules: Experimental Analyses and Applications 142

7 Avoidance and Punishment 172

8 Theories and Research on Operant Conditioning 201

9 Stimulus Control and Concept Learning 231

10 Comparative Cognition 261

11 Observational Learning and Motor Skills 293

12 Choice 328

Glossary 359
Author Index 374
Subject Index 387


Preface xvii
About the Author xix

1 History, Background, and Basic Concepts 1

The Search for General Principles of Learning 2
The Associationists 4

Aristotle 4
Box 1.1 Applying the Research. A Demonstration of Free Association 5

The British Associationists: Simple and Complex Ideas 5
Ebbinghaus’s Experiments on Memory 8
The Influence of the Associationists and Ebbinghaus 10

Behavioral and Cognitive Approaches to Learning 11
The Use of Animal Subjects 11
Ethical Issues and Animal Research 12
The Emphasis on External Events 13

Brain and Behavior 16
The Basic Characteristics of Neurons 16
Simple Sensations 17
Feature Detectors 19
The Neuroscience of Learning 20

Chemical Changes 20
Growth of New Synapses 21
Growth of New Neurons 22
Where Are “Complex Ideas” Stored in the Brain? 22

Summary 24
Review Questions 25
References 26

Detailed Contents


2 Innate Behavior Patterns and Habituation 29

Characteristics of Goal-Directed Systems 30
Reflexes 31
Tropisms 32

Kineses 32
Taxes 33

Sequences of Behavior 34
Fixed Action Patterns 34
Reaction Chains 35

Innate Human Abilities and Predispositions 37
Box 2.1 Spotlight on Research. We Have a Lot in Common: Human Universals 39
Habituation 40

General Principles of Habituation 42
Box 2.2 Spotlight on Research. Habituation and Psychological Functioning 44

Neural Mechanisms of Habituation 44
Habituation in Emotional Responses: The Opponent-Process Theory 47

The Temporal Pattern of an Emotional Response 47
The a-Process and b-Process 49
The Effects of Repeated Stimulation 50
Other Emotional Reactions 50
A Brief Evaluation 51

Summary 52
Review Questions 53
References 53

3 Basic Principles of Classical Conditioning 56

Pavlov’s Discovery and Its Impact 56
The Standard Paradigm of Classical Conditioning 57
The Variety of Conditioned Responses 58

Eyeblink Conditioning 58
Conditioned Suppression 59
The Skin Conductance Response 59
Taste-Aversion Learning 60

Pavlov’s Stimulus Substitution Theory 60
What Is Learned in Classical Conditioning? 61

Basic Conditioning Phenomena 63
Acquisition 63
Extinction 64
Spontaneous Recovery, Disinhibition, and Rapid Reacquisition 64
Conditioned Inhibition 66
Generalization and Discrimination 67

Box 3.1 Spotlight on Research. Classical Conditioning and the Immune System 68
The Importance of Timing in Classical Conditioning 69

CS–US Correlations 71
Second-Order Conditioning 72
Classical Conditioning Outside the Laboratory 73

Classical Conditioning and Emotional Responses 73
Applications in Behavior Therapy 74


Systematic Desensitization for Phobias 74
Box 3.2 Applying the Research. Virtual Reality Therapy 75

Aversive Counterconditioning 76
Treatment of Nocturnal Enuresis 78
Summary of the Classical Conditioning Therapies 78

Summary 79
Review Questions 80
References 80

4 Theories and Research on Classical Conditioning 84

Research and Theories on Associative Learning 85
The Blocking Effect 85
The Rescorla–Wagner Model 86

Acquisition 87
Blocking 88
Extinction and Conditioned Inhibition 88
Overshadowing 89
The Overexpectation Effect 89
Summary 91

Theories of Attention 91
Comparator Theories of Conditioning 92

Box 4.1 In the Media. Classical Conditioning in Advertising 93
Neuroscience and Classical Conditioning 94
Biological Constraints on Classical Conditioning 97

The Contiguity Principle and Taste-Aversion Learning 98
Biological Preparedness in Taste-Aversion Learning 98

Box 4.2 Spotlight on Research. Biological Preparedness in Human Learning 100
Biological Constraints and the General-Principle Approach 101

The Form of the Conditioned Response 103
Drug Tolerance and Drug Cravings as Conditioned Responses 103
Conditioned Opponent Theories 106

Summary 107
Review Questions 108
References 108

5 Basic Principles of Operant Conditioning 113

The Law of Effect 114
Thorndike’s Experiments 114
Guthrie and Horton: Evidence for a Mechanical Strengthening Process 115
Superstitious Behaviors 117

Box 5.1 In the Media. Superstitious Behaviors in Sports 119
The Procedure of Shaping, or Successive Approximations 120

Shaping Lever Pressing in a Rat 120
Shaping Behaviors in the Classroom 122
Shaping as a Tool in Behavior Modification 122

The Research of B. F. Skinner 124
The Free Operant 124
The Three-Term Contingency 126


Basic Principles of Operant Conditioning 126
Conditioned Reinforcement 127
Response Chains 128

Box 5.2 Applying the Research. Teaching Response Chains 130
Biological Constraints on Operant Conditioning 132

Instinctive Drift 132
Autoshaping 133

Autoshaping as Superstitious Behavior 133
Autoshaping as Classical Conditioning 134
Autoshaping as the Intrusion of Instinctive Behavior Patterns 135
Summary 136

Reconciling Reinforcement Theory and Biological Constraints 136
Summary 138
Review Questions 139
References 139

6 Reinforcement Schedules: Experimental Analyses and Applications 142

Plotting Moment-to-Moment Behavior: The Cumulative Recorder 143
The Four Simple Reinforcement Schedules 144

Fixed Ratio 144
Variable Ratio 146
Fixed Interval 147
Variable Interval 149

Box 6.1 In the Media. The Scalloped Cumulative Record of the
United States Congress 149

Extinction and the Four Simple Schedules 151
Other Reinforcement Schedules 152

Factors Affecting Performance on Reinforcement Schedules 152
Behavioral Momentum 153
Contingency-Shaped Versus Rule-Governed Behaviors 154

The Experimental Analysis of Reinforcement Schedules 155
Cause of the FR Postreinforcement Pause 156
Comparisons of VR and VI Response Rates 157

Applications of Operant Conditioning 159
Teaching Language to Children With Autism 159
Token Reinforcement 161

Box 6.2 Applying the Research. Organizational Behavior Management 164
Behavior Therapy for Marital Problems 165
Conclusions 166

Summary 167
Review Questions 167
References 168

7 Avoidance and Punishment 172

Escape and Avoidance 174
A Representative Experiment 174
Two-Factor Theory 175


One-Factor Theory 176
Cognitive Theory 177
Biological Constraints in Avoidance Learning 178
Conclusions About the Theories of Avoidance 180

Box 7.1 Applying the Research. The Procedure of Response Blocking (Flooding) 180
Learned Helplessness 181
Research on Punishment 183

Is Punishment the Opposite of Reinforcement? 183
Factors Influencing the Effectiveness of Punishment 184

Manner of Introduction 184
Immediacy of Punishment 185
Schedule of Punishment 185
Motivation to Respond 185
Reinforcement of Alternative Behaviors 186
Punishment as Discriminative Stimulus 187

Disadvantages of Using Punishment 187
Negative Punishment (Omission) 188

Behavior Decelerators in Behavior Therapy 189
Punishment 189

Box 7.2 In the Media. Punishment Can Be Effective, but Should
It Be Used in Therapy? 190

Negative Punishment: Response Cost and Time-Out 191
Overcorrection 192
Extinction 193
Escape Extinction 193
Response Blocking 194
Differential Reinforcement of Alternative Behavior 195
Stimulus Satiation 195

Summary 196
Review Questions 197
References 197

8 Theories and Research on Operant Conditioning 201

The Role of the Response 202
The Role of the Reinforcer 203

Is Reinforcement Necessary for Operant Conditioning? 203
Can Reinforcement Control Visceral Responses? 204
Biofeedback 206

Box 8.1 Applying the Research. Neurofeedback: Controlling Your Brain Waves 207
How Can We Predict What Will Be a Reinforcer? 209

Need Reduction 210
Drive Reduction 210
Trans-Situationality 211
Premack’s Principle 212
Premack’s Principle in Behavior Modification 215
Response Deprivation Theory 216
The Functional Analysis of Behaviors and Reinforcers 218


Behavioral Economics 220
Optimization: Theory and Research 220
Optimization and Behavioral Ecology 221
Elasticity and Inelasticity of Demand 222

Box 8.2 Applying the Research. Behavioral Economics and Drug Abuse 224
Other Applications 225

Summary 226
Review Questions 227
References 227

9 Stimulus Control and Concept Learning 231

Generalization Gradients 232
Measuring Generalization Gradients 232
What Causes Generalization Gradients? 232

How Experience Affects the Shape of Generalization Gradients 233
How Sensory Deprivation Affects the Shape of Generalization Gradients 235

Is Stimulus Control Absolute or Relational? 236
Transposition and Peak Shift 237
Spence’s Theory of Excitatory and Inhibitory Gradients 239
The Intermediate-Size Problem 240
Other Data, and Some Conclusions 240

Behavioral Contrast 242
Errorless Discrimination Learning 244
Box 9.1 Applying the Research. Errorless Learning in Education 246
Concept Learning 247

The Structure of Natural Categories 248
Animal Studies on Natural Concept Learning 249

Box 9.2 Spotlight on Research. Stimulus Equivalence Training 252
Stimulus Control in Behavior Modification 253

Study Habits and Health Habits 253
Insomnia 255

Summary 256
Review Questions 257
References 257

10 Comparative Cognition 261

Memory and Rehearsal 262
Short-Term Memory, or Working Memory 262

Delayed Matching to Sample 263
The Radial-Arm Maze 266

Rehearsal 267
Maintenance Rehearsal 268
Associative Rehearsal 269

Long-Term Memory, Retrieval, and Forgetting 270
Box 10.1 Spotlight on Research. Chunking of Information by Animals 272
Timing and Counting 274

Experiments on an “Internal Clock” 274
Counting 276


Animal Language 278
Research With Chimpanzees 278
Research With Other Species 280
Some Conclusions 281

Reasoning by Animals 282
Object Permanence 282
Analogies 283
Transitive Inference 284
Tool Use and Manufacture 285

Box 10.2 Spotlight on Research. Metacognition: Do Animals
Know What They Know? 286
Conclusions 287
Summary 287
Review Questions 288
References 288

11 Observational Learning and Motor Skills 293

Theories of Imitation 294
Imitation as an Instinct 294
Imitation as an Operant Response 296
Imitation as a Generalized Operant Response 296
Bandura’s Theory of Imitation 298
Generalized Imitation Versus Bandura’s Theory 300
Mirror Neurons and Imitation 300

Effects of the Mass Media 302
Box 11.1 In the Media. The Effects of Video Games and Popular Music 303
Modeling in Behavior Therapy 305

Facilitation of Low-Probability Behaviors 305
Acquisition of New Behaviors 305
Elimination of Fears and Unwanted Behaviors 306
Video Self-Modeling 307

Learning Motor Skills 308
Variables Affecting Motor Learning and Performance 308

Reinforcement and Knowledge of Results 308
Knowledge of Performance 309
Distribution of Practice 311
Observational Learning of Motor Skills 312
Transfer From Previous Training 312

Theories of Motor-Skill Learning 313
Adams’s Two-Stage Theory 313
Schmidt’s Schema Theory 316

Box 11.2 Applying the Research. What Is the Best Way to Practice? 318
Learning Movement Sequences 319

The Response Chain Approach 319
Motor Programs 319

Summary 322
Review Questions 323
References 323


12 Choice 328

The Matching Law 329
Herrnstein’s Experiment 329
Other Experiments on Matching 330
Deviations From Matching 331
Varying the Quality and Amount of Reinforcement 332

Matching and Reinforcement Relativity 333
Theories of Choice Behavior 334

Matching as an Explanatory Theory 334
Optimization Theory 335
Tests of Optimization Versus Matching 336
Momentary Maximization Theory 337

Box 12.1 Applying the Research. Can You Use a Momentary 338
Self-Control Choices 340
Box 12.2 Spotlight on Research. Measuring Delay Discounting 341

The Ainslie–Rachlin Theory 342
Animal Studies on Self-Control 344
Factors Affecting Self-Control in Children 346
Techniques for Improving Self-Control 347

Other Choice Situations 349
Risk Taking 349
The Tragedy of the Commons 350

Summary 353
Review Questions 354
References 354

Glossary 359
Author Index 374
Subject Index 387

The purpose of this book is to introduce the reader to the branch of psychology that deals
with how people and animals learn and how their behaviors are later changed as a result of
this learning. This is a broad topic, for nearly all of our behaviors are influenced by prior
learning experiences in some way. Because examples of learning and learned behaviors are
so numerous, the goal of most psychologists in this field has been to discover general prin-
ciples that are applicable to many different species and many different learning situations.
What continues to impress and inspire me after many years in this field is that it is indeed
possible to make such general statements about learning and behavior. This book describes
some of the most important principles, theories, controversies, and experiments that have
been produced by this branch of psychology in its first century.

This text is designed to be suitable for introductory or intermediate-level courses in
learning, conditioning, or the experimental analysis of behavior. No prior knowledge of
psychology is assumed, but the reading may be a bit easier for those who have had a course
in introductory psychology. Many of the concepts and theories in this field are fairly abstract,
and to make them more concrete and more relevant, I have included many real-world
examples and analogies.

Roughly speaking, the book proceeds from the simple to the complex, with respect to
both the difficulty of the material and the types of learning that are discussed. Chapter 1
discusses the behavioral approach to learning and contrasts it with the cognitive approach.
It also describes some of the earliest theories about the learning process; then it presents some
basic findings about the neural mechanisms of learning. Chapter 2 discusses innate behaviors
and the simplest type of learning, habituation. Many of the terms and ideas introduced here
reappear in later chapters on classical conditioning, operant conditioning, and motor-skills
learning. The next two chapters deal with classical conditioning. Chapter 3 begins with
basic principles and ends with some therapeutic applications. Chapter 4 describes more
recent theoretical developments and experimental findings in this area.

The next three chapters discuss the various facets of operant conditioning: Chapter 5
covers the basic principles and terminology of positive reinforcement, Chapter 6 covers
schedules of reinforcement and applications, and Chapter 7 covers negative reinforcement
and punishment. Chapters 8 and 9 have a more theoretical orientation. Chapter 8 presents
differing views on such fundamental questions as what constitutes a reinforcer and what
conditions are necessary for learning to occur. Chapter 9 takes a more thorough look at
generalization and discrimination, and it also examines research on concept learning.

Chapter 10 surveys a wide range of findings in the rapidly growing area of comparative
cognition. Chapter 11 discusses two types of learning that are given little or no emphasis in



many texts on learning—observational learning and motor-skills learning. A substantial
portion of human learning involves either observation or the development of new motor
skills. Readers might well be puzzled or disappointed (with some justification) with a text
on learning that includes no mention of these topics. Finally, Chapter 12 presents an over-
view of behavioral research on choice.

This book includes a number of learning aids for students. Each chapter begins with a
list of learning objectives and ends with a summary of the main points covered. Each
chapter also includes practice quizzes and review questions to help students determine if
they are learning and understanding the key points. The book also includes a glossary of
all important terms. The website for this text has a number of additional resources. For
instructors, there is a test bank of multiple-choice and short-essay questions, PowerPoint
slides for use in class, and Internet resources. For students, there are online quizzes for each
chapter, definitions of key terms, chapter outlines, and Internet links related to many of the
topics covered in the text.

New to this eighth edition are boxes in each chapter that highlight topics that should be
of special interest to students. The boxes are focused on three themes: In the Media, covering
topics related to learning and behavior that have been covered by various media sources,
Spotlight on Research, taking a closer look at current research on specific topics, and Applying
the Research, presenting real-world applications of the principles described in the text. This
edition also includes many new figures and illustrations to help students understand and
remember important concepts, principles, experimental procedures, and applications. To
enhance the relevance of this material for today’s students, a number of older and somewhat
technical topics from previous editions have been removed, and there are more examples of
how behavioral and cognitive principles of learning can be observed in people’s everyday
behaviors. Most of the chapters include sections that describe how the theories and prin-
ciples of learning have been used in the applied field of behavior modification.

I owe thanks to many people for the help they have given me as I wrote this book. Many
of my thoughts about learning and about psychology in general were shaped by my discus-
sions with the late Richard Herrnstein—my teacher, advisor, and friend. I am most grateful
to Debra Riegert and Rachel Severinovsky of Taylor and Francis for all the advice and
assistance they provided me throughout the work on this edition. Thanks go to the reviewers
of various editions of this book:

Matthew C. Bell, Mark Branch, Thomas Brown, Maureen Bullock, Gary Brosvic, Valerie
Farmer-Dougan, April Fugett, Adam Goodie, Kenneth P. Hillner, Peter Holland, Ann Kelley,
Melinda Leonard, Kathleen McCartney, Harold L. Miller, Jr., David Mostofsky, Thomas
Moye, Jack Nation, Erin Rasmussen, David Schaal, James R. Sutterer, Edward Wasserman,
Steve Weinert, and Joseph Wister. In addition, I thank Marge Averill, Stan Averill, John Bailey,
Chris Berry, Paul Carroll, David Coe, David Cook, Susan Herrnstein, Margaret Makepeace,
Margaret Nygren, Steven Pratt, and James Roach for their competent and cheerful help on
different editions of this book. Finally, I thank my wife, Laurie Averill, who drew many of
the illustrations and gave me plenty of valuable help on this and previous editions.

J. E. M.

James E. Mazur obtained his B.A. in Psychology from Dartmouth College in 1973 and
his Ph.D. in Experimental Psychology from Harvard University in 1977. He taught at Har-
vard as an assistant professor and associate professor from 1980 to 1988, and since then he
has taught at Southern Connecticut State University, where he was honored with the title
of CSU Professor in 2010. He is now Professor Emeritus and continues to teach part-time.
He has conducted research on operant conditioning and choice for over 40 years. He has
been a reviewer and associate editor for several journals, and he served as editor for the
Journal of the Experimental Analysis of Behavior. He has published numerous journal articles
and chapters on such topics as reinforcement schedules, conditioned reinforcement, self-
control, risk taking, procrastination, and mathematical models of choice.

About the Author

Learning Objectives

After reading this chapter, you should be able to

• describe the early theories of memory proposed by the Associationists and
the early memory studies of Hermann Ebbinghaus

• explain the behavioral and cognitive approaches to studying learning and how
they differ

• explain the advantages and disadvantages of using animals in psychological

• discuss intervening variables and the debate over whether they should be used
in psychology

• explain how our sensory receptors respond to “simple sensations” and how
feature detectors in the visual system respond to more complex patterns

• list three main types of changes that can take place in the brain as a result of
a learning experience, and present evidence for each type

C H A P T E R 1

History, Background,
and Basic Concepts

If you know nothing about the branch of psychology called learning, you may have some
misconceptions about the scope of this field. I can recall browsing through the course catalog
as a college freshman and coming across a course offered by the Department of Psychology
with the succinct title “Learning.” Without bothering to read the course description, I
wondered about the contents of this course. Learning, I reasoned, is primarily the occupation
of students. Would this course teach students better study habits, better reading, and better
note-taking skills? Or did the course examine learning in children, covering such topics as
the best ways to teach a child to read, to write, to do arithmetic? Did it deal with children


who have learning disabilities? It was difficult to imagine spending an entire semester on
these topics, which sounded fairly narrow and specialized for an introductory-level course.

My conception of the psychology of learning was wrong in several respects. First, a psy-
chology course emphasizing learning in the classroom would probably have a title such as
“Educational Psychology” rather than “Learning.” My second error was the assumption that
the psychology of learning is a narrow field. A moment’s reflection reveals that students do
not have a monopoly on learning. Children learn a great deal before ever entering a class-
room, and adults must continue to adapt to an ever-changing environment. Because learning
occurs at all ages, the psychological discipline of learning places no special emphasis on
classroom learning. Furthermore, since the human being is only one of thousands of species
on this planet that have the capacity to learn, the psychological discipline of learning is by
no means restricted to the study of human beings. For reasons to be explained, a large per-
centage of all psychological experiments on learning have used nonhuman subjects. Though
they may have their faults, psychologists in the field of learning are not chauvinistic about
the human species.

Although even specialists have difficulty defining the term learning precisely, most would
agree that it is a process of change that occurs as a result of an individual’s experience. Psy-
chologists who study learning are interested in this process wherever it occurs—in adults,
school children, other mammals, reptiles, and even insects. This may sound like a large
subject, but the field of learning is even broader than this because psychologists study not
only the process of learning but also the product of learning—the long-term changes in one’s
behavior that result from a learning experience.

An example may help to clarify the distinction between process and product. Suppose
you glance out the window and see a raccoon near some garbage cans in the backyard. As
you watch, the raccoon gradually manages to knock over a garbage can, remove the lid, and
tear open the garbage bag inside. If we wanted to study this raccoon’s behavior, many dif-
ferent questions would probably come to mind. Some questions might deal with the learn-
ing process itself: Did the animal open the can purely by accident, or was it guided by some
“plan of action”? What factors determine how long the raccoon will persist in manipulating
the garbage can if it is not immediately successful in obtaining something to eat? These
questions deal with what might be called the acquisition phase, or the period in which the
animal is acquiring a new skill.

Once the raccoon has become skillful at opening garbage cans, we can ask questions about
its long-term performance. How frequently will the raccoon visit a given backyard, and how
will the animal’s success or failure affect the frequency of its visits? Will its visits occur at
the most advantageous times of the day or week? Such questions concern the end product
of the learning process, the raccoon’s new behavior patterns. This text is entitled Learning
and Behavior, rather than simply Learning, to reflect the fact that the psychology of learning
encompasses both the acquisition process and the long-term behavior that results.


Because the psychology of learning deals with all types of learning and learned behaviors
in all types of creatures, its scope is broad indeed. Think, for a moment, of the different
behaviors you performed in the first hour or two after rising this morning. How many of


those behaviors would not have been possible without prior learning? In most cases, the
decision is easy to make. Getting dressed, washing your face, making your bed, and going to
the dining room for breakfast are all examples of behaviors that depend mostly or entirely
on previous learning experiences. The behavior of eating breakfast depends on several dif-
ferent types of learning, including the selection of appropriate types and quantities of food,
the proper use of utensils, and the development of coordinated hand, eye, and mouth move-
ments. It is hard to think of human behaviors that do not depend on prior learning.

Considering all of the behaviors of humans and other creatures that involve learning, the
scope of this branch of psychology may seem hopelessly broad. How can any single disci-
pline hope to make any useful statements about all these different instances of learning? It
would make no sense to study, one by one, every different example of learning that a person
might come across, and this is not the approach of most researchers who study learning.
Instead, their strategy has been to select a relatively small number of learning situations, study
them in detail, and then try to generalize from these situations to other instances of learning.
Therefore, the goal of much of the research on learning has been to develop general prin-
ciples that are applicable across a wide range of species and learning situations.

B. F. Skinner, one of the most influential figures in the history of psychology, made his
belief in this strategy explicit in his first major work, The Behavior of Organisms (1938). In his
initial studies, Skinner chose white rats as subjects and lever pressing as a response. An indi-
vidual rat would be placed in a small experimental chamber containing little more than a
lever and a tray into which food was occasionally presented after the rat pressed the lever.
A modern version of such a chamber is shown in Figure 1.1. In studying the behavior of

Figure 1.1 An experimental chamber in which a rat can receive food pellets by pressing a lever.


rats in such a sparse environment, Skinner felt that he could discover principles that govern
the behavior of many animals, including human beings, in the more complex environments
found outside the psychological laboratory. The work of Skinner and his students will be
examined in depth beginning in Chapter 5, so you will have the opportunity to decide for
yourself whether Skinner’s strategy has proven to be successful.

Attempts to discover principles or laws with wide applicability are a part of most scientific
endeavors. For example, a general principle in physics is the law of gravity, which predicts,
among other things, the distance a freely falling object will drop in a given period of time.
If an object starts from a stationary position and falls for t seconds, the equation d = 16t2
predicts the distance (in feet) that the object will fall. The law of gravity is certainly a general
principle because in theory it applies to any falling object, whether a rock, a baseball, or a
skydiver. Nevertheless, the law of gravity has its limitations. As with most scientific prin-
ciples, it is applicable only when certain criteria are met. Two restrictions on the equation
are that it applies (1) only to objects close to the earth’s surface and (2) only as long as no
other force, such as air resistance, plays a role. Therefore, the law of gravity can be more
accurately studied in the laboratory, where the role of air resistance can be minimized
through the use of a vacuum chamber. For similar reasons, principles of learning and behav-
ior are often best studied in a laboratory environment. Every chapter in this book will
introduce several new principles of learning and behavior, nearly all of which have been
investigated in laboratory settings. To demonstrate that these principles have applicability to
more natural settings, each chapter will also describe real-world situations in which these
principles play an important role.

Within the field of psychology, researchers have studied the topic of learning in several
different ways. The remainder of this chapter gives an overview of these different approaches,
plus a brief history of the field and some background information that will help you to
understand the topics covered in later chapters. We will begin with some of the earliest
recorded thoughts about learning and memory, and then we will examine and compare two
modern approaches to learning—the behavioral and cognitive approaches. Finally, this chap-
ter will introduce a third approach to studying learning—the neuroscience approach—
which examines what happens in the brain and in individual nerve cells when we learn.



The Greek philosopher Aristotle (c. 350 B.C.) is generally acknowledged to be the first
Associationist. He proposed three principles of association that can be viewed as an ele-
mentary theory of memory. Aristotle suggested that these principles describe how one
thought leads to another. Before reading about Aristotle’s principles, you can try something
Aristotle never did: You can conduct a simple experiment to test these principles. Before
reading further, take a few moments to try the demonstration in Box 1.1.

Aristotle’s first principle of association was contiguity: The more closely together (con-
tiguous) in space or time two items occur, the more likely will the thought of one item lead
to the thought of the other. For example, the response chair to the word table illustrates
association by spatial contiguity since the two items are often found close together. The


response lightning to the word thunder is an example of association by temporal contiguity.
Other examples of association by contiguity are bread-butter and dentist-pain.

Aristotle’s other two principles of association were similarity and contrast. He stated
that the thought of one concept often leads to the thought of similar concepts. Examples
of association by similarity are apple-orange or blue-green. By the principle of contrast, Aris-
totle meant that an item often leads to the thought of its opposite (e.g., night-day, girl-boy,
sunset-sunrise). Most people who try this simple free-association experiment conclude that
Aristotle’s principles of association have both strengths and weaknesses. His list of factors
that affect the train of thought seems incomplete, but it is not bad as a first step in the devel-
opment of a theory about the relationship between experience and memory.

The British Associationists: Simple and Complex Ideas

For some philosophers who wrote about Associationism several centuries after Aristotle, this
topic assumed a much greater significance: Associationism was seen as a theory of all knowl-
edge. The British Associationists included John Locke (1690), James Mill (1829), and


A Demonstration of Free Association

This exercise, which should take only a minute or two, can be called a study of free asso-
ciation. Take a piece of paper and a pencil, and write numbers 1 through 12 in a column
down the left side of the paper. Below is a list of words also numbered 1 through 12.
Reading one word at a time, write down the first one or two words that come to mind.

1. apple
2. night
3. thunder
4. bread
5. chair
6. bat
7. girl
8. dentist
9. quiet
10. sunset
11. elephant
12. blue

Once you have your list of responses to the 12 words, look over your answers and try
to develop some rules that describe how you came up with your responses. Can you
guess any of Aristotle’s three principles?


John Stuart Mill (1843). These writers are also called Empiricists because of their belief that
every person acquires all knowledge empirically, that is, through experience. This viewpoint
is typified by John Locke’s statement that the mind of a newborn child is a tabula rasa (a
blank slate) onto which experiences make their marks. The Empiricists believed that every
memory, every idea, and every concept a person has is based on previous experiences.

The opposite of Empiricism is Nativism, or the position that some ideas are innate and
do not depend on an individual’s past experience. For instance, Immanuel Kant (1781)
believed that the concepts of space and time are inborn and that through experience new
concepts are built on the foundation of these original, innate concepts. As we will see many
times throughout this book, modern research has uncovered numerous examples that sup-
port Nativism and contradict the extreme Empiricist position that all knowledge is learned
through experience. Nevertheless, we can grant that some concepts are innate, but many
concepts are developed through experience.

The British Empiricists offered some hypotheses both about how old concepts become
associated in memory and about how new concepts are formed. According to the Associa-
tionists, there is a direct correspondence between experience and memory. Experience
consists of sensations, and memory consists of ideas. Furthermore, any sensory experience
can be broken down into simple sensations. For instance, if a person observes a red box-
shaped object, this might be broken down into two simple sensations: red and rectangular.
Later, the person’s memory of this experience would consist of the two corresponding simple
ideas of red and rectangular (see Figure 1.2a). A simple idea was said to be a sort of faint replica
of the simple sensation from which it arose.

Now suppose that the person repeatedly encounters such a red box-shaped object.
Through the principle of contiguity, an association should develop between the ideas of red
and rectangle, as shown in Figure 1.2b. Once such an association is formed, if the person
experiences the color red, this will not only invoke the idea of red, but by virtue of the
association the idea of rectangular will be invoked as well (Figure 1.2c).

Of course, the Associationists realized that many of our concepts are more complex than
the simple ideas of red, rectangular, thunder, and lightning. In an attempt to come to grips with
the full range of memories and knowledge that all people have, some Associationists specu-
lated about the formation of complex ideas. James Mill (1829) proposed that if two or more
simple sensations are repeatedly presented together, a product of their union may be a com-
plex idea. For instance, if the sensations red and rectangular occur together repeatedly, a new,
complex idea of brick may form. Figure 1.2d shows one way to depict Mill’s hypothesis
graphically. Once such a complex idea is formed, it can also be evoked by the process of
association when the sensation of either red or rectangle occurs. Mill went on to say that com-
plex ideas could themselves combine to form larger duplex ideas. In the following passage,
Mill (1829) describes the formation of a hierarchy of ideas of increasing complexity:

Some of the most familiar objects with which we are acquainted furnish instances of
these unions of complex and duplex ideas. Brick is one complex idea, mortar is another
complex idea; these ideas, with ideas of position and quantity, compose my idea of a
wall. . . . In the same manner my complex idea of glass, and wood, and others, compose
my duplex idea of a window; and these duplex ideas, united together, compose my idea
of a house, which is made up of various duplex ideas.

(pp. 114–116)


There are both strengths and weaknesses in this hypothesis. Some types of learning do seem
to progress from simple to complex concepts. For example, only after children understand
the concepts of addition and repetition are they taught the more complex concept of multiplica-
tion, and it is often introduced as a procedure for performing repeated additions. However,
other concepts do not seem to follow as nicely from Mill’s theory, including his own example
of the concept of house. A 2-year-old may know the word house and use it appropriately
without knowing the “simpler” concepts of mortar, ceiling, or rafter. With house and many
other complex concepts, people seem to develop at least a crude idea of the entire concept
before learning all of the components of the concept, although according to Mill’s theory
this should not be possible. Thus, although it appears to have validity in some cases, Mill’s
theory is at best incomplete.

Another Associationist, Thomas Brown (1820), tried to expand Aristotle’s list by adding
some additional principles. For example, he proposed that the length of time two sensations



















Figure 1.2 Some principles of Associationism. (a) One-to-one correspondence between simple sensa-
tions and simple ideas. (b) After repeated pairings of the two sensations, an association forms between
their respective ideas. (c) Once an association is formed, presenting one stimulus will activate the ideas
of both. (d) With enough pairings of two simple ideas, a complex idea encompassing both simple
ideas is formed. The complex idea may now be evoked if either of the simple stimuli is presented.


coexist determines the strength of the association, and the liveliness or vividness of the sensa-
tions also affects the strength of the association. According to Brown, intense stimuli or
emotional events will be more easily associated and better remembered. He also proposed
that a stronger association will also occur if the two sensations have been paired frequently or
if they have been paired recently.

The ideas of the Associationists can be called the earliest theories of learning, for they
attempted to explain how people change as a result of their experiences. However, the Asso-
ciationists never conducted any experiments to test their ideas. In retrospect, it is remarkable
that despite an interest in principles of learning spanning some 2,000 years, no systematic
experiments on learning were conducted until the end of the nineteenth century. This
absence of research of learning was not a result of technological deficiencies because the first
experiments on learning were so simple that they could have been performed centuries

Ebbinghaus’s Experiments on Memory

Hermann Ebbinghaus (1885) was the first to put the Associationists’ principles to an experi-
mental test. In his memory experiments, Ebbinghaus served as his own subject. This is not
an acceptable arrangement by modern standards because his performance could have been
biased by his expectations. Yet despite this potential problem, all of his major findings have
been replicated by later researchers using modern research procedures.

To avoid using stimuli that had preexisting associations (such as coffee-hot), Ebbinghaus
invented the nonsense syllable—a meaningless syllable consisting of two consonants separated
by a vowel (e.g., HAQ, PIF, ZOD). He would read a list of nonsense syllables out loud at a
steady pace, over and over. Periodically, he would test his memory by trying to recite the list
by heart, and he would record the number of repetitions needed for one perfect recitation.
He then might allow some time to pass and then try to learn the list to perfection a second
time, again recording how many repetitions were needed. He could then calculate his
savings—the decrease in the number of repetitions needed to relearn the list. For example,
if he needed 20 repetitions to learn a list the first time, but only 15 repetitions to relearn the
list at a later time, this was a savings of 5 repetitions, or 25%.

A few examples will show how Ebbinghaus tested the Associationists’ principles. One of
Thomas Brown’s principles was that the frequency of pairings affects the strength of an
association. Obviously, this principle is supported by the simple fact that with enough repeti-
tions Ebbinghaus could learn even long lists of nonsense syllables. However, one of Ebbing-
haus’s findings provided additional support for the frequency principle. If he continued to
study a list beyond the point of one perfect recitation (e.g., for an additional 10 or 20 repeti-
tions), his savings after 24 hours increased substantially. In other words, even after he appeared
to have perfectly mastered a list, additional study produced better performance in a delayed
test. Continuing to practice after performance is apparently perfect is called overlearning,
and Ebbinghaus demonstrated that Brown’s principle of frequency applies to periods of
overlearning as well as to periods in which there is visible improvement during practice.

Another of Thomas Brown’s principles was recency: The more recently two items have
been paired, the stronger will be the association between them. Ebbinghaus tested this prin-
ciple by varying the length of time that elapsed between his study and test periods. As shown


in Figure 1.3, he examined intervals as short as 20 minutes and as long as 1 month. This
graph is an example of a forgetting curve, for it shows how the passage of time has a
detrimental effect on performance in a memory task. The curve shows that forgetting is
rapid immediately after a study period, but the rate of additional forgetting slows as more
time passes. The shape of this curve is similar to the forgetting curves obtained by later
researchers in numerous experiments with both humans and animals, although the time scale
on the x-axis varies greatly, depending on the nature of the task and the species of the sub-
jects. Forgetting curves of this type provide strong confirmation of Brown’s principle of

A final example will show how Ebbinghaus tested Aristotle’s principle of contiguity.
He reasoned the strongest associations in his lists should be between adjacent syllables, but
there should also be measurable (though weaker) associations between nonadjacent items.
He devised an ingenious method for testing this idea, which involved rearranging the items
in a list after they were memorized and then learning the rearranged list. His technique is
illustrated in Table 1.1.

The designations I1 through I16 refer to the 16 items as they were ordered in the original
list (List 0). Once this list is memorized, there should be a strong association between I1 and
I2, a somewhat weaker association between I1 and I3 (since these were separated by one item
in the original list), a still weaker association between I1 and I4, and so on. There should be
similar gradations in strength of association between every other item and its neighbors.

The rearranged list, called List 1 in Table 1.1, was used to test for associations between
items one syllable apart. Notice that every adjacent item in List 1 was separated by one syl-
lable in the original list. If there is any association between I1 and I3, between I3 and I5, and
so on, then List 1 should be easier to learn than a totally new list. In a similar fashion, List 2

Figure 1.3 Ebbinghaus’s forgetting curve. The percentage savings is shown for various time intervals
between his initial learning and relearning of lists of nonsense syllables. (After Ebbinghaus, 1885)





20 min





1 hr 8.8 hr 1 day 2 days 6 days 31 days


tests for associations between items that were two syllables apart in the original list. Ebbing-
haus found that if List 0 was simply relearned after 24 hours, the savings amounted to about
33%. In comparison, he found an average savings of 11% if List 1 was studied 24 hours after
List 0 and a savings of 7% if List 2 was used. Although the amount of savings with these
rearranged lists was not large, the pattern of results was orderly: As the number of skipped
syllables increased in the rearranged lists, the amount of savings was diminished. These results
therefore support the principle of contiguity because they show that the strength of an
association between two items depends on their proximity in the original list.

The Influence of the Associationists and Ebbinghaus

Several themes from the Associationists and Ebbinghaus can still be seen in the work of
present-day psychologists. During the twentieth century, two major approaches to the study
of learning arose—the behavioral and cognitive approaches. Many researchers from both
the behavioral and cognitive traditions have adopted the idea that learning involves the
formation of associations, as the next several chapters will show. Both behavioral and cogni-
tive psychologists continue to be interested in how factors such as contiguity, similarity
among stimuli, repetition, and the passage of time affect what we learn and what we remem-
ber. They continue to investigate how people (and animals) learn complex concepts and
novel ideas. Now that we have surveyed the contributions of these early thinkers, we can
turn to the modern-day learning researchers who followed them.

Table 1.1 Ebbinghaus’s rearranged list experiment. An original list of 16 nonsense syllables (represented
here by the symbols I1 through I16) was rearranged to test for possible associations between items separated
by one syllable (List 1) or associations between items separated by two syllables (List 2).

List 0
(Original list)

List 1
(1 item skipped)

List 2
(2 items skipped)

I1 I1 I1

I2 I3 I4
I3 I5 I7
I4 I7 I10
I5 I9 I13
I6 I11 I16
I7 I13 I2
I8 I15 I5
I9 I2 I8

I10 I4 I11
I11 I6 I14
I12 I8 I3
I13 I10 I6
I14 I12 I9
I15 I14 I12
I16 I16 I15



The field of learning is frequently associated with a general approach to psychology called
behaviorism, which was the dominant approach to the investigation of learning for the
first half of the twentieth century. During the 1960s, however, a new approach called cogni-
tive psychology began to develop, and one of the reasons for its appearance was that its
proponents were dissatisfied with the behavioral approach. This book considers both per-
spectives, but it places more emphasis on the behavioral approach. Two of the most notable
characteristics of the behavioral approach are (1) a heavy reliance on animal subjects and (2)
an emphasis on external events (environmental stimuli and overt behaviors) and a reluctance
to speculate about processes inside the organism that cannot be seen.

The Use of Animal Subjects

A large proportion of the studies described in this text used animals as subjects, especially
pigeons, rats, and rabbits. Researchers in this field frequently choose to conduct their experi-
ments with nonhuman subjects for a number of reasons. First, in research with humans,
subject effects can sometimes pose serious problems. A subject effect occurs when those
who are participating in an experiment change their behavior because they know they are
being observed. Whereas people may change the way they behave when they know a psy-
chologist is watching, subject effects are unlikely to occur with animal subjects. Most studies
with animal subjects are conducted in such a way that the animal does not know its behavior
is being monitored and recorded. Furthermore, it is unlikely that an animal subject will be
motivated to either please or displease the experimenter.

A second reason for using animal subjects is convenience. The species most commonly
used are easy and inexpensive to care for, and animals of a specific age and sex can be
obtained in the quantities the experimenter needs. Once animal subjects are obtained, their
participation is as regular as the experimenter’s: Animal subjects never fail to show up for
their appointments, which is unfortunately not the case with human participants.

Probably the biggest advantage of domesticated animal subjects is that their environment
can be controlled to a much greater extent than is possible with either wild animals or
human subjects. This is especially important in experiments on learning, where previous
experience can have a large effect on a subject’s performance in a new learning situation.
When a person tries to solve a brainteaser as part of a learning experiment, the experimenter
cannot be sure how many similar problems the subject has encountered in his or her lifetime.
When animals are bred and raised in the laboratory, however, their environments can be
constructed to ensure they have no contact with objects or events similar to those they will
encounter in the experiment.

A final reason for using animal subjects is that of comparative simplicity. Just as a child
trying to learn about electricity is better off starting with a flashlight than a cell phone,
researchers may have a better chance of discovering the basic principles of learning by
examining creatures that are less intelligent and less complex than human beings. The
assumption here is that although human beings differ from other animals in some respects,
they are also similar in some respects, and it is these similarities that can be investigated with
animal subjects.


One disadvantage of research with animals is that many of the most advanced human
abilities cannot be studied with animals. Although there has been some research with animals
on skills such as language and problem solving (see Chapter 10), most behavioral psycholo-
gists would agree that some complex abilities are unique to human beings. The difference
between behavioral psychologists and cognitive psychologists seems to be only that cognitive
psychologists are especially interested in those complex abilities that only human beings
possess, whereas behavioral psychologists are typically more interested in learning abilities
that are shared by many species. This is nothing more than a difference in interests, and it is
pointless to argue about it.

A second argument against the use of animal subjects is that human beings are so different
from all other animals that it is not possible to generalize from animal behavior to human
behavior. This is not an issue that can be settled by debate; it can only be decided by col-
lecting the appropriate data. As will be shown throughout this book, there is abundant
evidence that research on learning with animal subjects produces findings that are also
applicable to human behavior.

A third concern about the use of animals as research subjects is an ethical one. Is it right
to use animals in research and, if so, under what conditions? This complex and controversial
issue is discussed in the next section.

Ethical Issues and Animal Research

In recent years there has been considerable debate about the use of animals as research sub-
jects. Viewpoints on this matter vary tremendously. At one extreme, some of the most radical
animal rights advocates believe that animals should have the same rights as people and that
no animals should be used in any type of research whatsoever (Regan, 1983). Others, both
animal welfare advocates and members of the general public, take less extreme positions but
believe that steps should be taken to minimize and eventually phase out the use of animals
in research.

In response to such arguments, scientists have emphasized that many of the advances in
medicine, including vaccines, surgical techniques, and prescription drugs, would not have
been possible without research on animals. They warn that if research with animals were to
stop, it would severely impede progress in medical research and hamper efforts to improve
the health of the world population. In psychology, researchers have documented the many
benefits that have resulted from animal research in the treatment of disorders ranging from
anxiety and depression to drug addictions and memory loss (N. E. Miller, 1985). They argue
that progress in dealing with mental health problems would be jeopardized if animals were
no longer used as subjects in psychological research (Baldwin, 1993; Brennan, Clark, &
Mock, 2014).

Because of ethical concerns, many new regulations have been put in place in an effort to
improve the well-being of animal subjects. In the United States, most colleges, universities,
and research centers that use animal subjects are required to have an Institutional Animal
Care and Use Committee (IACUC) to oversee all research projects involving animals. The
IACUC must review each project with animal subjects before it begins to ensure that all
governmental regulations are met and that the animals are well cared for. Any pain or dis-
comfort to the animals must be minimized to the extent possible. For example, if an animal


undergoes surgery, appropriate anesthesia must be used. Regulations also require that all
research animals have adequate food and water; clean and well-maintained living environ-
ments with appropriate temperature, humidity, and lighting conditions; and the continual
availability of veterinary care.

It should be clear that recent research has been governed by increasingly strict regulations
designed to ensure the humane treatment of animal subjects. Older studies were conducted
during times when there were fewer regulations about animal research. Nevertheless, it is
probably safe to say that even before the advent of tighter regulations, the vast majority of
the experiments were done by researchers who took very good care of their animals because
they realized that one of the best ways to obtain good research results is to have subjects that
are healthy and well treated.

The Emphasis on External Events

The term behaviorism was coined by John B. Watson (1919), who is often called the first
behaviorist. Watson criticized the research techniques that prevailed in the field of psychol-
ogy at that time. A popular research method was introspection, which involves reflecting
on, reporting, and analyzing one’s own mental processes. Thus, a psychologist might attempt
to examine and describe his thoughts and emotions while looking at a picture or performing
some other specific task. A problem with introspection was that it required considerable
practice to master this skill, and, even then, two experienced psychologists might report dif-
ferent thoughts and emotions when performing the same task. Watson recognized this
weakness, and he argued that verbal reports of private events (sensations, feelings, states of
consciousness) should have no place in the field of psychology.

Watson’s logic can be summarized as follows: (1) We want psychology to be a science;
(2) sciences deal only with events everyone can observe; therefore, (3) psychology must deal
only with observable events. According to Watson, the observable events in psychology are
the stimuli that a person senses and the responses a person makes; they are certainly not the
subjective reports of trained introspectionists.

Whereas Watson argued against the use of unobservable events as psychological data,
B. F. Skinner criticized the use of unobservable events in psychological theories. Skinner
(1950) asserted that it is both dangerous and unnecessary to point to some unobservable
event, or intervening variable, as the cause of behavior. Consider an experiment in which
a rat is kept without water for a certain number of hours and is then placed in a chamber
where it can obtain water by pressing a lever. We would probably find an orderly relation-
ship between the independent variable, the number of hours of water deprivation, and the
dependent variable, the rate of lever pressing. The rule that described this relationship is
represented by the arrow in Figure 1.4a.

Skinner has pointed out that many psychologists would prefer to go further, however, and
postulate an intervening variable such as thirst, which is presumably controlled by the hours
of deprivation and which in turn controls the rate of lever pressing (see Figure 1.4b).
According to Skinner, this intervening variable is unnecessary because it does not improve
our ability to predict the rat’s behavior—we can do just as well simply by knowing the hours
of deprivation. The addition of the intervening variable needlessly complicates our theory.
Now our theory must describe two relationships: the relationship between hours of depriva-
tion and thirst, and that between thirst and lever pressing.


Skinner also argued that the use of an intervening variable such as thirst is dangerous
because we can easily fool ourselves into thinking we have found the cause of a behavior
when we are actually talking about a hypothetical and unobservable entity. Suppose that
when a father is asked why his son does not do his homework, he answers, “Because he is
lazy.” In this case, laziness, an unobservable entity, is offered as an explanation, and accepting
this explanation could prematurely curtail any efforts to improve the problem behavior.
After all, if the cause of a behavior is inside the person, how can we control it? However,
Skinner proposed that the causes of many behaviors can be traced back to the external
environment, and by changing the environment, we can change the behavior. Perhaps the
boy spends all afternoon playing video games, eats dinner with the family at a fairly late
hour, and then is too tired to do his assignments. If so, the parents might be able to change
the boy’s behavior by requiring him to complete his homework before playing any video
games. In short, the potential for controlling a behavior may be recognized if an intervening
variable such as laziness is rejected and an external cause of the behavior is sought.

Neal Miller (1959), another behavioral psychologist, disagreed with Skinner’s position
that intervening variables are always undesirable. Miller suggested that intervening variables
are often useful when several independent and dependent variables are involved. As shown
in Figure 1.5, he noted that besides hours of water deprivation, two other independent
variables that could affect the rat’s lever pressing might also increase if it were fed dry food
or if it were given an injection of a saline solution. Furthermore, the rate of lever pressing
is only one of many dependent variables that might be affected by water deprivation, dry
food, or a saline injection. Two other dependent variables are the volume of water consumed
and the amount of quinine (which would give the water a bitter taste) that would have to
be added to make the rat stop drinking.

Miller argued that once these additional independent and dependent variables are
considered, to account for the rat’s behavior we would need a theory with nine cause-
and-effect relationships, as symbolized by the nine crossing arrows in Figure 1.5a. This
fairly complicated theory could be simplified by including the intervening variable, thirst.
We can assume that each of the three independent variables affects an animal’s thirst, and
thirst controls each of the three dependent variables. Figure 1.5b shows that once the
intervening variable, thirst, is included in this way, only six cause-and-effect relationships
(represented by the six arrows in the figure) have to be described. In other words, when
there are multiple independent and dependent variables to consider, the theory with the
intervening variable is actually simpler (because there are fewer cause-and-effect relation-
ships to account for).

Some psychologists have also pointed out that intervening variables are commonplace
in other, firmly established sciences. For instance, many familiar concepts from physics

Figure 1.4 (a) A schematic diagram of a simple theory of behavior with no intervening variables. (b)
The same theory with an intervening variable added. In this example, the intervening variable, thirst,
is unnecessary, for it only complicates the theory. (From N.E. Miller, 1959, Liberalization of basic S-R
concepts, in S. Koch, Psychology: The study of a science, Vol. 2. © McGraw-Hill Education. Reprinted
by permission.)


Figure 1.5 (a) The arrows represent the nine relationships between independent and dependent
variables that must be defined by a theory without intervening variables. (b) The arrows represent
the six relationships the theory must define if it includes the intervening variable of thirst. Neal
Miller argued that the second theory is superior because it is more parsimonious. (From N.E. Miller,
1959, Liberalization of basic S-R concepts, in S. Koch, Psychology: The study of a science, Vol. 2.
© McGraw-Hill Education. Reprinted by permission.)

Hours of deprivation

Dry food

Saline injection

Rate of lever
Pressing for water

Volume consumed

Quinine tolerated

Hours of deprivation


Dry food

Saline injection

Rate of lever
Pressing for water

Volume consumedThirst

Quinine tolerated

(gravity, magnetism, force) are intervening
variables since they are not directly observ-
able. Some psychologists have therefore
reasoned that progress in psychology
would be needlessly restricted if the use of
intervening variables were disallowed
(Nicholas, 1984).

As Miller’s position shows, it is not cor-
rect to say that all behaviorists avoid using
intervening variables. As a general rule,
however, cognitive psychologists tend to use
intervening variables more freely and more
prolifically than do behavioral psycholo-
gists. The debate over the use of interven-
ing variables has gone on for decades, and
we will not settle it here. My own position
(though hardly original) is that the ultimate
test of a psychological theory is its ability to
predict behavior. If a theory can make
accurate predictions about behaviors that
were previously unpredictable, then the
theory is useful, regardless of whether it
contains any intervening variables. In this
book, we will encounter many useful theo-
ries of each type.

Practice Quiz 1: chaPter 1
1. Aristotle’s three principles of associa-

tion were ______, ______, and

2. Ebbinghaus’s forgetting curve shows
that the rate of forgetting in the first
few minutes after studying is ______
than the rate of forgetting a week

3. Animals have been used as research
subjects more often by ______ psy-
chologists than by ______

4. According to John B. Watson, if psy-
chology is to be a science it must
focus on observable events, namely
______ and ______.

5. According to B. F. Skinner, theories
in psychology should not include


1. contiguity, similarity, contrast 2. faster 3. behavioral,
cognitive 4. stimuli, responses 5. intervening variables



What happens in the nervous system when two stimuli are repeatedly paired and a person
begins to associate the two? How do our sensory systems allow it to recognize complex
stimuli such as bricks, automobiles, or people’s faces? Neuroscientists, who study the brain
and nervous system, have attempted to answer these questions and many others like them,
with varying degrees of progress so far. The rest of this chapter gives a brief overview of
some of this research. To understand this material you need to have some understanding
of how neurons (nerve cells) function, so the following section provides a short summary
of the basic points.

The Basic Characteristics of Neurons

The nervous systems of all creatures on earth are composed of specialized cells called neu-
rons, whose major function is to transmit information. The human brain contains many
billions of neurons, and there are many additional neurons throughout the rest of the body.
Although they vary greatly in size and shape, the basic components of all neurons, and the
functions of those components, are quite similar.

Figure 1.6 shows the structure of a typical neuron. The three main components are the
cell body, the dendrites, and the axons. The cell body contains the nucleus, which
regulates the basic metabolic functions of the cell, such as the intake of oxygen and the
release of carbon dioxide. In the transmission of information, the dendrites and the cell
body are on the receptive side; that is, they are sensitive to certain chemicals called trans-
mitters that are released by other neurons. When its dendrites and cell body receive
sufficient stimulation, a neuron is said to “fire”—it exhibits a sudden change in electrical
potential lasting only a few milliseconds (thousandths of a second). The more stimulation
a neuron receives, the more rapidly it fires: It may fire only a few dozen times a second
with low stimulation but several hundred times a second with high stimulation. The axons
are involved on the transmission side. Each time a neuron fires, enlarged structures at the
ends of the axons, the axon terminals, release a transmitter that may stimulate the dendrites
of other neurons. Therefore, within a single neuron, the flow of activity typically begins

Figure 1.6 A schematic diagram of a neuron.


with the dendrites, travels down the axons, and ends with release of transmitter by the
axon terminals.

The term synapse refers to a small gap between the axon terminal of one neuron (called
the presynaptic neuron) and the dendrite of another neuron (called the postsynaptic neuron). As
Figure 1.7 shows, the presynaptic neuron releases its transmitter into the synapse. This trans-
mitter can affect the postsynaptic neuron in one of two ways. In an excitatory synapse, the
release of transmitter makes the postsynaptic neuron more likely to fire. In an inhibitory
synapse, the release of transmitter makes the postsynaptic neuron less likely to fire. A single
neuron may receive inputs, some excitatory and some inhibitory, from thousands of other
neurons. At any moment, a neuron’s firing rate reflects the combined influence of all its
excitatory and inhibitory inputs.

Simple Sensations

One theme of the Associationists that has been uniformly supported by subsequent brain
research is the hypothesis that our sensory systems analyze the complex stimulus envi-
ronment that surrounds us by breaking it down into “simple sensations.” The nervous
system’s only contact with the stimuli of the external environment comes through a
variety of specialized neurons called receptors. Instead of dendrites that are sensitive
to the transmitters of other neurons, receptors have structures that are sensitive to

Figure 1.7 A schematic diagram of a synapse between two neurons. The chemical transmitter released
by the axon terminal of the presynaptic neuron causes changes in the dendrite of the postsynaptic
neuron that makes the neuron more likely to fire (in an excitatory synapse) or less likely to fire (in an
inhibitory synapse).


specific types of external stimuli. In the visual system, for example, receptors sensitive
to light are located on the retina. As shown in Figure 1.8, light entering the eye is
focused by the cornea and lens and is projected onto the retina. A miniature inverted
image of the visual world is focused on the retina, which lines the inside surface of the
eyeball. Some of the receptors on the retina are called cones (because of their shape),
and different cones are especially sensitive to different colors in the spectrum of visible
light. In the normal human eye, there are three classes of cones, which are most effec-
tively stimulated by light in the red, green, and blue regions of the spectrum, respectively.
A red-sensitive cone, for example, is most responsive to red light, but it will also exhibit
a weaker response when stimulated by other colors in the red region of the spectrum,
such as orange, violet, and yellow. Although we have only three types of cones, we can
distinguish many subtle differences in color because they produce different patterns of
activity in the three types of cones. A particular shade of yellow, for example, will pro-
duce a unique pattern of activity: The red and green cones may be activated to approxi-
mately the same extent, and the blue cones will exhibit very little activity. Since no
other color will produce exactly the same pattern of activity in the cones, this pattern
is the visual system’s method of encoding the presence of a particular shade of yellow.
We can think of the cones as receptors that decompose the complex visual world into
what the Associationists called “simple sensations.”

Similarly, all of our other senses have specialized receptors that are activated by simple
features. The skin contains a variety of tactile receptors, some sensitive to pressure, some to
pain, some to warmth, and some to cold. In the auditory system, single neurons are tuned
to particular sound frequencies so that one neuron might be most sensitive to a tone with
a frequency of 1,000 cycles/second. This neuron would be less sensitive to tones of higher
or lower pitches. Regarding the sense of taste, most experts believe that all gustatory sensa-
tions can be decomposed into four simple tastes: sour, salty, bitter, and sweet (and possibly a
fifth, savory). Some very exacting experiments by von Bekesy (1964, 1966) showed that
individual taste receptors on the tongue are responsive to one and only one of these simple
tastes. In summary, the evidence from sensory physiology is clear: All sensory systems begin
by breaking down incoming stimuli into simple sensations.

Figure 1.8 How light from an object in the environment enters the eye and is focused on the retina
as an inverted image.


Feature Detectors

Whereas our visual systems start by detecting the basic features of a stimulus—color, bright-
ness, and location—each of us can recognize complex visual patterns, such as the face of a
friend or a written word. The same is true of our other senses. We do not simply hear sounds
of different pitches and intensities; we can perceive spoken sentences, automobile engines,
and symphonies. When we eat, we do not just detect the basic tastes; we perceive the com-
plex tastes of a pepperoni pizza or a strawberry sundae. How do our nervous systems start
with simple sensations and arrive at these much more complex perceptions?

In their groundbreaking research, Hubel and Wiesel (1965, 1979) found neurons in the
brain that can be called feature detectors because each neuron responded to a specific
visual stimulus. Using an anesthetized monkey or cat, Hubel and Wiesel would isolate a
single neuron somewhere in the visual system and record its electrical activity while present-
ing a wide range of visual stimuli (varying in color, size, shape, and location in the visual
field) to the animal. The question Hubel and Wiesel wanted to answer was simple: What
type of feature detector is this neuron? That is, what type of visual stimuli will make the
neuron fire most rapidly?

Hubel and Wiesel found several different types of feature detectors in the visual cortex,
an area in the back of the head, just beneath the skull. One class of cells, which they called
simple cells, fired most rapidly when the visual stimulus was a line of a specific orientation,
presented in a specific part of the visual field. For example, one simple cell might fire most
rapidly in response to a line at a 45-degree angle from the horizontal. If the orientation of
the line were changed to 30 or 60 degrees, the cell would fire less rapidly, and with further
deviations from 45 degrees, the cell would respond less and less. Other simple cells responded
to lines of other orientations.

It is not hard to imagine how neural signals from the rods and cones on the retina could
combine to produce a line detector. Imagine that a simple cell in the cortex receives (through
a chain of intervening neurons) excitatory inputs from individual receptors that are posi-
tioned in a row on the surface of the retina. A line of just the right angle will stimulate this
entire row of retinal cells, and so there will be a very strong input to the simple cell in the
visual cortex. Lines of other orientations will only stimulate a few of the retinal cells, so there
will be less stimulation (and less response) of the simple cell in the visual cortex. So far, no
one has actually managed to trace the “wiring diagram” for a simple cell, but it is clear from
Hubel and Wiesel’s results that some such integration of information must occur between
the retina and the line-detecting cells in the visual cortex. Hubel and Wiesel also found
more complex feature detectors in the visual cortex. Some cells responded only to shapes
with two edges intersecting at a specific angle. For instance, one cell might respond to the
corner of a rectangle—two edges forming a 90-degree angle. Another cell might be most
responsive to part of a triangle—two edges forming an angle of, say, 45 degrees.

When Hubel and Wiesel (1963) examined cells in the visual cortex of newborn kittens
with no previous visual experience, they found feature detectors similar to those found in
adult cats (though the neurons of kittens were more sluggish in their response). This shows
that individual neurons in a kitten’s visual cortex are prewired to respond to specific visual
features (lines, angles) before the kitten has seen any visual patterns whatsoever. A Nativist
might call this an example of “innate knowledge”: The newborn kitten already knows how
to extract information from the visual world. However, feature detectors are also affected


by experience. Blakemore and Cooper (1970) found that kittens raised in an environment
with large vertical stripes on the walls had more vertical line detectors as adult cats, and those
raised in an environment with horizontal lines had more horizontal line detectors. Therefore,
both heredity and environment contribute to the types of visual feature detectors found in
the adult animal.

The most complex visual detectors ever reported are cortical neurons in macaque mon-
keys that could be called “hand detectors” and “face detectors” (Desimone, Albright, Gross,
& Bruce, 1984). For instance, the face detectors responded vigorously to human or monkey
faces, whereas a variety of other stimuli (shapes, textures, pictures of other objects) evoked
little or no response. Extrapolating from these remarkable findings, does this mean that the
brain has individual neurons in the visual system for every complex stimulus one can rec-
ognize, such as the face of a friend or a 2010 Porsche? Current research suggests that the
answer is “no.” From studies on human visual perception, there is evidence from both infants
and adults that large parts of the visual cortex are activated when people perceive human
faces, and it is the entire pattern of brain activity that allows us to recognize a face (Nichols,
Betts, & Wilson, 2010). And although human face perception may be different in some ways
from other types of object perception, many different areas of the brain are involved when
we perceive other objects as well (Konen & Kastner, 2008). Yet even with modern brain-
imaging technology and extensive research on this topic, there is much that neuroscientists
still do not understand about what takes place in the brain when a person recognizes a
familiar object.

The Neuroscience of Learning

There are several possible ways in which the brain might change during learning. One pos-
sibility is that learning involves chemical changes at the level of individual synapses that alter
flow of communication among neurons. A second possibility is that neurons may grow new
axons and/or new dendrites as a result of a learning experience so that new synaptic con-
nections are formed. A third possibility is that completely new neurons are grown during
a learning experience. Let us examine each of these possibilities.

Chemical Changes

There is now plenty of evidence that some changes in the brain do not depend on the
growth of new synapses but rather on chemical changes in already existing synapses. For
example, say the neurons in a slice of rat brain tissue are given a brief burst of electrical
stimulation; this action can produce long-lasting increases in the strength of existing con-
nections between neurons. The increase in the strength of excitatory synapses as a result of
electrical stimulation is called long-term potentiation, and the effect can last for weeks
or months (Bliss & Lomo, 1973). Long-term potentiation has also been observed in human
brain tissue removed during the course of surgical procedures (Chen et al., 1996) and even
in the intact brains of humans (Heidegger, Krakow, & Ziemann, 2010). Long-term potentia-
tion has been demonstrated in brain areas that are implicated in the storage of long-term
memories, such as the hippocampus and the cerebral cortex. For this reason, some investiga-
tors believe that long-term potentiation may be a basic process through which the brain can


change as a result of a learning experience. There is growing evidence that it may play a role
in the learning of new associations (Wang & Morris, 2010).

What type of chemical changes could cause an increase in the strength of a synaptic
connection? One possibility is that as a result of a learning experience, the axon terminal
of the presynaptic neuron develops the capacity to release more transmitter. Another
possibility is that the cell membrane of the postsynaptic neuron becomes more sensitive
to the transmitter, so its response to the same amount of transmitter is greater. In experi-
ments on long-term potentiation, researchers have found evidence that both presynaptic
and postsynaptic changes may be involved (Bourne, Chirillo, & Harris, 2013; Meis,
Endres, & Lessmann, 2012). It seems that the mammalian brain has at its disposal a num-
ber of different chemical mechanisms for altering the strengths of the connections
between neurons.

Growth of New Synapses

There is now abundant evidence that learning experiences can lead to the growth of new
synaptic connections between neurons. Some of the earliest evidence for the hypothesis
that new synapses are developed as a result of experience came from studies in which
animals were exposed to enriched living environments. Rosenzweig and his colleagues
(Rosenzweig, 1966; Rosenzweig, Mollgaard, Diamond, & Bennet, 1972) placed young rats
in two different environments to determine how early experience influences the develop-
ment of the brain. Some rats were placed in an environment rich in stimuli and in possible
learning experiences. These animals lived in groups of 10 to 12, and their cages contained
many objects to play with and explore—ladders, wheels, platforms, mazes, and the like.
Other rats were raised in a much more impoverished environment. Each animal lived in
a separate, empty cage, and it could not see or touch other rats. These rats certainly had
far fewer sensory and learning experiences. After the rats spent 80 days in these environ-
ments, Rosenzweig and colleagues found that the brains of the enriched rats were signifi-
cantly heavier than those of impoverished rats. Differences in weight were especially
pronounced in the cerebral cortex, which is thought to play an important role in the
learning process. Many recent studies have found evidence that growth in specific parts
of the cerebral cortex and other brain areas can result from a variety of different learning
experiences, ranging from rats learning mazes (Lerch et al., 2011) to people learning to
juggle (Draganski et al., 2004). It seems clear that learning experiences can produce
growth in brain tissue.

What types of changes at the cellular level accompany these differences in overall brain
size? Microscopic examinations have revealed a variety of changes in the brain tissue of rats
exposed to enriched environments, including more branching of dendrites (indicating more
synaptic connections between axons and dendrites) and synapses with larger surfaces. Other
studies have found that exactly where neural changes take place in the brain depends on
what the learning task involved. Spinelli, Jensen, and DiPrisco (1980) trained young kittens
to flex one foreleg to avoid a shock to that leg. After a few brief sessions with this proce-
dure, there was a substantial increase in the amount of dendritic branching in the area of
the cortex that controlled the movement of that foreleg. Studies like this provide compel-
ling evidence that relatively brief learning experiences can produce significant increases in
the number, size, and complexity of synaptic connections.


Many neuroscientists believe that the growth of new dendrites and synaptic connec-
tions underlies the formation of long-term memories (Kolb & Gibb, 2008). In humans,
studies have shown that dramatic arborization, or the branching of dendrites, occurs in
the months before birth and in the first year of life. At the same time, other connections
between neurons disappear. It is not clear how much of this change is due to maturation
and how much to the infant’s learning experiences. It appears, however, that as a child
grows and learns, numerous new synaptic connections are formed and other unneeded
connections are eliminated. These neural changes continue at least until the adolescent
years (Huttenlocher, 1990).

Growth of New Neurons

In the past, it was generally believed that except before birth and possibly during early
infancy, no new neurons can grow in the brains of animals. According to this view, all
learning takes the form of changes in existing neurons (through chemical changes or
synaptic growth), and any neurons that are lost due to illness or injury cannot be
replaced. Today, however, there is convincing evidence that this traditional view of
neural growth is incorrect and that new neurons continue to appear in the brains of
adult mammals (Fuchs & Flügge, 2014). For example, research with adult macaque
monkeys has found new neurons developing in several areas of the cerebral cortex
(Gould, Reeves, Graziano, & Gross, 1999). The growth of new neurons, called neuro-
genesis, has also been observed in other species, and in some cases this growth appears
to be related to learning experiences. For instance, in one experiment, some rats learned
tasks that are known to involve the hippocampus, and other rats learned tasks that do
not involve the hippocampus. For the first group of rats, after the learning period, new
neurons were found in a nearby area of the brain that receives inputs from the hippo-
campus. For the second group of rats, no new neurons were found in this area. These
results suggest that new neurons can grow during a learning experience and that exactly
where they grow may depend on the specific type of learning that is involved (Gould,
Beylin, Tanapat, Reeves, & Shors, 1999).

Studies of adult humans have shown that their brains also continue to produce new
neurons and that neurogenesis may play an important part in the functioning of the adult
brain. If a person’s level of neurogenesis is unusually low, this may be related to various
types of psychological disorders. Adults suffering from clinical depression have decreased
levels of neurogenesis, and antidepressant medications appear to increase neurogenesis
(Boldrini et al., 2013). After a brain injury, neurogenesis may help restore some level of
brain functioning in the damaged area. There is evidence that after such an injury, new
brain cells grow through mitosis (cell division), and they appear to develop some of the
same physical characteristics and neural connections as the neurons that were damaged
(Kokaia & Lindvall, 2003).

Where Are “Complex Ideas” Stored in the Brain?

Before concluding this brief survey of the physiological approach to learning, let us take one
final look at James Mill’s concept of complex ideas. What happens in the brain when a child
learns the concept house or when a kitten learns to recognize and respond appropriately to


a snake? Although the answer to this question is not yet known, a number of different pos-
sibilities have been proposed.

One hypothesis is that every learning experience produces neural changes that are
distributed diffusely over many sections of the brain. This hypothesis was supported by
some classic experiments by Karl Lashley (1950). After training rats to run through a
maze, Lashley removed sections of the cerebral cortex (different sections for different
rats) to see whether he could remove the memories of the maze. If he could, this would
show where the memories about the maze were stored. However, Lashley’s efforts to
find the location of these memories were unsuccessful. When a small section of cortex
was removed, this had no effect on a rat’s maze performance, no matter which section
was removed. When a larger section of cortex was removed, this caused a rat’s perfor-
mance in the maze to deteriorate, no matter which section was removed. Lashley con-
cluded that memories are stored diffusely throughout the brain and that removing small
sections of the brain will not remove the memory. Many later studies have also provided
support for the view that large sections of the brain undergo change during simple
learning experiences and that many brain regions are also involved when these learning
experiences are remembered at a later time (Shimamura, 2014; Tomie, Grimes, &
Pohorecky, 2008).

A very different hypothesis is that the information about individual concepts or ideas is
localized, or stored in small, specific sections of the brain. For example, some psychologists
have suggested that the cerebral cortex may contain many unused or dormant neurons.
Through an animal’s learning experiences, one (or a few) of these dormant neurons might
come to respond selectively to a particular complex object (Konorski, 1967). To take a simple
example, after an animal has had experience with a complex object such as an apple, some
cortical neuron might develop excitatory inputs from detectors responsive to the apple’s red
color, roughly spherical shape, specific odor, and other characteristics. In this way, an animal
that at birth had no complex idea of an apple might develop the ability to recognize apples
as a result of its experience.

Some evidence supporting this idea came from the pioneering research of Penfield (1959),
who electrically stimulated areas of the cerebral cortex of human patients during brain
surgery. When Penfield stimulated small areas of the cortex, his patients, who were anesthe-
tized but awake, reported a variety of vivid sensations, such as hearing a specific piece of
music or experiencing the sights and sounds of a circus. Although it might be tempting to
conclude that the electrical stimulation had triggered a site where specific memories of the
past were stored, Penfield’s findings can be interpreted in many ways, and their significance
is not clear.

Better evidence for localized memories comes from reports of people who suffered
damage to small sections of the brain as a result of an accident or stroke. Brain injury can,
of course, produce a wide range of psychological or physical problems, but in a few indi-
viduals the result was a loss of very specific information. For example, one man had diffi-
culty naming any fruit or vegetable, whereas he had no trouble identifying any other types
of objects (Hart, Berndt, & Caramazza, 1985). Another person could not name objects
typically found in a room, such as furniture and walls (Yamadori & Albert, 1973). Another
could no longer remember the names of well-known celebrities, but he had no problem
with the names of other famous people, such as historical and literary figures (Lucchelli,
Muggia, & Spinnler, 1997). There is also evidence from brain-imaging studies that specific


but different areas of the brain are acti-
vated when people look at pictures of ani-
mals versus pictures of tools (Chouinard &
Goodale, 2010). These findings suggest
that specific concepts are stored in specific
areas of the brain and that concepts
belonging to a single category are stored
close together.

The debate over whether the neural
representation of complex ideas is local-
ized or distributed has gone on for many
years, and it has not yet been resolved. It
is possible that both hypotheses are par-
tially correct, with some types of learning
producing changes in fairly specific parts
of the brain and others producing changes
over large portions of the brain. Modern-
day neuroscientists continue to investigate
the question asked by James Mill over a
century and a half ago: What are complex
ideas, and how does the human brain
acquire them and retain them? If and
when neuroscientists eventually discover
exactly how the brain stores information
about complex concepts and ideas, this
will be a milestone in the psychology of


The field of learning is concerned with both how people and animals learn and how their
long-term behavior changes as a result of this learning. The earliest ideas about learning
were developed by the Associationists, who proposed principles about how the brain forms
associations between different thoughts and ideas. Aristotle proposed the principles of con-
tiguity, similarity, and contrast. James Mill developed a theory of how two or more simple
ideas can be combined to form more complex ideas. Hermann Ebbinghaus conducted some
of the first studies on learning and memory using lists of nonsense syllables as his stimuli
and repeating the lists to himself until he memorized them. He demonstrated several basic
principles of learning, including contiguity, recency, and overlearning.

Two main approaches to studying learning are the behavioral and cognitive approaches.
Behavioral psychologists have often used animal subjects because they are interested in gen-
eral principles of learning that are shared by many species, because animals are less complex
than human subjects, and because animal environments can be controlled to a greater degree.
Critics of animal research have questioned whether we can generalize from animals to
people, and they have raised ethical concerns about the use of animal subjects. Behaviorists

Practice Quiz 2: chaPter 1
1. In communication between neurons,

a chemical transmitter is released by
the ______ of one neuron and received
by the ______ of another neuron.

2. There are three types of cones in the
human retina that respond to three
different types of stimuli: ______,
______, and ______.

3. The “simple cells” in the visual cortex
found by Hubel and Wiesel respond
specifically to ______.

4. Three main types of changes that
can occur in the brain as a result of
a learning experience are ______,
______, and ______.

5. By removing different parts of the
brains of rats after they learned a
maze, Lashley concluded that mem-
ories are stored ______.

1. axon terminals, dendrites 2. red, green, and blue
3. lines of specific orientations 4. chemical changes,
growth of new synapses, growth of new neurons
5. diffusely throughout the brain


have argued that psychology should deal only with observable events, whereas cognitive
psychologists regularly use intervening variables such as hunger, memory, and attention.
B. F. Skinner argued that intervening variables make scientific theories more complex than
necessary. However, Neal Miller showed that if a theory includes many independent vari-
ables and many dependent variables, then using intervening variables can actually simplify
a theory.

Specialized sensory neurons in the eyes, ears, and other sense organs respond to very
simple sensory properties, much as the Associationists suggested. Neurons in the eye
respond to specific colors, and neurons in the ear respond to specific pitches of sound.
In the brain, the inputs from many sensory neurons are often combined, so that individual
neurons may respond to features such as edges, angles, and corners of a visual stimulus.
How the nervous system combines all this information so that we can perceive and
identify objects in our environments is still not well understood, but there is evidence
that object recognition involves patterns of brain activity across large sections of the

Neuroscientists assume that whenever an individual learns something new, there is a
physical change somewhere in the brain or nervous system. Some axon terminals may begin
to produce neurotransmitters in greater quantities, some dendrites may become more sensi-
tive to existing neurotransmitters, new synapses may form between neurons, and completely
new neurons may grow. There is solid evidence for each of these different types of changes.
Lashley’s early research with rats suggested that many different sections of the brain are
changed during a simple learning experience. However, research on humans with brain
injuries suggests that some types of information may be stored in fairly small, specific areas
of the brain.

Review Questions

1. Describe Aristotle’s three principles of association and some of the additional
principles proposed by Brown. Illustrate these principles by giving some exam-
ples from your own life of words or concepts that you tend to associate.

2. What procedure did Ebbinghaus use to study memory? How did his results offer
evidence for the principles of frequency, recency, and contiguity?

3. What are some of the advantages and disadvantages to using animals as sub-
jects in research on learning?

4. Why did B. F. Skinner believe that intervening variables should not be used in
psychological theories? In your opinion, what is the biggest disadvantage of
using intervening variables? What do you consider the biggest advantage?

5. Describe some research results that provide evidence that learning can result in
chemical changes in the brain, the growth of new synaptic connections, and the
growth of new neurons.



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When any animal is born, it is already endowed with a variety of complex abilities. Its
immediate survival depends on the ability to breathe and to pump blood through its veins.
If it is a mammal, it has the ability to regulate its temperature within narrow limits. If its
survival depends on the ability to flee from predators, it may start to walk and run within
minutes after birth. Newborn animals are also equipped with a range of sensory capacities.
One major purpose of this chapter is to provide examples of the types of behavioral abilities
that an animal may already possess as it enters the world. There are good reasons for examin-
ing innate behavior patterns in a book about learning. First, many learned behaviors are
derivatives, extensions, or variations of innate behaviors. Second, many of the features of

C H A P T E R 2

Innate Behavior Patterns
and Habituation

Learning Objectives

After reading this chapter, you should be able to

• describe the major concepts of control systems theory, and apply the concepts
to both living and nonliving examples of goal-directed behavior

• describe four different types of innate behavior patterns, and explain how they

• describe some human abilities and predispositions that may be inborn
• define habituation, and list the general principles of habituation that are found

in all animal species
• discuss what is known about the neural mechanisms of habituation
• describe opponent-process theory, and diagram the typical pattern of an

emotional response to a new stimulus and to a stimulus that has been repeated
many times


learned behaviors (e.g., their control by environmental stimuli, their mechanisms of temporal
sequencing) have parallels in inborn behavior patterns. Besides surveying different types of
innate behaviors, this chapter will also examine the phenomenon of habituation, which is
often said to be the simplest type of learning.

One characteristic that is common to many behaviors, both learned and unlearned, is that
they appear to be purposive, or goal directed. As we will see, this is true of some of our most
primitive reflexes as well as our most complex skills. For this reason, it will be useful to begin
this chapter with some concepts from control systems theory, a branch of science that
deals with goal-directed behaviors in both living creatures and inanimate objects.


Control systems theory provides a general framework for analyzing a wide range of goal-
directed systems. The terminology used here is based on the work of McFarland (1971). A
simple example of an inanimate goal-directed system is a house’s heating system. The goal
of the heating system is to keep the house temperature above some minimum level, say 65°F.
If the house temperature drops below 65°F, the heating system “spontaneously” springs into
action, starting the furnace. Once the temperature goal is reached, the heating system turns
off. Of course, we know there is nothing magical about this process. The activity of the
heating system is controlled by a thermostat, which relies on the expansion and contraction
of metal components to open or close a switch that turns the furnace off or on.

The thermostat is an example of a fundamental concept in control systems theory, the
comparator. As shown in Figure 2.1, a comparator receives two types of input, called the
reference input and the actual input. The reference input is often not a physical entity but a
conceptual one (the temperature that, when reached, will be just enough to open the switch
and stop the furnace). On the other hand, the actual input measures some actual physical
characteristic of the present environment, in this case, the air temperature in the vicinity of
the thermostat.

Any comparator has rules that it follows to determine, based on the current actual input
and reference input, what its output will be. In the case of a thermostat, the output is an on/off





(air temperature
near thermostat)





(warm air

from radiators)



Figure 2.1 Concepts of control systems theory as applied to a house’s heating system.


command to the furnace, which is an example of an action system. The rules that the ther-
mostat follows might be these: (1) If the air temperature becomes one degree lower than the
reference input, turn on the furnace; (2) if the air temperature becomes one degree higher
than the reference input, turn off the furnace. With a setting of 65°F, these rules would keep
the air temperature between 64°F and 66°F.

The product of the action system is simply called the output—the entry of warm air from
the radiators in this example. As Figure 2.1 shows, the output of the action system feeds
back and affects the actual input to the comparator. For this reason, such a goal-directed
system is frequently called a feedback system or a closed-loop system. However, the actual input
can also be affected by other factors, such as the disturbance depicted in Figure 2.1, an open
window. A window open on a cold day can disrupt this feedback system by keeping the
house cold even if the furnace runs continuously.

This example illustrates six of the most important concepts of control systems theory:
comparator, reference input, actual input, action system, output, and disturbance. We will
encounter many examples of goal-directed behaviors in this book, and it will often be useful
to try to identify the different components of the feedback loop in these examples. The next
section is the first of many in this text that will make use of the concepts of control systems


A reflex is a stereotyped pattern of movement of a part of the body that can be reliably
elicited by presenting the appropriate stimulus. You are probably familiar with the patellar
(knee-jerk) reflex: If a person’s leg is supported so that the foot is off the ground and the
lower leg can swing freely, a light tap of a hammer just below the kneecap will evoke a small
kicking motion from the leg. As with all reflexes, the patellar reflex involves an innate con-
nection between a stimulus and a response. The stimulus in this example is the tapping of
the tendon below the kneecap, and the response is the kicking motion.

A normal newborn child displays a variety of reflexes. A nipple placed in the child’s
mouth will elicit a sucking response. If the sole of the foot is pricked with a pin, the child’s
knees will flex, pulling the feet away from the painful stimulus. If an adult places a finger in
the child’s palm, the child’s fingers will close around it in a grasping reflex. Some of the
newborn’s reflexes disappear with age. Others, such as the constriction of the pupils and the
closing of the eyes in response to a bright light or coughing in response to a throat irritation,
persist throughout life.

If you ever accidentally placed your hand on a hot stove, you probably exhibited a flexion
reflex—a rapid withdrawal of the hand caused by a bending of the arm at the elbow. The
response is very rapid because the association between sensory and motor neurons occurs
directly in the spinal cord. Figure 2.2 depicts a cross section of the spinal cord and some of
the neural machinery involved in this reflex. The hand contains sensory neurons sensitive
to pain, and their lengthy axons travel all the way into the spinal cord before synapsing with
other neurons. In the flexion reflex, one or more small neurons, called interneurons, separate
the sensory neurons from motor neurons. The motor neurons have cell bodies within the
spinal cord, and their axons exit through the front of the spinal cord, travel back down the
arm, and synapse with individual muscle fibers in the arm. When excited, the muscle fibers


contract, thereby producing the response. The physiology of this reflex is sometimes called
the spinal reflex arc, after the shape of the path of neural excitation shown in Figure 2.2.
Not one but many such sensory neurons, interneurons, and motor neurons are involved in
producing the reflexive response.

There is more to the flexion reflex than the simple stimulus-response relation shown in
Figure 2.2, however. Even this basic reflex is actually a simple feedback system. Within the
muscles of the arm are structures called stretch receptors, which serve as the comparators of
the feedback system. We will not go into detail about how this happens, but the stretch
receptors compare (1) the goal or reference input—the commands sent from the motor
neurons to the muscle fibers telling them to contract—and (2) the actual amount that the
muscles have contracted. Just because some motor neurons have sent their commands to
the muscle, this does not guarantee that the arm is safely withdrawn from the dangerous
object. There might be a disturbance—an obstruction that impedes the movement of the
arm. If the muscles have not contracted sufficiently for any such reason, the stretch receptors
begin to stimulate the motor neurons (which in turn stimulate the muscle fibers more vigor-
ously), and this stimulation continues until the contraction is completed. In short, the com-
parators (the stretch receptors) continue to stimulate the action system (the motor neurons
and muscle fibers) until the goal (a successful muscle contraction) is achieved. Feedback can
play a crucial role in even the simplest reflexive behaviors.


Whereas a reflex is the stereotyped movement of a part of the body, a tropism is a move-
ment or change in orientation of the entire animal. The first to study tropisms was Jacques
Loeb (1900), who called tropisms forced movements to suggest that no intelligence, will, or
choice was involved. Later researchers (e.g., Fraenkel & Gunn, 1940) grouped tropisms into
two major categories: kineses (plural of kinesis) and taxes (plural of taxis).


A common example of a kinesis is the humidity-seeking behavior of the wood louse. This
creature, though actually a small crustacean, resembles an insect, and it spends most of its
time under a rock or a log in the forest. The wood louse must remain in humid areas in

Figure 2.2 A cross section of the spinal cord, along with the components of the spinal withdrawal reflex.


order to survive; if the air is too dry, it will die of dehydration in a matter of hours. Fortu-
nately for the wood louse, nature has provided it with a simple yet effective technique for
finding and remaining in moist areas. To study the wood louse’s strategy, Fraenkel and Gunn
(1940) placed several wood lice in the center of a chamber in which the air was moist at
one end and dry at the other. They found that the wood lice usually kept walking when
they were in the dry end of the chamber, but they frequently stopped for long periods of
time in the moist end. As a result, wood lice tended to congregate in the moist end of the

What distinguishes a kinesis from a taxis is that in a kinesis the direction of the movement
is random in relation to a stimulus. The wood louse does not head directly toward a moist
area or away from a dry one because it has no means of sensing the humidity of a distant
location—it can only sense the humidity of its present location. Nevertheless, its tendency
to keep moving when in a dry area and stop when in a moist area is generally successful in
keeping the creature alive. Kineses can also help to keep creatures away from predators. For
instance, one species of slugs displays rapid movement when exposed to a chemical produced
by a predatory beetle and less movement when the chemical is not present (Armsworth,
Bohan, Powers, Glen, & Symondson, 2005).

The wood louse’s humidity-seeking behavior is another example of a feedback sys-
tem. Although we do not know exactly how the wood louse measures humidity, its
behavior tells us that it must have a comparator that can detect the actual input (current
humidity) and compare it to the reference input (the goal of high humidity). The
action system in this case is the creature’s locomotion system, that is, the motor neurons,
muscles, and legs that allow it to move about. Locomotion is, of course, the output of
this action system, but there is no guarantee that locomotion will lead to the goal of
high humidity. The wood louse may move about incessantly if it finds itself in a dry
location, but if there are no humid areas nearby, the goal of high humidity will not be


Unlike kineses, in a taxis, the direction of movement bears some relationship to the location
of the stimulus. One example of a taxis is a maggot’s movement away from any bright light
source. If a bright light is turned on to the maggot’s right, it will promptly turn to the left
and move in a fairly straight line away from the light. The maggot accomplishes this direc-
tional movement by using a light-sensitive receptor at its head end. As the maggot moves,
its head repeatedly swings left and right, and this oscillating movement allows it to compare
the brightness of light in various directions and to move toward the direction where the
light is less intense.

A more sophisticated taxis is exhibited by the ant, which can use the sun as a navigational
aid when traveling to or from its home. On a journey away from home, the ant travels in a
straight path by keeping the sun at a constant angle to its direction of motion. To return
home, the ant changes the angle by 180 degrees. The ant’s reliance on the sun can be dem-
onstrated by providing it with an artificial sun that the experimenter can control. If this light
source is gradually moved, the ant’s direction of travel will change to keep its orientation
constant with respect to the light (Schneirla, 1933).



So far we have discussed innate behaviors that consist of either a brief movement or a con-
tinuous series of adjustments. The innate behavior patterns we will now examine are more
complex, for they consist of a series of different movements performed in an orderly

Fixed Action Patterns

A fixed action pattern is a sequence of behaviors that has the following characteristics:
(1) It is a part of the repertoire of all members of a species, and it may be unique to that
species; (2) suitable experiments have confirmed that the animal’s ability to perform the
behavior is not a result of prior learning experiences; and (3) the behaviors occur in a rigid
order regardless of whether they are appropriate in a particular context. Once a fixed action
pattern is started, it will continue to completion.

As an example of a fixed action pattern, Eibl-Eibesfeldt (1975) described the nut-burying
behavior of a particular species of squirrel:

The squirrel Sciurus vulgaris L. buries nuts in the ground each fall, employing a quite
stereotyped sequence of movement. It picks a nut, climbs down to the ground, and
searches for a place at the bottom of a tree trunk or a large boulder. At the base of such
a conspicuous landmark it will scratch a hole by means of alternating movements of the
forelimbs and place the nut in it. Then the nut is rammed into place with rapid thrusts
of its snout, covered with dirt by sweeping motions and tamped down with the

(p. 23)

Although all members of the species exhibit this behavior pattern, this does not prove that
the behavior is innate. Each squirrel may learn how to bury nuts by watching its parents
early in life. To determine whether the behavior pattern is innate, Eibl-Eibesfeldt con-
ducted a deprivation experiment in which all possible means of learning the behavior
were removed. A squirrel was separated from its parents at birth and raised in isolation so
that it had no opportunity to observe other squirrels burying nuts (or doing anything else,
for that matter). In addition, the squirrel received only liquid food and it lived on a solid
floor, so it had no experience in handling food or in digging or burying objects in the
ground. The animal was kept well fed so that it had little chance of discovering that stor-
ing away food for a time of need is a good strategy. When the squirrel was full grown,
Eibl-Eibesfeldt finally gave it some nuts, one at a time. At first the squirrel ate the nuts
until apparently satiated. When given additional nuts, it did not drop them but carried
them around in its mouth as it searched about the cage. It seemed to be attracted by verti-
cal objects, such as a corner of the cage, where it might drop the nut. Obviously, it could
not dig a hole in the floor, but it would scratch at the floor with its forepaws, push the
nut into the corner with its snout, and make the same covering and tamping-down
motions seen in the burying sequence of a wild squirrel. This careful experiment dem-
onstrates conclusively that the squirrel’s nut-burying repertoire is innate. The caged


squirrel’s scratching, covering, and tamping-down motions in the absence of dirt show
how the components of a fixed action pattern will occur in their usual place in the
sequence even when they serve no function.

It usually takes a fairly specific stimulus, called a sign stimulus, to trigger a fixed action
pattern. In the case of the squirrel, the sign stimulus is clearly the nut, but without further
experiments we cannot tell which features—its size, shape, color, and so on—are essential
ingredients for eliciting the response. For other fixed action patterns, systematic investigation
has revealed which features of a stimulus are important and which are irrelevant. In humans,
Provine (1989) has found evidence that contagious yawning (the tendency to yawn when
someone else yawns) is a fixed action pattern that may occur if we see the entire face of a
yawning person. Seeing only the yawner’s eyes or only the mouth is not enough to elicit
contagious yawning.

A surprising finding is that sometimes an unrealistic stimulus can elicit a stronger
response than the actual sign stimulus itself. One example is provided by the oyster-
catcher, a bird that lays white eggs with brown spots. If one of its eggs rolls out of its
nest, the bird will retrieve it with stereotyped head and neck movements. However, if
given a choice between one of its own eggs and a replica that is four times as large, it
prefers this supernormal stimulus to the normal one and strains to bring this “egg” to
its nest (Figure 2.3).

Reaction Chains

Whereas fixed action patterns continue until completion once started, in a reaction chain
the progression from one behavior to the next depends on the presence of the appropriate
stimulus. If the stimulus is not present, the chain of behaviors will be interrupted. On the
other hand, if a stimulus for a behavior in the middle of a chain is presented at the outset,
the earlier behaviors will be omitted.

Figure 2.3 An oystercatcher attempts to roll a supernormal egg back to its nest.



An interesting example of a reaction chain is provided by the hermit crab. The hermit
crab has no shell of its own; instead, it lives in the empty shells of mollusks. Frequently
during its life, the hermit crab grows too large for its present shell and must find a larger
one. Reese (1963) identified at least eight separate behaviors that usually occur in a
sequence as a crab searches for and selects a new shell. A crab that needs a new shell
exhibits a high level of locomotion. Eventually the crab spots a shell visually, at which
point it approaches the shell and touches it. The crab grasps the shell with its two front
legs, then climbs on top of it. Its cheliped (claw) is used to feel the texture of the surface—
a rough texture is preferred. The crab then climbs down and rotates the shell in its legs,
exploring the external surface. When the aperture of the shell is located, this too is
explored by inserting the cheliped as far as possible. If there is sand or other debris in the
aperture, it is removed. Once the aperture is clear, the crab turns around and inserts its
abdomen deeply into the shell and then withdraws it, evidently to determine whether the
size of the interior is acceptable. If the shell is suitable, the crab turns the shell upright,
enters it once again, and then goes on its way.

The steps of this response chain are diagrammed in Figure 2.4, which emphasizes a key
point about reaction chains: Each response usually produces the stimulus for the next


No shell

in sight

within reach

External surface



Interior size

right side up


Approach shell

Explore external
surface (lift, climb)

Search for aperture

Explore aperture
with cheliped,
remove any debris

Insert abdomen

Turn shell
right side up

Enter shell


Figure 2.4 The hermit crab’s reaction chain of shell searching and selecting behaviors. The behaviors
form a chain because each successive behavior usually leads to the stimulus for the next behavior in
the chain.


response in the chain. For instance, the first response, locomotion, eventually leads to the
sight of a shell, which is the stimulus for the second response, approach. The response of
approach brings the shell within reach, which is the stimulus for the third response, lifting,
and so on. However, unlike fixed action patterns, the behaviors of a reaction chain do not
always occur in the full and complete sequence. The sequence can stop at any point if the
stimulus required for the next step is not there. For example, Reese (1963) found that shells
filled with plastic would elicit the first five behaviors in Figure 2.4, but since the aperture
was not open, the sixth behavior did not occur and the crab would eventually walk away.
Conversely, the beginning steps of the sequence may be omitted if the stimulus for a behavior
in the middle of the sequence occurs. When crabs were presented with a suitable shell with
the aperture directly in front of them, they would often omit the first five behaviors of the
sequence and proceed with the last. This dependence on external stimulus support makes
reaction chains more variable, but at the same time more adaptable, than fixed action


Although human beings have a variety of reflexes, plus a few fixed action patterns and other
inborn behaviors, these innate responses certainly constitute a very small portion of what
we do. As noted in Chapter 1, almost all of our daily behaviors are products of our learning
experiences. Because learning plays such a large role in human behavior, some philosophers,
such as the British Empiricists, have maintained that all human behavior is based on prior
learning. (Recall John Locke’s statement that the mind of a child at birth is a tabula rasa, or
blank slate.) This viewpoint about the all-important role of experience was shared by many
psychologists, including the behaviorist John B. Watson (1925), whose bold statement about
the importance of upbringing is often quoted:

Give me a dozen healthy infants, well-formed, and my own specified world to
bring them up in and I’ll guarantee to take any one at random and train him to
become any type of specialist I might select—doctor, lawyer, artist, merchant-chief,
and yes, even beggar-man and thief, regardless of his talents, penchants, tendencies,
abilities, vocations, and race of his ancestors. I am going beyond the facts and I
admit it, but so have advocates of the contrary, and they have been doing it for
thousands of years.

(p. 82)

Watson believed that the environment could play such a dominant role in determining
what type of adult a child will become because he thought heredity had little or nothing
to do with how people behave. In The Blank Slate, Steven Pinker (2002) argued that this
point of view, though widely held in modern society, is incorrect and that heredity plays
a much larger role than is commonly assumed. Pinker surveyed evidence from various
areas of scientific research, including neurophysiology, genetics, psychology, and anthro-
pology, to support his contention that all human beings have in common a large set of
inborn abilities, tendencies, and predispositions, which collectively might be called
“human nature.” He maintained that the human brain is not simply a batch of uniform,


undifferentiated neurons that are waiting to be shaped by whatever the environment
presents. He reviewed evidence that neurons in different parts of the brain are special-
ized to perform certain functions or to respond to the environment in certain preestab-
lished ways.

As one example, it is well known that certain parts of the human brain play a critical role
in our ability to use language. A section of the cerebral cortex called Wernicke’s area is
essential for language comprehension: If this area is damaged through accident or illness, a
person cannot understand spoken language. Another area of the cerebral cortex, Broca’s area,
is necessary for speech production, and if this area is damaged, a person loses the ability to
speak in coherent sentences. Pinker maintains that the presence of neurons specifically
designed to respond to human speech is what allows young children to learn language so
easily. Although chimpanzees, dolphins, and a few other species can be taught to use human-
like language to a certain degree (as described in Chapter 10), no other species comes close
to what young children can do.

A strategy used by Pinker (and by other scientists) to support the claim that a particular
characteristic of human beings is innate is to demonstrate that this characteristic is found in
people everywhere on earth. We cannot conduct deprivation experiments with people as
Eibl-Eibesfeldt (1975) did with a squirrel, but we can demonstrate that people living in vastly
different cultures and environments all exhibit a particular characteristic. There are many
different languages on earth, but all human societies have verbal language, and all human
languages have nouns, verbs, adjectives, and adverbs. Although different languages use dif-
ferent word orders, there are certain commonalities in the way sentences are structured
(Baker, 2001). Children of all cultures babble before they learn to speak, and even deaf
children babble at an early age (Lenneberg, 1967). These and other cross-cultural universals
have been used as evidence for an innate human ability to acquire language. However, some
researchers have argued that upon closer inspection, the similarities across human languages
are not really as universal as they may appear (Evans & Levinson, 2009), and this issue has
not been settled.

One other aspect of human behavior that may be innate is the range of emotions people
experience, how emotions are reflected in their facial expressions, and how others interpret
these facial expressions. The psychologist Paul Ekman has found that facial expressions
can be understood by people from cultures around the world (Ekman, 1973; Ekman &
Matsumoto, 2011). Ekman showed people from many different cultures photographs of
faces that depicted six different emotions (happiness, disgust, surprise, sadness, anger, and
fear) and asked them to classify the emotion of the person in the photograph. Regardless
of where they lived, people showed a high degree of accuracy in classifying the emotions
shown in the photographs. Ekman and his colleagues have also suggested that there is a
cross-cultural ability to recognize basic emotions through a person’s vocalizations, such as
screams or laughs (Sauter, Eisner, Ekman, & Scott, 2010). Some of Ekman’s hypotheses
remain controversial, but many psychologists now agree that there is cross-cultural uni-
formity in how people express emotions and interpret facial expressions. However, learn-
ing is also involved because some types of facial expressions are culture specific. For
example, in China, sticking out your tongue is a way of showing surprise, and this is not
so in Western societies.



We Have a Lot in Common: Human Universals

Scientists who study human behavior have concluded that people around the world share
many basic characteristics besides simple reflexes. The anthropologist Donald E. Brown
(1991) has compiled a list of human universals—abilities or behaviors that are found in
all known human cultures. The list contains about 400 items, and it includes some very
specific behaviors such as dance, music, death rituals, hygienic care, jokes, and folklore, as
well as some major characteristics of human life, such as marriage, inheritance rules, tool
making and tool use, government, sanctions for crimes, and division of labor. Learning and
experience clearly affect just about every item on Brown’s list: Dance, music, and folklore
vary tremendously from culture to culture. So do a society’s type of government, what is
considered a crime and how people are punished, what types of tools people make, and
how labor is divided among individuals. However, Brown’s point is that every human society
has some type of dance, some type of government, some type of division of labor, and so on
(Figure 2.5). He maintains that because these characteristics of human existence are found
in all cultures, even those that are completely isolated from the modern world, they most
likely reflect innate human tendencies. Over the years, other researchers have suggested
possible additions to Brown’s original list based on new findings about cross-cultural simi-
larities in human behavior (e.g., Aknin et al., 2013; Saucier, Thalmayer, & Bel-Bahar, 2014).

Figure 2.5 Dance is a human universal. (Filipe Frazao/


Deciding that a particular behavioral tendency or characteristic is innate is not an easy
matter. The fact that a behavioral characteristic is found in all human cultures does not,
by itself, constitute proof that the characteristic is innate. Another possibility is that the
behavior is seen in people everywhere because the environment places similar constraints
on people everywhere. For example, one could argue that division of labor is advanta-
geous in all environments because it is more efficient for an individual to become an expert
in one line of work than to try to master dozens of different skills. Perhaps future research
on human genetics will help sort out which of these universal human characteristics are
hereditary, which are the products of similar environments, and which are a combination
of the two. Whatever the case may be, Brown’s list of human universals is interesting
to contemplate because it shows, in a world full of people with vastly different lifestyles,
interests, beliefs, and personalities, how much all people have in common.

Can you think of other human universals besides those mentioned above? Take a few
moments and try to list a few examples of behaviors, practices, or customs that are found
in all human cultures. You can then compare your examples with Brown’s complete list,
which can be found at:


Habituation is defined as a decrease in
the strength of a response after repeated
presentation of a stimulus that elicits the
response. Here is a typical example. For his
vacation, Dick has rented a cottage on a
picturesque lake deep in the woods. The
owner of the cottage has advised Dick that
although the area is usually very quiet,
members of the fish and game club just
down the shore often engage in target
practice for a few hours during the eve-
ning. Despite this forewarning, the first
loud rifle shot elicits a startle reaction from
Dick—he practically jumps out of his
chair, his heart beats rapidly, and he
breathes heavily for several seconds. After
about half a minute, Dick has fully recov-
ered and is just returning to his novel
when he is again startled by a second gun-
shot. This time, the startle reaction is not
as great as the first one: Dick’s body does
not jerk quite as dramatically, and there is
not so large an increase in heart rate. With
additional gunshots, Dick’s startle response

Practice Quiz 1: chaPter 2
1. In control systems theory, the com-

parator compares the ______ and
the ______, and if they do not match,
the comparator signals the ______.

2. In the flexion reflex, pain receptors
in the hand have synaptic connec-
tions with ______, which in turn have
synapses with ______.

3. A kinesis is a ______ movement in
response to a stimulus, and a taxis
is a ______ movement in response
to a stimulus.

4. The main difference between fixed
action patterns and reaction chains
is that ______.

5. Abilities or behaviors that are found
in all known human cultures are
called ______.


1. actual input, reference input, action system
2. interneurons, motor neurons 3. random, direc-
tional 4. the behavior sequence occurs in a rigid order
in fixed action patterns, but it is more flexible in reaction
chains 5. human universals


decreases until it has habituated completely; that is, the noise no longer disrupts his con-
centration on his novel.

Another behavior that often displays habituation is the orienting response. If a new
sight or sound is presented to a dog or other animal, the animal may stop its current activity,
lift its ears and its head, and turn in the direction of the stimulus. If the stimulus is presented
repeatedly but is of no consequence, the orienting response will disappear. Similarly, if an
infant is played a tape recording of an adult’s voice, the infant will turn its head in the direc-
tion of the sound. If, however, the same word is played over and over, the infant will soon
stop turning toward the sound. Therefore, both animals and humans will typically exhibit
an orienting response to a novel stimulus, and they will both exhibit habituation of the
orienting response if the same stimulus is presented many times.

The function that habituation serves for the individual should be clear. In its everyday
activities, a creature encounters many stimuli, some potentially beneficial, some potentially
dangerous, and many neither helpful nor harmful. It is to the creature’s advantage to be able
to ignore the many insignificant stimuli it repeatedly encounters. Being continually startled
or distracted by such stimuli would be a waste of the creature’s time and energy. A study by
Dielenberg and McGregor (1999) shows how animals can habituate to a fear-provoking
stimulus if the stimulus repeatedly proves to be insignificant. Rats were presented with a cat
collar that contained a cat’s odor, and the response of the rats was to run into a hiding place
and remain there for quite a while. However, Figure 2.6 shows that after several presentations
of the cat collar, the rats’ hiding times decreased and came close to those of the control group
of rats that were exposed to a cat collar that had no cat odor on it.

Figure 2.6 The amount of time rats spent hiding when exposed to a cat collar with cat odor exhibits
habituation over successive days of exposure. The filled circles are from a control group of rats exposed
to a cat collar that had no cat odor. (From Dielenberg, R.A. & McGregor, I.S., 1999, Habituation of
the hiding response to cat odor in rats, Journal of Comparative Psychology, 113, 376–387. © American
Psychological Association. Adapted with permission.)







1 2 3








4 5


Because habituation is a simple yet very useful type of learning, it is not surprising that
it can be found throughout the animal kingdom. Habituation can be seen in hydra, whose
diffuse networks of neurons are among the most primitive nervous systems found on our
planet (Rushford, Burnett, & Maynard, 1963). There have even been reports of habituation
in protozoa (one-celled organisms). In one study, Wood (1973) found a decline in the
contraction response of the protozoan Stentor coeruleus with repeated presentations of a
tactile stimulus. At the same time, its responsiveness to another stimulus, a light, was

General Principles of Habituation

We have seen that habituation occurs in species ranging from one-celled organisms to
human beings. Furthermore, it is not just the existence of habituation but its properties that
are similar in such diverse species. In a frequently cited article, Thompson and Spencer
(1966) listed some of the main principles of habituation that have been observed in people
and in a wide variety of other species.

1. Course of Habituation. Habituation of a response occurs whenever a stimulus is repeat-
edly presented, but it is usually a gradual process that progresses over a number of trials. The
decrements in responding from trial to trial are large at first but get progressively smaller as
habituation proceeds.

2. Effects of Time. If after habituation the stimulus is withheld for some period of time, the
response will recover. The amount of recovery depends on how much time has elapsed. We
might say that habituation is “forgotten” as time passes. Suppose that after Dick’s startle
response to the gunshots has habituated, there are no more gunshots for 30 minutes, but then
they begin again. Dick is likely to exhibit a weak startle reaction to the first sound of gunshot
after the break. (Thus, there is some savings over time, but also some forgetting.) In com-
parison, if there were no further shooting until the following evening, Dick’s startle reac-
tion after this longer time interval would be larger.

3. Relearning Effects. Habituation may disappear if the stimulus is not presented for a
long period of time, but if the same stimulus then begins again, the rate of habituation
should be faster the second time. Later, if there is a third or fourth series of stimulus pre-
sentations, the habituation should be faster each time. To use Ebbinghaus’s term, there will
be savings from the previous periods of habituation. For example, although Dick’s initial
startle response to the sound of gunfire on the second evening of his vacation might be
almost as large as on the first evening, the response should disappear more quickly the
second time.

4. Effects of Stimulus Intensity. We have already seen that a reflexive response is frequently
stronger with a more intense stimulus. Such a response is also more resistant to habituation.
Habituation proceeds more rapidly with weak stimuli, and if a stimulus is very intense, there
may be no habituation at all.

5. Effects of Overlearning. As in Ebbinghaus’s experiments, further learning can occur in
habituation even if the response to a stimulus has completely disappeared. Thompson and
Spencer called this below-zero habituation because it occurs at a time when there is no
observable response to the stimulus. Suppose that after 20 gunshots, Dick’s startle response
has completely disappeared. After a 24-hour interval, however, he might show little savings


from the previous day’s experience. In comparison, if there were 100 gunshots on the first
evening, Dick would probably show less of a startle response on the second evening. In
other words, although the additional 80 gunshots produced no additional changes in
Dick’s behavior at the time, they did increase his long-term retention of the

6. Stimulus Generalization. The transfer of habituation from one stimulus to new but
similar stimuli is called generalization. For example, if on the third evening the sounds of
the gunshots are somewhat different (perhaps because different types of guns are being used),
Dick may have little difficulty ignoring these sounds. The amount of generalization depends
on how similar the new stimulus is to the habituated stimulus.

Developmental psychologists can use habituation (along with stimulus generalization),
as a tool to determine exactly which stimuli an infant finds similar, and in doing so they
can learn a lot about infants’ sensory and cognitive abilities. For example, Johnson and
Aslin (1995) presented 2-month-old infants with a display that featured a dark rod moving
from side to side behind a white box (Figure 2.7). At first, the infants would look at this
display for many seconds, but after repeated presentations, this orienting response habitu-
ated. Then, the infants were tested with two new stimuli: a solid rod moving back and
forth with no box in front and a broken rod moving back and forth. The infants showed
more generalization of habituation to the solid rod than to the broken rod. Based on this
finding, Johnson and Aslin concluded that the infants treated the original stimulus as a
solid rod (not a broken rod) moving behind the box, even though the middle part of the
rod could not be seen.

Many experiments have used similar procedures to examine a wide range of skills in
human infants, including their ability to recognize visual stimuli (Singh et al., 2015), to
discriminate different musical excerpts (Flom & Pick, 2012), and to analyze cause and
effect in a chain of events (Kosugi, Ishida, Murai, & Fujita, 2009). This strategy of using
habituation to measure surprise or changes in attention has proven to be a valuable tech-
nique for studying the perceptual and mental abilities of infants, even those less than a
month old.

Habituation Stimulus Test Stimuli

Figure 2.7 In a study by Johnson and Aslin (1995), infants were repeatedly shown the stimulus on
the left until their orienting responses to the stimulus habituated. They were then tested for general-
ization, using each of the two stimuli on the right.


Neural Mechanisms of Habituation

What takes place in a creature’s nervous system when it habituates to a stimulus? To investigate
this question, some scientists have studied fairly primitive creatures, a strategy known as the
simple systems approach. A good example is the work of Eric Kandel and his colleagues
(Abbott & Kandel, 2012; Castellucci, Pinsker, Kupfermann, & Kandel, 1970), who have spent
several decades studying both the behavior and the nervous system of Aplysia, a large marine
snail. They chose to study this animal because its nervous system is relatively simple—it
contains only a few thousand neurons compared to the billions in a mammal’s nervous system.
Kandel and his coworkers investigated the process of habituation in one of Aplysia’s reflexes,
the gill-withdrawal reflex. If Aplysia’s siphon (described as a “fleshy spout”) is touched lightly,


Habituation and Psychological Functioning

Although habituation is a very simple type of learning, it is a useful and important one.
A creature that was unable to habituate to insignificant stimuli would probably have a
difficult time attending to more important stimuli. In fact, there is some evidence that
the speed at which human infants and children habituate to repetitive stimuli is related
to their mental abilities later in life. Laucht, Esser, and Schmidt (1994) found that infants
who displayed faster habituation to repetitive stimuli at 3 months of age obtained, on
average, slightly higher scores on intelligence tests when they were 4½ years old. Even
before birth, the human fetus exhibits habituation to such stimuli as vibration or sounds,
and one study found that a fetus’s rate of habituation was related to performance on
tests of cognitive functioning 6 months after birth (Gaultney & Gingras, 2005).

Other studies have compared rates of habituation in human adults who are or are not
suffering from various psychological disorders such as schizophrenia or severe depres-
sion. In some research, habituation was measured in brain activity as the people were
repeatedly presented with visual images of faces or other objects. The findings were
that habituation in some brain areas, such as the cerebellum and the visual cortex, was
slower in individuals with schizophrenia or depression than in the normal population
(Williams, Blackford, Luksik, Gauthier, & Heckers, 2013). Slower habituation in people
with depression was also found when the habituation was measured in simple overt
behaviors such as the eyeblink reflex.

These are correlational studies, not experiments, so it would be a mistake to try to
draw any conclusions about cause and effect from them. Still, this research does sug-
gest that the ability to habituate to repetitive, unimportant stimuli early in life may be one
predictor of later mental abilities and that this very simple type of learning may be related
to overall mental health.


its gill contracts and is drawn inside the mantle for a few seconds (Figure 2.8a). The neural
mechanisms that control this reflex are well understood. The siphon contains 24 sensory
neurons that respond to tactile stimulation. Six motor neurons control the gill-withdrawal
response. Each of the 24 sensory neurons has a monosynaptic connection (i.e., a direct con-
nection that involves just one synapse) with each of the six motor neurons. In addition, other
axons from the sensory neurons are involved in polysynaptic connections (indirect connec-
tions mediated by one or more interneurons) with these same motor neurons. Figure 2.8b
depicts a small portion of this neural circuitry.

If the siphon is stimulated about once every minute for 10 or 15 trials, the gill-withdrawal reflex
habituates. Complete habituation lasts for about an hour, and partial habituation may be observed
for as long as 24 hours. If such trials are given on three or four successive days, long-term habitu-
ation (lasting several weeks) can be observed. Through a series of elaborate tests, Kandel’s group
was able to determine that during habituation, a decrease in excitatory conduction always occurred
at the synapses involving the axons of the sensory neurons (the points marked by arrows in
Figure 2.8b). There was no change in the postsynaptic neuron’s sensitivity to the transmitter. What
had changed was the amount of transmitter released by the presynaptic (sensory) neurons: With
repeated stimulus presentations, less transmitter was released into the synapse.

Figure 2.8 (a) The marine snail Aplysia. If the siphon is lightly touched, the gill reflexively withdraws
beneath the hard mantle. (b) A small portion of the neural circuitry involved in Aplysia’s gill-
withdrawal reflex. Habituation occurs because after repeated stimulation the sensory neurons release
less neurotransmitter at the points indicated by the arrows.


Kandel then proceeded to ask questions at a deeper level: What chemical mechanisms are
responsible for the depressed transmitter release of the sensory neurons? Each time a neuron
fires, there is an influx of calcium ions into the axon terminals, and this calcium current is
thought to cause the release of transmitter into the synapse. Klein, Shapiro and Kandel (1980)
found that the calcium current grew weaker during habituation, and in the recovery period
after habituation, both the calcium current and the response of the postsynaptic (motor)
neuron increased at the same rate. The experimenters concluded that a decrease in the cal-
cium current causes a decrease in the amount of transmitter released into the synapse, which
in turn decreases the excitation of the motor neuron, producing a weakened gill-withdrawal

The work of Kandel and associates nicely illustrates the potential advantages of the simple
systems strategy in physiological research on learning. Because of the comparative simplicity
of Aplysia’s neural networks, researchers have been able to pinpoint the neural changes
responsible for habituation. This research shows that, at least in some cases, learning depends
on changes at very specific neural locations, not on widespread changes in many parts of the
nervous system. Furthermore, this learning involved no anatomical changes, such as the
growth of new axons, but merely changes in the effectiveness of already established connec-
tions between neurons.

Because the nervous system of a typical mammal is so much more complex than that of
Aplysia, it is much more difficult to identify the individual neurons that undergo change
during habituation to a stimulus. Nevertheless, substantial progress has been made in locating
the brain locations involved in habituation, at least in certain specific cases. Michael Davis
(1989) has conducted extensive research on one such specific case: habituation of a rat’s
startle response to a sudden loud noise. The startle response is measured by testing a rat in
a chamber that sits on springs so that the rat’s movement when it is startled shakes the cham-
ber slightly, and this movement is measured by a sensor. Through many careful studies, Davis
and colleagues were able to trace the entire neural circuit involved in a rat’s startle response
(Davis, Gendelman, Tischler, & Gendelman, 1982). The circuit begins in the auditory nerve,
works its way through auditory pathways to the brainstem, then proceeds to motor pathways
that controlled the muscles involved in the startle response. Davis found that habituation of
the startle reflex takes place in the early portions of this circuit—the auditory pathways.
Although the exact neurons responsible for the habituation have not been identified, these
findings are similar to those from Aplysia in two respects. First, the neurons that undergo
change during habituation are on the sensory side of the circuit. Second, the changes take
place within the reflex circuit itself, rather than being the result of new inputs from neurons
elsewhere in the nervous system.

Other studies with mammals extend but also complicate the physiological picture of
habituation. In some cases of habituation, higher sections of the brain seem to be involved,
including the auditory cortex. Using guinea pigs, Condon and Weinberger (1991) found
that if the same tone was presented repeatedly, individual cells in the auditory cortex
“habituated”; that is, they decreased their sensitivity to this tone, but not to tones of higher
or lower pitch.

With modern brain-imaging techniques, such as positron emission tomography
(PET) and functional magnetic resonance imaging (fMRI), it has become possible
to identify brain areas that are involved in habituation in humans. With fMRI, researchers
can measure the activity of different parts of the brain in real time, as a person performs


some task or is presented with some stimulus. For instance, one study using fMRI found
habituation in many different parts of the brain, including the cerebral cortex and the hip-
pocampus, when people were repeatedly shown the same pictures of human faces (Fischer
et al., 2003). Other brain areas show habituation when people are presented with repeated
speech sounds (Joanisse, Zevin, & McCandliss, 2007). PET scans have displayed changes in
the cerebellum as a person’s startle response to a loud noise habituates (Timmann et al.,
1998). There is growing evidence that many different areas of the brain and nervous system
can undergo habituation when the same stimulus is repeatedly presented.

Neuroscientists use the term plasticity to refer to the nervous system’s ability to change as
a result of experience or stimulation. All in all, the physiological studies of habituation
demonstrate that plasticity is possible in many different levels of the nervous system.
Although simple chemical changes such as neurotransmitter depletion may be responsible
for some types of habituation, in other cases the neural mechanisms appear to be quite
complex (Thompson, 2014).

Habituation in Emotional Responses:
The Opponent-Process Theory

Richard Solomon and John Corbit (1974) proposed a theory of emotion that has attracted
a good deal of attention. The theory is meant to apply to a wide range of emotional reac-
tions. The type of learning they propose is quite similar to the examples of habituation we
have already examined: In both types of learning, an individual’s response to a stimulus
changes simply as a result of repeated presentations of that stimulus. However, according to
the opponent-process theory of Solomon and Corbit, with stimulus repetition, some
emotional reactions weaken while others are strengthened.

The Temporal Pattern of an Emotional Response

Imagine that you are a premedical student taking a course in organic chemistry. You received
a C+ on the midterm, and your performance in laboratory exercises was fair. You studied
hard for the final exam, but there were some parts of the exam that you could not answer.
While leaving the examination room, you overheard a number of students say that it was a
difficult test. Later you receive your grades for the semester, and you learn to your surprise
that your grade in organic chemistry was an A–! You are instantly ecstatic and you tell the
good news to everyone you see. You are too excited to do any serious work, but as you run
some errands, none of the minor irritations of a typical day (long lines, impolite salespeople)
bother you. By evening, however, your excitement has settled down, and you experience a
state of contentment. The next morning you receive a call from the registrar’s office. There
has been a clerical error in reporting the grades, and it turns out that your actual grade in
organic chemistry was B–. This news provokes immediate feelings of dejection and despair.
You reevaluate your plans about where you will apply to medical school, and you wonder
whether you will go at all. Over the course of a few hours, however, your emotional state
gradually recovers and returns to normal.

This example illustrates all of the major features of a typical emotional episode as pro-
posed by opponent-process theory. Figure 2.9 presents a graph of your emotional states


during this imaginary episode. The solid bar at the bottom marks the time during which
some emotion-eliciting stimulus is present. In this example, it refers to the time when you
believed your grade was an A–. The y-axis depicts the strength of an individual’s emotional
reactions both while the stimulus is present and afterward. (The response to the stimulus
itself is always plotted in the positive direction, regardless of whether the emotion is “pleas-
ant” or “unpleasant.”) According to the theory, the onset of such a stimulus produces the
sudden appearance of an emotional reaction, which quickly reaches a peak of intensity (the
initial ecstasy in this example). This response then gradually declines to a somewhat lower
level, or plateau (your contentment during the evening). With the offset of the stimulus
(the telephone call), there is a sudden switch to an emotional after-reaction that is in some
sense the opposite of the initial emotion (the dejection and despair). This after-reaction
gradually declines, and the individual’s emotional reaction returns to a neutral state.

As an example where the initial reaction was unpleasant, Solomon and Corbit described
a study in which dogs received a series of shocks (Church, LoLordo, Overmier, Solomon, &
Turner, 1966). When a shock began, the dogs would show obvious signs of fear and distress,
and their heart rates rose rapidly from a resting state of about 120 beats/minute to about
200 beats/minute and then began to decline. At the termination of the shock, a typical dog’s
behavior was characterized as “stealthy, hesitant, and unfriendly” (1966, p. 121). These after-
reactions may not sound like the opposite of fear, but they were certainly different from the
initial reaction, and there was a rebound effect in which the dogs’ heart rates dropped to
about 90 beats/minute and then returned to normal after a minute or so.

Figure 2.9 The typical pattern of an emotional response according to the opponent-process theory.
The solid bar shows the time during which an emotion-eliciting stimulus is present. The solid bar
shows the time during which an emotion-eliciting stimulus is present. (From Solomon, R.L., & Corbit,
J.D., 1974, An opponent-process theory of motivation: I. Temporal dynamics of affect, Psychological
Review, 81, 119–145. © American Psychological Association. Reprinted with permission.)




Peak of Affective

Decay of

Peak of Primary
Affective Reaction

Adaption Phase

Steady Level






Standard Pattern of Affective

Intensity of


Intensity of




The a-Process and b-Process

Solomon and Corbit proposed that many emotional reactions follow the pattern
shown in Figure 2.9. They theorized that this pattern is the result of two opposing
internal processes that they called the a-process and the b-process. The a-process is
largely responsible for the initial emotional response, and the b-process for the after-
reaction. The left half of Figure 2.10 shows how these two processes supposedly
combine to produce the pattern in Figure 2.9. Solomon and Corbit described the
a-process as a fast-acting response to a stimulus that rises to a maximum and remains
there as long as the stimulus is present. When the stimulus ends, the a-process decays
very quickly (see the middle left graph in Figure 2.10). In the heart-rate study with
dogs, the a-process would be some hypothetical internal mechanism (perhaps the flow
of adrenaline) that produces, among other responses, an increase in heart rate. The
opposing b-process is supposedly activated only in response to the activity of the
a-process, and it is more sluggish both to rise and to decay. The middle left graph in
Figure 2.10 also shows the more gradual increase and decrease in the b-process. In
the heart-rate example, the b-process would be some internal mechanism causing a
decrease in heart rate.

Note in Figure 2.10 that the b-process begins to rise while the stimulus (the shock)
is still present. Solomon and Corbit proposed that when both the a- and b-processes




Panel A.
First Few Stimulations

Panel B.
After Many Stimulations










Figure 2.10 According to opponent-process theory, a person’s emotional reaction (or “manifest affec-
tive response”) is jointly determined by the underlying a- and b-processes. The proposed time course
of these processes during the first few presentations of an emotion-eliciting stimulus is shown on the
left. The right side shows the predicted patterns after many repetitions of the same stimulus. (From
Solomon, R.L., & Corbit, J.D., 1974, An opponent-process theory of motivation: I. Temporal dynam-
ics of affect, Psychological Review, 81, 119–145, © American Psychological Association. Reprinted with


are active to some degree, the resulting emotional response can be predicted by simple
subtraction. That is, the action of the a-process will be countered to some extent by
the action of the b-process, and the emotional response will be weaker. This is why
there is a drop in the initial emotional reaction from the peak to the plateau. When
the stimulus ends and the a-process quickly decays, all that remains is the b-process,
which produces the emotional after-reaction. You should see how the two processes
in the middle left graph of Figure 2.10 combine to produce the pattern in the upper
left graph.

The Effects of Repeated Stimulation

One of the most important parts of the opponent-process theory concerns how the pattern
of an emotional response changes with repeated presentations of the same stimulus. To put
it simply, the theory states that with repeated exposures to a stimulus, the initial emotional
response exhibits a sort of habituation—it becomes progressively smaller—while at the same
time there is a marked increase in the size and duration of the after-reaction. The top right
graph in Figure 2.10 shows the predicted pattern of an emotional response after many stimu-
lations. The middle right graph shows that, according to the theory, this change is the result
of an increase in the size of the b-process. Whereas the a-process does not change, the
b-process is strengthened with use and weakened with disuse. With repeated stimulations,
the b-process rises more quickly, reaches a higher maximum, and is slower to decay after the
stimulus is terminated.

In support of these predictions Solomon and Corbit described what happened to the
emotional responses and heart rates of the dogs in the experiment of Church et al. (1966).
After several sessions, there was little, if any, increase in heart rate during the shock. However,
after shock termination, heart rates decreased by as much as 60 beats/minute, and they took
from 2 to 5 minutes (instead of 1 minute or less) to return to normal. The dogs’ overt
behaviors also changed: During the shocks, the researchers noted that one dog “appeared
pained, annoyed, anxious, but not terrified. . . . Then, when released suddenly at the end of
the session, the dog rushed about, jumped up on people, wagged its tail, in what we called
at the time ‘a fit of joy.’ Finally, several minutes later, the dog was its normal self: friendly, but
not racing about” (Solomon & Corbit, 1974, p. 122).

In short, with extended experience, the changes in the dogs’ heart rates and overt behav-
iors were similar: The reaction to the shock was smaller than before, but the after-reaction
was larger and lasted longer.

Other Emotional Reactions

Solomon and Corbit (1974) claimed that their theory describes many different types of
emotional experiences. They discussed the emotional responses of parachutists on their
initial jumps and on later jumps, as reported by S. M. Epstein (1967). Overall, the emotional
experiences of parachutists resemble those of the dogs in the heart-rate study. Novice para-
chutists appear terrified during a jump; after the jump, they look stunned for a few minutes
and then return to normal. Experienced parachutists appear only moderately anxious dur-
ing a jump, but afterward they report feelings of exhilaration and euphoria that can last for


hours. They claim that this feeling of euphoria is one of the main reasons they continue
to jump.

As another example, Solomon and Corbit discussed the powerful reactions people have
to addictive drugs such as opiates. After a person’s first opiate injection, an intense feeling
of pleasure (a “rush”) is experienced. This peak of emotion declines to a less intense state
of pleasure. As the effect of the drug wears off, however, the aversive after-reactions set
in—nausea, insomnia, irritability, anxiety, inability to eat, and other physical problems,
along with feelings of craving for the drug. The withdrawal symptoms can last for hours
or a few days.

For an experienced opiate user, the pattern changes. The injection no longer brings an
initial rush, but only mild feelings of pleasure, if any. This decrease in the effects of a drug
with repeated use is called tolerance, and it is observed with many drugs besides opiates.
According to the opponent-process theory, drug tolerance is the product of a strengthened
b-process. The stronger b-process also explains why, with repeated opiate use, the withdrawal
symptoms become more severe, and they may last for weeks or longer. Solomon and Corbit
proposed that opponent-process theory provides a framework for understanding not only
opiate use but all addictive behaviors (such as smoking, alcoholism, and the use of barbitu-
rates and amphetamines). We will see in Chapter 4, however, that other researchers who
study drug use disagree with the details of the opponent-process theory.

A Brief Evaluation

The predictions that opponent-process theory makes about the pattern of emotional
responses have been tested in numerous experiments. In many cases the theory’s predictions
have been supported (e.g., Vargas-Perez, Ting-A-Kee, Heinmiller, Sturgess, & van der Kooy,
2007), but sometimes they have not (Newton, Kalechstein, Tervo, & Ling, 2003). To its credit,
the theory has been applied to a diverse range of human behaviors, including the effects of
exercise (Lochbaum, 1999), the depression that can follow the loss of a loved one (Moss,
2013), and how people experience the sensations of pain followed by relief when the pain
ends (Leknes, Brooks, Wiech, & Tracey, 2008).

Recent research on the brain mechanisms of drug addiction supports the assumptions of
opponent-process theory about the weakening of the a-process (the pleasures derived from
a drug dose) and the strengthening of the b-process (the withdrawal symptoms, Koob & Le
Moal, 2008). Some studies with rats have identified specific sections of the brain that appear
to be involved in both the initial positive reaction to opiates and the negative after-reactions
(Radke, Rothwell, & Gewirtz, 2011), and these findings provide neuroanatomical support
for the basic ideas of opponent-process theory. Research on brain changes in drug addiction
is consistent with the idea that addicts are motivated to keep using drugs not so much
because they continue to provide pleasure but rather because they provide temporary relief
from the unpleasant withdrawal symptoms (Baker, Piper, McCarthy, Majeskie, & Fiore,

Critics of opponent-process theory have pointed out that the different examples used
by Solomon and Corbit exhibit vastly different time courses. In the heart-rate studies with
dogs, the b-process lasts only seconds or a few minutes. In an addiction, the b-process may
continue for months. Is it likely that the same physiological mechanisms are involved in


emotional events whose durations differ
by a factor of 10,000 or more? The critics
have argued that there may be nothing
more than a superficial resemblance among
the different examples Solomon and Cor-
bit present.

In defense of opponent-process theory,
we might assert that as long as emotional
responses conform to the predictions of
the theory, it does not matter whether
these patterns are based on a single physi-
ological mechanism or on a dozen differ-
ent ones. On a strictly descriptive level,
the major characteristics of emotional
episodes emphasized by opponent-process
theory (the peak, the plateau, the after-
effect, the changes with repeated stimula-
tion) appear to be fairly well documented
by case histories, systematic observations,
and experiments. Whether or not these
patterns share a common physiological
mechanism, the theory captures some
characteristics of emotional responses that
seem to be quite general. This may be the
theory’s greatest virtue. The broad view-
point provided by opponent-process theory allows us to see commonalities among our
emotions that would probably go unnoticed in a more myopic analysis of individual
emotional responses.


One of the simplest types of innate behaviors is the reflex, which is a simple response to a
specific stimulus, such as blinking when a bright light is shined in the eye. Kineses are ran-
dom movements in response to a specific stimulus, whereas taxes are directed movements
(such as an ant using the sun as a compass). Fixed action patterns are sequences of behavior
that always occur in a rigid order, whereas reaction chains are more flexible sequences that
can be adapted to current circumstances. The concepts of control systems theory, which
describe a comparison between the actual state of the world and a goal state, are helpful in
analyzing these innate behavior patterns. Few innate behavior patterns have been found in
humans, but there is evidence that humans may have quite a few innate abilities and predis-
positions, including language skills, how emotions are displayed in facial expressions, and a
variety of other social behaviors.

Habituation is the decline and eventual disappearance of a reflexive response when the
same stimulus is repeatedly presented. Habituation gives a creature the ability to ignore

Practice Quiz 2: chaPter 2
1. The second time a stimulus under-

goes habituation, the time course of
habituation is ______.

2. More intense stimuli habituate ______
than weaker stimuli.

3. Research with Aplysia has found that
habituation involves ______ changes
in the ______ neurons.

4. In opponent-process theory, with
repeated stimulation, the ______
does not change, but the ______
starts earlier, becomes stronger, and
lasts longer.

5. In drug addiction, the b-process
appears as ______, whereas in para-
chute jumping, the b-process
appears as ______.


1. more rapid 2. more slowly 3. chemical, sensory
4. a-process, b-process 5. cravings and withdrawal
symptoms, euphoria


unimportant, repetitive events. In both simple and complex creatures, habituation exhibits
the same set of properties, such as forgetting, overlearning, and stimulus generalization.
Research with simple creatures such as the snail Aplysia, as well as with mammals, has traced
the physiological and chemical changes that occur in the brain during habituation and
specific brain structures involved in habituation in a few cases.

The opponent-process theory of Solomon and Corbit states that many emotional reac-
tions consist of an initial response called the a-process and a later, opposing response called
the b-process. Repeated presentations of the same stimulus strengthen the b-process, so
that the initial reaction grows weaker and the after-reaction grows stronger and lasts lon-
ger. This theory has been applied to a wide variety of emotional reactions, including drug
addiction, the emotions involved in parachute jumping, and responses to painful or aver-
sive stimuli.

Review Questions

1. Describe an example of each of the following innate behavior patterns: reflex,
kinesis, taxis, fixed action pattern, and reaction chain. Select one of these exam-
ples and show how it can be analyzed using the concepts of control systems

2. What types of evidence do scientists use to support claims that human beings
are born with certain abilities and predispositions? Which examples of innate
human predispositions do you find most convincing, and which do you find less
convincing? Explain your reasoning.

3. If you bought a clock for your room that made a loud ticking sound, you would
probably soon habituate to the sound. Use this example to illustrate the general
principles of habituation. Why is this simple type of learning useful?

4. How has habituation been studied in human infants, and what has been found?
5. Draw a diagram that shows the pattern of a typical emotional response to a new

stimulus according to opponent-process theory. Now diagram the changed pat-
tern that occurs in response to a stimulus that has been frequently repeated. Use
a specific example, such as drug addiction or smoking, to explain the diagrams.


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Learning Objectives

After reading this chapter, you should be able to

• describe the procedure of classical conditioning and some of the most com-
mon ways it is studied in the laboratory

• explain Pavlov’s stimulus substitution theory, and describe its strengths and

• describe the basic principles of classical conditioning, including acquisition,
extinction, spontaneous recovery, conditioned inhibition, generalization, and

• explain how the timing of the stimuli in classical conditioning affects the results
• give examples of classical conditioning that are found in everyday life
• describe some behavior therapies that are based on classical conditioning,

and evaluate their effectiveness

C H A P T E R 3

Basic Principles of
Classical Conditioning


The Russian scientist Ivan Pavlov is one of the most famous figures in the history of psy-
chology. Pavlov was interested in the various substances secreted by an animal’s digestive
system to break down the food eaten, including saliva. He used dogs in his research, and
he developed a surgical technique that enabled him to redirect the saliva from one of the
dog’s salivary ducts through a tube and out of the mouth, so that it could be measured
(see Figure 3.1). A dog might receive several test sessions on successive days. In each ses-
sion the animal would be given food, and its salivation would be recorded as it ate. Pavlov’s


important observation came when studying dogs that had been through the testing pro-
cedure several times. Unlike a new dog, an experienced one would begin to salivate even
before the food was presented. Pavlov reasoned that some stimuli that always preceded the
presentation of food, such as the sight of the experimenter, had developed the ability to
elicit the response of salivation. Pavlov concluded that his dogs were exhibiting a simple
type of learning: Salivation, which began as a reflexive response to the stimulus of food in
the dog’s mouth, was now elicited by a new stimulus. This phenomenon is now known
as classical conditioning. Pavlov discovered many of the findings described in this
chapter, and he developed a set of procedures for studying classical conditioning that is
still in use today.

The Standard Paradigm of Classical Conditioning

To conduct a typical experiment in classical conditioning, an experimenter first selects some
stimulus that reliably elicits a characteristic response. The stimulus of this pair is called the
unconditioned stimulus (US), and the response is called the unconditioned response
(UR). The term unconditioned indicates that the connection between the stimulus and
response is unlearned (innate). In Pavlov’s experiments on the salivary response, the US was
the presence of food in the dog’s mouth, and the UR was the secretion of saliva. The third
element of the classical conditioning paradigm is the conditioned stimulus (CS), which
can be any stimulus that does not initially evoke the UR (e.g., a bell). The term conditioned
stimulus means that the bell will elicit the response of salivation only after conditioning has
taken place.

Figure 3.2 shows the sequence of events on the first trial of classical conditioning and on
a later trial. In its simplest form, a classical conditioning trial involves the presentation of the

Figure 3.1 Pavlov’s salivary conditioning situation. A tube redirects drops of saliva out of the dog’s
mouth so they can be recorded automatically. (From Yerkes & Morgulis, 1909)


CS (e.g., a bell) followed by the US (e.g., the food). On the initial trials, only the US will
elicit the response of salivation. However, as the conditioning trials continue, the dog will
begin to salivate as soon as the CS is presented. Any salivation that occurs during the CS
but before the US is referred to as a conditioned response (CR) because it is elicited by
the CS, not the US.

The Variety of Conditioned Responses

Although classical conditioning can be obtained with many different responses, much
of the research has been conducted with a small number of conditioning preparations
(conditioning situations using a particular US, UR, and species) that can be studied easily
and efficiently. The following conditioning preparations are among the most commonly

Eyeblink Conditioning

Conditioning of the eyeblink reflex has been studied with humans, rabbits, rats, and other
animals. Figure 3.3 shows a modern apparatus for eyeblink conditioning with humans.
The US is a puff of air directed at the eye, and the UR is of course an eyeblink. Eyeblinks
are recorded by a photocell that measures movement of the eyelid. In eyelid conditioning
research with rabbits, the US can be an air puff or a mild electric shock delivered to the
skin in the vicinity of the eye, which also reliably elicits an eyeblink as a UR. The CS
may be a light, a tone, or some tactile stimulus such as a vibration of the experimental
chamber, and the duration of the CS is typically about 1 second. Like the UR, the CR
is an eyeblink. Eyeblink conditioning can be slow: It may take over 100 pairings before
a CR is observed on 50% of the trials. Research in eyeblink conditioning has helped
scientists to map the brain areas and chemical mechanisms involved in conditioning, to
diagnose psychological disorders, to study the effects of aging, and in other ways (e.g.,
Radell & Mercado, 2014).

Figure 3.2 Events of a classical conditioning trial both before (left) and after (right) a CR is


Conditioned Suppression

In the conditioned suppression procedure, also called the conditioned emotional response (CER)
procedure, the subjects are usually rats, and the US is an aversive event such as a brief electric
shock delivered through the metal bars that form the floor of the experimental chamber. A
rat is first trained to press a lever at a steady pace by occasionally delivering a food pellet after
a lever press. Occasionally, a CS (a light, sound, vibration, etc.) is presented for perhaps a minute,
followed by the US, the shock. At first, the rat will continue to press the lever during the
1-minute CS. However, after several trials on which the CS was followed by shock, the rat will
slow down or stop its lever pressing when the CS is on, as if it is anticipating the upcoming
shock. Therefore the CR in this procedure is the suppression of lever pressing, and it indicates
the rat has learned the association between CS and US. As soon as the shock is over, the rat
will resume lever pressing at its normal rate. Conditioning in this procedure can be rapid:
Strong conditioned suppression can often be found in fewer than 10 trials, and in some cases
significant suppression can be observed after just one CS–US pairing.

The Skin Conductance Response

A conditioning preparation called skin conductance response (SCR), sometimes referred
to as the electrodermal response, uses human participants. The SCR is a change in the electrical
conductivity of the skin. To measure a person’s SCR, two coin-shaped electrodes are attached

Figure 3.3 An eyeblink conditioning arrangement. The participant wears a headset that has a tube to
direct a puff of air to the eye, a photocell to measure movement of the eyelid, and earphones for the
presentation of auditory stimuli.


to the palm, and they measure momentary fluctuations in the conductivity of the skin
(caused by small changes in perspiration). The conductivity of the skin is altered by emotions
such as fear or surprise, which is why the SCR is often one measure used in lie detector tests.
One stimulus that reliably produces a large increase in skin conductivity is electric shock,
and a similar increase in conductivity can be conditioned to any CS that is paired with
shock. For instance, the CS might be a tone, the US a shock to the left wrist, and the response
an increase in conductivity of the right palm. The SCR is of value to researchers because it
can be quickly and reliably conditioned with human participants, and many complex stimuli
(such as spoken or written words) can be examined as CSs.

Taste-Aversion Learning

The CS in this procedure is the taste of something the animal eats or drinks, often a food it
has never tasted before. After eating or drinking, the animal is given an injection of a poison
(the US) that makes it ill. Several days later, the animal is again given the opportunity to
consume the food that served as the CS. The animal typically consumes little or none of
this food. Therefore, the measure of conditioning is the degree to which the animal avoids
the food.

A taste aversion is something that many people experience at least once in their lives.
Perhaps there is some type of food that you refuse to eat because you once became ill after
eating it. You may find the very thought of eating this food a bit nauseating, even though
most people enjoy the food. If you have such a taste aversion, you are not unusual—one
study found that more than half of the college students surveyed had at least one taste aver-
sion (Logue, Ophir, & Strauss, 1981).

Pavlov’s Stimulus Substitution Theory

Pavlov proposed a theory of classical conditioning that is called the stimulus substitution
theory. The theory states that in classical conditioning the CS becomes a substitute for the
US, so that the response initially elicited only by the US is now also elicited by the CS. At
first glance, this theory seems to provide a perfectly satisfactory description of what takes
place in many common examples of classical conditioning. In salivary conditioning, at first
only the food elicits salivation, but later the CS also elicits salivation. In eyeblink condition-
ing, both the UR and the CR are eyelid closures. In SCR conditioning, an increase in skin
conductance is first elicited by a shock, and after conditioning there is a similar skin con-
ductance response to some initially neutral stimulus.

Despite these apparent confirmations of stimulus substitution theory, today most psy-
chologists believe that the theory is not correct. There are several problems. First, the CR is
almost never an exact replica of the UR. For instance, in eyeblink conditioning, the UR to
an air puff is a large and rapid eyelid closure, but the CR is a smaller and more gradual eyelid
closure. Second, not all parts of the UR become part of the CR. For example, Zener (1937)
noted that when a dog is presented with food as a US, many responses, such as chewing and
swallowing the food, occur in addition to salivation. However, a well-trained CS such as a
bell will usually elicit only salivation, not chewing and swallowing responses. Third, a CR
may include some features that are not part of the UR. For instance, using a bell as a CS,


Zener found that many dogs would turn their heads and look at the bell when it was rung.
Sometimes a dog would move its entire body closer to the ringing bell. Obviously, these
behaviors were not a normal part of the dog’s UR to food. Fourth, in some cases the CR is
opposite of the UR. For instance, one response to an electric shock is an increase in heart
rate, but in studies with guinea pigs, Black (1965) observed conditioned heart rate decreases
to a CS paired with shock. As another example, one of the URs to a morphine injection is
an increase in body temperature. However, if rats are repeatedly presented with some CS
followed by a morphine injection, the CS will later produce decreases in body temperature.
Conditioned responses that are the opposite of the UR are called conditioned compensa-
tory responses.

In summary, although its simplicity is appealing, stimulus substitution theory does not
provide a full and complete description of what occurs in classical conditioning. Classical
conditioning is not simply a transfer of a response from one stimulus to another. Because of
the problems described here, it is often difficult to predict in advance what the CR will look
like in a specific instance. It may resemble the UR, or it may be very different.

Because of the problems with the stimulus substitution approach, other theories have
been proposed. According to the sign-tracking theory (Costa & Boakes, 2009; Hearst &
Jenkins, 1974), the CS does not become a substitute for the US but rather a sign or signal for
the upcoming US. The theory states that animals tend to orient themselves toward, approach,
and explore any stimuli that are good predictors of important events, such as the delivery of
food. For instance, if a bell is repeatedly paired with food, a dog may exhibit an orienting
response: It may raise its ears, look in the direction of the bell, and possibly approach the
bell. Therefore, it is not surprising that some components of the orienting response to the
CS are retained as part of the CR. In summary, the form of the CR may include features
that are part of the animal’s natural response to the signal, as well as features of its natural
response to the US.

What Is Learned in Classical Conditioning?

Besides recording the behavior of his animals, Pavlov also speculated about what changes
might take place in the brain during classical conditioning. He proposed that there is a
specific part of the brain, which we can call the US center, that becomes active whenever a
US (such as food) is presented. Similarly, for every different CS (a tone, a light), there is a
separate CS center, which becomes active whenever that particular CS is presented. From
what we know about the physiology of the sensory systems (Chapter 1), these assumptions
seem quite reasonable. Pavlov also assumed that for every UR (say, salivation) there is a part
of the brain that can be called a response center, which, when activated, sends the neural
commands that produce the observed response. There is an innate connection between the
US center and the response center (see Figure 3.4). During classical conditioning, some
new association develops, so that now the CS activates the response center (and a CR is

As Figure 3.4 shows, there are at least two types of new associations that would give
the CS the capacity to elicit a CR. On one hand, a direct association between the CS
center and the response center might form during conditioning, which can be called a
stimulus-response association, or S-R association. On the other hand, an association might


form between the CS center and the US center (an association between two stimuli, or
S-S association). Later, when the CS is presented, the CS center is activated, which activates
the US center (through the S-S association), which in turn activates the response center
(through the innate association). Pavlov tended to favor the S-S alternative, but he had
little data on which to decide between the two.

Later experimenters devised some clever techniques to try to distinguish between these
two alternatives. Rescorla (1973) used the following reasoning: If the S-S position is correct,
then after conditioning, the occurrence of a CR depends on the continued strength of
two associations—the learned association between the CS center and the US center and
the innate association between the US center and the response center (see Figure 3.4). If the
US-response connection is somehow weakened, this should also weaken the CR since the
occurrence of the CR depends on this connection. However, if the S-R position is correct,
the strength of the CR does not depend on the continued integrity of the US-response
association but only on the direct association between the CS center and the response center.
But how can a reflexive US-response association be weakened? Rescorla’s solution was to
rely on habituation.

Rescorla used a conditioned suppression procedure with rats, with a loud noise as the
US, because he knew that there would be habituation to the noise if it were repeatedly
presented. The design of the experiment is shown in Table 3.1. In Phase 1, two groups
of rats received identical classical conditioning with a light as the CS and the noise as
the US. In Phase 2, the habituation group received many presentations of the noise by
itself in order to habituate the rats’ fear of the noise. The technique of decreasing the
effectiveness of the US after an excitatory CS has been created is called US devaluation.
The control rats spent equal amounts of time in the experimental chamber in Phase 2,
but no stimuli were presented, so there was no opportunity for the noise to habituate
in this group. In the test phase, both groups were presented with the light by itself for
a number of trials, and their rates of lever pressing were recorded. When the light came

Figure 3.4 Two possible versions of Pavlov’s stimulus substitution theory. During classical condition-
ing, an association might develop from the CS center to the US center or from the CS center directly
to the response center.

Nervous System



(e.g., bell)

(e.g., food)

(e.g., salivation)

? ?



on, lever pressing was greatly suppressed in the control group but not in the habitua-
tion group. Rescorla concluded that the strength of the CR depends on the continued
strength of the US-response association, as predicted by the S-S position but not the
S-R position.

Similar studies have been conducted with human subjects, some using the skin conduc-
tance response (SCR) preparation. For example, a CS (such as a picture of some common
object) is paired with a loud noise or a shock, and then the intensity of the US is changed.
If the US intensity is decreased, SCRs to the CS decrease as well. Conversely, if the intensity
of the US is increased, SCRs to the CS also increase (Schultz, Balderston, Geiger, & Helm-
stetter, 2013; White & Davey, 1989). These results also support the S-S position because they
show that the response to the CS changes depending on the current strength of the response
to the US. Other research on the associations formed during classical conditioning will be
described in Chapter 4.



The part of a conditioning experiment in which the learner first experiences a series of
CS–US pairings, and during which the CR gradually appears and increases in strength, is
called the acquisition phase. Figure 3.5 shows the results of an acquisition phase in an
experiment on eyeblink conditioning with human participants (Gerwig et al., 2010). The
participants received three sessions of 100 trials per day, in which a brief tone was followed
by an air puff directed at the eye. For the normal adults, the percentage of trials with a CR
gradually increased until it leveled off at about 55%. This value—the maximum level of
conditioned responding that is gradually approached as conditioning proceeds—is called the
asymptote. Figure 3.5 also shows the results from a group of adults who had suffered strokes
that caused damage to the cerebellum, a part of the brain that plays an important role in
eyeblink conditioning. These participants showed weaker levels of conditioned responding,
which reached an asymptote of about 30%.

In general, if a stronger stimulus is used as a US (a stronger puff of air, a larger amount of
food), the asymptote of conditioning will be higher (a higher percentage of conditioned
eyeblinks, more salivation). Strong USs also usually result in faster conditioning; that is, it
may take fewer trials for a CR to appear with a strong US than with a weak one. The same
is true about the intensity of the CS (e.g., classical conditioning will be faster with a loud
tone than with a soft tone).

Table 3.1 Design of Rescorla’s (1973) experiment on S-S versus S-R connections.

Group Phase 1 Phase 2 Test

Habituation Light→Noise Noise (habituation) Light
Control Light→Noise No stimuli Light



A simple technique for producing a reduction and eventual disappearance of the CR is
extinction, which involves repeatedly presenting the CS without the US. Suppose we fol-
lowed the acquisition phase with an extinction phase in which the bell was presented for
many trials but no food was delivered. The first two panels in Figure 3.6 show, in an idealized
form, the likely results of our hypothetical experiment. As the bell is presented trial after
trial without food, the amount of salivation gradually decreases, and eventually it disappears
altogether. When the extinction phase is completed, we have a dog that behaves like a dog
that is just beginning the experiment—the bell is presented and no salivation occurs. We
might conclude that extinction simply reverses the effects of the previous acquisition phase.
That is, if the animal has formed an association between the CS and the US during the
acquisition phase, perhaps this association is gradually destroyed during the extinction phase.
This hypothesis seems very reasonable, but it is almost certainly wrong, as explained in the
next section.

Spontaneous Recovery, Disinhibition, and Rapid Reacquisition

Suppose that after an acquisition phase on Day 1 and an extinction phase on Day 2, we
return the dog to the experimental chamber on Day 3 and conduct another series of extinc-
tion trials with the bell. Figure 3.6 shows that on the first several trials of Day 3, we are likely

Figure 3.5 The acquisition of eyeblink CRs by normal adults and by those who had suffered strokes that
caused damage to the cerebellum. (Adapted from Behavioural Brain Research, Vol. 2, Gerwig, M. et al.,
Evaluation of multiple-session delay eyeblink conditioning comparing patients with focal cerebellar lesions
and cerebellar degeneration, 143–151. Copyright 2010, with permission from Elsevier.)








0 5 10 15 20 25 30

Blocks of 10 trials





Normal adults

Day 3Day 2Day 1

Cerebellum injury


to see some conditioned responding to the bell, even though no CRs were observed at the
end of Day 2. Pavlov called this reappearance of conditioned responding spontaneous
recovery, and he treated it as proof that the CS–US association is not permanently destroyed
in an extinction procedure.

Several different theories about spontaneous recovery have been proposed. One popular
theory, which we can call the inhibition theory, states that after extinction is complete, the
subject is left with two counteracting associations (Konorski, 1948). The CS–US association
formed during acquisition is called an excitatory association because through this association
the CS now excites, or activates, the US center. According to this theory, a parallel but inhibi-
tory association develops during extinction. When extinction is complete, the effects of the
excitatory and inhibitory associations cancel out, so that the US center is no longer activated
when the CS is presented. However, the theory states that inhibitory associations (at least
newly formed ones) are more fragile than excitatory associations, and they are more severely
weakened by the passage of time. Therefore, at the beginning of Day 3, the weakened inhibi-
tory association can no longer fully counteract the excitatory association, and so some CRs
are observed. Further extinction trials on Day 3 strengthen the inhibitory association, and
so conditioned responding once again disappears.

If we were to conduct further extinction sessions on Days 4, 5, 6, and so on, we might
again observe some spontaneous recovery, but typically the amount of spontaneous recovery
would become smaller and smaller until it no longer occurred (see Figure 3.6). According
to the inhibition theory, this happens because the inhibitory association becomes progres-
sively stronger with repeated extinction sessions.

The inhibition theory is just one of several theories about why spontaneous recovery
occurs. Some experiments by Robbins (1990) supported a theory that during extinction,
the subject stops “processing” or “paying attention to” the CS. Conditioned responses then
disappear because when the animal stops paying attention to the CS, it stops responding to
the CS. Later, when the animal is brought back to the conditioning chamber after some
time has passed, the animal’s attention to the CS is revived for a while, leading to a spontane-
ous recovery of CRs.

Another theory of spontaneous recovery states that the CS becomes an ambiguous stimu-
lus because it has been associated both with the US and then with the absence of the US
(Bouton, 2000; Capaldi, 1966). As you can see, there is disagreement among the experts on
exactly what causes extinction and spontaneous recovery. Modern neurophysiological
research suggests that several different processes may contribute to extinction, including

Figure 3.6 Idealized changes in the strength of a CR across one acquisition day followed by 4 days of




f C


Day 1 Day 2 Day 3 Day 4 Day 5



inhibition and a partial erasure or weakening of the original association (Delamater & West-
brook, 2014).

More evidence that extinction is not the complete erasure of previous learning comes
from the phenomenon of disinhibition. Suppose that an extinction phase has progressed
to the point where the CS (a bell) no longer evokes any salivation. Now, if a novel stimulus
such as a buzzer is presented a few seconds before the bell, the bell may once again elicit a
CR of salivation. Pavlov called this effect disinhibition because he believed that the presenta-
tion of a distracting stimulus (the buzzer) disrupts the fragile inhibition that supposedly
develops during extinction. According to the inhibition theory, the more stable excitatory
association is less affected by the distracting stimulus than is the inhibitory association, and
the result is a reappearance of the conditioned salivary response.

The phenomenon of rapid reacquisition is a third piece of evidence that extinction
does not completely eliminate what was learned in the acquisition phase. Rapid reacquisition
is similar to the “savings” that are found in experiments on list learning (Chapter 1) or
habituation (Chapter 2). In classical conditioning, if a subject receives an acquisition phase,
an extinction phase, and then another acquisition phase with the same CS and the same US,
the rate of learning is substantially faster in the second acquisition phase—the reacquisition
phase (Bouton, Woods, & Pineño, 2004). Furthermore, if an animal receives repeated cycles
of extinction and reacquisition, the rate of learning tends to get faster and faster (Hoehler,
Kirschenbaum, & Leonard, 1973).

These three phenomena—spontaneous recovery, disinhibition, and rapid reacquisition—
make it very clear that there is no simple way to get a subject to “unlearn” a CR and that
no amount of extinction training can completely wipe out all the effects of a classical con-
ditioning experience. Extinction can cause a CR to disappear, and after a while spontaneous
recovery may disappear, but the subject will never be exactly the same as before the condi-
tioning began.

Conditioned Inhibition

There is plenty of evidence that, depending on the procedures used in classical condition-
ing, a CS may become either excitatory or inhibitory (Miller & Spear, 1985). An excit-
atory CS is simply one that elicits a CR. An inhibitory CS (also called a conditioned
inhibitor or a CS–) is one that prevents the occurrence of a CR or reduces the size of the
CR from what it would otherwise be. Pavlov discovered a fairly simple and effective pro-
cedure for changing a neutral stimulus into a conditioned inhibitor. Suppose we repeatedly
pair the sound of the buzzer with food until a dog always salivates at the sound of the
buzzer. The buzzer can now be called an excitatory CS (or CS+) because it regularly elicits
a CR. In the second phase of the experiment, the dog receives two types of trials. Some
trials are exactly like those of Phase 1 (buzzer plus food). However, on other trials both the
buzzer and a light are presented simultaneously, but no food is delivered. The simultaneous
presentation of two or more CSs, such as the buzzer and the light, is called a compound
CS. After many trials of both types, the dog will eventually salivate on trials with the buzzer
alone but not on trials with both the buzzer and the light. It appears that the light has
become a conditioned inhibitor: It prevents the response of salivation to the buzzer that
would otherwise occur.


One way to provide a convincing demonstration that the light has become a conditioned
inhibitor is to show that it can prevent salivation to some other CS, not just to the buzzer
with which it was trained. Suppose that a third stimulus, a fan blowing air into the chamber,
is paired with food until it reliably elicits salivation. Now, for the first time, the animal
receives a trial with a compound CS consisting of the fan and the light. If the light is truly
a conditioned inhibitor, it should have the capacity to reduce the salivation produced by any
CS, not just by the buzzer with which it was originally presented. In this test, we would find
that the light reduced or eliminated the CR to the fan, even though these two stimuli were
never presented together before. This shows that the light is a general conditioned inhibitor
because it seems to have the ability to block or diminish the salivation elicited by any excit-
atory CS.

Generalization and Discrimination

After classical conditioning with one CS, other, similar stimuli will also elicit CRs, even though
these other stimuli have never been paired with the US. This transfer of the effects of condi-
tioning to similar stimuli is called generalization, which is illustrated in Figure 3.7. In this
experiment, rabbits received eyeblink conditioning with a 1,200-Hz tone as the CS and a
shock near the eye as a US (Liu, 1971). Then the rabbits were tested with tones of five different
frequencies, but no US was presented. As can be seen, the 1,200-Hz tone elicited the highest
percentage of CRs. The two tones closest in frequency to the 1,200-Hz tone elicited an inter-
mediate level of responding, and the more distant tones elicited the fewest responses. The
function in Figure 3.7 is a typical generalization gradient. It shows that the more similar a
stimulus is to the training stimulus, the greater will be its capacity to elicit CRs.

Figure 3.7 A typical generalization gradient. After eyeblink conditioning with a 1,200-Hz tone, rabbits
were tested with tones of higher and lower pitches. (From Liu, S. S., 1971, Differential conditioning and
stimulus generalization of the rabbit’s nictating membrane response. Journal of Comparative and Physiologi-
cal Psychology, 77, 136–142. © American Psychological Association. Reprinted with permission.)









800 1,200

Test stimulus (Hz)





t C


1,600 2,000



Classical Conditioning and the Immune System

As you probably know, the body’s immune system is designed to fight off infections.
Whenever bacteria, viruses, or foreign cells enter a person’s body, the immune system
produces antibodies that attack and kill these invaders. There is convincing evidence
that the immune system can be influenced by classical conditioning. Ader and Cohen
(1975) conducted a landmark study in this area. They gave rats a single conditioning trial
in which the CS was saccharin-flavored water and the US was an injection of cyclophos-
phamide, a drug that suppresses the activity of the immune system. A few days later,
the rats were injected with a small quantity of foreign cells (red blood cells from sheep)
that their immune systems would normally attack vigorously. One group of rats was then
given saccharin-flavored water once again, whereas a control group received plain water.
Ader and Cohen found that for rats in the saccharin-water group, the response of the
immune system was weaker than for rats in the plain-water group; that is, fewer antibod-
ies were produced by rats in the saccharin-water group. In other words, they found that
the saccharin, which normally has no effect on the immune system, now produced a CR,
a weakening of the immune system.

The activity of the immune system can also be increased through classical condition-
ing. Solvason, Ghanata, and Hiramoto (1988) exposed mice to the odor of camphor
as a CS, and then they were injected with the drug interferon as the US. Interferon nor-
mally causes an increase in the activity of natural killer cells in the bloodstream—cells
that are involved in combating viruses and the growth of tumors. After a few pairings of
the camphor odor and interferon, presenting the camphor odor by itself was enough
to produce an increase in activity of the natural killer cells. A similar study with healthy

Generalization can be used by advertisers to help them sell their products. Till and Priluck
(2000) found that if consumers have a favorable attitude toward a particular brand name of
a product, this favorable attitude generalizes to other brands that have similar names and to
other products with the same brand name. This can help to explain why many products you
see in supermarkets and department stores have names and package designs similar to those
of well-known brands.

The opposite of generalization is discrimination, in which an individual learns to
respond to one stimulus but not to a similar stimulus. We have seen that if a rabbit’s eyeblink
is conditioned to a 1,200-Hz tone, there will be substantial generalization to an 800-Hz
tone. However, if the 800-Hz tone is never followed by food, but the 1,200-Hz tone is always
followed by food, the animal will eventually learn a discrimination in which the 1,200-Hz
tone elicits an eyeblink and the 800-Hz tone does not. This type of discrimination learning
is important in many real-world situations. For instance, it is appropriate for you to have a
fear reaction if you are rapidly driving into an intersection and you see that the light has
turned red, but not if you see the light is green.



In all types of classical conditioning, the
precise timing of the CS and the US can
have a major effect in several ways. The
timing of events can affect (1) how strong
the conditioning will be, (2) whether a CS
will become excitatory or inhibitory, and
(3) exactly when the CR occurs.

All of the experiments discussed so far
used short-delay conditioning in which
the CS begins a second or so before the US
(as diagrammed in Figure 3.8). This tem-
poral arrangement usually produces the
strongest and most rapid conditioning.
Studies have shown that it is important for
the CS to begin slightly before the US
does: In simultaneous conditioning,
where the CS and US begin at the same
moment (see Figure 3.8), conditioned
responding is much weaker than in short-
delay conditioning (Smith & Gormezano,
1965). This may be so for a few reasons.
For one thing, if the US begins at the same
moment as the CS, the learner may respond
to the US but fail to notice the CS. Also, if

human adults also obtained increases in natural killer cells through classical conditioning
(Buske-Kirschbaum, Kirschbaum, Stierle, Jabaij, & Hellhammer, 1994).

Researchers have recognized the potential importance of this phenomenon, and they
have begun to understand the brain mechanisms that make conditioning of the immune
system possible (Kusnecov, 2014). For people whose immune systems have been tempo-
rarily weakened through illness or fatigue, developing psychological techniques to strengthen
immune activity could be beneficial. In other cases, decreasing the activity of the immune
system may be what is needed. For example, common allergies are the product of an over-
active immune system. In one study, people who were allergic to dust mites were given five
trials in which flavored water was paired with an antihistamine (a drug that reduces the allergic
reaction). Later, when they received a trial with the flavored water but no drug, they showed
the same signs of relief from their allergy symptoms as when they actually received the drug
(Goebel, Meykadeh, Kou, Schedlowski, & Hengge, 2008). Human research on classical con-
ditioning and the immune system is still fairly limited, but this type of research may eventually
produce ways to better control immune system activity for the benefit of the patient.

Practice Quiz 1: chaPter 3
1. In eyeblink conditioning, a tone could

be used as the ______ and an air
puff as the ______; an eyeblink is
the ______.

2. A problem with Pavlov’s stimulus sub-
stitution theory is that the ______ does
not always resemble the ______.

3. Rescorla’s (1973) experiment sup-
ported the theory of S-S associa-
tions because after responding to
the US (loud noise) was reduced thr –
ough habituation, responding to the
CS ______.

4. Three phenomena that show that
extinction is not the complete elimi-
nation of a learned association are
______, ______, and ______.

5. After classical conditioning with one
CS, the appearance of CRs to new
but similar stimuli is called ______.


1. CS, US, CR and UR 2. CR, UR 3. decreased
4. spontaneous recovery, disinhibition, rapid reacqui-
sition 5. generalization


the CS does not precede the US, it cannot serve to signal or predict the arrival of the US.
As we will see again and again, the predictiveness of a CS is an important determinant of
the degree of conditioning the CS undergoes and of whether this conditioning is excitatory
or inhibitory. The following rules of thumb, though not perfect, are usually helpful in pre-
dicting the outcome of a conditioning arrangement:

• If a CS is a good predictor of the presence of the US, it will tend to become excitatory.
• If a CS is a good predictor of the absence of the US, it will tend to become inhibitory.

Keep these rules in mind when examining the other conditioning arrangements discussed
in this section.

As shown in Figure 3.8, trace conditioning refers to the case in which the CS and US
are separated by some time interval in which neither stimulus is present. The name reflects
the idea that since the CS is no longer physically present when the US occurs, the learner
must rely on a “memory trace” of the CS if conditioning is to occur. In various studies on
classical conditioning, the amount of time elapsing between CS and US presentations, or the
CS–US interval, was systematically varied. That is, one group might receive a series of con-
ditioning trials with a 2-second CS–US interval, another group with a 5-second CS–US
interval, and so on. As the duration of the CS–US interval is increased, the level of condi-
tioning declines systematically (Ellison, 1964).

A similar pattern emerges in long-delay conditioning, where the CS begins at least
several seconds before the US, but the CS continues until the US is presented (Figure 3.8).
In long-delay conditioning, the CS–US interval is the delay between the onsets of the CS
and US. Here, too, the strength of the conditioned responding decreases as the CS–US
interval increases, but the effects of delay are usually not as pronounced as in trace

Figure 3.8 The temporal relationships between CS and US in five types of classical conditioning.


conditioning (which is understandable since in long-delay conditioning, the learner does
not have to rely on memory of the CS).

In long-delay conditioning, Pavlov noted that the timing of the CRs changed over trials.
Early in training, a dog would salivate as soon as the CS was presented, although the CS–US
interval might be 10 seconds. As conditioning trials continued, however, these early CRs
would gradually disappear, and the dog would salivate shortly before the food was presented
(8 or 9 seconds after CS onset). This pattern is consistent with the rule that the stimulus
that is the best predictor of the US will be the most strongly conditioned. In this example,
what stimulus is a better predictor of the US than CS onset? It is the compound stimulus—
CS onset plus the passage of about 10 seconds. Therefore, it is this combination that eventu-
ally elicits the most vigorous CRs.

The bottom of Figure 3.8 shows the procedure of backward conditioning, in which
the CS is presented after the US. Even if the CS is presented immediately after the US, the
level of conditioning is markedly lower than in simultaneous or short-delay conditioning.
This finding illustrates a limitation of the contiguity principle: Besides their temporal prox-
imity, the order of the stimuli is important. Although backward conditioning may result in
a weak excitatory association, there is evidence that after more trials, a backward CS becomes
inhibitory (Siegel & Domjan, 1971). Once again, the predictiveness rule can serve as a useful
guide: In backward conditioning, the CS signals a period of time in which the US will be
absent. As long as the CS is present, the learner can be certain that no US will occur.

One hypothesis about classical conditioning that addresses the timing of events is the
temporal coding hypothesis (Arcediano, Escobar, & Miller, 2005; Matzel, Held, & Miller,
1988). This hypothesis states that in classical conditioning, more is learned than a simple
association between CS and US—the individual also learns about the timing of these two
events, and this learning affects when the CR occurs. This hypothesis can explain why the
CR may occur just before the onset of the US in long-delay conditioning—the individual
has learned that a delay of a certain duration separates the onset of the CS and the onset of
the US. A variety of experiments have demonstrated the role of temporal coding in both
excitatory and inhibitory conditioning. For instance, if animals learn that food is likely to be
presented 10 seconds after a CS begins, but food is unlikely to be presented 30 seconds after
the CS begins, many CRs occur around the 10-second mark, and very few occur around 30
seconds (Williams, Johns, & Brindas, 2008). Such experiments on the timing of CRs make
it very clear that animals learn about temporal relations between CS and US, not just CS–US
associations (Kirkpatrick & Church, 2004; Polack, Molet, Miguez, & Miller, 2013).

CS–US Correlations

In all of the conditioning arrangements shown in Figure 3.8, the CS and US are presented
on every trial, and the temporal pattern is the same. In the real world, however, things are
not always so regular. For example, a sound in the night could be a signal of danger (a burglar
in the house), but most of the time it might just be the house creaking. Similarly, for a rabbit
in the forest, the rustling of leaves could be a predator, or it could be simply a breeze.
Although the relationships among stimuli are variable and uncertain in the real world, it is
important for both people and animals to know which stimuli are the most dependable
signals of important events, both good and bad. In the laboratory, classical conditioning


procedures can be used to evaluate an animal’s ability to detect imperfect correlations
between stimuli.

A series of experiments by Rescorla (1966, 1968) showed how the probability of the US
in the presence of the CS and in its absence combine to determine the strength of the CR.
In a conditioned suppression procedure with rats, the CS was a 2-minute tone that was
presented at random intervals. In one condition, there was a 40% chance that a shock would
occur at some point when the tone was on and a 20% chance of shock when the tone was
off. In this case, where the probability of shock was greater in the presence of the tone, the
tone became an excitatory CS (lever pressing was suppressed when the tone came on). In
another condition, the probability of shock was the same in the presence and absence of the
tone (e.g., a 40% chance of shock both when the tone was on and when it was off). In this
case, the rats showed no suppression at all to the tone. In a third condition, the probability
of shock was lower when the tone was on than when it was off (so the tone signaled a rela-
tive level of safety from shock), and in this case the tone became an inhibitory CS.

Based on these results, Rescorla concluded that an important variable in classical condi-
tioning is the correlation between CS and US. If the correlation is positive (the CS predicts
a higher-than-normal probability of the US), the CS will become excitatory. If there is no
correlation between CS and US (the probability of the US is the same whether or not the
CS is present), the CS will remain neutral. If the correlation between CS and US is negative
(the CS signals a lower-than-normal probability of the US), the CS will become inhibitory.
These results provide another instance where the predictiveness rule is a useful guide: If a
CS predicts that the US is likely to occur, the CS will become excitatory; if the CS predicts
that the US is not likely to occur, the CS will become inhibitory. This rule is not perfect,
but it works well in most cases.


So far we have examined only procedures in which a CS is paired with (or correlated with)
a US. However, this is not the only way a CS can acquire the ability to elicit a CR. In
second-order conditioning, a CR is transferred from one CS to another. Pavlov described
the following experiment to illustrate this process. First, the ticking of a metronome was
used as a CS in salivary conditioning until the sound of the metronome elicited salivation.
Because it was paired with the US, the metronome is called a first-order CS. Then another
stimulus, a black square, was presented and immediately followed by the sound of the met-
ronome on a number of trials, but no food was presented on these trials. After a few trials
of this type, the black square began to elicit salivation on its own, even though it was never
paired directly with the food (but only with the metronome). In this example, the black
square is called a second-order CS because it acquired its ability to elicit a CR by being paired
with a first-order CS, the metronome.

Second-order conditioning has also been demonstrated with humans. For example, in a
procedure called evaluative conditioning, people are asked to evaluate different stimuli—
to rate how much they like them using a scale that ranges from “very disliked” to “very
liked.” The first-order CSs are typically words that people consistently rate as being positive
(e.g., honest or friendly) or negative (e.g., cruel or arrogant). These words are first-order
CSs, not unconditioned stimuli, because they would certainly have no value to someone who


did not know the English language. For English speakers, these words presumably attained
their positive or negative values because they have been associated with good or bad experi-
ences in the past. In one interesting study, pictures of people’s faces were the second-order
stimuli, and, while looking at some of these faces, the participants heard either positive or
negative adjectives (Figure 3.9). The participants later rated the faces as being “liked” if they
had been paired with positive adjectives and “disliked” if they had been paired with negative
adjectives. These positive or negative ratings of the faces occurred even if the participants
could not remember the adjectives that had been paired with individual faces. In other
words, participants knew they liked some faces and disliked others, but they could not say
exactly why (Baeyens, Eelen, Van den Bergh, & Crombez, 1992).


In everyday life, classical conditioning is important in at least two ways. First, it gives us a
way of understanding “involuntary” behaviors, those that are automatically elicited by cer-
tain stimuli whether we want them to occur or not. As discussed in the next section, many
emotional reactions seem to fall into this category. Second, research on classical conditioning
has led to several major treatment procedures for behavior disorders. These procedures can
be used to strengthen desired “involuntary” responses or to weaken undesired responses.
The remainder of this chapter examines the role of classical conditioning in these nonlabora-
tory settings.

Classical Conditioning and Emotional Responses

Everyday emotional responses such as feelings of pleasure, happiness, anxiety, or excitement
are frequently triggered by specific stimuli. In many cases, the response-eliciting properties
of a stimulus are not inborn but acquired through experience. Suppose you open your
mailbox and find a card with the return address of a close friend. This stimulus may imme-
diately evoke a pleasant and complex emotional reaction that you might loosely call affec-
tion, warmth, or fondness. Whatever you call the emotional reaction, there is no doubt that

Figure 3.9 In evaluative conditioning, initially neutral stimuli such as pictures of faces are paired with
positive or negative adjectives. After conditioning, people will have positive or negative reactions to
the faces as well.


this particular stimulus—a person’s handwritten address on an envelope—would not elicit
the response from you shortly after your birth, nor would it elicit the response now if you
did not know the person who sent you the letter. The envelope is a CS that elicits a pleasant
emotional response only because the address has been associated with your friend. Other
stimuli can elicit less pleasant emotional reactions. For many college students, examination
periods can be a time of high anxiety. This anxiety can be conditioned to stimuli associated
with the examination process—the textbooks on one’s desk, a calendar with the date of the
exam circled, or the sight of the building where the exam will be held.

Classical conditioning can also affect our emotional reactions to other people. In one
study using evaluative conditioning, participants were asked to look at photographs of peo-
ple’s faces, and each photograph was paired with either a pleasant, neutral, or unpleasant
odor. When they later had to evaluate their preferences for the people in the photographs
(with no odors present), they gave the highest ratings to faces previously paired with pleasant
odors and the lowest ratings to those paired with unpleasant odors (Todrank, Byrnes, Wrz-
esniewski, & Rozin, 1995). This research surely encourages companies that sell mouthwash,
deodorant, and perfume.

Applications in Behavior Therapy

Systematic Desensitization for Phobias

One of the most widely used procedures of behavior therapy is systematic desensitiza-
tion, a treatment for phobias that arose directly out of laboratory research on classical con-
ditioning. A phobia is an excessive and irrational fear of an object, place, or situation. Phobias
can be quite debilitating. A fear of insects or snakes may preclude going to a picnic or taking
a walk in the woods. A fear of crowds may make it impossible for a person to go to the
supermarket, to a movie, or to ride on a bus or train.

How do phobias arise? After Pavlov’s discovery, classical conditioning was seen as one
possible source of irrational fears. In a famous (or, more accurately, infamous) experiment
by John B. Watson and Rosalie Rayner (1921), a normal 11-month-old infant named Albert
was conditioned to fear a white rat (which Albert initially did not fear) with a loud noise
of a hammer hitting a steel bar (which made him cry). After several trials on which the
white rat was paired with the noise, Albert would cry when he was presented with the white
rat by itself. This fear also generalized to a white rabbit and to other white furry objects,
including a ball of cotton and a Santa Claus mask. If this experiment sounds cruel and
unethical, rest assured that modern standards would not allow such an experiment to be
conducted today. However, the experiment demonstrated how a fear could be acquired
through classical conditioning. It also suggests a possible treatment: If a phobia can be
acquired through classical conditioning, perhaps it can be eliminated through extinction.

Systematic desensitization is a procedure in which the patient is exposed to the phobic
object gradually, so that fear and discomfort are kept to a minimum and extinction is allowed
to occur. The treatment has three parts: the construction of a fear hierarchy, training in
relaxation, and the gradual presentation of items in the fear hierarchy to the patient. The
fear hierarchy is a list of fearful situations of progressively increasing intensity. At the bottom
of the list is an item that evokes only a very mild fear response in the patient, and at the top
is the most highly feared situation. Once the fear hierarchy is constructed, patients are given



Virtual Reality Therapy

A therapy technique that combines classical conditioning and modern computer tech-
nology is virtual reality therapy, in which the patient wears a headset that displays
realistic visual images that change with every head movement, simulating a three-
dimensional environment. One application is in the treatment of phobias. In one case,
for example, a man with a fear of flying was exposed to more and more challenging
simulations of riding in a helicopter, and eventually his fear of flying diminished. Virtual
reality therapy has been successfully used for fears of animals, heights, public speak-
ing, and so on (Baus & Bouchard, 2014; North, North, & Coble, 2002). This technique
has several advantages over traditional systematic desensitization. The procedure does
not rely on the patient’s ability to imagine the objects or situations. The stimuli are very
realistic, they can be controlled precisely, and they can be tailored to the needs of each
individual patient.

Virtual reality therapy has also been used to treat individuals with post-traumatic
stress disorder (PTSD), including combat veterans and others who have been exposed

a session of deep muscle relaxation, after which they typically report that they feel very calm
and relaxed. The therapist then begins with the weakest item in the hierarchy, describes the
scene to the patient, and asks the patient to imagine this scene as vividly as possible. For
example, in the treatment of a teenager who developed a fear of driving after an automobile
accident, the first instruction was to imagine “looking at his car as it was before the accident”
(Kushner, 1968). Because the patient is in a relaxed state, and because the lowest item did
not evoke much fear to begin with, it usually can be imagined with little or no fear. After
a short pause in which the patient is told to relax, the first item is again presented. If the
patient reports that the item produces no fear, the therapist moves on to the second item on
the list, and the procedure is repeated. The therapist slowly progresses up the list, being
certain that the fear of one item is completely gone before going on to the next item.

The results of numerous studies on systematic desensitization involving thousands of
patients have been published, and in most of these reports about 80 to 90% of the patients
were cured of their phobias—a very high success rate for any type of therapy in the realm
of mental health (Paul, 1969). In some cases, real stimuli are used instead of relying on the
patient’s imagination. Sturges and Sturges (1998) treated an 11-year-old girl with a fear of
elevators by systematically exposing her to an elevator (beginning by having her just stand
near an elevator and ending with her riding alone on the elevator). In another variation of
systematic desensitization, humor was used in place of relaxation training, based on the
reasoning that humor would also counteract anxiety. Individuals with an extreme fear of
spiders were asked to create jokes about spiders, and they were presented with humorous
scenes involving spiders. This treatment proved to be just about as effective as the more
traditional relaxation training in reducing spider phobias (Ventis, Higbee, & Murdock, 2001).


Aversive Counterconditioning

The goal of aversive counterconditioning is to develop an aversive CR to stimuli associ-
ated with an undesirable behavior. For instance, if a person has alcoholism, the procedure
may involve conditioning the responses of nausea and queasiness of the stomach to the sight,
smell, and taste of alcohol. The term counterconditioning is used because the technique is
designed to replace a positive emotional response to certain stimuli (such as alcohol) with a
negative one. In the 1940s, Voegtlin and his associates treated over 4,000 individuals with
alcoholism who volunteered for this distinctly unpleasant therapy (Lemere, Voegtlin, Broz,
O’Hallaren, & Tupper, 1942; Voegtlin, 1940). Over a 10-day period, a patient received about

to violence in their lives. In one controlled study, active-duty soldiers with PTSD were
randomly assigned to two groups. One group received “treatment as usual”—the
standard treatment provided by mental health facilities for PTSD, which included such
things as cognitive therapy, relaxation training, thought control, and group therapy. The
other group received virtual reality therapy over a period of about 10 weeks, with one
or two therapy sessions a week. The patients wore 3-D goggles that depicted scenes
and events that were very similar to those they had experienced in combat, such as
the sights and sounds of their base camp, engaging in house-to-house searches, and
being attacked by enemy forces. As in systematic desensitization, the patients were
also given relaxation training, and the stimuli were presented in a graduated sequence,
going from mild to increasingly stressful situations. Besides relying on the patients’
reports of their anxiety levels during this treatment, the therapists used medical record-
ing equipment to continually measure the patients’ arousal and anxiety levels to make
sure their emotional reactions did not become too intense. This study found substantial
improvement in most of those in the virtual reality group (at least a 30% decrease in
PTSD symptoms), and this was significantly better than the group that received treat-
ment as usual (McLay et al., 2011). Other clinical tests have confirmed that virtual reality
therapy can be an effective treatment for PTSD (Botella, Serrano, Baños, & Garcia-
Palacios, 2015).

In addition to phobias and PTSD, virtual reality therapy has been used with some
success for other disorders, including chronic pain, smoking, alcoholism, and drug
addictions. Of course, the treatment methods can vary greatly depending on the type
of problem being treated. For instance, in the treatment for drug addictions, images
of stimuli associated with the drug are repeatedly presented in an effort to reduce the
drug cravings produced by these stimuli (see the section on cue-exposure therapy in
Chapter 4). As the technology continues to improve and as therapists discover the most
effective ways to apply it in conjunction with other treatment methods, it seems likely
that the use of computer-generated stimuli will become more widespread in behavior
therapy in the future.


a half dozen treatment sessions in which alcoholic beverages were paired with an emetic (a
drug that produces nausea). First, the patient received an emetic, and soon the first signs of
nausea would begin. The patient was then given a large glass of whiskey and was instructed
to look at, smell, and taste the whiskey, and after a few minutes the drug caused the patient
to vomit. In later conditioning sessions, other liquors were used to ensure that the aversion
was not limited to whiskey. It is hard to imagine a more unpleasant therapy, and the patients’
willingness to participate gives an indication both of their commitment to overcome their
alcoholism and of their inability to do so on their own.

Figure 3.10 shows the percentages of patients who were totally abstinent for various
lengths of time after the therapy. The percentage was high at first but declined as years
passed. The diminishing percentages may reflect the process of extinction: If over the years
a person repeatedly encounters the sight or smell of alcohol (at weddings, at parties, on
television) in the absence of the US (the emetic), the CR of nausea should eventually wear
off. At least two types of evidence support the role of extinction. First, patients who
received “booster sessions” (further conditioning sessions a few months after the original
treatment) were, on average, abstinent for longer periods of time. The reconditioning ses-
sions presumably counteracted the effects of extinction. Second, those who continued to
associate with old drinking friends (and were thereby exposed to alcohol) were the most
likely to fail.

If the declining percentages in Figure 3.10 seem discouraging, it is important to realize
that a similar pattern of increasing relapses over time occurs with other treatments for
alcoholism. Furthermore, Voegtlin used a very strict criterion for success—total absti-
nence. Individuals who drank with moderation after the treatment, or had just one relapse,
were counted as failures. Figure 3.10 therefore presents the most pessimistic view possible
about the success of this treatment. Despite the evidence for its effectiveness, in recent

Figure 3.10 The percentages of Voegtlin’s clients who remained completely abstinent for various
amounts of time following aversive counterconditioning for alcoholism. (Based on Lemere &
Voegtlin, 1950)

1 2





Years after aversive counterconditioning










years aversive counterconditioning has not often been used as a treatment for alcoholism.
When used, it is often included as one component of multifaceted treatment programs
that also involve family counseling, self-control training, and other techniques (Smith &
Frawley, 1990).

Aversive counterconditioning has also been applied to other behavioral problems,
including drug use, cigarette smoking, overeating, and sexual deviations. Different aversive
stimuli have been used, including electric shock, unpleasant odors, or disgusting mental
images. One method that has been used to help people quit smoking cigarettes is called
“rapid smoking”: The smoker inhales cigarette smoke at a rapid pace, which makes it a
sickening experience. This technique has had a respectable success rate (Gifford & Shoe-
nberger, 2009).

In summary, aversive counterconditioning is a procedure that attempts to decrease
unwanted behaviors by conditioning aversive reactions to stimuli associated with the
behaviors. Its effectiveness is variable. It appears to be a useful procedure for eliminating
some sexual deviations, such as fetishes and exhibitionism. When used as a treatment for
alcoholism or smoking, some clients have had relapses, but others have remained abstinent
for years.

Treatment of Nocturnal Enuresis

Nocturnal enuresis (bedwetting during sleep) is a fairly common problem with children. If
it continues at age 5 or older, it can become a frustrating problem for both children and
parents. Fortunately, most cases can be cured by a straightforward procedure developed by
Mowrer and Mowrer (1938) called the bell-and-pad method. The pad, a water-detecting
device, is placed beneath the child’s sheets; a single drop of urine will activate the device and
ring the bell to wake up the child. The child is instructed in advance to turn off the alarm,
go to the toilet and urinate, and then go back to sleep. The bell and pad are used every night
until the problem disappears.

In this procedure, the bell is a US that elicits two responses in the child: (1) awakening
and (2) the tightening of those muscles necessary to prevent further urination (responses that
occur because the child has no difficulty retaining urine when awake). The goal of the
procedure is to transfer either or both of these responses to an internal CS—the sensations
associated with having a full bladder. For simplicity, let us call the CS a full bladder. By
repeatedly pairing a full bladder with the bell, the response of awakening and/or tightening
the muscles so as to retain one’s urine should eventually be elicited by the full bladder alone,
before the bell sounds.

Various studies have found success rates of about 80% for the bell-and-pad method.
Relapses are a frequent problem, but they can be readily treated with additional bell-and-pad
training. Evidence from a number of different studies has shown that the bell-and-pad
method is more effective than other treatments for enuresis (Brown, Pope, & Brown, 2011).

Summary of the Classical Conditioning Therapies

Behavior therapies based on principles of classical conditioning have been used to strengthen,
eliminate, or replace behaviors. The bell-and-pad treatment for nocturnal enuresis is an
example of a procedure designed to strengthen a behavior (i.e., night-time retention).


Systematic desensitization is used to elimi-
nate the emotional responses of fear and
anxiety. Aversive counterconditioning is
designed to replace pleasant emotional
responses to such stimuli as alcohol and
cigarette smoke with aversion. Each of
these procedures has its share of failures and
relapses, but each can also boast of long-
term successes for a significant percentage
of those who receive treatment.


In its simplest form, classical conditioning
involves the repeated pairing of a CS with
a US that naturally elicits a UR. After
repeated pairings, the CS starts to elicit a
CR. Pavlov used the salivation response of
dogs to study classical conditioning, but in
modern research, some common condi-
tioning preparations are eyeblink condi-
tioning, conditioned suppression, the skin
conductance response, and taste-aversion

According to Pavlov’s stimulus substi-
tution theory, the CS should produce the same response that the US originally did. In
reality, however, sometimes the CR is different in form, and sometimes it is actually the
opposite of the UR. In the brain, it has been proposed that neural centers for the CS
become connected to either the center for the US (an S-S connection) or directly to the
center for the response (an S-R connection). Some experiments on US devaluation favor
the S-S view.

Throughout the animal kingdom, instances of classical conditioning exhibit the same
basic principles, including acquisition, extinction, spontaneous recovery, disinhibition, con-
ditioned inhibition, generalization, and discrimination. The most effective temporal arrange-
ment for conditioning occurs in short-delay conditioning; weaker conditioning usually
occurs in simultaneous, long-delay, or trace conditioning. In backward conditioning, the CS
may become a conditioned inhibitor. In second-order conditioning, a CR is transferred not
from US to CS but from one CS to another.

In everyday life, classically CRs can be seen in our emotional reactions to many different
stimuli. In behavior therapy, systematic desensitization is used to extinguish phobias by
gradually presenting more and more intense fear-provoking stimuli while the patient is in
a relaxed state. Aversive counterconditioning is used to replace positive responses to certain
stimuli (e.g., alcohol, cigarettes) with negative responses. The bell-and-pad method is used
to train children to avoid bedwetting.

Practice Quiz 2: chaPter 3
1. When the CS and US are separated

by some time interval, this is called

2. The temporal arrangement that usu-
ally produces the strongest excitatory
conditioning is ______ conditioning.

3. In an evaluative conditioning proce-
dure in which pictures of people are
paired with either positive or negative
adjectives, the adjectives are ______
and the pictures of people are ______.

4. When the effectiveness of aversive
counterconditioning for alcoholism
weakens over time, this could be an
example of the conditioning principle
of ______.

5. In the classical conditioning treatment
for bedwetting, the US is ______.


1. trace conditioning 2. short-delay 3. first-order
CSs, second-order CSs 4. extinction 5. an alarm that
wakes up the child



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1. Define CS, US, UR, and CR. Use the examples of salivary conditioning and con-
ditioning of the skin conductance response to illustrate these four concepts.

2. What three different types of evidence show that extinction does not simply
erase the association that was formed during classical conditioning?

3. Describe one temporal arrangement between CS and US that produces strong
excitatory conditioning, one that produces weak excitatory conditioning, and
one that produces inhibitory conditioning. Give a reasonable explanation of why
each different procedure produces the results that it does.

4. Explain how television advertisers can use classical conditioning to give viewers
a positive feeling about their product. How could they use classical condition-
ing to give viewers a negative reaction to other brands? Can you think of actual
commercials that use these techniques?

5. Explain how systematic desensitization is used to treat phobias. Explain how
extinction and generalization are important parts of the procedure. Why don’t
phobias extinguish by themselves, without the need for treatment?


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Learning Objectives

After reading this chapter, you should be able to

• explain the blocking effect and why it is important
• describe the basic concepts of the Rescorla–Wagner model and how it accounts

for conditioning phenomena such as acquisition, extinction, blocking, and
conditioned inhibition

• summarize research findings on the neural mechanisms of classical condition-
ing in primitive animals, mammals, and humans

• explain how heredity can influence what animals and people learn through
classical conditioning

• discuss the role that classical conditioning plays in drug tolerance and

C H A P T E R 4

Theories and Research on
Classical Conditioning

This chapter surveys some major themes and issues in the field of classical conditioning.
The chapter is divided into four sections, each of which addresses different questions. The
first section covers theories about when and how different types of conditioning will occur:
Under what conditions will a stimulus become an excitatory CS, or become an inhibitory
CS, or remain neutral? The second section examines classical conditioning from the per-
spective of neuroscience. We will take a brief look at how classical conditioning alters the
functioning of individual neurons and what areas of the brain are involved. The third sec-
tion, on biological constraints, examines the role that hereditary factors play in associative
learning. The final section addresses the question of what form a CR will take. Will the
CR be similar to the UR, the opposite of the UR, or something entirely different? This


question has important practical implications, as when a stimulus that has been associated
with a drug might later elicit a response that either mimics or opposes the reaction to the
drug itself.


One of the oldest principles of associative learning is the principle of frequency: The more
frequently two stimuli are paired, the more strongly will a learner associate the two. Thomas
Brown first proposed this principle, and it has been a basic assumption of many theories of
learning. Because of widespread acceptance of the frequency principle as a fundamental rule
of learning, an experiment by Leon Kamin that contradicted this principle attracted con-
siderable attention.

The Blocking Effect

To simplify the description of Kamin’s (1968) experiment and others in this chapter, the
following notation will be used. We will use capital letters to represent different CSs (e.g.,
T will represent a tone, and L will represent a light). A plus sign (+) will indicate that a US
was presented after a CS. For example, T+ will indicate a trial on which a tone was presented
and was followed by a US. The notation TL will refer to a trial on which two CSs, a tone
and a light, were presented simultaneously but were not followed by the US.

Kamin’s original experiment used rats in a conditioned suppression procedure. Table 4.1
outlines the design of the experiment. There were two groups of rats, a blocking group
and a control group. In Phase 1, rats in the blocking group received a series of L+ trials (a
light followed by shock), and by the end of this phase, L elicited a strong CR (suppression of
lever pressing when L was on). In Phase 2, the blocking group received a series of LT+ trials:
The light and a tone were presented together, followed by shock. Finally, in the test phase,
T was presented by itself (with no shock) for several trials so as to measure the strength of
conditioning to the tone.

There was only one difference in the procedure for the control group: In Phase 1, no
stimuli were presented at all. Therefore, the first time these rats were exposed to L, T, and
shock was in Phase 2. The important point is that both groups received exactly the same
number of pairings of T and shock. So the frequency principle predicts that conditioning to T
should be equally strong in the two groups. However, this is not what Kamin found: Whereas
he observed a strong fear response (conditioned suppression) to T in the control group, there
was almost no fear response at all to T in the blocking group. Kamin concluded that the
prior conditioning with L somehow “blocked” the later conditioning of T. Since Kamin’s

Table 4.1 Design of Kamin’s blocking experiment.

Group Phase 1 Phase 2 Test Phase Results

Blocking L+ LT+ T T → no fear
Control – LT+ T T → fear


pioneering work, the blocking effect has been demonstrated in numerous experiments using
a variety of conditioning situations with both animals and people.

An intuitive explanation of the blocking effect is not difficult to construct: To put it
simply, T was redundant in the blocking group; it supplied no new information. By the end
of Phase 1, rats in the blocking group had learned that L was a reliable predictor of the
US—the US always occurred after L and never at any other time. Adding T to the situation
in Phase 2 added nothing to the rat’s ability to predict the US. This experiment suggests that
conditioning will not occur if a CS adds no new information about the US.

This experiment demonstrates that conditioning is not an automatic result when a CS
and a US are paired. Conditioning will occur only if the CS is informative, only if it
predicts something important, such as an upcoming shock. For two psychologists, Robert
Rescorla and Allan Wagner (1972), the blocking effect and related findings led them to
develop a new theory that has become one of the most famous theories of classical

The Rescorla–Wagner Model

The Rescorla–Wagner model is a mathematical model about classical conditioning, and
for some people the math makes the model difficult to understand. However, the basic ideas
behind the theory are quite simple and reasonable, and they can be explained without the
math. This section is designed to give you a good understanding of the concepts behind the
model without using any equations.

Classical conditioning can be viewed as a means of learning about signals (CSs) for
important events (USs). The Rescorla–Wagner model is designed to predict the outcome
of classical conditioning procedures on a trial-by-trial basis. For each trial in a condition-
ing procedure, there could be excitatory conditioning, inhibitory conditioning, or no
conditioning at all. According to the model, two factors determine which of these three
possibilities actually occurs: (1) the strength of the subject’s expectation of what will occur
and (2) the strength of the US that is actually presented. The model is a mathematical
expression of the concept of surprise: It states that learning will occur only when the
learner is surprised, that is, when what actually happens is different from what the learner
expected to happen.

You should be able to grasp the general idea of the model if you learn and understand
the following six rules:

1. If the strength of the actual US is greater than the strength of the learner’s expecta-
tion, all CSs paired with the US will receive excitatory conditioning.

2. If the strength of the actual US is less than the strength of the learner’s expectation,
all the CSs paired with the US will receive some inhibitory conditioning.

3. If the strength of the actual US is equal to the strength of the learner’s expectation,
there will be no conditioning.

4. The larger the discrepancy between the strength of the expectation and the strength
of the US, the greater the conditioning (either excitatory or inhibitory).

5. More salient (more noticeable) CSs will condition faster than less salient (less notice-
able) CSs.


6. If two or more CSs are presented together, the learner’s expectation will be equal to
their total strength (with excitatory and inhibitory stimuli tending to cancel each
other out).

To demonstrate how these six rules work, we will now examine several different examples.
For all of the examples, imagine that a rat receives a conditioning procedure in which a CS
(a light, a tone, or some other signal) is followed by food as a US. In this conditioning situ-
ation, the CR is activity, as measured by the rat’s movement around the conditioning cham-
ber (which can be automatically recorded by movement detectors). In actual experiments
using this procedure, the typical result is that as conditioning proceeds, the rat becomes more
and more active when the CS is presented, so its movement can be used as a measure of the
amount of excitatory conditioning.


Suppose a light (L) is paired with one food pellet (Figure 4.1). On the very first condition-
ing trial, the rat has no expectation of what will follow L, so the strength of the US (the
food pellet) is much greater than the strength of the rat’s expectation (which is zero).
Therefore, this trial produces some excitatory conditioning (Rule 1). But conditioning is
rarely complete after just one trial. The second time L is presented, it will elicit a weak
expectation, but it is still not as strong as the actual US, so Rule 1 applies again, and more
excitatory conditioning occurs. For the same reason, further excitatory conditioning
should take place on Trials 3, 4, and so on. However, with each conditioning trial, the rat’s
expectation of the food pellet should get stronger, and so the difference between the
strength of the expectation and the strength of the US gets smaller. Therefore, the fastest
growth in excitatory conditioning occurs on the first trial, and there is less and less addi-
tional conditioning as the trials proceed (Rule 4). Eventually, when L elicits an expectation
of food that is as strong as the actual food pellet itself, the asymptote of learning is reached,
and no further excitatory conditioning will occur with any additional L and food

Figure 4.1 According to the Rescorla–Wagner model, during acquisition, the actual US is greater than
the expected US, so there is excitatory conditioning (an increase in the strength of the CS–US associa-
tion). The strength of the expected US is greater on later acquisition trials, so the amount of condi-
tioning is not as great as on the first trial.



Continuing with this same example, now suppose that after the asymptote of conditioning
is reached for L, a compound CS of L and tone (T) are presented together and are followed
by one food pellet (Figure 4.2). According to Rule 6, when two CSs are presented, the
learner’s expectation is based on the total expectations from the two. T is a new stimulus, so
it has no expectations associated with it, but L produces an expectation of one food pellet.
One food pellet is in fact what the animal receives, so the expected US matches the actual
US, and no additional conditioning occurs (Rule 3); that is, L retains its excitatory strength,
and T retains zero strength.

This, in short, is how the Rescorla–Wagner model explains the blocking effect: No con-
ditioning occurs to the added CS because there is no surprise—the strength of the learner’s
expectation matches the strength of the US.

Extinction and Conditioned Inhibition

Suppose that after conditioning with L, a rat receives extinction trials in which L is presented
without food (Figure 4.3). The expected US is food, but the actual US is nothing (no food
is presented). This is a case where the strength of the expected US is greater than that of the
actual US, so according to Rule 2, there will be a decrease in the association between L and
food. Further extinction trials will cause more and more decline in the association between
L and food.

Now think about a slightly different example. Suppose that after conditioning with L
has reached its asymptote, the rat receives trials in which L and T are presented together,
but no food pellet is delivered on these trials. This is another case where Rule 2 applies:
The strength of the expected US will be greater than the strength of the actual US.
According to Rule 2, both CSs, L and T, will acquire some inhibitory conditioning on
these extinction trials.

Let us be clear about how this inhibitory conditioning will affect L and T. Because L
starts with a strong excitatory strength, the trials without food (and the inhibitory condition-
ing they produce) will begin to counteract the excitatory strength. This is just another
example of extinction. In contrast, T begins the extinction phase with zero strength because
it has not been presented before. Therefore, the trials without food (and the inhibitory
conditioning they produce) will cause T’s strength to decrease below zero—it will become
a conditioned inhibitor.

Figure 4.2 According to the Rescorla–Wagner model, the blocking effect occurs because there is no
learning on a conditioning trial if the expected US is equal to the actual US.



In an experiment with a compound CS consisting of one intense stimulus and one weak
one, Pavlov discovered a phenomenon he called overshadowing. After a number of con-
ditioning trials, the intense CS would produce a strong CR if presented by itself, but the
weak CS by itself would elicit little, if any, conditioned responding. It was not the case that
the weak CS was simply too small to become an effective CS, because if it were paired with
the US by itself, it would soon elicit CRs on its own. However, when the two CSs were
conditioned together, the intense CS seemed to mask, or overshadow, the weaker CS. Over-
shadowing has been observed in experiments with both animals and humans (Spetch, 1995;
Stockhorst, Hall, Enck, & Klosterhalfen, 2014).

The Rescorla–Wagner model’s explanation of overshadowing is straightforward (Figure 4.4).
According to Rule 5, more salient stimuli will condition faster than less salient stimuli. If, for
example, a dim light and a loud noise are presented together and followed by a food pellet, the
noise will acquire excitatory strength faster than the light. When the total expectation based
on both the noise and the light equals the strength of the actual US, food, conditioning will
stop. Because the noise is more salient, it will have developed much more excitatory strength
than the light. If the dim light is presented by itself, it should elicit only a weak CR.

The Overexpectation Effect

One characteristic of a good theory is the ability to stimulate research by making new pre-
dictions that have not been previously tested. The Rescorla–Wagner model deserves good
grades on this count, because hundreds of experiments have been conducted to test the

Figure 4.3 According to the Rescorla–Wagner model, during extinction, the expected US is greater
than the actual US, so there is inhibitory conditioning (a decrease in the strength of the CS–US

Figure 4.4 According to the Rescorla–Wagner model, overshadowing occurs because the amount of
conditioning depends on the salience of a stimulus. Here, the noise is more salient, so there is a larger
increase in the noise–food association than in the light–food association.


model’s predictions. Research on a phenomenon known as the overexpectation effect is
a good example, because it is a case where the Rescorla-Wagner model makes a prediction
that many people find surprising and counterintuitive, yet the prediction turns out to be

Table 4.2 presents the design of an experiment on the overexpectation effect. Two CSs,
L and T, are involved. For Phase 1, the notation L+, T+ means that on some trials L is
presented by itself and followed by a food pellet, and on other trials T is presented by itself
and followed by a food pellet. Consider what should happen on each type of trial. On L+
trials, the strength of the expectation based on L will continue to increase and eventually
approach the strength of one food pellet. Similarly, on T+ trials, the strength of the expec-
tation based on T will grow and also approach the strength of one food pellet. At the end
of Phase 1, the rat expects one food pellet when L is presented, and it also expects one food
pellet when T is presented. In Phase 2, rats in the control group receive no stimuli, so no
expectations are changed. Therefore, in the test phase, these rats should exhibit a strong CR
to both L and T, and they do.

The results should be quite different for rats in the overexpectation group. In Phase 2,
these rats receive a series of trials with the compound stimulus, LT, followed by one food
pellet. On the first trial of Phase 2, a rat’s total expectation, based on the sums of the strengths
of L and T, should be roughly equal to the strength of two food pellets (because both L and
T have a strength of about one food pellet). Loosely speaking, we might say that the rat
expects a larger US because two strong CSs are presented, but all it gets is a single food pellet
(Figure 4.5). Therefore, compared to what it actually receives, the animal has an overexpecta-
tion about the size of the US, and Rule 2 states that under these conditions, both CSs will
experience some inhibitory conditioning (they will lose some of their associative strength).

Table 4.2 Design of an experiment on the overexpectation effect.

Group Phase 1 Phase 2 Test Phase Results

Overexpectation L+, T+ LT+ L, T moderate CRs
Control L+, T+ no stimuli L, T strong CRs

Figure 4.5 According to the Rescorla–Wagner model, the overexpectation effect occurs because when
two separately conditioned stimuli are presented together, the expected US is greater than the actual
US, so there is inhibitory conditioning (a decrease in the strength of each CS–food association).


With further trials in Phase 2 for the overexpectation group, the strengths of L and T
should continue to decrease as long as the total expectation from the two CSs is greater than
the strength of one food pellet. In the test phase, the individual stimuli L and T should
produce weaker CRs in the overexpectation group because their strengths were decreased
in Phase 2. Experiments with both animals and people have confirmed this prediction: CRs
are weaker in the overexpectation group than in the control group (Kremer, 1978; Ruprecht,
Izurieta, Wolf, & Leising, 2014).

The model’s accurate prediction of the overexpectation effect is especially impressive
because the prediction is counterintuitive. If you knew nothing about the Rescorla–Wagner
model when you examined Table 4.2, what result would you predict for this experiment?
Notice that subjects in the overexpectation group actually receive more pairings of L and
T with the US, so the frequency principle would predict stronger CRs in the overexpecta-
tion group. Based on the frequency principle, the last thing we would expect from more
CS–US pairings is a weakening of the CS–US associations. Yet this result is predicted by the
Rescorla–Wagner model, and the prediction turns out to be correct.


The Rescorla–Wagner model might be called a theory about US effectiveness: It states that an
unpredicted US is effective in promoting learning, whereas a well-predicted US is ineffec-
tive. As the first formal theory that attempted to predict when a US will promote associative
learning and when it will not, it is guaranteed a prominent place in the history of psychol-
ogy. The model has been successfully applied to many conditioning phenomena, but it is
not perfect. Some research findings are difficult for the model to explain. We will now take
a brief look at some of these findings, and at other theories of classical conditioning that are
based on different assumptions about the learning process.

Theories of Attention

Some theories of classical conditioning focus on how much attention the learner pays to the
CS (e.g., Mackintosh, 1975; Pearce & Hall, 1980). One common feature of these theories is
the assumption that the learner will pay attention to informative CSs but not to uninforma-
tive CSs. If the learner does not pay attention to a CS, there will be no conditioning of that
CS. These theories might also be called theories of CS effectiveness, because they assume that
the conditionability of a CS, not the effectiveness of the US, changes from one situation to
another. A phenomenon called the CS preexposure effect provides one compelling piece of
evidence for this assumption.

The CS preexposure effect is the finding that classical conditioning proceeds more
slowly if a CS is repeatedly presented by itself before it is paired with the US. For example,
if a rat receives presentations of a tone by itself, and these are followed by tone-food pairings,
conditioning of the tone-food association will take more trials than if there were no tone
preexposure trials (Lubow & Moore, 1959). A simple explanation is that because the tone
is presented repeatedly but predicts nothing during CS preexposure, the animal gradually
pays less and less attention to this stimulus. We might say that the rat learns to ignore the
tone because it is not informative, and for this reason it takes longer to associate the tone
with the US when conditioning trials begin and the tone suddenly becomes informative.


The problem for the Rescorla–Wagner model is that it does not predict the CS preexpo-
sure effect. It is easy to see why. When a new CS is presented by itself, the expected US is
zero, and the actual US is zero. Because the expected US equals the actual US, according to
the Rescola–Wagner model there should be no learning of any kind. But, evidently, subjects
do learn something on CS preexposure trials, and what they learn hinders their ability to
develop a CS–US association when the two stimuli are paired at a later time.

Unlike the Rescorla–Wagner model, attentional theories such as those of Mackintosh
(1975) and Pearce and Hall (1980) can easily explain the CS preexposure effect: Because the
CS predicts nothing during the preexposure period, attention to the CS decreases, and so
conditioning is slower when the CS is first paired with the US at the beginning of the
conditioning phase. The attentional theories can also account for other basic conditioning
phenomena. For example, they can explain the overshadowing effect by assuming that ani-
mals pay more attention to the more salient CS.

Experiments designed to compare the predictions of the Rescorla–Wagner model and
attentional theories have produced mixed results, with some of the evidence supporting each
theory (Balaz, Kasprow, & Miller, 1982; Hall & Pearce, 1983). Perhaps these findings indicate
that both classes of theory are partly correct; that is, perhaps the effectiveness of both CSs
and USs can change as a result of a subject’s experience. If a US is well predicted, it may
promote no conditioning (which is the basic premise of the Rescorla–Wagner model).
Likewise, if nothing surprising follows a CS, it may become ineffective (the basic premise
of the attentional theories).

Comparator Theories of Conditioning

Other theories of classical conditioning, called comparator theories, assume that the animal
compares the likelihood that the US will occur in the presence of the CS with the likelihood
that the US will occur in the absence of the CS (Miller & Schachtman, 1985; Stout & Miller,
2007). Comparator theories differ from those we have already examined in two ways. First,
comparator theories do not make predictions on a trial-by-trial basis because they assume
that what is important is not the events of individual trials but rather the overall, long-term
correlation between a CS and a US. Second, comparator theories propose that the correlation
between CS and US does not affect the learning of a CR but rather its performance.

As a simple example, suppose that the probability of a US is 50% in the presence of some
CS, but its probability is also 50% in the absence of this CS. The comparator theories predict
that this CS will elicit no CR, which is what Rescorla (1968) found, but not because the
CS has acquired no excitatory strength. Instead, the theories assume that both the CS and
contextual stimuli—the sights, sounds, and smells of the experimental chamber—have
acquired equal excitatory strengths because both have been paired with the US 50% of the
time. Comparator theories also assume that a CS will not elicit a CR unless it has greater
excitatory strength than the contextual stimuli. Unlike the Rescorla–Wagner model, how-
ever, comparator theories assume that an animal in this situation has indeed learned some-
thing about the CS—that the US sometimes occurs in its presence—but the animal will not
respond to the CS unless it is a better predictor of the US than the context.

To test comparator theories, one common research strategy is to change the strength of
one stimulus and try to show that the conditioned responding changes to another stimulus.



Classical Conditioning in Advertising

Because classical conditioning can alter a person’s feelings about a stimulus, even with-
out the person’s awareness, it should come as no surprise that classical conditioning
has been used in advertising for a long time. For example, a television commercial may
present a certain brand of snack food along with stimuli that most viewers will evaluate
positively, such as young, attractive people having a good time. Advertisers hope that
viewers will be attracted to the people and that this positive reaction will become condi-
tioned to the product being sold. If the conditioning is successful, you may later have a
positive reaction when you see the product in a store, regardless of whether or not you
remember the commercial.

In a similar way, many advertisements feature popular celebrities or athletes endors-
ing products, based on the idea that viewers’ positive reactions to the celebrities will

For example, suppose that after conditioning, an animal exhibits only a weak CR to a light
because both the light and the contextual stimuli have some excitatory strength. According
to comparator theories, one way to increase the response to the light would be to extinguish
the excitatory strength of the context by keeping the subject in the context and never pre-
senting the US. If the response to the light depends on a comparison of the light and the
context, extinction of the context should increase the response to the light. Experiments of
this type have shown that extinction of the context does increase responding to the CS
(Matzel, Brown, & Miller, 1987).

In a related experiment by Cole, Barnet, and Miller (1995), one CS was followed by a
US every time it was presented, whereas a second CS was followed by a US only 50% of the
time. At first, the second CS did not elicit much conditioned responding. However, after
responding to the first CS was extinguished, CRs to the second CS increased dramatically.
The Rescorla–Wagner model does not predict these effects, because it states that the condi-
tioned strength of one CS cannot change if that CS itself is not presented. According to
comparator theories, however, subjects may learn an association between a CS and a US that
cannot initially be seen in their performance, but this learning can be unmasked if the
strength of a competing CS is weakened.

Research designed to test comparator theories has provided mixed results. Some studies
have provided support for this approach (e.g., San-Galli, Marchand, Decorte, & Di Scala,
2011), whereas other studies have supported the predictions of traditional associative learning
theories such as the Rescorla–Wagner model (e.g., Dopson, Pearce, & Haselgrove, 2009).
Because there is evidence supporting both types of theories, it may be that future theories
of classical conditioning will need to take into account both learning and performance
variables to accommodate the diverse findings that researchers have obtained.


transfer from the celebrities to the product. Marketing research suggests that this
transfer of positive reactions to the product or brand does indeed occur. In laboratory
research, associations can form between CSs that are paired with the same US, and
this can also occur in advertising when a celebrity acts as a spokesperson for more than
one product. If a celebrity endorses a popular line of athletic shoes and a less familiar
brand of other types of athletic equipment, the positive reaction can transfer from one
product to the other (Chen, Chang, Besherat, & Baack, 2013).

Some research has shown that the use of a popular celebrity in a commercial can
do more than simply give the viewer a vague positive feeling about a product; specific
characteristics of the celebrity can become associated with the product. For instance,
the handsome and popular actor George Clooney has been a sponsor for a particular
brand of expresso. According to Förderer and Unkelbach (2014), viewers of these ads
come to associate the specific attributes of Clooney (such as sexy, cosmopolitan, and
glamorous) with the product brand. Förderer and Unkelbach refer to this phenomenon
as attribute conditioning because the attributes or characteristics of one stimulus
(the celebrity) are transferred to another stimulus (the product).

Even the music in a commercial can influence people’s reactions to the product.
In one experiment, college students viewed pictures of pens that differed only in their
color, while they listened to music that they either liked or disliked. Later, when asked
to choose of one the pens, most showed a preference for the color of pen that was
associated with the music they liked (Gorn, 1982).

Classical conditioning can also be used in advertising in the opposite way—to
develop negative associations with a competing product. Some ads show people who
are frustrated or unhappy when using a competitor’s product. Negative advertising is
especially common in political commercials. An ad may associate pictures of the other
candidate with somber or disturbing music, with images of unpleasant scenes, and
with angry or unhappy faces. The goal, of course, is to make voters associate nega-
tive emotions with the opponent. Many voters say they dislike these negative ads, and
you may wonder why they are used so frequently in political campaigns. The answer is
simple: They work.


Our understanding of classical conditioning would be greatly enhanced if we knew exactly
what changes take place in the nervous system during the acquisition and subsequent per-
formance of a new CR. This topic has been the focus of intense research efforts for many
years, and much has been learned about the neural mechanisms of classical conditioning.
This section can provide only a brief survey of some of the major developments.

Some of the research on the neural mechanisms of classical conditioning has been done
with primitive creatures. Kandel and his colleagues have been able to study classical


conditioning in the gill-withdrawal reflex of Aplysia (see Figure 2.8). In this research, the
US was a shock to the tail, and the UR was the gill-withdrawal response. The CS was weak
stimulation of the siphon, which initially produced only a minor gill withdrawal response.
After several pairings of the CS and US, however, the CS began to elicit a full gill-withdrawal
response (Carew, Hawkins, & Kandel, 1983). The researchers determined that this CR (the
increased response to siphon stimulation) was due to an increase in the amount of transmitter
released by the sensory neurons of the siphon. Note that precisely the opposite neural
change (decreased transmitter release by the sensory neurons) was found to be responsible
for habituation of the gill-withdrawal response (see Chapter 2). However, other changes
have been observed in Aplysia’s nervous system during classical conditioning as well. Glan-
zman (1995) found that the dendrites of the postsynaptic neurons in the circuit develop
enhanced sensitivity, so they exhibit stronger responses to chemical stimulation. In addition
to these chemical changes, repeated classical conditioning trials can produce more perma-
nent, structural changes in Aplysia’s nervous system—the growth of new synapses between
the sensory neurons in the siphon and motor neurons (Bailey & Kandel, 2009). These are
important findings because they show that even in the simple nervous system of Aplysia, a
variety of chemical and structural changes can take place during a simple learning

With humans and other mammals, research on this topic is proceeding in several different
directions. Some studies have examined human participants with damage in specific areas
of the brain due to accident or illness; these individuals are trained in a classical conditioning
paradigm, such as eyeblink conditioning, to determine the effects of brain damage. Another
strategy is to condition people without brain damage while using modern imaging tech-
nologies to measure activity in different parts of the brain. With nonhuman subjects,
researchers have examined chemical mechanisms and the effects of lesions to different brain
areas. Our brief review of this research will focus on five main points.

1. The neural pathways involved in the CR are often different from those involved in the UR. This
can be shown through procedures that eliminate one of these responses but not the other.
For example, in baboons, a certain part of the hypothalamus appears to be intimately involved
in the conditioned heart-rate changes elicited by CSs paired with shock. If this part of the
hypothalamus is destroyed, heart-rate CRs disappear, whereas unconditioned heart-rate
responses are unaffected (Smith, Astley, DeVito, Stein, & Walsh, 1980).

In rabbit eyeblink conditioning, the cerebellum, a part of the brain that is important for
many skilled movements, plays a critical role (Tracy, Thompson, Krupa, & Thompson, 2013).
As in heart-rate conditioning, different neural pathways are involved in eyeblink URs and
CRs. The eyeblink UR to an air puff directed at the eye seems to be controlled by two
distinct pathways—a fairly direct pathway in the brainstem and a more indirect pathway
passing through the cerebellum. Considerable evidence shows that the eyeblink CR is con-
trolled by this second, indirect pathway. If sections of this pathway in the cerebellum are
destroyed, eyeblink CRs disappear and cannot be relearned (Knowlton & Thompson, 1992).
If neurons in this same part of the cerebellum are electrically stimulated, eyeblink responses
similar to the CR are produced (Thompson, McCormick, & Lavond, 1986). If this diver-
gence of UR and CR pathways is found in other response systems, it would help to explain
why the forms of the UR and CR are often different.

2. Many different brain structures may be involved in the production of a simple CR. For example,
although the cerebellum is important in rabbit eyeblink conditioning, many other brain areas


are involved as well. When humans receive eyeblink conditioning, brain-imaging techniques
such as positron emission tomography (PET) reveal increased blood flow in one side of the
cerebellum (corresponding to the side of the eye involved in conditioning), but there is
increased blood flow in many other parts of the brain as well (Molchan, Sunderland, McIn-
tosh, Herscovitch, & Schreurs, 1994). In other species, several different brain sites have been
implicated in heart-rate conditioning, including parts of the amygdala, hypothalamus, and
cingulate cortex (Schneiderman et al., 1987).

With humans, fMRI techniques have been used to obtain detailed maps of brain
activity when people receive classical conditioning. During classical conditioning with
the SCR, fMRIs show complex patterns of activity involving many parts of the brain,
which again supports the idea that multiple brain structures are involved in classical
conditioning. In one study, pictures of faces were used as CSs, and both pleasant and
unpleasant odors were used as USs. Particularly interesting was the finding that even
within the same conditioning preparation (pairing faces with odors), the areas of activa-
tion were different for pleasant odors and unpleasant odors (Gottfried, O’Doherty, &
Dolan, 2002).

3. Different conditioning phenomena may involve different brain locations. This point has been
made in a variety of studies with different species. For example, if the hippocampus is
removed from a rabbit, the animal will fail to exhibit the blocking effect (Solomon, 1977),
but removal of the hippocampus does not prevent the development of conditioned inhibi-
tion. In mice, damage to an area near the hippocampus called the entorhinal cortex interferes
with the CS preexposure effect (Lewis & Gould, 2007). Another brain area, the amygdala,
seems important for associations involving both contextual stimuli and typical CSs (Cum-
mins, Boughner, & Leri, 2014).

4. Different CRs involve different brain locations. For example, whereas the cerebellum is
important in eyeblink conditioning, other brain areas are involved in heart-rate condition-
ing. One study compared a group of people with damage to the cerebellum to a group of
people without such brain damage. The people without brain damage quickly learned a
conditioned eyeblink response, but those with damage to the cerebellum did not learn this
response. However, the air puff itself did elicit an eyeblink UR in those with brain damage,
which shows that they had not simply lost motor control of this response (Daum et al.,
1993). The deficit appears to be a problem in forming the necessary associations for the
eyeblink CR to a neutral stimulus. This does not mean, however, that the people with dam-
age to the cerebellum suffered a general inability to associate stimuli, because measurements
of their heart rates and SCRs showed that they had indeed learned the association between
CS and the air puff. These results indicate that different parts of the brain are involved in
the conditioning of different response systems.

5. Activity and growth in individual neurons seems to be related to the acquisition and production
of CRs. For example, one study with adult mice found the sprouting of new axons and
the growth of new axon terminals and synapses in the cerebellum during eyeblink con-
ditioning (Boele, Koekkoek, De Zeeuw, & Ruigrok, 2013). Other researchers found that
when rabbits were presented with a series of conditioning trials with a CS such as a tone,
the activity of certain cells in the cerebellum increased at about the same rate as the eye-
blink CR. When the eyeblink CR decreased during extinction, so did the activity of these
cells. Moreover, the cellular activity during a single presentation of the CS paralleled the
pattern of the eyeblink CR, with the neuron’s activity preceding the eyeblink response by


about 30 milliseconds. Along with other
evidence, this finding suggests that these
cells play an important role in the devel-
opment of the CR (McCormick &
Thompson, 1984). Neurons with similar
properties have been found in the hip-
pocampus, a brain structure that plays an
important role in learning and memory
(Berger & Weisz, 1987).

One study used trace conditioning of a
fear response (measured by an increased
heart rate) with rabbits. For two different
groups of rabbits, a CS was followed by
shock after a gap of either 10 or 20 sec-
onds. After conditioning, the rabbits
received trials in which the CS was pre-
sented without shock. The researchers
identified individual neurons in the hip-
pocampus whose activity increased after
the CS and peaked either 10 or 20 seconds
later (matching the CS–US interval with
which the rabbits were trained). These
neurons, therefore, seemed to be involved
in the timing of the CR (McEchron, Tseng,
& Disterhoft, 2003).

As we have seen, brain research on clas-
sical conditioning is proceeding on a num-
ber of different levels, including research on entire brain structures, on individual neurons,
and on chemical mechanisms. Both primitive and more advanced species are being studied.
Much is still unknown about the brain mechanisms of classical conditioning, but one point
seems certain: Anyone hoping for a simple physiological explanation is going to be disap-
pointed. Classical conditioning, one of the simplest types of learning, appears to involve a
very complex system of neural and chemical mechanisms.


As discussed in Chapter 1, probably the most fundamental assumption underlying research
on animal learning is that it is possible to discover general principles of learning that are not
dependent in any important way on an animal’s biological makeup. During the 1960s,
researchers began to report findings that questioned the validity of the general-principle
approach to learning. For the most part, these findings took the form of alleged exceptions
to some of the best-known general principles of learning. As this type of evidence began to
accumulate, some psychologists started to question whether the goal of discovering general
principles of learning was realistic. They reasoned, if we find too many exceptions to a rule,
what good is the rule?

Practice Quiz 1: chaPter 4
1. Kamin’s blocking effect was surpris-

ing because it seemed to violate the
______ principle of associative

2. According to the Rescorla–Wagner
model, excitatory conditioning occurs
when the ______ is greater than the

3. According to the Rescorla–Wagner
model, extinction is a case where the
______ is greater than the ______,
so ______ conditioning occurs.

4. If conditioning to a weak CS is
impaired because it is presented
along with a more intense CS, this
is known as ______.

5. One brain structure that plays an
important role in eyeblink condition-
ing is the ______.

1. frequency 2. actual US, expected US 3. expected
US, actual US, inhibitory 4. overshadowing
5. cerebellum


This section will examine the evidence against the general-principle approach in the area
of classical conditioning, and it will attempt to come to some conclusions about its signifi-
cance for the psychology of learning. Biological constraints on other types of learning will
be discussed in later chapters.

The Contiguity Principle and Taste-Aversion Learning

As discussed in Chapter 1, the principle of contiguity is the oldest and most persistent prin-
ciple of association, having been first proposed by Aristotle. We saw in Chapter 3 that
CS–US contiguity is an important factor in classical conditioning. A popular textbook from
the early 1960s summarized the opinion about the importance of contiguity that prevailed
at that time: “At the present time it seems unlikely that learning can take place at all with
delays of more than a few seconds” (Kimble, 1961, p. 165).

Given this opinion about the importance of contiguity, it is easy to see why the work of
John Garcia and his colleagues on long-delay learning attracted considerable attention.
Garcia’s research involved a classical conditioning procedure in which poison was the US
and some novel taste was the CS. In one study (Garcia, Ervin, & Koelling, 1966), rats were
given the opportunity to drink saccharin-flavored water (which they had never tasted
before), and they later received an injection of a drug that produces nausea in a matter of
minutes. For different rats the interval between drinking and the drug injection varied from
5 to 22 minutes. Although these durations were perhaps a hundred times longer than those
over which classical conditioning was generally thought to be effective, all rats developed
aversions to water flavored with saccharin. Many later experiments replicated this finding,
and taste aversions were found even when delays as long as 24 hours separated the CS from
the poison US (Etscorn & Stephens, 1973). As a result, some psychologists proposed that
taste-aversion learning is a special type of learning, one that does not obey the principle of
contiguity. Taste-aversion learning was seen by some as an exception to one of the most
basic principles of association.

Biological Preparedness in Taste-Aversion Learning

A crucial assumption underlying most research on classical conditioning is that the experi-
menter’s choice of stimuli, responses, and species of subject is relatively unimportant. Sup-
pose, for example, that an experimenter wishes to test some hypothesis about learning using
the salivary conditioning preparation. The subjects will be dogs, and the US will be food
powder, but what stimulus should be used as the CS? According to what Seligman and Hager
(1972) called the equipotentiality premise, it does not matter what stimulus is used; the
decision is entirely arbitrary. The equipotentiality premise does not state that all stimuli and
all responses will result in equally rapid learning. We know that CSs differ in their salience,
and a bright light will acquire a CR more rapidly than a dim light. What the equipotentiality
premise does say is that a stimulus (or a response) that is difficult to condition in one situa-
tion should also be difficult to condition in other situations. For example, if a dim light is a
poor CS in a salivary conditioning experiment, it should also be a poor CS in an eyeblink
conditioning experiment. In short, the equipotentiality premise states that a given stimulus
will be an equally good (or equally bad) CS in all contexts.


The simplicity of the equipotentiality premise might seem appealing, but plenty of evi-
dence has shown that it is wrong. Garcia and Koelling (1966) conducted an important
experiment showing that the same two stimuli can be differentially effective in different
contexts. Two groups of rats were each presented with a compound stimulus consisting of
both taste and audiovisual components. Each rat received water that had a distinctive flavor,
and whenever the rat drank the water, there were flashing lights and a clicking noise. For
one group, the procedure consisted of typical taste-aversion learning: After drinking the
water, a rat was injected with a poison, and it soon became ill. For the second group, there
was no poison; instead, a rat’s paws were shocked whenever it drank.

Garcia and Koelling then conducted extinction tests (no shock or poison present) in
which the taste and audiovisual stimuli were presented separately. The results were very dif-
ferent for the two groups. The group that received poison showed a greater aversion to the
saccharin taste than to the lights and noises. However, exactly the opposite pattern was
observed for the group that received the shock. These animals consumed almost as much of
the saccharine-flavored water as in baseline, but when drinking was accompanied by the
lights and noises, they drank very little.

Figure 4.6 summarizes the results of this experiment, using thick arrows to represent
strong associations and thin arrows to represent weak associations. Garcia and his colleagues
concluded that because of a rat’s biological makeup, it has an innate tendency to associate
illness with the taste of the food it had previously eaten. The rat is much less likely to associ-
ate illness with visual or auditory stimuli that are present when a food is eaten. On the other
hand, the rat is more likely to associate a painful event like shock with external auditory and
visual stimuli than with a taste stimulus.

Seligman (1970) proposed that some CS–US associations might be called prepared
associations because the animal has an innate propensity to form such associations quickly
and easily (e.g., a taste–illness association). Other potential associations might be called con-
traprepared associations because even after many pairings, an animal may have difficulty form-
ing an association between the two stimuli (such as taste and shock). It should be clear that

Figure 4.6 In the experiment of Garcia and Koelling (1966), rats acquired a strong association between
lights and noises and shock but only a weak association between the taste of saccharin and shock. The
opposite was found for rats that had saccharin, lights, and noises paired with a poison.


the concept of preparedness is at odds with the equipotentiality premise. It implies that to
predict how effective a particular CS will be, it is not enough to know how effective this
CS has been in other contexts. We must also know what US will be used and whether this
CS–US pair is an example of a prepared or contraprepared association.

To complicate matters further, the predisposition to associate two stimuli can vary across
different species. Although rats may be predisposed to associate taste stimuli with illness, other
animals may not be. Wilcoxon, Dragoin, and Kral (1971) compared the behaviors of rats and
bobwhite quail that became ill after drinking water that had a distinctive (sour) taste, water
with a distinctive (dark blue) color, or water that both tasted sour and was dark blue. As we
would expect, rats displayed aversions to the sour taste but not to the blue color. In contrast,
quail developed aversions both to the sour water and the blue water, and the blue color was
actually the more effective stimulus for these animals. This difference between these species
makes sense, because quail rely more on vision in searching for food, whereas rats rely more
on taste and smell. Still, these findings show that attempting to generalize about preparedness
or ease of learning from one species to another can be a dangerous strategy.


Biological Preparedness in Human Learning

People can also develop a strong aversion to a food that is followed by illness, even
if the illness follows ingestion of the food by several hours. Logue, Ophir, and Strauss
(1981) used questionnaires to ask several hundred college students about any food
aversions they might have that developed as a result of an illness that occurred after
they ate the food. About 65% reported at least one food aversion. Most said the taste of
the food was now aversive to them, but some said that the smell, texture, or sight of the
food was also aversive. In many cases, people develop an aversion to some food even
though they know that their illness was caused by something completely unrelated to
the food, such as the flu or chemotherapy treatment (Bernstein, Webster, & Bernstein,
1982; Scalera & Bavieri, 2009).

Preparedness may also play a part in how people develop fears or phobias (Figure 4.7).
Öhman and colleagues have proposed that humans have a predisposition to develop
fears of things that have been dangerous to our species throughout our evolutionary his-
tory, such as snakes, spiders, and thunder (Öhman & Mineka, 2001). Quite a few experi-
ments have tested this hypothesis, often by pairing shock with pictures of such objects as
snakes, spiders, flowers, and mushrooms. Once a fear response has been conditioned,
some studies have found greater resistance to extinction in the spider/snake groups com-
pared to the flower/mushroom groups (Öhman, Dimberg, & Ost, 1985; Schell, Dawson, &
Marinkovic, 1991). There is also evidence that both adults and young children can detect
a snake or spider in a visual array faster than more “neutral” stimuli such as flowers and
mushrooms (LoBue & DeLoache, 2008; Öhman, Flykt, & Esteves, 2001).


Biological Constraints and the General-Principle Approach

The findings of Garcia and his colleagues were certainly a surprise to traditional learning
theorists, but, in retrospect, they have not proven to be damaging to the general-principle
approach to learning. It is true that taste-aversion learning can occur with long delays

Figure 4.7 Many people have a fear of spiders, and this might be due to a human biological
predisposition. (Cara-Foto/

Dimberg and Öhman (1996) proposed that people are also predisposed to associ-
ate angry faces with aversive consequences. Their reasoning is that throughout our
evolutionary history, when one person stared with an angry expression toward another
person, the angry person often followed this expression with some attempt to hurt or
intimidate the other person. As a result, human beings have become prepared to pro-
duce a fearful or defensive reaction to an angry face. This hypothesis has been tested
with discrimination procedures similar to those used for spiders and snakes, except
that angry faces are used in one group and happy or neutral faces are used in a control
group. Once again, the results have been varied; some studies found support for the
preparedness hypothesis (Dimberg & Öhman, 1983), and others found none (Packer,
Clark, Bond, & Siddle, 1991). Overall, the evidence for preparedness in human pho-
bias remains inconclusive. Based on the data that are available, it is possible that both
heredity and experience combine to determine what types of fears and phobias people
are most likely to develop (Mallan, Lipp, & Cochrane, 2013).


between CS and US, but so can other types of learning. For instance, Lett (1973) found that
rats could learn the correct choice in a T-maze even when the delay to the food reinforcer
was 60 minutes. Other studies have found learning when stimuli are separated by as much
as 24 hours (Capaldi, 1966). Furthermore, contiguity does make a difference in taste-aversion
learning, only on a different time scale than more typical learning tasks. To make this point,
Figure 4.8 compares the suppressing effects of shock and poison as the delays to these aversive
stimuli were increased. The top panel shows the data from rats pressing a lever for food when
shocks were used to suppress their responding. As the delay between a response and shock
increased, there was less and less suppression of responding. The bottom panel shows the
results from a study on taste-aversion learning in which different groups of rats experienced
different delays between exposure to a saccharin solution and a poison injection. Observe
the similarity in the shapes of the two functions. Both sets of results are consistent with the
principle of contiguity—the shorter the interval between a response and an aversive event,

Figure 4.8 The suppression of lever pressing by shocks delivered after different delays (top, based on
data from Baron, Kaufman, & Fazzini, 1969) is compared to the suppression of drinking a saccharine
solution by poison delivered after different delays (bottom, based on data from Andrews & Braveman,
1975). Notice the different time scales in the two panels.







10 20 30

Delay to Shock (seconds)

Andrews & Braveman, 1975

Baron, kaufman & Fazzini, 1969





f L




40 50 60







105 15

Delay to Poison (hours)





f D


20 25


the stronger the effect of the aversive stimulus. The only major difference between the two
experiments is the scale on the x-axis (seconds versus hours). What we need to account for
in these two sets of results are just different time scales, not different principles of learning.

At one time, the evidence for biological preparedness in taste-aversion learning (and in
other learning situations) was also seen as a problem for the general-principle approach.
Notice, however, that the concept of preparedness also deals with differences in the speed of
learning or the amount of learning, not in the kind of learning that takes place. It is not
impossible for rats to develop an association between a visual stimulus and illness; it simply
requires more trials. The same can be said for a taste–shock association. Once again, this
alleged evidence against general principles of learning merely amounts to a quantitative dif-
ference, not a qualitative one: To account for differences in the speed of learning, we simply
need different numbers, not different laws.

Seligman and Hager (1972) had proposed that taste-aversion learning is a unique type of
learning that does not obey the laws of traditional learning theory. However, in a review of
the findings on this topic, Logue (1979) described considerable evidence that there is actually
nothing unique about taste-aversion learning. She noted that many of the most familiar
phenomena of classical conditioning, including generalization gradients, extinction, condi-
tioned inhibition, blocking, and second-order conditioning, have all been observed in taste-
aversion learning. Later studies also found overshadowing (Nagaishi & Nakajima, 2010) and
stimulus preexposure effects (Lubow, 2009) in taste-aversion learning. Based on findings like
these, many researchers have concluded that taste-aversion learning violates no traditional
principles of learning and requires no new principles of learning. In fact, taste-aversion
learning has joined the conditioned suppression and eyeblink paradigms as a commonly used
procedure for studying classical conditioning. This fact, perhaps more than any other, should
put to rest the notion that taste-aversion learning is inconsistent with the general-principle
approach to learning theory.


As discussed in Chapter 3, predicting the form of a CR is often difficult. In some cases, the
CR is quite similar to the UR, and in others it is the opposite of the UR. When a CR is the
opposite of the UR, it is sometimes called a compensatory CR, because it tends to compensate
for, or counteract, the UR. In this section, we will first investigate how classical conditioning
can affect an individual’s reaction to a drug. In this area of research, both mimicking and
compensatory CRs have been observed. We will then examine some theories that try to
explain why CRs assume the variety of forms that they do.

Drug Tolerance and Drug Cravings as Conditioned Responses

A heroin user’s first injection produces a highly pleasurable response of euphoria, but with
later injections of the same dosage, the intensity of this positive emotional response becomes
smaller and smaller. The decrease in effectiveness of a drug with repeated use is called toler-
ance, and it occurs with many drugs. There are a variety of hypotheses about why tolerance
occurs (including Solomon and Corbit’s opponent-process theory, described in Chapter 2).


Shepard Siegel (1975, 2005) has developed a theory of drug tolerance that is based on clas-
sical conditioning. In short, Siegel claims that drug tolerance is due, at least in part, to a
compensatory CR that is elicited by CSs that regularly precede a drug administration. These
CSs may include the contextual stimuli (environmental surroundings) and stimuli associated
with drug administration (needles, drug paraphernalia, etc.). A description of a few of Siegel’s
experiments will illustrate how he came to these conclusions.

One of the URs produced by the drug morphine is analgesia, or a decreased sensitivity to
pain. In one experiment with rats, Siegel (1975) found that a decrease in analgesia over suc-
cessive morphine injections (i.e., tolerance of the analgesic response) was controlled by
contextual stimuli. To measure the rats’ sensitivity to pain, Siegel would place them on a
metal plate that was an uncomfortably warm temperature of about 54°C. When a rat’s paws
become painfully hot, the rat makes an easily measurable response—it lifts its forepaws and
licks them. By timing the latency of this paw-lick response, Siegel could measure a rat’s
sensitivity to pain.

Rats in a control group received four test trials (separated by 48 hours) on which they
were brought into a special experimental room, given an injection of a saline solution (as a
placebo), and later placed on the metal surface. The paw-lick latencies for these control
subjects were short and roughly the same on all four trials, which shows that pain sensitivity
for the control group did not change over trials. The procedure for one experimental group
was exactly the same, except that these rats received four morphine injections, not saline
injections. On the first trial, the average paw-lick latency for this group was nearly double
that of the control group. This result shows that the morphine had its expected analgesic
effect. However, the latencies for this group decreased over the next three trials, and on the
fourth trial, their latencies were about the same as those of the control group. Therefore, in
four trials these rats had developed a tolerance to the morphine—it no longer had an anal-
gesic effect.

According to Siegel’s hypothesis, this tolerance occurred because the stimuli that accom-
panied each morphine injection (the sights, sounds, and smells of the experimental room)
were CSs that acquired the capacity to elicit a compensatory CR of hyperalgesia, or an
increased sensitivity to pain. By Trial 4, this compensatory CR of hyperalgesia completely
counteracted the UR of analgesia, so the net effect was no change in pain sensitivity. If this
hypothesis was correct, it should be possible to eliminate the tolerance simply by changing
the stimuli on the final trial. To accomplish this, a third group of rats received their first three
morphine injections in their home cages, but on their fourth trial, they received their mor-
phine injections in the experimental room for the first time. Since this stimulus was com-
pletely new, it should elicit no compensatory CRs. As Siegel predicted, these animals showed
a strong analgesic response: They looked like rats that had never received a morphine injec-
tion before. This big difference between the two morphine groups was obtained simply by
changing the room in which the morphine was injected.

Some of the most convincing evidence for the compensatory CR theory has come from
studies in which the CS is presented without the drug US, and a compensatory CR has been
observed directly. Rozin, Reff, Mack, and Schull (1984) showed that for regular coffee drink-
ers, the smell and taste of coffee can serve as a CS that elicits a compensatory CR counter-
acting the effects of caffeine. Besides its effects on arousal and alertness, caffeine normally
causes an increase in salivation. However, for regular coffee drinkers, this increase in saliva-
tion is minimal (a tolerance effect). Rozin and colleagues had regular coffee drinkers drink


a cup of coffee that either did or did not contain caffeine (and they were not told which).
After drinking coffee with caffeine, these participants showed only a small increase in saliva-
tion, as would be expected of habitual coffee drinkers. However, after they drank coffee
without caffeine, they showed a substantial decrease in salivation. This decrease was a com-
pensatory CR that was elicited by the stimuli that were usually paired with caffeine (the
smell and taste of coffee). In addition, when these coffee drinkers drank a cup of hot apple
juice containing caffeine, they showed substantial increases in salivation, which shows that
they had not developed a general tolerance to the effects of caffeine—their tolerance was
found only when the caffeine was paired with the usual CS, coffee.

Similar evidence for compensatory CRs has been obtained with many other pharmaco-
logical agents, including adrenalin and alcohol. One experiment found that the effects of an
alcoholic drink can be stronger if a person is in an unfamiliar setting as compared to an
environment previously associated with drinking alcohol (Birak, Higgs, & Terry, 2011). Just
as the rats’ tolerance to morphine disappeared when they were given the drug in a new room
in Siegel’s (1975) experiment, these people’s tolerance to alcohol was diminished when they
drank it in a new setting.

If it is generally true that classical conditioning contributes to the phenomenon of drug
tolerance, it should be possible to find evidence for this effect in nonlaboratory settings.
Siegel, Hinson, Krank, and McCully (1982) presented some evidence from regular heroin
users who died, or nearly died, after a heroin injection. Of course, an overdose of heroin can
be fatal, but in some cases the dosage that caused a death was one the user had tolerated on
the previous day. Siegel proposes that in some cases of this type, the user may have taken the
heroin in an unusual stimulus environment, where the user’s previously acquired compensa-
tory CRs to the heroin injection would be decreased. He states that survivors of nearly fatal
injections frequently report that the circumstances of the drug administration were different
from those under which they normally injected the drug.

Another implication of the research on conditioned drug responses is that stimuli in the
individual’s environment can produce drug cravings and withdrawal symptoms, which make
it difficult for a recovering addict to remain abstinent. As Siegel and Ramos (2002) have
noted, there is abundant evidence that stimuli previously associated with an addictive sub-
stance can elicit cravings, and this has been known for a long time. Well before Pavlov
studied classical conditioning, Macnish (1859) described how environmental stimuli can
affect an alcoholic:

Man is very much the creature of habit. By drinking regularly at certain times he feels
the longing for liquor at the stated return of these periods—as after dinner, or imme-
diately before going to bed, or whatever the period may be. He even finds it in certain
companies, or in a particular tavern at which he is in the habit of taking his libations.

(p. 151)

Because conditioned stimuli can elicit cravings for a drug, some drug treatment programs
have included cue exposure treatment in which clients are exposed to stimuli normally
associated with a drug (but no drug), so that conditioned drug cravings can be extinguished
(Drummond, Tiffany, Glautier, & Remington, 1995). For example, in a smoking-cessation
program, the smoker might be presented with cigarettes to look at, handle, and light up (but
not smoke), so that the cravings associated with these cues can gradually extinguish.


Smoking-related stimuli can also be presented using computer-generated images in virtual
environments, and this has been shown to decrease cravings for cigarettes. In one study,
Moon and Lee (2009) used functional magnetic resonance imaging (fMRI) to show that
areas of the brain that are normally active when a person has nicotine cravings were less
active after smokers were given computer-generated stimuli. Cue exposure treatment has
been used in the treatment of heroin addictions, alcoholism, and even chocolate cravings
(Van Gucht et al., 2008). The success of this approach has been mixed, however. Siegel and
Ramos (2002) recommend several ways to make it more effective. One way is to give cue
exposure treatment in several different contexts, making them as similar as possible to various
real-life situations. (Otherwise, a person who normally smoked a lot at work might well have
a relapse when he returned to the work environment.) Also, because spontaneous recovery
is a property of classical conditioning, several cue exposure sessions, given over a period of
time, may be necessary.

Conditioned Opponent Theories

Schull (1979) proposed an interesting theory about compensatory CRs. He called his theory
a conditioned opponent theory because he accepted most of the assumptions of the
Solomon and Corbit opponent-process theory (Chapter 2) but made one important change.
Whereas Solomon and Corbit proposed that the b-process is increased by a nonassociative
strengthening mechanism, Schull proposed that any increase in the size of the b-process is
based on classical conditioning. To take a specific example, a person’s response to an initial
heroin injection is a very pleasurable sensation followed by unpleasant withdrawal symp-
toms. The initial pleasure is the a-process, and the unpleasant after-effect is the b-process.
Now, according to Schull, only the b-process can be classically conditioned. Let us assume
the stimuli that accompany the heroin injection—the needle, the room, and so on—serve
as CSs that, after a few pairings with heroin, begin to elicit the withdrawal symptoms by
themselves. These CSs have several effects. First, they tend to counteract the a-process, so a
heroin injection no longer produces much of a pleasurable sensation. Second, they combine
with the b-process to produce more severe and longer lasting withdrawal symptoms. Third,
when no heroin is available, these stimuli can still produce withdrawal symptoms and crav-
ings for the drug. Thus, Schull proposed that classically conditioned stimuli may contribute
to many of the debilitating characteristics of drug addiction.

Schull’s conditioned opponent theory deals exclusively with the conditioning of b-pro-
cesses, but Wagner (1981) proposed a general theory that is meant to apply to all CRs,
whether or not we would want to call them “b-processes.” Wagner called this theory a
sometimes opponent process (SOP) theory because it predicts that in some cases a CR
will be the opposite of the UR, but in other cases a CR will mimic the UR. How can we
predict what type of CR we will see in a particular conditioning situation? According to
SOP, the CR will mimic the UR if the UR is monophasic, but it will be the opposite of the
UR if the UR is biphasic. In essence, the terms monophasic and biphasic concern whether a
b-process can be observed in the UR. For example, the heart-rate UR to shock is biphasic
because it consists of an increase in heart rate when the shock is on, followed by a decrease
in heart rate below baseline when the shock is terminated. Because the UR exhibits such a
“rebound effect,” SOP predicts that the CR will be the opposite of the UR, and animal


research has demonstrated that this is the
case. On the other hand, the UR of an eye-
blink to a puff of air is monophasic: The
eye closes, then opens, but there is no
rebound—the eye does not open wider
than it was initially. For this reason, SOP
predicts that the CR will mimic the UR in
eyeblink conditioning, which is of course
the case. A number of studies have found
support for the predictions of SOP (e.g.,
Albert, Ricker, Bevins, & Ayres, 1993;
McNally & Westbrook, 2006). Although
these conditioned opponent theories are
complex, one basic message is clear: Many
factors can affect the type of CR that is
elicited by any particular CS, so the size
and form of the CR may be difficult to
predict in advance.


In Kamin’s experiment on the blocking
effect, rats first received conditioning trials
with a light paired with shock and then
trials with both the light and a tone paired
with the shock. In the test phase, presenting
the tone alone produced no fear response. To account for this and similar results, the
Rescorla–Wagner model states that conditioning will occur only if there is a discrepancy
between the strength of the US and the strength of the subject’s expectation. This model
can account for many conditioning phenomena, such as overshadowing, conditioned inhibi-
tion, and the overexpectation effect. However, the model has difficulty explaining certain
phenomena such as the CS preexposure effect. Attentional theories of classical conditioning
maintain that the effectiveness of a CS decreases if the CS is not informative. Comparator
theories propose that subjects may learn a CS–US association but not perform a CR unless
the CS is a better predictor of the US than are the contextual stimuli.

Research with simple creatures such as Aplysia has discovered specific neural and chemical
changes that occur during classical conditioning. Research with vertebrates, including
humans, has shown that many brain structures may be involved in the development of a
simple CR and that different brain structures seem to be involved for different CRs and
different conditioning phenomena.

Animals appear to be biologically prepared to learn certain conditioned associations more
easily than others. In taste-aversion learning, animals and people can learn to associate a taste
with illness, even if the illness occurs several hours after eating. Rats can quickly learn an
association between a taste and illness or between audiovisual stimuli and shock, but they
are slow to learn the opposite associations. Although biological constraints cannot be

Practice Quiz 2: chaPter 4
1. Because taste aversions can be

learned with long delays between
eating and illness, some psycholo-
gists said they violated the ______

2. Rats more easily associate auditory
and visual stimuli with ______, and
they more easily associate tastes
with ______.

3. There is some evidence that people
may be predisposed to develop pho-
bias to such stimuli as ______.

4. Morphine produces decreased sen-
sitivity to pain, and a CS associated
with morphine produces ______.

5. In ______, people with drug or alco-
hol addictions are presented with
stimuli that can trigger withdrawal
symptoms because they are associ-
ated with the drug.

1. contiguity 2. shock, illness 3. spiders or snakes
4. increased sensitivity to pain 5. cue exposure treatment


ignored, the same general principles seem to apply to taste-aversion learning as to other
forms of classical conditioning.

When a CS has been paired with a drug US, the CS will often elicit compensatory
CRs—physiological responses that are the opposite of those produced by the drug—and
these compensatory CRs can show up as drug tolerance. Conditioned opponent theories
have attempted to explain these compensatory CRs and to predict when a CR will mimic
the UR and when it will be the opposite of the UR.

Review Questions

1. Describe Kamin’s experiment on the blocking effect. Why was the result

2. Under what conditions does the Rescorla–Wagner model predict that there will
be excitatory conditioning, inhibitory conditioning, or no conditioning? Give a
specific example of each case.

3. What are some of the main findings and conclusions that can be drawn from
neurophysiological research on classical conditioning?

4. Why were evidence for long-delay taste-aversion learning and other examples
of biological constraints on classical conditioning seen as threats to the general-
principle approach to learning? How has this issue been settled?

5. What are conditioned compensatory responses? What role do they play in drug
tolerance and addiction?


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Unlike classically conditioned responses, many everyday behaviors are not elicited by a spe-
cific stimulus. Behaviors such as walking, talking, eating, drinking, working, and playing do
not occur automatically in response to any particular stimulus. In the presence of a stimulus
such as food, an animal might eat or it might not, depending on the time of day, the time
since its last meal, the presence of other animals, and so on. Because it appears that the animal
can choose whether to engage in behaviors of this type, people sometimes call them “vol-
untary” behaviors and contrast them with the “involuntary” behaviors that are part of
unconditioned and conditioned reflexes. Some learning theorists state that whereas classical
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untary behaviors. The term voluntary may not be the best term to use because it is difficult

C H A P T E R 5

Basic Principles of
Operant Conditioning

Learning Objectives

After reading this chapter, you should be able to

• describe Thorndike’s Law of Effect and experiments on animals in the puzzle box
• discuss how the principle of reinforcement can account for superstitious

• describe the procedure of shaping and explain how it can be used in behavior

• explain B. F. Skinner’s free-operant procedure, three-term contingency, and the

basic principles of operant conditioning
• define instinctive drift, and explain why some psychologists believed that it

posed problems for the principle of reinforcement
• define autoshaping and discuss different theories about why it occurs


to define in a precise, scientific way, but whatever we call nonreflexive behaviors, this chapter
should make one thing clear: Just because there is no obvious stimulus preceding a behavior,
this does not mean that the behavior is unpredictable. The extensive research on operant
conditioning might be described as an effort to discover general principles that can predict
what nonreflexive behaviors an individual will perform and under what conditions.


Thorndike’s Experiments

E. L. Thorndike (1898, 1911) was the first researcher to investigate systematically how an
animal’s nonreflexive behaviors can be modified as a result of its experience. In Thorndike’s
experiments, a hungry animal (a cat, a dog, or a chicken) was placed in a small chamber that
Thorndike called a puzzle box. If the animal performed the appropriate response, the door
to the puzzle box would be opened, and the animal could exit and eat some food placed just
outside the door. For some animals, the required response was simple: pulling on a rope,
pressing a lever, or stepping on a platform. Figure 5.1 shows one of Thorndike’s more dif-
ficult puzzle boxes, which required a cat to make three separate responses. The first time an
animal was placed in a puzzle box, it usually took a long time to escape. A typical animal
would move about and explore the various parts of the chamber in a seemingly haphazard
way, and eventually it would perform the response that opened the door. Based on his
observations, Thorndike concluded that an animal’s first production of the appropriate
response occurred purely by accident.

To determine how an animal’s behavior would change as a result of its experience, Thorn-
dike would return the animal to the same puzzle box many times and measure how long it

Figure 5.1 One of Thorndike’s puzzle boxes. A cat could escape from this box by pulling a string, stepping
on the platform, and turning one of the two latches on the front of the door. (From Thorndike, 1898)


took the animal to escape each trial. Figure 5.2 presents a typical result from one of Thorn-
dike’s cats, which shows that as trials progressed, the cat’s time to escape gradually declined
(from 160 seconds on the 1st trial to just 7 seconds on the 24th trial). Thorndike attributed
this gradual improvement over trials to the progressive strengthening of an S-R connection:
The stimulus was the inside of the puzzle box, and the response was whatever behavior
opened the door. To account for the gradual strengthening of this connection, Thorndike
(1898) formulated a principle of learning that he called the Law of Effect: If, in a specific
situation, a response is followed by a satisfying state of affairs, the response will become
associated with that situation and will be more likely to occur again in that same situation.
Thorndike defined a “satisfying state of affairs” as “one which the animal does nothing to
avoid, often doing such things as attain and preserve it” (p. 245).

The application of the Law of Effect to the puzzle-box experiments is straightforward:
Certain behaviors, those that opened the door, were closely followed by a satisfying state of
affairs (escape and food), so when the animal was returned to the same situation it was more
likely to produce those behaviors than it had been at first. In modern psychology, the phrase
“satisfying state of affairs” has been replaced by the term reinforcer, but the Law of Effect
(or the principle of positive reinforcement) remains as one of the most important con-
cepts of learning theory.

Guthrie and Horton: Evidence for a Mechanical
Strengthening Process

Two researchers who followed Thorndike, E. R. Guthrie and G. P. Horton (1946), provided
more convincing evidence that the learning that took place in the puzzle box involved the
strengthening of whatever behavior happened to be followed by escape and food. They
placed cats in a puzzle box with a simple solution: A pole in the center of the chamber had
only to be tipped in any direction to open the door. A camera outside the chamber photo-
graphed the cat at the same instant that the door swung open, thereby providing a permanent

Figure 5.2 The number of seconds required by one cat to escape from a simple puzzle box on 24
consecutive trials. (From Thorndike, 1898)




1 12











record of exactly how the cat had performed the effective response on each trial. The pho-
tographs revealed that after a few trials, each cat settled on a particular method of manipulat-
ing the pole that was quite consistent from trial to trial. However, different cats developed
different styles for moving the pole; for example, one cat would always push the pole with
its left forepaw, another would always bite the pole, and another would lie down next to the
pole and roll over into it (Figure 5.3).

In summary, Guthrie and Horton found that after their cats mastered the task, there was
relatively little variability from trial to trial for a given cat, but there was considerable vari-
ability from one cat to another. These results provide evidence for a particular version of
the Law of Effect that Brown and Herrnstein (1975) called the stop-action principle.
According to this principle, there is a parallel between the action of the camera and the
reinforcer in the experiments of Guthrie and Horton. Like the camera, the occurrence of
the reinforcer serves to stop the animal’s ongoing behavior and strengthen the association
between the situation (the puzzle box) and those precise behaviors that were occurring at
the moment of reinforcement.

Figure 5.3 These drawings illustrate the sorts of behaviors cats displayed in the puzzle box of Guthrie
and Horton. Each cat developed a unique style of moving the pole and used it trial after trial.


The stop-action principle states that because of this strengthening process, the specific
bodily position and the muscle movements occurring at the moment of reinforcement will
have a higher probability of occurring on the next trial. If the cat repeats the bodily position
and movements on the next trial, this will produce a second reinforcer, thereby further
strengthening that S-R association even more. This sort of positive feedback process should
eventually produce one S-R connection that is so much stronger than any other that this
particular response pattern will occur with high probability, trial after trial. For each cat,
whatever random behavior happened to get reinforced a few times would become dominant
over other behaviors.

Superstitious Behaviors

The mechanical nature of the stop-action principle suggests that behaviors may sometimes
be strengthened “by accident.” Skinner (1948) conducted a famous experiment, now often
called the superstition experiment, which made a strong case for the power of accidental
reinforcement. Eight pigeons were placed in separate experimental chambers, and grain was
presented every 15 seconds regardless of what the pigeons were doing. After a pigeon had
spent some time in the chamber, Skinner observed the bird’s behavior. He found that six of
his eight pigeons had developed clearly defined behaviors that they performed repeatedly
between food presentations. One bird made a few counterclockwise turns between reinforc-
ers, another made pecking motions at the floor, and a third repeatedly poked its head into
one of the upper corners of the chamber. A fourth bird was observed to toss its head in an
upward motion, and two others swayed from side to side. These behaviors occurred repeat-
edly despite the fact that no behavior was required for reinforcement. Similar results have
been found by other researchers who repeated (with some variations) the basic idea of Skin-
ner’s experiment (Gleeson, Lattal, & Williams, 1989; Neuringer, 1970).

According to Skinner, whatever behavior happened to be occurring when the reinforcer
was delivered was strengthened. If the first reinforcer occurred immediately after a pigeon
had tossed its head upward, this behavior of head tossing would be more likely to occur in
the future. Therefore, there was a good chance that the next reinforcer would also follow a
head-tossing motion. The accidental strengthening process is self-perpetuating because once
any one behavior develops a somewhat higher frequency of occurrence than all other behav-
iors, it has a greater chance of being reinforced, which increases its frequency still further,
and so on.

Skinner (1948) proposed that many of the superstitious behaviors and rituals that
people perform are produced by the same mechanism that caused his pigeons to exhibit
such peculiar behaviors: accidental reinforcement. Superstitious behaviors frequently arise
when an individual actually has no control over the events taking place, as in card playing
or other types of gambling, where winning or losing depends on chance. In laboratory
experiments, Matute (1994, 1995) observed superstitious behaviors in situations where peo-
ple had no control over events. In one case, college students were exposed to unpleasantly
loud tones and were told that they could turn off the tones by typing the correct sequence
of keys on a keyboard. In reality, the participants had no control over the tones, which went
on and off no matter what keys they typed. Nevertheless, most of the students developed
superstitious behaviors—they tended to type the same key sequences each time a tone came


on. At the end of the experiment, many of the participants said they believed that their
typing responses did turn off the tones.

Herrnstein (1966) pointed out that Skinner’s analysis is most applicable to idiosyncratic
superstitions, like those of a gambler or an athlete. It seems likely that such personal supersti-
tions arise out of a person’s own experience with reinforcement. On the other hand, super-
stitions that are widely held across a society (e.g., the belief that it is bad luck to walk under
a ladder, or that the number 13 is unlucky) are probably acquired through communication
with others, not through individual experience (Figure 5.4). How some of these common
superstitions began is not known, but Herrnstein suggested that they may be the residue of
previous contingencies of reinforcement that are no longer in effect. As an example, he cited
the belief that it is bad luck to light three cigarettes on a single match. This superstition arose
in the trenches during World War I. At that time, there was some justification for this belief
because every second that a match remained lit increased the chances of being spotted by
the enemy. This danger is not present in everyday life, but the superstition is still passed on
from generation to generation. Herrnstein speculated that superstitions may be perpetuated
by stories of occasional individuals who violate the rule and meet with an unfortunate fate.
Thus, Herrnstein claimed that some superstitions were originally valid beliefs and are now
perpetuated by rumor and/or occasional coincidences. It is easy to imagine how some
superstitions (such as the one about walking under a ladder) may have begun, whereas the
origins of others are less clear.

Figure 5.4 Crossing one’s fingers for good luck is a common superstitious behavior. (Misha Beliy/



Superstitious Behaviors in Sports

Superstitious behaviors are common among athletes. In a study of college football,
track, and gymnastics teams, Bleak and Frederick (1998) found that an average player
performed about 10 different superstitious behaviors, such as wearing a lucky charm or
item of clothing, eating the same meal before each competition, or taping a part of the
body that was not injured. Burger and Lynn (2005) found that superstitious behaviors
were widespread among professional baseball players in both the United States and
Japan. Some superstitious behaviors occur without the athlete’s awareness. Ciborowski

Skinner’s analysis of his superstition experiment is not the only possible interpretation.
Staddon and Simmelhag (1971) conducted a careful replication of the superstition experi-
ment, recorded the pigeons’ behaviors more thoroughly than Skinner did, and came to
different conclusions. They found that certain behavior patterns tended to occur frequently
during the intervals between food deliveries, and they called these interim and terminal
behaviors. Interim behaviors occurred in the early part of the interval, when the next
reinforcer was still some time away. Interim behaviors included pecking toward the floor,
turning, and moving along the front wall of the chamber. Terminal behaviors tended to
occur as the time of food delivery drew near. Two of the most frequent terminal behaviors
were orienting toward the food magazine and pecking in the vicinity of the magazine.
Staddon and Simmelhag proposed that some of the behaviors that Skinner called “supersti-
tious behaviors” may actually have been interim or terminal behaviors. These behaviors
are not produced by accidental reinforcement but are simply innate behaviors that animals
tend to perform when the likelihood of reinforcement is low (interim behaviors) or when
food is about to be delivered (terminal behaviors). Many other studies have shown that the
periodic delivery of food or some other reinforcer can give rise to a variety of stereotyped
behaviors that have been collectively called adjunctive behaviors. These innate behaviors
occur when the next reinforcer is some time away and the animal must do something to
“pass the time.”

Still, it seems clear that Skinner’s analysis of superstitious behaviors was at least partly
correct: Sometimes behaviors do increase in frequency because of accidental reinforcement.
In the laboratory, experiments with both adults and children have found that they have a
tendency to develop superstitious behaviors when free reinforcers were periodically deliv-
ered (Sheehan, Van Reet, & Bloom, 2012). These superstitious behaviors tend to increase
just before a reinforcer is delivered, and they are distinctly different for different participants.
For instance, in a study where a mechanical clown delivered marbles every now and then,
one child developed the behavior of kissing the clown on the nose, another child swung his
hips, and another puckered his mouth (Wagner & Morris, 1987). Outside the laboratory,
many idiosyncratic superstitions can be easily traced to past reinforcement, including those
frequently seen in athletes.



Shaping Lever Pressing in a Rat

Imagine that as part of your laboratory work in a psychology course, you are given a rat in
an experimental chamber equipped with a lever and a pellet dispenser, and your task is to
train the rat to press the lever at a modest rate. You have a remote-control button that, when
pressed, delivers one food pellet to a food tray in the chamber. You instructor tells you that
an important first step is to establish the sound of the pellet dispenser as a conditioned rein-
forcer. A conditioned reinforcer is a previously neutral stimulus that has acquired the
capacity to strengthen responses because that stimulus has been repeatedly paired with food
or some other primary reinforcer. A primary reinforcer is a stimulus that naturally
strengthens any response it follows. Primary reinforcers include food, water, sexual pleasure,
and comfort. If you repeatedly expose your rat to the sound of the pellet dispenser followed
by the delivery of a food pellet, the sound of the dispenser should become a conditioned
reinforcer. You can be sure that this has been accomplished when the rat will quickly return
to the food tray from any part of the chamber as soon as you operate the dispenser. The
importance of the conditioned reinforcer is that it can be presented immediately after the
rat makes any desired response. We have seen that the contiguity between response and
reinforcer is very important—whatever behavior immediately precedes reinforcement will
be strengthened. With a conditioned reinforcer such as the sound of the pellet dispenser, a
response can be immediately reinforced even if it takes the rat several seconds to reach the
primary reinforcer, the food.

Once you have established the sound of the pellet dispenser as a conditioned reinforcer,
you might just wait until the rat presses the lever and then immediately deliver a food pellet.
However, suppose the lever is 5 inches above the floor of the chamber, and it takes an

(1997) asked college baseball players to describe the behaviors they performed between
pitches while batting (e.g., touching parts of the body or clothing, gripping the bat in cer-
tain ways, touching the ground or plate with the bat). The players were able to list most of
them, but not all. However, when asked how many times they repeated these behaviors,
the players’ estimates were too low by a factor of four. Amazingly, Ciborowski found that
the average player made 82 such movements in one time at bat.

Superstitious behaviors are also frequently seen in sports fans as they watch their
favorite teams play. Wann et al. (2013) found that some of the most common supersti-
tions of sports fans are wearing specific types of sports apparel, consuming specific
types of food or drink, and choosing either to watch or not watch the action during
critical parts of a game. It is easy to imagine how these behaviors could have been
accidentally reinforced in the past, when performing the behaviors was followed by a
team’s success.


effortful push from the rat to fully depress the lever. Under these circumstances, you might
wait for hours and the rat might never depress the lever. And, of course, you cannot reinforce
a response that never occurs.

This is where the process of shaping, or successive approximations, becomes very useful.
A good way to start would be to wait until the rat is below the lever and then reinforce any
detectable upward head movement. After 5 or 10 reinforcers for such a movement, the rat will
probably be making upward head movements quite frequently. Once this behavior is well
established, the procedure of shaping consists of gradually making your criterion for reinforce-
ment more demanding. For example, the next step might be to wait for an upward head
movement of at least half an inch before delivering a food pellet. Soon the rat will be making
these larger responses regularly. You can then go on to require upward movements of 1 inch,
1.5 inches, and so on, until the rat is bringing its head close to the lever. The next step might
be to require some actual contact with the lever, then contact with one forepaw, then some
downward movement of the lever, and so on, until the rat has learned to make a full lever press.

Figure 5.5 provides a graphic illustration of how the procedure of shaping makes use of
the variability in the subject’s behavior. Suppose that before beginning the shaping process,
you simply observed the rat’s behavior for 5 minutes, making an estimate every 5 seconds
about the height of the rat’s head above the floor of the chamber. Figure 5.5 provides an
example of what you might find: The y-axis shows the height of the rat’s head to the nearest
half inch, and the x-axis shows the number of times this height occurred in the 5-minute
sample. The resulting frequency distribution indicates that the rat usually kept its head about
1.5 inches from the floor, but sometimes its head was lower and sometimes much higher.
Given such a distribution, it might make sense to start the shaping process with a requirement
that the rat raise its head to a height of at least 2.5 inches before it is reinforced. Figure 5.5
shows how the frequency distribution would probably shift after the shaping process began.

Figure 5.5 Hypothetical distributions showing the height of a rat’s head as observed at regular intervals
before shaping (solid line) and after selective reinforcement of head heights greater than 2.5 inches
(dotted line). Rachlin (1970) presents a similar analysis of the shaping process.






0 10
Number of observations

Criterion for

Before shaping
After shaping


t o

f r







Shaping Behaviors in the Classroom

Shaping can be used to produce totally new behaviors in people as well as in laboratory rats.
At many colleges and universities, there are stories about how the students in a large lecture
course collaborated to shape the behavior of their professor. In one such story, a professor
who usually stood rigidly behind the lectern was reinforced by his students for any move-
ment, and by the end of the hour he was pacing back and forth and gesturing wildly with
his arms. In another story, a professor in an introductory psychology course lectured from
an elevated stage. The students secretly agreed to reinforce the professor for any movement
to the left. The reinforcers they used were listening attentively, nodding their heads in appar-
ent understanding of what he was saying, and taking notes. Whenever the professor moved
to the right, however, they stopped delivering these reinforcers—they would stop taking
notes, yawn, look bored, and look around the room. This systematic delivery of reinforcers
for movement to the left was apparently quite successful, for legend has it that about halfway
through the lecture the professor fell off the left side of the stage (which was only about 18
inches high). Stories like this suggest that shaping can work even when the subject is
unaware of what is going on.

Shaping as a Tool in Behavior Modification

Not all examples of shaping are as frivolous as those described in the previous section. Shap-
ing is frequently used as a method to establish new or better behaviors in a wide range of
settings. As one example, Scott, Scott, and Goldwater (1997) used a shaping technique to
improve the performance of a university pole-vaulter. This 21-year-old had been competing
in the sport for 10 years, and he had taken part in international events, but there was one
aspect of the skill of pole-vaulting that he had difficulty mastering. To obtain the maximum
height of a vault, it is important for the athlete to raise his or her arms and the pole as high
overhead as possible when the pole is planted in the ground at the moment of takeoff. This
vaulter was not extending his arms completely at takeoff, and he knew it, but he could not
seem to break this bad habit.

Videotapes of the pole-vaulter showed that on an average attempt, his arms were
extended to a height of 2.25 meters. To train him to reach higher, the researchers set up
a photoelectric beam and sensor slightly above this point, at 2.30 meters. On every practice
trial, a trainer shouted “Reach!” as he was running down the runway, and if his hands
broke the photoelectric beam, there was a beep to signal that he had extended his arms
to the criterion level. Because this beep was associated with a better performance, it can
be called a conditioned reinforcer, just as the sound of the pellet dispenser serves as a
conditioned reinforcer for a rat learning a new response. Once the pole-vaulter achieved
a success rate of 90% at one hand height, the criterion was gradually increased (to 2.35
meters, then to 2.40 meters, and so on). The improvement took many months of practice,
but eventually the vaulter was extending his arms to just about their maximum possible
height. From his perspective, the most important result was that with each increase in the
height of the photoelectric beam, the height of the bar that he was able to clear rose to a


new personal best. Therefore, this systematic shaping procedure produced the results he
was trying to attain.

In another example of shaping, therapists used toys and other desired items as reinforcers
to get an 8-year-old boy with intellectual disabilities to use a mask that delivered medication
he needed to treat a serious respiratory condition (Hagopian & Thompson, 1999). The boy
initially resisted using the mask for any length of time, so the therapists started by giving
him a reinforcer when he wore the mask for just 5 seconds. The criterion for reinforcement
was gradually increased over a period of several weeks until he was using the mask for the
full duration of 40 seconds that he needed.

Shaping can be used with groups as well as with individuals. In a program at a drug
treatment clinic, cocaine users were given standard methadone treatment, and a shaping
procedure was used to gradually decrease their use of cocaine. Over the course of several
weeks, patients received vouchers that could be exchanged for items such as movie tickets
if their urine samples showed at least a 25% reduction in cocaine metabolites compared to
their previous test. Eventually, they could earn vouchers only if there was no sign of cocaine
in their urine samples. The researchers found that this shaping procedure was more effective
in reducing cocaine use than requiring complete abstinence from the very start of the pro-
gram (Preston, Umbricht, Wong, & Epstein, 2001). Similar procedures have been used to
help people quit smoking (Stoops et al., 2009). Because shaping can help to improve behav-
iors even under difficult circumstances, it has become a common component of many
behavior modification programs.

In some ways, shaping is more of an art than an exact science. Many split-second deci-
sions must be made about which behaviors to reinforce and which not to reinforce, how
quickly the criterion for reinforcement should be increased, what to do when the learner
has a setback, and so on. However, the procedure of shaping can be made more precise by
using a percentile schedule. In a percentile schedule, a response is reinforced if it is better
than a certain percentage of the last several responses that the learner has made (J. R. Platt,
1973). For example, imagine that a boy in math class does not complete his assignments
on time because he is easily distracted. A behavior therapist might use a percentile sched-
ule to shape more and more rapid completion of his work. Suppose the student is told to
work on a series of math problems, and at the end of every minute, the boy earns a rein-
forcer if he completes more problems than he did in 7 of the last 10 minutes. The rein-
forcers could be points that can later be exchanged for money, snacks, or some other
tangible reinforcers. As the boy earns reinforcers, the criterion for future reinforcers
should gradually increase, because his performance is always being compared to how well
he did in the last 10 minutes.

Percentile schedules have been successfully applied to cases ranging from the academic
performance of children with developmental disabilities (Athens, Vollmer, & St. Peter Pipkin,
2007) to increasing activity levels in adults to improve their health (Washington, Banna, &
Gibson, 2014). Percentile schedules can also be used in computer software that keeps track
of each student’s performance and tailors the difficulty of the material to the child’s rate of
improvement. In this way, slower learners are given additional practice with simpler concepts
until they master them, and faster learners are not held back but are given more difficult
material to keep them challenged (Figure 5.6).



Whereas Thorndike deserves credit for the
first systematic research on reinforcement,
B. F. Skinner was primarily responsible for
the increasing interest in this topic during
the middle of the twentieth century. Skinner
himself discovered many of the most basic
and most important properties of reinforce-
ment. In addition, he trained several genera-
tions of students whose research has enriched
our knowledge about how reinforcement
affects the behavior of people and animals.

The Free Operant

In his research on operant conditioning,
Skinner modified Thorndike’s procedure in
a simple but important way. Research with
the puzzle box involved a discrete trial pro-
cedure: A trial began each time an animal
was placed in the puzzle box, and the ani-
mal could make one and only one response
on each trial. The primary dependent vari-
able was response latency. After each trial,
the experimenter had to intervene, physi-
cally returning the animal to the puzzle box
for the next trial. This procedure was time

Figure 5.6 Some types of educational software use shaping procedures that increase the difficulty of
the material based on each child’s rate of progress. (Money Business Images/

Practice Quiz 1: chaPter 5
1. Thorndike referred to the principle of

strengthening a behavior by its con-
sequences as ______; in modern
terminology, this is called ______.

2. In photographing cats in the puzzle
box, Guthrie and Horton found that
the behaviors of an individual cat
were ______ from trial to trial, but
they were ______ from cat to cat.

3. Superstitious behaviors are more
likely to occur when an individual has
______ of the reinforcer.

4. When using food to shape the
behavior of a rat, the sound of the
food dispenser is a ______, and the
food itself is a ______.

5. A shaping procedure in which a
behavior is reinforced if it is better
than a certain percentage of the last
few responses the individual has
made is called a ______.


1. the Law of Effect, reinforcement 2. similar, different
3. little or no control 4. conditioned reinforcer, primary
reinforcer 5. percentile schedule


consuming and cumbersome, and only a small number of trials could be conducted each day.
Other early operant conditioning procedures, such as those involving runways or mazes with
a reinforcer at the end, shared these same disadvantages.

Skinner’s innovation was to make use of a response that the animal could perform
repeatedly without the intervention of the experimenter. In experiments with rats, lever
pressing is often the operant response. With pigeons, the most common response is the
key peck: One or more circular plastic disks, called response keys, are recessed in one wall
of the experimental chamber (Figure 5.7), and the bird’s pecks at these keys are recorded.
Procedures that make use of lever pressing, key pecking, or similar responses are called
free-operant procedures to distinguish them from the discrete trial procedures of the
puzzle box or maze. The distinguishing characteristics of a free-operant procedure are
that (1) the operant response can occur at any time and (2) the operant response can
occur repeatedly for as long as the subject remains in the experimental chamber. In addi-
tion, responses such as lever pressing and key pecking require so little effort that a subject
can make thousands of responses in a single session. With so many responses to observe,
the experimenter can study the moment-to-moment variations in response rate that
occur as a subject learns about the experimental situation or as some external stimulus
is changed.

Figure 5.7 A pigeon pecking at a lighted key in a typical operant conditioning chamber. Grain is
provided as a reinforcer through the square opening beneath the keys.


The Three-Term Contingency

In its simplest form, a contingency is a rule that states that some event, B, will occur if and
only if another event, A, occurs. Simple classical conditioning provides one example: The
US will occur if and only if the CS occurs first. It is sometimes said that in operant con-
ditioning, there is a contingency between response and reinforcer—the reinforcer occurs
if and only if the response occurs. Skinner pointed out, however, that there are actually
three components in the operant conditioning contingency: (1) the context or situation
in which a response occurs (i.e., those stimuli that precede the response); (2) the response
itself; and (3) the stimulus that follow the response (i.e., the reinforcer). To be more spe-
cific, the contingency in operant conditioning usually takes the following form: In the
presence of a specific stimulus, often called a discriminative stimulus, the reinforcer
will occur if and only if the operant response occurs. Because of the three components—
discriminative stimulus, response, and reinforcer—Skinner called this relationship a three-
term contingency.

Suppose a pigeon learns to peck a key for food pellets in a chamber that has a bright
yellow light just above the key. When the light is on, each response produces a food pellet,
but when the light is off, no food pellets are delivered. If the light is periodically turned on
and off during the course of the experiment, the pigeon will learn to discriminate between
these two conditions and respond only when the light is on. This type of discrimination
learning is important in many real-world situations, because a response that is reinforced in
one context may not be reinforced in another. For example, a child must learn that the
behavior of telling jokes may be reinforced if it occurs during recess but punished if it occurs
during math class. The term stimulus control refers to the broad topic of how stimuli that
precede a behavior can control the occurrence of that behavior. Chapter 9 will examine this
topic in detail.

Basic Principles of Operant Conditioning

Many of the principles of operant conditioning have counterparts in classical condition-
ing that we have already examined, so a brief discussion of them will suffice here.
Thorndike’s results (as in Figure 5.2) demonstrate that the acquisition of an operant
response, like that of a CR, is usually a gradual process. In operant conditioning, the
procedure of extinction involves no longer following the operant response with a
reinforcer, and, as in classical conditioning, the response will weaken and eventually
disappear. If the subject is returned to the experimental chamber at some later time,
spontaneous recovery of the operant response will typically be observed, just as it is
observed in classical conditioning.

In the previous section, we saw that discrimination learning can occur in operant
conditioning as well as in classical conditioning. The opposite of discrimination, gen-
eralization, is also a common phenomenon in operant conditioning. Let us return to
the example of the pigeon that learned to discriminate between the presence and
absence of a bright yellow light. Suppose the color of the light changed to green or
orange, and no more reinforcers were delivered. Despite this change in color, the
pigeon would probably continue to peck at the key for a while until it learned that no


more reinforcers were forthcoming. In other words, the pigeon generalized from the
yellow light to a light of another color, even though it had never been reinforced for
pecking in the presence of this other color. If we tested a number of different colors,
we would probably obtain a typical generalization gradient in which responding was
most rapid in the presence of yellow and less and less rapid with colors less and less
similar to yellow.

Conditioned Reinforcement

As already explained, if a neutral stimulus is repeatedly paired with a primary reinforcer, it
can become a conditioned reinforcer. The conditioned reinforcer can then act as a surrogate
for the primary reinforcer, increasing the strength of any response that it follows. In an early
study on conditioned reinforcement, Skinner (1938) presented rats with repeated pairings
of a clicking sound and food. In the second phase of the experiment, food was no longer
presented; nevertheless, the rats learned to press a lever when this response produced only
the clicking sound. Naturally, since the clicking sound was no longer paired with food, it is
not surprising that the lever pressing did not persist for long. To maintain its reinforcing
power, a conditioned reinforcer must continue to be paired (at least occasionally) with the
primary reinforcer.

Skinner used the term generalized reinforcers to refer to a special class of conditioned
reinforcers—those that are associated with a large number of different primary reinforcers.
Perhaps the best example of a generalized reinforcer is money. The potency of this rein-
forcer in maintaining the behaviors of workers in our society is clear. Money is a general-
ized reinforcer (and a powerful one) precisely because it can be exchanged for so many
different stimuli that are inherently reinforcing for most people (food, clothing, material
possessions, entertainment, etc.). Although money is a powerful reinforcer, it should be clear
that its power, like that of all conditioned reinforcers, depends on its continued association
with primary reinforcers. If money could no longer be exchanged for any primary rein-
forcers, it would be difficult to find individuals willing to work simply to obtain their
weekly paychecks.

Both laboratory findings and real-world examples of conditioned reinforcers (money;
exam grades; praise from a parent, teacher, or boss, etc.) demonstrate the powerful effects
that they can have on an individual’s behavior. What is still being debated by psycholo-
gists, however, is exactly how conditioned reinforcers exert their effects. One basic ques-
tion is whether conditioned reinforcers affect behavior because they provide information
(about the future delivery of a primary reinforcer) or because they add value to the situ-
ation (i.e., they add additional reinforcing value above what the primary reinforcer
already provides).

To illustrate the difference between providing information and adding value, Rachlin
(1976) asked readers to imagine two hotels. In hotel A, a dinner bell rings before each meal.
This bell should become a conditioned reinforcer because it is paired with the primary
reinforcer, food. In hotel B, a dinner bell also rings before each meal, but the bell also rings
at other times, when there is no meal. Which hotel will people prefer? If the bell adds value
to the situation, hotel B should be preferred, because the bell rings more often. But, accord-
ing to Rachlin, it seems obvious that people would prefer hotel A, where the bell provides


accurate information about when meals will be served. An experiment with pigeons by
Schuster (1969) supported Rachlin’s prediction. The pigeons preferred a situation in which
the conditioned reinforcers (a light and a buzzer) always signaled food (as in hotel A) over
a situation where the light and buzzer were presented extra times without food (as in
hotel B). This experiment supports the information theory of conditioned reinforcement—
the strongest conditioned reinforcers are those that provide the best information about the
delivery of primary reinforcers.

Many other studies on conditioned reinforcement have been conducted, and unfor-
tunately they do not offer a simple answer to the question of exactly what conditioned
reinforcers do. Some experiments seemed to provide evidence that conditioned rein-
forcers do add value to the situation (Bell & Williams, 2013; Williams & Dunn, 1991).
Williams (1994) also suggested that conditioned reinforcers may play other roles, includ-
ing marking and bridging. Marking is providing immediate feedback for a particular
response, as when the sound of the food dispenser immediately after an appropriate
response makes it easier for an animal trainer to shape new behaviors. Bridging occurs
when a conditioned reinforcer fills the time period between a response and the delivery
of a primary reinforcer, which may help the learner to associate the response and the

In a review of the many complex and often conflicting laboratory findings about condi-
tioned reinforcers, Shahan (2010) concluded that most of the results are consistent with the
idea that conditioned reinforcers act as “signposts” that “serve to guide rather than strengthen
behavior” (p. 279). In other words, he argues in favor of the information hypothesis. How-
ever, others continue to argue just as strongly for the reinforcing value hypothesis (e.g.,
McDevitt & Williams, 2010), so this issue has not been settled.

Response Chains

In Chapter 2, we examined the concept of a reaction chain, which is a sequence of innate
behaviors that occur in a fixed order. A similar concept involving learned behaviors is the
response chain, which is defined as a sequence of behaviors that must occur in a specific
order, with the primary reinforcer being delivered only after the final response of the
sequence. Some of the clearest examples of response chains are displayed by animals trained
to perform complex sequences of behavior for circus acts or other public performances.
Imagine a hypothetical performance in which a rat climbs a ladder to a platform, pulls a
rope that opens a door to a tunnel, runs through the tunnel to another small platform, slides
down a chute, runs to a lever, presses the lever, and finally receives a pellet of food. Ignoring
for the moment how the rat could be trained to do this, we can ask what maintains the
behavior once it has been learned.

The first response, climbing the ladder, brings the rat to nothing more than a platform
and a rope. These are certainly not primary reinforcers for a rat. Skinner would claim,
however, that these stimuli act as conditioned reinforcers for the response of climbing the
ladder because they bring the animal closer to primary reinforcement than it was before.
Besides serving as conditioned reinforcers, the platform and rope also act as discriminative
stimuli for the next response of the chain, pulling the rope. The conditioned reinforcer
for this response is the sight of the door opening, for this event brings the subject still


closer to primary reinforcement. Like the platform and rope, the open door also serves a
second function—it is a discriminative stimulus for the next response, running through
the tunnel.

We could go on to analyze the rest of the response chain in a similar fashion, but the
general pattern should be clear by now. Each stimulus in the middle of a response chain is
assumed to serve two functions: It is a conditioned reinforcer for the previous response and
a discriminative stimulus for the next response of the chain. This analysis is depicted graphi-
cally in Figure 5.8, where SD stands for “discriminative stimulus” and SR stands for “reinforc-
ing stimulus.”

How would an animal trainer go about teaching a rat to perform this sequence? One
very effective strategy, sometimes called backward chaining, is to start with the last response
of the chain and work backward. After teaching the rat where to obtain its food reinforce-
ment and establishing the sound of the food dispenser as a conditioned reinforcer, the
trainer could start to shape the last response of the chain, pressing the lever. Once this
response is well established, the trainer might place the rat on the bottom of the chute. It
is very likely that the rat would move from this position to the lever, since the lever will
now act as a conditioned reinforcer (having been previously paired with food). By addi-
tional shaping, the animal could be trained to slide down the chute to reach the lever, then
to travel through the tunnel to reach the chute, and so on. Some shaping with food as a
primary reinforcer might be required for some links of the chain (e.g., pulling the rope).
Once the response was established, however, the primary reinforcement could be removed

Figure 5.8 The alternating sequence of stimuli and responses in the hypothetical response chain
described in the text. Each stimulus within the chain serves as a conditioned reinforcer for the previ-
ous response and as a discriminative stimulus for the next response.

Stimuli Responses

Ladder (SD) Climb

Pull rope

Run through tunnel

Slide down chute

Run to lever

Press lever

Platform, rope (SR , SD)

Open door (SR , SD)

Chute (SR , SD)

Sight of lever (SR , SD)

Lever within reach (SR , SD)

Food pellet (SR)


and the behavior would be maintained by the conditioned reinforcement provided by the
next stimulus of the chain, a stimulus that signaled that the animal was one step closer to
the primary reinforcer.

Not surprisingly, the behaviors of a response chain will eventually disappear if the
primary reinforcement is eliminated. It is also interesting to observe what happens if
one of the conditioned reinforcers in the middle of the chain is eliminated. The general
rule is that all behaviors that occur before the “broken link” of the chain will be extin-
guished, whereas those that occur after the broken link will continue to occur. For
example, suppose that pulling the rope no longer opens the door to the tunnel. The
response of rope pulling will eventually stop occurring, as will the behavior of climbing
the ladder that leads to the platform and rope. On the other hand, if the rat is placed
beyond the broken link (inside the tunnel or at the top of the chute), the remainder of
the chain should continue as long as the final response is followed by the primary

Because they are the farthest from the primary reinforcer, responses near the beginning
of a response chain should be the weakest, or the most easily disrupted. Behavior therapists
frequently make use of this principle when attempting to break up a response chain that
includes some unwanted behaviors (e.g., walking to the drugstore, buying a pack of ciga-
rettes, opening the pack, lighting a cigarette, and smoking it). Efforts to interrupt this chain
should be most effective if applied to the earliest links of the chain.


Teaching Response Chains

Many everyday activities are examples of response chains. Tasks such as doing the
laundry, making your bed, changing a flat tire, installing new software on a computer,
preparing a meal, and many others involve sequences of behaviors that must be com-
pleted in the right order, and only then is the primary reinforcer obtained (Figure 5.9).
Being able to perform response chains like these is an important part of daily life, but
some children and adults with autism or other developmental disabilities have difficulty
learning them. Educators and behavior analysts have therefore looked for ways to teach
response chains effectively.

Backward chaining is one good way to teach a response chain, but it is not the
only way. In forward chaining, the teacher starts by reinforcing the first response of
the chain, then gradually adds the second response, the third response, and so on.
For example, in learning to use a laundromat, adolescents with developmental dis-
abilities were first reinforced just for finding an empty washing machine. Next, they
were reinforced for finding an empty machine and putting in the soap, then for finding
an empty machine, putting in the soap, loading the clothes, and so on (McDonnell &
McFarland, 1988).


Figure 5.9 Doing the laundry is an everyday example of a response chain. (Monkey Business

Another training method is the total task method: The individual is taught all of the
steps of a response chain at once, and the teacher uses prompts to elicit the appropri-
ate response at each step. A prompt is an extra discriminative stimulus that makes the
correct response more likely to occur. A prompt can be a verbal instruction, physical
guidance, or having the teacher model the behavior first, but in all cases the goal is
to eventually eliminate the prompt so that the child can perform the task without the
guidance of the teacher. This may sound simple enough, but in practice, the teacher
must make many decisions about what types of prompts to use and then about how
to remove them. One method is the most-to-least approach, where the teacher begins
by using the strongest prompts and gradually shifts to weaker ones. For instance, in
teaching a child with autism how to build a structure out of Lego blocks, a teacher could
use the most-to-least approach by first manually guiding the child’s hands each step of
the way, then by lightly guiding the child’s movements by touching the elbow, and finally
by using no physical prompt at all. The opposite method is the least-to-most approach,
in which the teacher begins with the least intrusive prompt (such as a light touch on the
shoulder) and only proceeds to stronger prompts if they are necessary.

All of these methods for teaching response chains (and many other variations) have
been used successfully in teaching life skills to children and adults with severe dis-
abilities (Shrestha, Anderson, & Moore, 2013). A challenge for the teacher or behavior
analyst is to determine which methods will work best in a particular case.



Just as biological factors affect what is learned in classical conditioning, they play an impor-
tant role in operant conditioning. Two phenomena, instinctive drift and autoshaping, both
discovered in the 1960s, raised serious questions about the power of reinforcement to modify
and control a creature’s behavior. The stories of how these phenomena were discovered and
the theoretical debates surrounding them provide valuable lessons about the strengths and
limitations of the general principles of learning.

Instinctive Drift

Two psychologists who attempted to apply the principles of operant conditioning outside
the laboratory were Keller and Marian Breland. After studying with B. F. Skinner, the Bre-
lands became animal trainers and worked with many different species, teaching complex and
frequently amusing patterns of behavior. Their animals were trained for zoos, fairs, television
commercials, and other public performances. The Brelands’ business was successful, and over
the years they trained several thousand animals. Despite their successes, however, the Brelands
began to notice certain recurrent problems in their use of reinforcement techniques. They
referred to these problems as “breakdowns of conditioned operant behavior.” In an article
entitled “The Misbehavior of Organisms” (Breland & Breland, 1961), they described several
of their “failures” in the use of reinforcement.

In one example, they trained a pig to pick up coins, one at a time, and drop them in a
piggy bank a few feet away. The pig learned this quite easily, and at first it would go back
and forth quickly, carrying the coins and putting them in the piggy bank. But as weeks
passed, its behavior became slower and slower, and other, unreinforced behaviors appeared:
“He might run over eagerly for each dollar, but on the way back, instead of carrying the
dollar and depositing it simply and cleanly, he would repeatedly drop it, root it, drop it
again, root it along the way, pick it up, toss it up in the air, drop it, root it some more, and
so on. . . . Finally it would take the pig about 10 minutes to transport four coins a distance
of about 6 feet. This problem behavior developed repeatedly in successive pigs” (Breland
& Breland, 1961, p. 683).

This example differs from the instances of contraprepared associations discussed in Chap-
ter 4. Here, the problem was not in learning the new response but in maintaining it over
time. New behaviors appeared that were not reinforced, behaviors that were part of the pig’s
natural food-gathering repertoires. The Brelands called this phenomenon instinctive drift:
With extensive experience, the animal’s performance drifted away from the reinforced
behaviors and toward instinctive behaviors that occur when it is seeking the reinforcer (food)
in a natural environment.

A similar problem occurred when the Brelands tried to train a raccoon to pick up coins
and place them in a small container. With just one coin, the raccoon learned, with a little
difficulty, to pick it up and drop it in the container, after which it received food as a rein-
forcer. When given two coins simultaneously, however, the raccoon would hold onto the
coins for several minutes, frequently rubbing them together, and occasionally dipping
them into the container and pulling them out again. These behaviors became more and
more prevalent over time, and the swift sequence of depositing the coins that the Brelands


desired was never achieved. As with the pigs, the raccoon’s intruding behaviors resemble
those of its food-gathering repertoire. A raccoon may repeatedly dip a piece of food in a
stream before eating it, and the rubbing motions are similar to those it might use in remov-
ing the shell from a crustacean. Notice, however, that in the present context these behav-
iors were inappropriate in two ways: (1) The coins were not food, the container was not
a stream, and there was no shell to be removed by rubbing the coins together and (2) the
intruding behaviors did not produce food reinforcement—indeed, they actually postponed
its delivery.

The Brelands observed many other examples of this sort of instinctive drift, and they
claimed that they constituted “a clear and utter failure of conditioning theory” (1961, p.
683). The problem was perfectly clear: Animals exhibited behaviors that the trainers did not
reinforce in place of behaviors the trainers had reinforced.


In 1968, P. L. Brown and Jenkins published an article on a method for training pigeons to
peck a key that was easier and less time consuming than manual shaping. Naive pigeons
were deprived of food and taught to eat from the grain dispenser. After this, a pigeon was
exposed to the following situation: At irregular intervals averaging 60 seconds, the response
key was illuminated with white light for 8 seconds; then the key was darkened and food was
presented. Although no response was necessary for the delivery of food, after a number of
trials all of the pigeons began to peck at the lighted key.

Although autoshaping is a good method for training the response of key pecking, psy-
chologists soon realized the more important theoretical significance of the Brown and
Jenkins result. Key pecking had been used in countless experiments because it was consid-
ered to be a “typical” operant response—a response that is controlled by its consequences.
Yet here was a situation where the key-peck response was not necessary for reinforcement,
but it occurred anyway. Why did the pigeons peck at the key? Several different explanations
were proposed.

Autoshaping as Superstitious Behavior

Brown and Jenkins suggested that autoshaping might be an example of a superstitious
behavior, as discussed earlier in this chapter. It is possible that approaching, making contact,
and pecking the lighted key were accidentally reinforced by the food deliveries that soon
followed. However, an experiment by Rachlin (1969) suggested that this hypothesis is not
correct. Using a procedure similar to that of Brown and Jenkins, Rachlin photographed
pigeons on each trial at the moment reinforcement was delivered. The photographs revealed
no tendency for the birds to get progressively closer to the key and finally peck it. On the
trial immediately preceding the trial of the first key peck, a pigeon might be far from the
key, looking in another direction, at the moment of reinforcement. There was no hint of a
gradual shaping process at work.

More evidence against the superstition interpretation came from a study in which the
food reinforcer was eliminated from any trial on which the pigeon pecked at the lighted
key (Williams & Williams, 1969). The results of this experiment were quite remarkable: Even


though no food ever followed a key peck, pigeons still acquired the key-peck response and
persisted in pecking at the lighted key on about one third of the trials. This experiment
showed quite convincingly that key pecking in an autoshaping procedure is not an instance
of superstitious behavior.

Autoshaping as Classical Conditioning

Some researchers have proposed that autoshaping is simply an example of classical condi-
tioning intruding into what the experimenter sees as an operant conditioning situation
(Moore, 1973). Pigeons eat grain by pecking at the kernels with jerky head movements.
We might say that pecking is the pigeon’s unconditioned response to the stimulus of grain.
According to the classical conditioning interpretation, this response of pecking is trans-
ferred from the grain to the key because the lighted key is repeatedly paired with food.
One type of evidence supporting this idea came from experiments by Jenkins and Moore
(1973) in which a lighted response key was regularly followed by food for some pigeons
and by water for other pigeons. In both cases the pigeons began to peck at the lighted
key. However, by filming the pigeons’ responses, Jenkins and Moore demonstrated that
the pigeons’ movements toward the key differed depending on which reinforcer was used.
When the reinforcer was food, a pigeon’s response involved an abrupt, forceful pecking
motion made with the beak open wide and eyelids almost closed (Figure 5.10, bottom
row). These movements are similar to those a pigeon makes when eating. When the
reinforcer was water, the response was a slower approach to the key with the beak closed

Figure 5.10 Photographs of a pigeon’s key pecks when the reinforcer was water (top row) and when
the reinforcer was grain (bottom row). Notice the different beak and eyelid movements with the two
different reinforcers. (From Jenkins, H.M. & Moore, B.R., The form of the auto-shaped response
with food or water reinforcers, Journal of the Experimental Analysis of Behavior, 20, 163–181. Copyright
1973 by the Society for the Experimental Analysis of Behavior, Inc.)


or nearly closed (Figure 5.10, top row). Sometimes, swallowing movements and a rhythmic
opening and closing of the beak were observed. All of these movements are part of the
pigeon’s characteristic drinking pattern. Jenkins and Moore proposed that these behaviors
were clear examples of Pavlov’s concept of stimulus substitution. The lighted key served
as a substitute for either food or water, and responses appropriate for either food or water
were directed at the key.

The term autoshaping is now used to refer to any situation in which an animal produces
some distinctive behavior in response to a signal that precedes and predicts an upcoming
reinforcer. Others have called this phenomenon sign-tracking because the animal watches,
follows, and makes contact with a signal for an upcoming reinforcer. In this broader sense,
autoshaping (or sign-tracking) has been observed in a wide range of species (e.g., Anselme,
Robinson, & Berridge, 2013; Morrow, Saunders, Maren, & Robinson, 2015). Many of the
examples are consistent with the stimulus substitution theory of classical conditioning
because the animal’s response to the stimulus that precedes a reinforcer is similar to the
response to the reinforcer itself.

Autoshaping as the Intrusion of Instinctive Behavior Patterns

Whereas some studies support for the stimulus substitution interpretation of autoshaping,
others do not. Wasserman (1973) observed the responses of 3-day-old chicks to a key light
paired with warmth. In an uncomfortably cool chamber, a heat lamp was turned on briefly
at irregular intervals, with each activation of the heat lamp being preceded by the illumina-
tion of a green key light. All chicks soon began to peck the key when it was green, but their
manner of responding was unusual: A chick would typically move very close to the key, push
its beak into the key, and rub its beak from side to side in what Wasserman called a “snug-
gling” behavior. These snuggling responses resembled behaviors a newborn chick normally
makes to obtain warmth from a mother hen: The chick pecks at the feathers on the lower
part of the hen’s body, then rubs its beak and pushes its head into the feathers. The problem
for stimulus substitution theory, however, is that the chicks’ responses to the heat lamp were
very different. There was no pecking or snuggling; instead, a chick would extend its wings
(which allowed it to absorb more of the heat) and stand motionless. On other trials, a chick
might extend its wings, lower its body, and rub its chest against the floor. There was virtually
no similarity between a chick’s responses to the key light and its responses to the heat lamp.
For this reason, Wasserman concluded that the stimulus substitution account of autoshaping
was incorrect.

Other experiments supported Wasserman’s conclusion (Bullock and Myers, 2009; Tim-
berlake & Grant, 1975). For example, Timberlake and Grant observed rats when the signal
preceding each food pellet was the entry of another rat (restrained on a small moving
platform) into the experimental chamber. Since a food pellet elicits biting and chewing
responses, the stimulus substitution interpretation of autoshaping predicts that a rat should
perform these same biting and chewing responses to the restrained rat, because the rat is a
signal for food. Not surprisingly, Timberlake and Grant observed no instances of biting or
chewing responses directed toward the restrained rat. However, they did observe a high
frequency of other behaviors directed toward the restrained rat, including approach, sniff-
ing, and social contact (pawing, grooming, and climbing over the other rat). Rats often feed
in groups, and these social behaviors toward the restrained rat resemble those that might


occur during a food-seeking expedition. Timberlake and Grant (1975) suggested the fol-
lowing interpretation:

As an alternative to stimulus substitution, we offer the hypothesis that autoshaped
behavior reflects the conditioning of a system of species-typical behaviors commonly
related to the reward. The form of the behavior in the presence of the predictive stimu-
lus will depend on which behaviors in the conditioned system are elicited and supported
by the predictive stimulus.

(p. 692)

Timberlake (1993) called this interpretation of autoshaped behaviors a behavior-
systems analysis to reflect the idea that different reinforcers evoke different systems
or collections of behaviors. An animal may have a system of food-related behaviors, a
system of water-related behaviors, a system of warmth-seeking behaviors, a system of
mating behaviors, and so on. Exactly which behavior from a given system will be elic-
ited by a signal depends on the physical properties of that signal. For instance, in Was-
serman’s study, the lighted response key (a distinctive visual stimulus about head high)
evidently lent itself more readily to snuggling than to wing extension, so that is how
the chicks responded.


Regardless of whether we emphasize the hereditary aspects of autoshaped behaviors or their
similarity to classically conditioned CRs, both autoshaped behaviors and instinctive drift
seem to pose severe difficulties for operant conditioners. Breland and Breland (1961) sum-
marized the problem nicely: “The examples listed we feel represent clear and utter failure
of conditioning theory. . . . The animal simply does not do what it has been conditioned
to do” (p. 683).

Reconciling Reinforcement Theory and Biological Constraints

How do those who believe in the general-principle approach to learning, and especially
in the principle of reinforcement, respond to these cases where the general principles do
not seem to work? When it was first discovered, autoshaping seemed to pose a major
problem for the principle of reinforcement because the response occurs again and again,
even though it is not required for reinforcement. This seemed to defy the basic concept
of reinforcement. However, later analyses suggested that autoshaping is simply an instance
of classical conditioning. Although autoshaped responses do not always follow the prin-
ciple of stimulus substitution (Wasserman, 1973), it is now commonly believed that
autoshaping is in fact a good example of classical conditioning. And like taste-aversion
learning (Chapter 4), autoshaping has been widely used as a procedure for studying basic
principles of classical conditioning (e.g., Balsam, Drew, & Yang, 2002; Locurto, Terrace, &
Gibbon, 1981). Autoshaping now appears quite consistent with the general-principle
approach to learning after all (but with the principles of classical conditioning rather than
operant conditioning).


The evidence on instinctive drift cannot be dealt with so easily. Here, when a trainer
tries to reinforce one behavior, other unreinforced behaviors appear and gradually
become more persistent. These behaviors are presumably part of the animal’s inherited
behavioral repertoire. As the Brelands discovered, these behaviors often cannot be
eliminated by using standard reinforcement techniques, so it might appear that the
principle of reinforcement is simply incorrect—it cannot explain why these behaviors
arise and are maintained. How might a psychologist who relies heavily on the concept
of reinforcement react to this? The reactions of B. F. Skinner are worth examining.
First of all, it is important to realize that Skinner has always maintained that an organ-
ism’s behavior is determined by both learning experiences and heredity. Well before
biological constraints on learning became a popular topic, Skinner had written about
the hereditary influences on behavior (Heron & Skinner, 1940; Skinner, 1966). Later,
Skinner (1977) stated that he was neither surprised nor disturbed by phenomena such
as instinctive drift or autoshaping. He asserted that these are simply cases where phy-
logenetic (hereditary) and ontogenetic (learned) influences on behavior are operating
simultaneously: “Phylogeny and ontogeny are friendly rivals and neither one always
wins” (p. 1009). In other words, we should not be surprised that hereditary factors can
compete with and sometimes overshadow the reinforcement contingencies as deter-
minants of behavior.

We have seen that if reinforcers are delivered at regular, periodic intervals, a variety of
unreinforced behaviors appear between reinforcers, and they are called adjunctive behav-
iors. In laboratory animals, adjunctive behaviors can take a variety of forms, including
aggression, wheel running, and drinking large amounts of water. Adjunctive behaviors have
also been observed in humans. In one study, college students played a game of backgammon
in which they had to wait for fixed periods of time (and could not watch) as their oppo-
nents made their moves. When these waiting periods were long, several behaviors unrelated
to playing the game—bodily movement, eating, and drinking—increased in frequency
(Allen & Butler, 1990).

Although it might appear on the surface that adjunctive behaviors are distinctly different
from typical operant behaviors, appearances can sometimes be deceiving. In a thoughtful
review, Killeen and Pellón (2013, p. 1) make a convincing case that “adjunctive behaviors
are operants.” The details of their arguments are complex and will not be presented here,
but one of their points is that adjunctive behaviors follow predictable patterns that depend
on the frequency, timing, and number of reinforcers delivered. They also propose that
adjunctive behaviors are similar in some ways to superstitious behaviors in the sense that
both appear even though there is no contingency between the response and the delivery
of reinforcement.

In summary, there is a growing consensus in the field of learning that the research on
biological constraints does not forecast the end of the general-principle approach, but
rather that it has provided the field with a valuable lesson (Domjan, 2008; Green & Holt,
2003; Logue, 1988). This research shows that an animal’s hereditary endowment plays an
important part in many learning situations, and the influence of heredity cannot be
ignored. However, critics who use these data to claim that the principle of reinforcement
should be abandoned appear to be making a serious logical mistake: They conclude that
because a theoretical concept cannot explain everything, it is deficient and should be
discarded. This reasoning is just as incorrect as claiming that all behaviors are learned,


none innate. This chapter (and the next
several) provides overwhelming evidence
that the delivery of reinforcement contin-
gent upon a response is a powerful means
of controlling behavior. No amount of
evidence on the hereditary influences on
behavior can contradict these findings.


If a response is followed by a reinforcer, the
frequency of that response will increase.
Thorndike demonstrated this principle in his
experiments with cats in the puzzle box, and
he called it the Law of Effect. Using photog-
raphy, Guthrie and Horton found that what-
ever motion a cat happened to make at the
moment of reinforcement tended to be
repeated on later trials. Different cats learned
distinctly different styles of making the same
response. If a response is strengthened when,
by mere coincidence, it is followed by a rein-
forcer, it is called a superstitious behavior.
B. F. Skinner reported seeing superstitious
behaviors in a famous experiment with
pigeons. Accidental reinforcement may
account for the unusual rituals performed by
some gamblers and athletes.

The procedure of shaping, or successive approximations, involves reinforcing any small
movement that comes closer to the desired response and then gradually changing the criterion
for reinforcement until the desired behavior is reached. Shaping is a common part of many
behavior modification procedures.

B. F. Skinner used the term three-term contingency to describe the three-part relation
between a discriminative stimulus, an operant response, and a reinforcer. Responses can be
strengthened by either primary reinforcers or conditioned reinforcers. Contingencies
between stimuli and responses can have more than three components, as in a response chain,
which consists of an alternating series of stimuli and responses, and only the last response is
followed by a primary reinforcer.

While using operant conditioning techniques to train animals, Breland and Breland discov-
ered instinctive drift: The animals would begin to display innate behaviors associated with the
reinforcers, even though these behaviors were not reinforced. When Brown and Jenkins
repeatedly paired a lighted key with food, pigeons eventually began to peck at the key. They
called this phenomenon autoshaping. Such examples of biological constraints in operant con-
ditioning do not mean that the principle of reinforcement is incorrect, but they do show that
behavior is often controlled by a mixture of learning and hereditary influences.

Practice Quiz 2: chaPter 5
1. Thorndike’s research with the puzzle

box is an example of a ______ pro-
cedure, whereas Skinner’s research
used a ______ procedure.

2. The three parts of a three-term con-
tingency are the ______, the ______,
and the ______.

3. Each stimulus in the middle of a
response chain serves as a ______
for the previous response and as a
______ for the next response.

4. The procedure in which pigeons start
to peck at a lighted response key
when it precedes food deliveries is
called ______.

5. The Brelands used the term instinc-
tive drift to refer to cases where an
animal stopped performing ______
behaviors and started performing
______ behaviors as its training


1. discrete trial, free-operant 2. discriminative stimulus,
operant response, reinforcer 3. conditioned reinforcer,
discriminative stimulus 4. autoshaping 5. reinforced,



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1. Describe Thorndike’s experiments with the puzzle box and how they demon-
strated his Law of Effect. What did Guthrie and Horton find when they photo-
graphed cats in the puzzle box, and what does this tell us about the principle of

2. Explain how you could use shaping to teach a dog to jump over a tall hurdle.
3. Give a concrete example of how shaping can be used in a behavior modification

program with a human learner.
4. Explain how response chains include all of the following: discriminative stimuli,

operant responses, conditioned reinforcers, and a primary reinforcer. Describe at
least two different techniques for teaching a response chain.

5. What is autoshaping? Describe three different theories about why autoshaping
occurs. Which theory do you think is best and why?


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Learning Objectives

After reading this chapter, you should be able to

• describe the four simple reinforcement schedules and the types of behavior
they produce during reinforcement and extinction

• give examples of reinforcement schedules from everyday life
• explain the difference between contingency-shaped and rule-governed

• describe different theories about why there is a postreinforcement pause on

fixed-ratio schedules, and explain which theory is best
• discuss explanations of why responding is faster on variable-ratio schedules

than on variable-interval schedules
• give examples of how the principles of operant conditioning have been used

in behavior modification with children and adults

C H A P T E R 6

Reinforcement Schedules
Experimental Analyses and Applications

Among B. F. Skinner’s many achievements, one of the most noteworthy was his experi-
mental analysis of reinforcement schedules. A reinforcement schedule is simply a rule
that states under what conditions a reinforcer will be delivered. To this point, we have
mainly considered cases in which every occurrence of the operant response is followed by
a reinforcer. This schedule is called continuous reinforcement (CRF), but it is only one
of an infinite number of possible rules for delivering a reinforcer. In the real world, responses
are sometimes, but not always, followed by reinforcers. A salesperson may make many
phone calls in vain before finally selling a magazine subscription. A typist may type dozens
of pages, comprised of thousands of individual keystrokes, before finally receiving payment


for a completed job. A lion may make several unsuccessful attempts to catch a prey before
it finally obtains a meal. Recognizing that most behaviors outside the laboratory receive
only intermittent reinforcement, Skinner devoted considerable effort to the investigation
of how different schedules of reinforcement have different effects on behavior (Ferster &
Skinner, 1957).


Skinner constructed a simple mechanical device, the cumulative recorder, which records
responses in a way that allows any observer to see at a glance the moment-to-moment pat-
terns of a subject’s behavior. Figure 6.1 shows how the cumulative recorder works. A slowly
rotating cylinder pulls a roll of paper beneath a pen at a steady rate, so the x-axis of the
resultant graph, the cumulative record, represents time. If the subject makes no response, a
horizontal line is the result. However, each response causes the pen to move up the page by
a small increment (in a direction perpendicular to the movement of the paper), so the y-axis
represents the cumulative number of responses the subject has made since the start of the

As Figure 6.1 shows, a cumulative record tells much more than the overall number of
responses. Segments of the record that have a fairly even linear appearance correspond to
periods in which the subject was responding at a steady rate—the greater the slope, the faster
the response rate. Figure 6.1 also shows how an acceleration or deceleration in response rate
would appear in the cumulative record. Finally, small downward deflections in a cumulative

Figure 6.1 A simplified drawing of a cumulative recorder and the type of graph it produces.


Fast responding

No responding


Responses m
ove pen this way

Slow responding

Paper moves this way
as time passes


record generally indicate those times at which a reinforcer was delivered. With these points
in mind, we can now examine how the schedule of reinforcement determines an individual’s
pattern of responding.


Fixed Ratio

The rule for reinforcement in a fixed-ratio (FR) schedule is that a reinforcer is deliv-
ered after every n responses, where n is the size of the ratio. For example, in an FR 20
schedule, every 20 responses will be followed by a reinforcer. If an animal begins with
an FR 1 schedule (which is the same as CRF) and then the ratio is gradually increased,
the animal can be trained to make many responses for each reinforcer. For example, many
animals will respond for food reinforcement on FR schedules where 100 or more
responses are required for each reinforcer. After an animal has performed on an FR
schedule for some time and has become acquainted with the requirements of the sched-
ule, a distinctive pattern of responding develops. As Figure 6.2 shows, responding on an
FR schedule exhibits a “stop-and-go” pattern: After each reinforcer, there is a pause in
responding that is sometimes called a postreinforcement pause. Once responding
resumes, however, the subject typically responds at a constant, rapid rate until the next
reinforcer is delivered.

Figure 6.2 Idealized cumulative records showing the typical patterns of behavior generated by the
four simple reinforcement schedules.











Outside the laboratory, a good example of FR schedules is the “piecework” method used
to pay factory workers in some companies (Figure 6.3). For instance, a worker operating a
semiautomatic machine that makes door hinges might be paid $10 for every 100 hinges
made. Long ago, I worked in a factory for several summers, and I had the opportunity to
observe workers who were paid by the piecework system. Their behavior was quite similar
to the FR pattern shown in Figure 6.2. Once a worker started up the machine, he almost
always worked steadily and rapidly until the counter on the machine indicated that 100
pieces had been made. At this point, the worker would record the number completed on a
work card and then display a postreinforcement pause—he might chat with friends, have a
soft drink or a cup of coffee, or glance at a newspaper for a few minutes. After this pause,
the worker would turn on the machine and produce another 100 hinges.

With FR schedules, the average size of the postreinforcement pause increases as the size
of the ratio increases. For example, the pause will be shorter with an FR 20 schedule than
with an FR 200 schedule. In contrast, an individual’s rate of responding after the postrein-
forcement pause remains fairly constant as the size of the ratio increases (Crossman, Bonem,
& Phelps, 1987; Powell, 1969). With very large ratios, however, the animal may start to
exhibit long pauses at times other than right after reinforcement. The term ratio strain is

Figure 6.3 In some jobs, employees are paid on FR schedules: They receive a certain amount of money
for every batch of products they complete.


sometimes used to describe the general weakening of responding that is found when large
ratios are used.

Variable Ratio

The only difference between an FR schedule and a variable-ratio (VR) schedule is that
on the latter, the number of required responses is not constant from reinforcer to reinforcer.
To be specific, the rule for reinforcement on a VR n schedule is that on average, a subject
will receive one reinforcer for every n responses, but the exact number of responses required
at any moment may vary widely. When an experiment is controlled by a computer, a VR
schedule is sometimes implemented by giving the computer a list of possible ratio sizes from
which it selects at random after each reinforcer to determine the number of responses
required for the next reinforcer. For example, a list for VR 10 might contain the ratios 1, 2,
3, 4, 5, 6, 10, 19, and 40. In the long run, an average of 10 responses will be required for each
reinforcer, but on a given trial, the required number may be as few as 1 or as many as 40.

Figure 6.2 shows a typical cumulative record from a VR schedule. The pattern of respond-
ing might be described as rapid and fairly steady. The major difference between FR perfor-
mance and VR performance is that postreinforcement pauses are typically quite brief on VR
schedules (Blakely & Schlinger, 1988). Intuitively, the reason for the shorter postreinforce-
ment pauses on VR schedules seems clear: After each reinforcer, there is always the possibility
that another reinforcer will be delivered after only a few additional responses.

Many forms of gambling are examples of VR schedules. Games of chance, such as slot
machines, roulette wheels, and lotteries, all exhibit the two important characteristics of VR
schedules: (1) A person’s chances of winning are directly proportional to the number of times
the person plays, and (2) the number of responses required for the next reinforcer is uncer-
tain. It is the combination of these two features that makes gambling an “addiction” for
some people; that is, gambling behavior is strong and persistent because the very next lottery
ticket or the very next coin in a slot machine could turn a loser into a big winner. Gamblers
tend to persist in playing even after long stretches without a win (Horsley, Osborne, Norman,
& Wells, 2012).

Although games of chance are among the purest examples of VR schedules outside the
laboratory, many other real-world activities, including most sports activities, have the proper-
ties of a VR schedule. Consider the behavior of playing golf. As one who is fond of this
activity, I know that golf offers many different reinforcers (companionship, exercise, sunshine,
fresh air, and picturesque scenery), but one of the strongest reinforcers is the thrill and satis-
faction that come from playing well, either through an entire round or on a single shot. Each
time a golfer walks to the first tee, a chance exists that this round will be his or her best. The
continual possibility of an outstanding round or at least a spectacular shot is probably an
important reason why the average golfer keeps returning to the course again and again.

Some other behaviors reinforced on VR schedules include playing practically any com-
petitive sport, fishing, hunting, playing card games or video games, watching the home team
play, and going to fraternity parties (Figure 6.4). The delivery of reinforcers for each of these
activities fits the definition of a VR schedule: The occasion of the next reinforcer is unpre-
dictable, but in the long run, the more often the behavior occurs, the more rapidly will
reinforcers be received.


Fixed Interval

In all interval schedules, the presentation of a reinforcer depends both on the subject’s behav-
ior and on the passage of time. The rule for reinforcement on a fixed-interval (FI) sched-
ule is that the first response after a fixed amount of time has elapsed is reinforced. For
example, in an FI 60-second schedule, immediately after one reinforcer has been delivered,
a clock starts to time the next 60-second interval. Any responses that are made during those
60 seconds have no effect whatsoever. However, at the 60-second mark, a reinforcer is
becomes available, and the next response will produce the reinforcer.

If the subject had either a perfect sense of time or access to a clock, the most efficient
behavior on an FI schedule would be to wait exactly 60 seconds, then make one response to
collect the reinforcer. However, because no subject has a perfect sense of time and because
a clock is usually not provided for the subject to watch, subjects on FI schedules typically
make many more responses per reinforcer than the one that is required. Figure 6.2 shows
the typical pattern of responding found on FI schedules. As on FR schedules, there is a
postreinforcement pause, but after this pause, the subject usually starts by responding quite
slowly (unlike the abrupt switch to rapid responding on an FR schedule). As the interval
progresses, the subject responds more and more rapidly, and just before reinforcement, the
response rate is quite rapid. For obvious reasons, the cumulative record pattern from this
class of schedule is sometimes called a fixed-interval scallop.

The FI schedule does not have many close parallels outside the laboratory because few
real-world reinforcers occur on such a regular temporal cycle. However, one everyday

Figure 6.4 Many sports are examples of variable-ratio schedules. (LuckyImages/


behavior that approximates the typical FI pattern of accelerating responses is waiting for a
bus. Imagine that you are walking to a bus stop and that just as you arrive you see a bus
leave. Suppose that you are not wearing a watch, but you know that a bus arrives at this stop
every 20 minutes so you sit down on a bench and start to read a book. In this situation, the
operant response is looking down the street for the next bus. The reinforcer for this response
is simply the sight of the next bus. At first, the response of looking for the bus may not occur
at all, and you may read steadily for 5 or 10 minutes before your first glance down the street.
Your next glance may occur 1 or 2 minutes later, and now you may look down the street every
minute or so. After 15 minutes, you may put away the book and stare down the street almost
continuously until the bus arrives.

Other situations in which important events occur at regular intervals can produce similar
patterns of accelerating behavior. Mawhinney, Bostow, Laws, Blumenfeld, and Hopkins
(1971) measured the study behavior of college students in a psychology course, and they
found that the pattern of this behavior varied quite predictably, depending on the schedule
of examinations. As mentioned earlier, the conditioned reinforcer of a good grade on an
exam can be an important reinforcer for studying. All the readings for the course were avail-
able only in a special room in the library, and the materials could not be taken out of this
room so that the students’ study behavior could be measured. At two points in the course,
there was a short quiz every class, which approximates a CRF schedule. At two other times
in the course, there were no daily quizzes, but a longer exam was given at the end of the
third week. This was more like an FI schedule because there was no immediate reinforcer
for studying during the early parts of the 3-week period. This arrangement is not exactly
like an FI schedule because studying early in the 3-week period presumably had some ben-
eficial effect in terms of the grade on the exam. Despite this difference, Figure 6.5 shows
that the patterns of the students’ study behavior during the two 3-week periods were similar
to typical FI performance: There was little studying during the early parts of the 3-week
period, but the amount of studying steadily increased as the exam approached. In contrast,

Figure 6.5 The average minutes of study per day by college students when the instructor gave daily
quizzes and when a larger exam was given at the end of a 3-week period. (From Mawhinney, V.T., et
al., A comparison of students’ studying behavior produced by daily, weekly and 3-week testing sched-
ules, Journal of Applied Behavior Analysis, 4, 257–264. Copyright 1971 by the Society for the Experi-
mental Analysis of Behavior.)




2 4 6 8 10 12 14 16 18 20


Daily Daily3-Week 3-Week





22 24 26 28 30 32 34 36 38 40


study behavior was more stable from day to day when the students had daily quizzes. This
experiment demonstrates that an instructor’s selection of a schedule of quizzes or exams can
have a large effect on the study behavior of the students in the course.

Variable Interval

Variable-interval (VI) schedules are like FI schedules except that the amount of time
that must pass before a reinforcer is stored varies unpredictably from reinforcer to reinforcer.
For example, in a VI 60-second schedule, the time between the delivery of one reinforcer
and the storage of another might be 6 seconds for one reinforcer, then 300 seconds for the
next, 40 seconds for the next, and so on. As on FI schedules, the first response to occur after
a reinforcer is stored collects that reinforcer, and the clock does not start again until the
reinforcer is collected.

As Figure 6.2 shows, VI schedules typically produce a steady, moderate response rate. This
response pattern seems sensible considering the characteristics of the schedule. Because a
reinforcer might be stored at any moment, a long pause after reinforcement would not be
advantageous. By maintaining a steady response rate, the subject will collect each reinforcer
soon after it is stored, thus keeping the VI clock moving most of the time. On the other
hand, a very high response rate, such as that observed on a VR schedule, would produce only
a minor increase in the rate of reinforcement.

An example of an everyday behavior that is maintained by a VI schedule of reinforcement
is checking for mail. The reinforcer in this situation is simply the receipt of mail. Most
people receive mail on some days but not on others, and the days when one will find some-
thing reinforcing (e.g., letters, as opposed to junk mail or bills) in the mailbox are usually
impossible to predict. The delivery of mail approximates a VI schedule because (1) it is
unpredictable; (2) if a reinforcer is stored (the mail has been delivered), only one response is
required to collect it; and (3) if the reinforcer has not yet been stored, no amount of respond-
ing will bring it forth. The resultant behavior is moderate and steady: Most people check
the mail every day, but usually only once a day.


The Scalloped Cumulative Record of the United States Congress

Reinforcement schedules not only control the behavior of individuals; they can also con-
trol the behavior of large groups of people. Some behavioral psychologists have noted
that both the reinforcement schedule and the performance of the U.S. Congress are
similar in some ways to an FI schedule (Critchfield, Haley, Sabo, Colbert, & Macropoulis,
2003; Weisberg & Waldrop, 1972). Each year, Congress begins its session in January,
and the session concludes near the end of each year, so there is a roughly fixed period
of time in which it must work. Weisberg and Waldrop suggested that one of the main


behaviors of Congress is to pass legislation, and one of the main reinforcers is adjourn-
ment, when members can go home to seek the support of their constituents (so they
can be reelected). Based on these assumptions, Critchfield et al. plotted the number of
bills passed by Congress over a 30-year period in the form of a cumulative record, with
time on the x-axis and cumulative bills passed on the y-axis. As shown in Figure 6.6,
the cumulative record of Congress’s behavior is a fine example of an FI scallop. In each
and every year, the number of bills passed starts at a slow pace and then accelerates
as the end of the session approaches.

Critchfield et al. (2003) point out that few real-world examples are exactly the same as
laboratory reinforcement schedules, and this is certainly true for congressional behavior.
For one thing, there is usually a large amount of preparatory work that must be done
before a bill is ready for a vote, so it would be unrealistic to expect that many bills would
be passed in the first week of a new session. Also, whereas a laboratory FI schedule
actually requires just one response for reinforcement, Congress has a certain amount




1991 1992 1993 1994 1995 1996

1997 1998 1999 2000




1981 1982 1983 1984 1985 1986 1987 1988 1989 1990




1971 1972 1973 1974 1975 1976 1977 1978 1979 1980





r o

f B


Figure 6.6 A cumulative record of bills passed by the U.S. Congress over a 30-year period. (From
Critchfield, T.S., et al., A half century of scalloping in the work habits of the United States
Congress. Journal of Applied Behavior Analysis, 36, 2003, 465–486. Copyright 2003 by the Society
for the Experimental Analysis of Behavior, Inc.)


of work that must be done each year, so there seem to be elements of an FR schedule
involved as well. However, the FI features seem to play a dominant role in the case of
Congress, as seen in the consistent scalloped pattern.

Another interesting feature of Congress’s cumulative record is that more bills are
passed in even-numbered years than in odd-numbered years. This is a very consis-
tent trend, which can be seen in the up-and-down heights of the cumulative record in
consecutive years. Can you think of an explanation for this pattern? Is there something
different about the reinforcers members of Congress receive in even years compared
to odd years?

Extinction and the Four Simple Schedules

What happens with the different reinforcement schedules if reinforcement stops? One gen-
eral finding is that extinction is more rapid after CRF than after a schedule of intermittent
reinforcement. This finding is called the partial reinforcement effect, an effect that
seemed paradoxical to early researchers. Why should a response that is only intermittently
followed by a reinforcer be stronger (more resistant to extinction) than a response that has
been followed by a reinforcer every time it has occurred? This dilemma has been named
Humphreys’s paradox, after the psychologist who first demonstrated the partial reinforce-
ment extinction effect (Humphreys, 1939).

One explanation of the partial reinforcement effect is called the discrimination
hypothesis (Mowrer & Jones, 1945). It states that in order for behavior to change once
extinction begins, the individual must be able to discriminate the change in reinforcement
contingencies. With CRF, where every response has been reinforced, the change to extinc-
tion is easy to discriminate, and so it does not take long for responding to disappear. For
example, a vending machine usually dispenses reinforcers (snacks, soft drinks) on a schedule
of CRF: Each time the correct amount of money is inserted, a reinforcer is delivered. If the
schedule is switched to extinction (the machine breaks down), a person will not continue
to put money in the machine for long.

Compare this situation to a slot machine, which dispenses reinforcers on a VR sched-
ule. If a slot machine appeared to be functioning normally but could never produce a
jackpot, a gambler might continue to pour many coins into the machine before giving
up. It would take a long time for the gambler to discriminate the change from a VR
schedule to extinction.

Although the discrimination hypothesis may be easy to understand, experimental evi-
dence suggests that a slightly different hypothesis, the generalization decrement hypoth-
esis (Capaldi, 1966), is better. Generalization decrement is simply a term for the decreased
responding one observes in a generalization test when the test stimuli become less and less
similar to the training stimulus. The generalization decrement hypothesis states that respond-
ing during extinction will be weak if the stimuli during extinction are different from those
that were present during reinforcement, but it will be strong if these stimuli are similar to
those encountered during reinforcement.


According to Capaldi, there is a large generalization decrement when the schedule switches
from CRF to extinction because the animal has never experienced a situation in which its
responses were not reinforced. In other words, the animal quickly stops responding because
it has never been taught to keep responding when its initial responses are not reinforced.
However, suppose an animal has been reinforced on a VR 50 schedule and now switches to
extinction. Here there will be much less generalization decrement because on many occasions
in the past the animal has made a long run of unreinforced responses, and eventually a rein-
forcer was delivered. For this animal, the stimuli present during extinction (long stretches of
unreinforced responses) are quite similar to the stimuli present during the VR schedule. For
this reason, the animal will probably continue to respond for a longer period of time.

Other Reinforcement Schedules

Although the four simple reinforcement schedules have been the most thoroughly investi-
gated, the number of possible rules for delivering reinforcement is unlimited. Many other
reinforcement schedules have been studied by behavioral psychologists. For example, under
a differential reinforcement of low rates (DRL) schedule, a response is reinforced if
and only if a certain amount of time has elapsed since the previous response. If the schedule
is DRL 10 seconds, every response that occurs after a pause of at least 10 seconds is rein-
forced. If a response occurs after 9.5 seconds, this not only fails to produce reinforcement
but it resets the 10-second clock to zero, so that now 10 more seconds must elapse before a
response can be reinforced. As you might imagine, DRL schedules produce very low rates
of responding, but they are not as low as would be optimal. Animals on a DRL schedule
often pause slightly less than the required duration; as a result, considerably more than half
of their responses go unreinforced (Richards, Sabol, & Seiden, 1993).

The opposite of DRL is the differential reinforcement of high rates (DRH) sched-
ule, in which a certain number of responses must occur within a fixed amount of time. For
example, a reinforcer might occur each time the subject makes 10 responses in 3 seconds or
less. Since rapid responding is selectively reinforced by this schedule, DRH can be used to
produce higher rates of responding than those obtained with any other reinforcement sched-
ule. Other common reinforcement schedules combine two or more simple schedules in
some way. For instance, in a concurrent schedule, the subject is presented with two or
more response alternatives (e.g., several different levers), each associated with its own rein-
forcement schedule. With more than one reinforcement schedule available simultaneously,
psychologists can determine which schedule the subject prefers and how much time is
devoted to each alternative. Some of these more complex reinforcement schedules will be
discussed in later chapters.


An individual’s behavior on a given reinforcement schedule can be affected by many
other factors besides the rule for reinforcement. Some of these factors—amount of rein-
forcement, rate of reinforcement, delay, and response effort—are fairly straightforward. Not


surprisingly, both people and animals will display stronger responding on a reinforcement
schedule if the reinforcers are large and if they are delivered at a high rate. However, if
the reinforcers are delayed, or if each response requires substantial effort, responding will
be slower. Another important factor is reinforcement history: Research has shown that how
an individual responds on one reinforcement schedule depends on what other reinforce-
ment schedules the individual has previously been exposed to. As an example, Weiner
(1964) had human participants press a response key to earn points in ten 1-hour sessions.
Some participants worked on an FR 40 schedule (on which more rapid responding led
to more reinforcers). Other participants worked on a DRL 20-second schedule (on which
only pauses longer than 20 seconds were reinforced). Then all participants were switched
to an FI 10-second schedule. The participants with FR experience responded rapidly on
the FI schedule, but those with DRL experience responded very slowly. These large dif-
ferences persisted even after 20 sessions with the FI schedule. Similar effects of prior
reinforcement history have been found with animals (Macaskill & Hackenberg, 2012;
Wanchisen, Tatham, & Mooney, 1989).

Some of these points may seem obvious, but other factors that can affect performance on
reinforcement schedules are not so obvious. Next, we will examine some factors that can
easily be overlooked.

Behavioral Momentum

When a heavy object starts moving, it acquires momentum and becomes difficult to stop.
Nevin (1992) has argued that there is an analogy between the momentum of a moving
object and the behavioral momentum of an ongoing operant behavior. Nevin has found
that a behavior’s resistance to change (which is a measure of behavioral momentum)
depends on the association between the discriminative stimulus and the reinforcer (i.e., on
how frequently the behavior has been reinforced in the presence of a certain discriminative

An experiment with pigeons (Nevin, 1974) illustrates the concept of behavioral momen-
tum. The pigeons earned food by pecking on a response key that was sometimes green and
sometimes red. VI schedules delivered 60 food presentations per hour when the key was
green but only 20 food presentations per hour when the key was red. As expected, the
pigeons pecked more rapidly when the key was green. Then, Nevin interrupted the green
and red keys with periods during which free food was delivered. When the free food deliv-
eries were very rapid, the pigeons’ rates of key pecking decreased by about 60% when the
key was green but by over 80% when the key was red. According to Nevin, pecking on the
green key had greater momentum because it was associated with a higher rate of reinforce-
ment, so this behavior was less disrupted by the free food deliveries than was pecking on the
red key. Studies with humans have also found that behaviors associated with higher rates of
reinforcement are harder to disrupt (Milo, Mace, & Nevin, 2010).

Nevin and Grace (2000) proposed that the concept of behavioral momentum has a num-
ber of implications for attempts to change behavior outside the laboratory. Behavior thera-
pists frequently want to make sure that a newly trained behavior (e.g., working steadily on
one’s job during work hours) will persist in the presence of potential disruptors (e.g., distrac-
tions by friends, reinforcers for competing behaviors). The newly trained behavior will have


more momentum and be more likely to persist despite such potential disruptors if the worker
has developed a strong association between the work environment and reinforcement for
appropriate work-related behavior.

As another example, the concept of behavioral momentum can help to explain why
some undesirable behaviors may relapse when a patient leaves a treatment facility (Podle-
snik & Shahan, 2010). For instance, someone who has received treatment for a drug
addiction may start taking drugs again when he leaves a treatment center and returns
home because the drugs are strongly associated with that environment (the patient’s
neighborhood and friends). Although he abstained from drugs in the treatment facility,
the association between the patient’s neighborhood and drugs has not been broken.
Because drug-taking behavior has strong momentum when the individual is in his old
neighborhood, it may persist in that environment despite the treatment the patient has
received elsewhere.

Contingency-Shaped Versus Rule-Governed Behaviors

As we have seen, each reinforcement schedule tends to produce its own characteristic pattern
of behavior (Figure 6.2). B. F. Skinner called these patterns contingency-shaped behav-
iors because behavior is gradually shaped into its final form as the individual gains more
and more experience with a particular reinforcement schedule (Ferster & Skinner, 1957).
However, some laboratory experiments with humans have found behavior patterns that were
quite different from those shown in Figure 6.2. For example, under FI schedules, some
humans show the accelerating pattern found with animals, others respond very quickly
throughout the interval, and others make only a few responses near the end of the interval
(Leander, Lippman, & Meyer, 1968). Discrepancies between human and animal behaviors
have been found with other reinforcement schedules as well (Lowe, 1979). Why should the
same reinforcement schedules produce different behavior patterns in humans and

One explanation is that the discrepancies between animal and human performance on
reinforcement schedules occur because people are capable of both contingency-shaped
behavior and rule-governed behavior. Skinner (1969) proposed that because people
have language, they can be given verbal instructions or rules to follow, and these rules
may or may not have anything to do with the prevailing reinforcement contingencies.
For example, a mother may tell a child, “Stay out of drafts or you will catch a cold,” and
the child may follow this rule for a long time, regardless of whether it is truly effective
in preventing colds. With respect to laboratory experiments on reinforcement schedules,
this theory states that human participants may behave differently from animals because
they are following rules about how to respond (e.g., “Press the response button as rapidly
as possible,” or “Wait for about a minute, and then respond”). They may form these rules
on their own, or they may get them from the instructions the experimenter provides
before the experiment begins. Once a human participant receives or creates such a rule,
the actual reinforcement contingencies may have little or no effect on his or her behavior.
For instance, if the experimenter says, “Press the key rapidly to earn the most money,”
the participant may indeed respond rapidly on an FI schedule even though rapid respond-
ing is not necessary on this schedule.


Several types of evidence support the idea that human performance on reinforcement
schedules is often rule governed, at least in part. Many studies have shown that the
instructions given to participants can have a large effect on their response patterns (e.g.,
Bentall & Lowe, 1987; Catania, Matthews, & Shimoff, 1982). If participants are given
no specific rule to follow, they may form one on their own. Human participants have
sometimes been asked, either during an experiment or at the end, to explain why they
responded the way they did. In some cases there is a close correspondence between a
person’s verbal descriptions and his or her actual response patterns (Wearden, 1988).
However, there are also cases where human participants asked to explain their behaviors
can give no rule, or the rule they give does not describe their actual behavior patterns
(Matthews, Catania, & Shimoff, 1985). Therefore, it seems likely that other variables
also contribute to the differences between human and nonhuman performance on
reinforcement schedules. For instance, a primary reinforcer, food, is usually used with
animal subjects, but with humans the reinforcers in many experiments have been con-
ditioned reinforcers (e.g., points that may later be exchanged for small amounts of
money). In addition, animals and humans usually come to the laboratory with very
different reinforcement histories. It is important to remember that operant behavior is
affected by many variables, and this can make the task of analyzing the behavior com-
plex and challenging.


Throughout the preceding discussions of
reinforcement schedules, the explanations
of why particular schedules produce spe-
cific response patterns have been casual
and intuitive. For example, we noted that
it would not “make sense” to have a long
postreinforcement pause on a VR schedule
or to respond at a very rapid rate on a VI
schedule. This level of discussion can make
the basic facts about reinforcement sched-
ules easier to learn and remember. How-
ever, such imprecise statements are no
substitute for a scientific analysis of exactly
which independent variables (which char-
acteristics of the reinforcement schedule)
control which dependent variables (which
aspects of the subject’s behavior). This sec-
tion presents a few examples that show
how a scientific analysis can either improve
on intuitive explanations of behavior or
distinguish among different explanations,
all of which seem intuitively reasonable.

Practice Quiz 1: chaPter 6
1. In a cumulative record, fast respond-

ing is indicated by a ______, and no
responding is indicated by a ______.

2. Responding on an FR schedule typi-
cally shows a(n) ______ pattern, and
responding on an FI schedule typi-
cally shows a(n) ______ pattern.

3. Responding on ______ schedules is
usually rapid and steady, and
responding on ______ schedules is
usually slower and steady.

4. A behavior has a high ______ if it is
not affected much by distractions or
environmental changes.

5. ______ behavior is controlled by the
schedule of reinforcement; ______
behavior is controlled by instructions
subjects are given or form on their


1. steep line, horizontal line 2. stop-and-go, accelerating
3. VR, VI 4. behavioral momentum 5. contingency-
shaped, rule-governed


Cause of the FR Postreinforcement Pause

Why do animals pause after reinforcement on FR schedules? Several possible explanations
seem intuitively reasonable. Perhaps the postreinforcement pause is the result of fatigue: The
subject has made many responses and has collected a reinforcer; now it rests to alleviate its
fatigue. A second possibility is satiation: Consuming the food causes a slight decrease in the
animal’s level of hunger, which results in a brief interruption in responding. A third explana-
tion is that on an FR schedule, the animal is farthest from the next reinforcer immediately
after receiving the previous reinforcer: Many responses will be required before another
reinforcer is delivered. We can call these three explanations of the FR postreinforcement
pause the fatigue hypothesis, the satiation hypothesis, and the remaining-responses hypoth-
esis. Each of these hypotheses sounds plausible, but how can we determine which is correct?
Several types of evidence help to distinguish among them.

First, there is the finding that postreinforcement pauses become larger as the size of the
FR increases. This finding is consistent with both the fatigue and remaining-responses
hypotheses, but it contradicts the satiation hypothesis. Because the subject can collect rein-
forcers at a faster rate on a small FR schedule, its level of hunger should be lower; according
to the satiation hypothesis, pauses should be longer on smaller FR schedules, not on larger
FR schedules.

Data that help to distinguish between the fatigue and remaining-responses hypotheses are
provided by studies that combine two or more different FR schedules into what is called a
multiple schedule. In a multiple schedule, the subject is presented with two or more dif-
ferent schedules, one at a time, and each schedule is signaled by a different discriminative
stimulus. For example, Figure 6.7 illustrates a portion of a session involving a multiple FR
10 FR 100 schedule. When the response key is blue, the schedule is FR 100; when it is red,
the schedule is FR 10. The key color remains the same until a reinforcer is earned, at which
point there is a 50% chance that the key color (and schedule) will switch.

The behavior shown in Figure 6.7, though hypothetical, is representative of the results
from actual studies that used multiple FR schedules (Mintz, Mourer, & Gofseyeff, 1967).
Examine the postreinforcement pauses that occurred at points a, b, c, d, e, and f. Notice that
sometimes the pause after FR 100 is long (f), but sometimes it is short (a, b). Sometimes the
pause after FR 10 is short (b), but sometimes it is long (c, e). The data show that we cannot

Figure 6.7 A hypothetical but typical pattern of response from a multiple schedule where the blue
key color signaled that the schedule was FR 100 and the red key color signaled that the schedule was
FR 10.

Key color Blue Blue

100 1010

a b c d e f

100 10 100 100





predict the size of the postreinforcement pause by knowing how many responses the subject
has just made. This fact forces us to reject the fatigue hypothesis.

However, it is possible to predict the size of the pause by knowing the size of the upcom-
ing ratio. Notice that the pause is short whenever the key color is red (points a, b, and d),
which is the discriminative stimulus for FR 10. The pause is long when the key color is blue
(points c, e, and f), the discriminative stimulus for FR 100. This pattern is exactly what would
be predicted by the remaining-responses hypothesis: The size of the postreinforcement pause
is determined by the upcoming FR requirement. Experiments of this type have shown quite
clearly that the size of the postreinforcement pause depends on how much work must be
done before the next reinforcer is delivered and that the factors of satiation and fatigue play
at most a minor role.

Comparisons of VR and VI Response Rates

Experiments with both humans and animals have shown that if a VR schedule and a VI
schedule deliver the same number of reinforcers per hour, subjects usually respond faster on
the VR schedule (Baxter & Schlinger, 1990; Matthews, Shimoff, Catania, & Sagvolden,
1977). Why is responding faster on VR schedules than on VI schedules even in cases where
the rates of reinforcement are the same for the two schedules?

One theory about this difference in response rates can be classified as a molecular
theory, which means that it focuses on small-scale events—the moment-by-moment rela-
tionships between responses and reinforcers. The other theory is a molar theory, one that
deals with large-scale measures of behavior and reinforcement. To be more specific, molecu-
lar theories usually discuss events that have time spans of less than 1 minute, whereas molar
theories discuss relationships measured over at least several minutes and often over the entire
length of an experimental session.

To account for the different response rates on VR and VI schedules, a popular molecular
theory is the interresponse time (IRT) reinforcement theory. IRT is the time between
two consecutive responses. In essence, this theory states that response rates are slower on VI
schedules than on VR schedules because long IRTs (long pauses between responses) are more
frequently reinforced on VI schedules. This theory was first proposed by Skinner (1938) and
later supported by others (Anger, 1956; Platt, 1979). Remember that a VI schedule has a
timer that sets up each reinforcer, so if there is a long pause in responding (a long IRT), there
is a good chance that a reinforcer will be waiting when the next response is finally made.
However, if there is a quick and rapid burst of responses (a series of short IRTs), the chances
of a reinforcer becoming available during that short period of time are small. Therefore, on
a VI schedule, long IRTs are more likely to be followed by a reinforcer. On a VR schedule,
however, the delivery of reinforcement depends entirely on the number of responses pro-
duced, not on the passage of time. Therefore, there is no selective strengthening of long
pauses on VR schedules. However, if the individual makes a burst of rapid responses, there
is an increasing chance that one of those will complete the VR requirement and deliver a

To provide evidence for their viewpoint, those who favor IRT reinforcement theory have
arranged schedules that reinforce different IRTs with different probabilities. For example,
Shimp (1968) set up a schedule in which only IRTs between 1.5 and 2.5 seconds or between


3.5 and 4.5 seconds were reinforced. As the theory of selective IRT reinforcement would
predict, IRTs of these two sizes increased in frequency, just as IRT reinforcement theory
predicted. In another experiment, Shimp (1973) mimicked the pattern of IRT reinforcement
that occurs in a typical VI schedule: He did not use a VI clock but simply reinforced long
IRTs with a high probability and short IRTs with a low probability. The result of this “syn-
thetic VI” schedule was a pattern of responding indistinguishable from that of a normal VI
schedule—moderate, steady responding with a mixture of long and short IRTs.

A molar theory of the VI–VR difference might be called the response-reinforcer cor-
relation theory (Baum, 1973; Green, Kagel, & Battalio, 1987), which focuses on the long-
term relation between response rate and reinforcement rate on these two schedules. Figure 6.8
shows the relationship between a subject’s average response rate and overall reinforcement
rate for a typical VR schedule and a typical VI schedule. On VR 60, as on all ratio schedules,
there is a linear relationship between response rate and reinforcement rate. For instance, a
response rate of 60 responses per minute will produce 60 reinforcers per hour, and a response
rate of 90 responses per minute will produce 90 reinforcers per hour. The relationship on
the VI 60-second schedule (as on all VI schedules) is very different. No matter how rapidly
the subject responds, it cannot obtain more than the scheduled 60 reinforcers per hour. The
reason that the reinforcement rate drops with very low response rates is that the VI clock
will sometimes be stopped (having stored a reinforcer), and it will not start again until the
subject makes a response and collects a reinforcer. But as long as the subject responds at a
modest rate, it will obtain close to 60 reinforcers per hour.

Let us try to understand how the different functions in Figure 6.8 could cause the
response rate difference between VI and VR. Suppose that after extensive experience on VR
60, a pigeon’s response rate is about 60 responses per minute (where the two functions cross
in Figure 6.8), and it is earning about 60 reinforcers per hour. Now suppose the schedule
is switched to VI 60 seconds. The same response rate would continue to produce 60 rein-
forcers per hour. However, the pigeon’s response rate is not completely steady: Sometimes

Figure 6.8 The relationship between rate of response and rate of reinforcement on a VR 60 schedule
and a VI 60-second schedule.






0 20 40 60

Responses per minute

VI 60 seconds

VR 60







80 100


the pigeon responds slightly slower and sometimes slightly faster, and it eventually learns that
slower responding has little effect on reinforcement rate on the VI schedule. The pigeon’s
behavior may gradually drop to, say, 20 responses per minute without much decrease in the
rate of reinforcement. Speaking loosely, we could say that on VI 60 seconds, the pigeon has
learned that the extra 40 responses per minute are “not worth it” because they produce only
a minor increase in reinforcement rate.

Probably the best way to decide between the molar and molecular theories is to use a
schedule in which the molar contingencies favor rapid responding and the molecular con-
tingencies favor slow responding (or vice versa). Experiments with pigeons (Vaughan, 1987)
and rats (Cole, 1999) used some complex schedules that had these properties. For instance,
one schedule used by Vaughan had the molar features of a VR schedule (faster response rates
produced more reinforcers) but the molecular features of a VI schedule (reinforcement was
more likely after a long IRT). As predicted by IRT reinforcement theory, the pigeons responded
slowly on this schedule (and thereby lost reinforcers in the long run). Conversely, the pigeons
responded rapidly on a schedule in which the molecular contingencies favored rapid respond-
ing (short IRTs) but the molar contingencies favored slow responding (so once again the
pigeons lost reinforcers in the long run). In a similar way, Tanno and Sakagami (2008) used
some complex schedules to compare the effects of molar and molecular variables. They found
that the long-term correlation between response rates and reinforcement had very little effect
on the rats’ responding. However, they responded rapidly when short IRTs were selectively
reinforced and slowly when long IRTs were selectively reinforced. All of these results support
the molecular approach, for they indicate that the animals were sensitive to the short-term
consequences of their behavior but not to the long-term consequences.


Within the field of behavior modification, operant conditioning principles have been applied
to so many different behaviors that they are too numerous to list, let alone describe, in a few
pages. Operant conditioning principles have been used to help people who wish to improve
themselves by losing weight, smoking or drinking less, or exercising more. They have been
applied to a wide range of children’s problems, including classroom disruption, poor aca-
demic performance, fighting, tantrums, extreme passivity, and hyperactivity. They have been
used in attempts to improve the daily functioning of adults and children who have more
serious behavior problems and must be institutionalized. These principles have also been
applied to problems that affect society as a whole, such as litter and pollution, the waste of
energy and resources, workplace accidents, delinquency, shoplifting, and other crimes.
Because of the number and diversity of these applications, this section can do no more than
describe a few representative examples.

Teaching Language to Children With Autism

Autism is a severe disorder that affects roughly 1 in every 100 children, usually appearing
when a child is a few years old. One major symptom of autism is extreme social withdrawal.
The child shows little of the normal interest in watching and interacting with other people.


Children with autism do not acquire normal language use: They either remain silent or
exhibit echolalia, which is the immediate repetition of any words they hear. These children
frequently spend hours engaging in simple repetitive behaviors such as rocking back and
forth or spinning a metal pan on the floor. Despite considerable research, the causes of autism
are not well understood.

Ivar Lovaas (1967) developed an extensive program based on operant conditioning prin-
ciples designed to teach children with severe autism to speak, to interact with other people,
and in general to behave more normally. Lovaas’s methods make use of many of the operant
conditioning principles we have already discussed, plus some new ones. At first, a therapist
uses some tasty food as a primary reinforcer and starts by reinforcing the child simply for
sitting quietly and looking at the experimenter. Next, using shaping, the therapist rewards
the child for making any audible sounds, and then for making sounds that more and more
closely mimic the word spoken by the therapist. For instance, if the child’s name is Billy, the
therapist may say the word “Billy” as a discriminative stimulus, after which any verbal
response that approximates this word will be reinforced. So as not to rely entirely on food
as a reinforcer (which would lose its effectiveness rapidly because of satiation), the therapist
establishes other stimuli as conditioned reinforcers. Before presenting the food, the therapist
might say “Good!” or give the child a hug—two stimuli that can eventually be used as
reinforcers by themselves.

Early in this type of training, the therapist might use her hand to aid the child in his
mouth and lip movements. This type of physical guidance is one example of a prompt. A
prompt is any stimulus that makes a desired response more likely. In this example, the thera-
pist’s prompt of moving the child’s lips and cheeks into the proper shape makes the produc-
tion of the appropriate response more likely. Whenever a prompt is used, it is usually
withdrawn gradually in a procedure known as fading. The therapist may do less and less of
the work of moving the child’s lips and cheeks into the proper position, then perhaps just
touch the child’s cheek lightly, then not at all. This type of training demands large amounts
of time and patience. It might take several days before the child masters his first word. How-
ever, the pace of progress quickens as additional words are introduced, and after a few weeks
the child may master several new words each day.

At this stage of training, the child is only imitating words he hears. The next step is to
teach him the meanings of these words. Training begins with concrete nouns such as nose,
shoe, and leg. The child is taught to identify the correct object in response to the word as a
stimulus (e.g., by rewarding an appropriate response to the instruction, “Point to your nose”)
and to produce the appropriate word when presented with the object (Therapist: “What is
this?” Billy: “Shoe”). Later in training, similar painstaking techniques are used to teach the
child the meanings of verbs and adjectives, of prepositions such as in and on, and of abstract
concepts such as first, last, more, less, same, and different. This program can produce dramatic
improvements in the behavior of children who typically show negligible improvement from
any other type of therapy. Over the course of several months, Lovaas typically found that a
child who was initially aloof and completely silent became friendly and affectionate and
learned to use language to answer questions, to make requests, or to tell stories.

How successful is this behavioral treatment in the long run? Lovaas (1987) compared
children who had received extensive behavioral treatment for autism (40 hours a week for
2 or more years) with children who had received minimal treatment (10 hours a week or
less). At ages 6 or 7, the differences between groups were dramatic: Nearly half of the


children from the treatment group had normal IQs and academic performance, as compared
to only 2% of the children from the control group. Six years later, the children in the treat-
ment group continued to maintain their advantage over those in the control group. Some
performed about as well as average children of the same age on tests of intelligence and
adaptive behavior (McEachin, Smith, & Lovaas, 1993). These results are encouraging, for
they suggest that if extensive behavioral treatment is given to young children with autism,
it can essentially eliminate the autistic symptoms in at least some of them.

To be most effective, behavioral treatment for autism should begin at an early age, and it
should be intensive: The child should receive many hours of treatment per week for a period
of several years. However, researchers have been testing new behavioral treatments for chil-
dren on the autism spectrum that are less time-consuming, and some seem promising. For
example, a technique called Pivotal Response Training targets important social and com-
munication skills that the child can later use in many different situations, thereby maximizing
their impact. In many cases, the child’s parents are taught to use this training at home. There
is a strong emphasis on keeping motivation high by giving the child choices among tasks
and reinforcers (Koegel, Bimbela, & Schreibman, 1996). Although more research on Pivotal
Response Training is needed, a number of studies found that it produced substantial improve-
ments in children’s verbal and social skills (Lydon, Healy, & Leader, 2011; Ventola et al.,

Token Reinforcement

In behavioral psychology, a token is defined as “an object or symbol that is exchanged for
goods or services” (Hackenberg, 2009, p. 257). Research with various species of animals
suggests that tokens can act as conditioned reinforcers—that is, their delivery can strengthen
operant responses. Wolfe (1936) taught chimpanzees to insert poker chips into a vending
machine to receive small amounts of food or water. The poker chips could then be used as
reinforcers to establish and maintain new behaviors, such as lifting a weighted lever arm.
Marbles have been used as tokens for rats (Malagodi, 1967) and illuminated lights as tokens
for pigeons (Bullock & Hackenberg, 2006). Tokens can produce response patterns that are
very similar to those obtained with primary reinforcers such as food. For instance, Malagodi’s
rats exhibited stop-and-go responding when marbles were delivered on an FR schedule and
slower, steady responding when they were delivered on a VI schedule. Tokens can also serve
as discriminative stimuli, signaling the upcoming delivery of a primary reinforcer (Mazur &
Biondi, 2013).

In human applications, tokens may be physical objects such as poker chips or gold stars
on a bulletin board, or they may simply be points added in a record book. Token systems
have been used in classrooms, psychiatric institutions, prisons, and homes for juvenile offend-
ers. What all token systems have in common is that each individual can earn tokens by
performing any of a number of different desired behaviors and can later exchange these
tokens for a variety of “backup” or primary reinforcers.

In the past, token systems have been used in psychiatric hospitals to improve the day-to-
day functioning of patients. In one such program, patients received tokens for specific behav-
iors from three broad categories: personal hygiene, social interaction, and job performance
(Schaefer & Martin, 1966). The purpose was to reinforce behaviors would be considered


normal and desirable not only within the hospital but also in the outside world. The tokens
were used to purchase food items, cigarettes, access to television, and recreational activities.
The token program remained in effect for 3 months, and there were substantial increases in
the reinforced behaviors. Other studies also found that token systems could produce impres-
sive improvements in psychiatric patients. However, the use of token systems in psychiatric
hospitals has declined over the years for several reasons (Glynn, 1990). These systems require
a good deal of time and effort and a well-trained staff. In addition, there has been an increas-
ing emphasis on pharmacological treatments for psychiatric patients. Some court rulings have
restricted what can legally be done with token systems. For these reasons, it seems unlikely
that token systems will be used extensively in psychiatric institutions in the foreseeable future.

Although their use with psychiatric patients has declined, token systems are now very
commonly used in classrooms. Tokens may be delivered for good academic performance or
for good behavior (Figure 6.9). In one instance, the classroom behavior of special education
students was improved by giving them tokens for such behaviors as paying attention, using
appropriate language, cooperating with others, and following instructions (Cavalier, Feretti,
& Hodges, 1997). Teachers can set up a system in which tokens are exchanged for snacks,
small prizes, or access to special activities.

In some cases, token reinforcement can be combined with modern technology to make
the process more efficient and convenient. Dallery, Glenn, and Raiff (2007) offered vouch-
ers to smokers for reducing their levels of carbon monoxide (which measures how much
they had been smoking). The entire project was conducted over the Internet. Twice a day,
participants used a webcam to record themselves taking a breath test for carbon monoxide.
Over a 4-week period, they earned vouchers for greater and greater decreases in carbon
monoxide levels, and the vouchers could be used to buy items from merchants on the
Internet. Figure 6.10 shows the carbon monoxide levels from all 20 participants during the
four weeks of the experiment. The individual results were variable, but many participants
showed dramatic reductions in smoking as the treatment progressed.

Figure 6.9 Many teachers use token systems in the classroom, such as these happy, neutral, and sad
faces that represent different levels of performance. Students who obtain enough happy faces earn
small prizes or special activities.

Figure 6.10 Carbon monoxide levels from 20 smokers are shown from a 4-week study in which they
earned tokens exchangeable for rewards by smoking less. (From Dallery, J., Glenn, I.M., & Raiff, B.R.,
2007, An Internet-based abstinence reinforcement treatment for cigarette smoking, Drug and Alcohol
Dependence, 86, 230–238. Reprinted by permission of Elsevier.)


























































0 10 20 30 40








50 60 0 10 20 30 40 6050 0 10 20 30 40 6050 0 10 20 30 40 6050

P39M26D56 R42






J04S17D90 P09

G11J08A29 D50


C23 E49
Sample Number

B30 T22




Organizational Behavior Management

An area of applied behavior analysis known as organizational behavior manage-
ment is devoted to using the principles of behavioral psychology to improve human
performance in the workplace (Johnson, Redmon, & Mawhinney, 2001). Researchers in
this field have addressed such matters as worker productivity, supervisor effectiveness,
accident prevention, quality improvement, and customer satisfaction. The idea of orga-
nizational behavior management is to apply a scientific approach to workplace behavior.

When behavior analysts serve as consultants for a business organization, the pro-
cess usually involves several different steps. The organization’s leaders must decide on
the goals they wish to achieve and describe them in concrete terms (e.g., reducing the
number of days per month that the average employee is absent from work). Current
practices of the company are then observed, and data are collected on the workers’
behaviors. The behavior analysts then make recommendations for changes of two main
types—antecedent-based interventions and consequence-based interventions (Wilder,
Austin, & Casella, 2009). Antecedent-based interventions focus on events that occur
before the work is done, including such matters as providing appropriate worker train-
ing, clarifying tasks, and setting goals. Consequence-based interventions focus on the
events that occur after the work is done, and they can include the use of praise, mon-
etary rewards, and feedback. Data are collected to evaluate whether the changes are
having the desired effects, and further adjustments are made as needed.

An early study by Wallin and Johnson (1976) used a simple VR schedule (a lottery)
to reduce employee absenteeism and tardiness in a small electronics company. Work-
ers were allowed to participate in a monthly lottery if their attendance record had been
perfect for the past month, and the winner received a $10 prize. In the first 11 months
of the lottery system, employee absenteeism was 30% lower than in the previous 11
months. This simple program saved the company thousands of dollars in sick-leave
expenditures. But monetary reinforcers are not always necessary. Camden and Ludwig
(2013) provided weekly personal and public feedback to nursing assistants in a health
care clinic about their absences and the problems they caused (the number of under-
staffed shifts and the names of coworkers who had to work extra hours to cover their
shifts). This simple change produced a decrease in absenteeism by nearly 50%.

Reinforcement procedures have also been used to decrease workplace accidents.
Workers in two open-pit mines were given trading stamps (exchangeable for various
types of merchandise) in return for accident-free performance. The workers could earn
stamps in a variety of ways: by completing a year without a lost-time accident, by fol-
lowing safety standards, and so on. They lost stamps if someone in their group had
an accident or damaged equipment. After the adoption of the trading-stamp program,


lost-time accidents decreased by more than two thirds. The monetary savings from
reduced accident rates were at least 15 times greater than the cost of the trading-
stamp program (Fox, Hopkins & Anger, 1987). Quite a few other studies have found that
such reinforcement programs can both reduce workplace accidents and save compa-
nies substantial amounts of money.

Often the workplace changes include several different components. In a hospital’s
operating room, behavior analysts used a combination of goal setting, task clarification,
and feedback to increase the use of safe methods of passing sharp surgical instru-
ments from one person to another. Baseline measurements showed that surgeons and
staff frequently used unsafe techniques that increased the risk of a cut or puncture
wound. In a meeting with operating room personnel, a “challenging but realistic” goal
for improvement was agreed upon. Task clarification was accomplished by having staff
model safe and unsafe techniques so that everyone understood the proper techniques.
For feedback, the operating room procedures were observed and recorded, and each
week the percentage of safe transfers was reported to the staff. Under this program,
the use of safe techniques increased by a factor of two (Cunningham & Austin, 2007).

Organizational behavior management has been used for many different types of com-
panies, both large and small, in many different sectors of the economy, including human
services, manufacturing, transportation, and education. Of course, no two companies are
exactly alike, but by comparing the results from many different cases, researchers can start
to draw general conclusions about what methods of behavior change are the most effective.

Behavior Therapy for Marital Problems

Some therapists have used behavioral principles to aid couples who seek help for marital prob-
lems. Jacobson and Follette (1985) recognized that in unhappy married couples, each spouse
tends to resort to threats, punishment, and retaliation in an attempt to get what he or she wants
from the other. For this reason, the initial phases of therapy are designed to promote more
positive interactions between partners. To encourage a reciprocal exchange of reinforcers
between spouses, a contingency contract is often used. A contingency contract is a written
agreement that lists the duties (behaviors) required of each party and the privileges (reinforcers)
that will result if the duties are performed. In most cases, both spouses play active roles in creat-
ing the contract and indicate their agreement with the terms of the contract by signing it.

A contingency contract can help to encourage the exchange of reinforcers and to let each
partner know what behaviors the other desires. For instance, the husband may agree to do the
dishes in the evening if and only if the wife took the children to school that morning. Con-
versely, the wife agrees to take the children to school the next morning if and only if the
husband did the dishes the night before. The use of a written contract ensures that both part-
ners understand what is expected of them. This part of behavioral marital therapy is called
behavior exchange because each spouse makes an effort to perform specific behaviors that will
please the other.


Behavior exchange is just one part of behavioral marital therapy. Another important
component is training in communication and problem-solving skills. N. S. Jacobson
(1977) observed that unhappy couples often have difficulty communicating, and they
find it difficult to solve even the simplest of problems that may arise. As part of their
therapy, a couple first reads a book about problem solving in marriage, then they try
to solve a very minor problem as the therapist watches. For instance, the wife may
complain that she does not like always having to remind the husband to take out the
garbage. The couple then tries to find a solution to this small problem that satisfies
both spouses. Whenever one spouse responds in an inappropriate way, the therapist
interrupts, points out the flaw, and suggests a better alternative. After a little trial and
error, the couple usually finds a solution to this minor problem. Over time, they gradu-
ally work up to bigger problems, and they are typically given “homework assignments”
in which they engage in problem solving on a regular basis between meetings with the

This general approach is often called cognitive-behavior therapy (CBT) because of its
emphasis on problem solving and communication between partners. It offers a promising
approach to the treatment of marital discord: One review of 17 separate studies concluded
that a couple’s chances of successfully resolving their marital difficulties more than doubled
if the couple received this type of therapy
(Halweg & Markman, 1988). Although
these techniques do not work for every-
one, they can help many unhappy couples
improve the quality of their marriages. As
with most types of behavior modification,
behavioral therapy for couples is not a
fixed and unchanging system; it continues
to evolve as therapists experiment with
new techniques and measure their effec-
tiveness (West, 2013).


The successful application of the princi-
ples of reinforcement to a wide array of
behavior problems provides one of the
strongest pieces of evidence that the
research of the operant conditioning labo-
ratory is relevant to real-world behavior.
The examples described here are just a
small sample of what has been accom-
plished in the field of applied behavior
analysis. Other examples will be presented
in later chapters. With further research in
this area, psychologists should continue to
develop a more complete understanding
of how “voluntary” behaviors are affected
by their consequences.

Practice Quiz 2: chaPter 6
1. Research results favor the ______

theory of FR postreinforcement
pauses over the ______ and ______

2. IRT reinforcement theory states that
longer IRTs are more likely to be rein-
forced on ______ schedules, but
bursts of responding are more likely to
be reinforced on ______ schedules.

3. ______ theories deal with long-term
relationships between behavior and
reinforcement, whereas ______ theo-
ries deal with moment-to-moment
relationships between behavior and

4. Physically guiding the movements of
a learner is an example of a ______;
gradually removing this physical
guidance is called ______.

5. In behavioral marital therapy, a writ-
ten agreement between spouses is
called a ______.


1. remaining-responses, fatigue, satiation 2. VI, VR
3. molar, molecular 4. prompt, fading 5. contingency



An FR schedule delivers a reinforcer after a fixed number of responses, and it typically pro-
duces a postreinforcement pause followed by rapid responding. A VR schedule delivers a
reinforcer after a variable number of responses, and it typically leads to rapid, steady respond-
ing. On an FI schedule, the requirement for reinforcement is one response after a fixed
amount of time. Subjects often exhibit a postreinforcement pause and then an accelerating
response pattern. VI schedules are similar except that the time requirement is variable, and
they typically produce moderate, steady responding.

Performance on a reinforcement schedule can be affected by the quality and amount of
reinforcement, the response effort, and the individual’s level of motivation and past experi-
ence. People may also respond according to rules they have been taught or have learned on
their own. When reinforcement is discontinued, extinction is usually rapid after CRF, slower
after FI or FR, and slowest after VI or VR.

Experimental analysis has shown that the postreinforcement pause on FR schedules
occurs primarily because each reinforcer is a signal that many responses must be completed
before the next reinforcer. Regarding the question of why VR schedules produce faster
responding than VI schedules, IRT reinforcement theory states that long pauses are often
reinforced on VI schedules, whereas bursts of rapid responses are more likely to be reinforced
on VR schedules. A different theory states that subjects learn that more rapid responding
yields more reinforcers on VR schedules but not on VI schedules.

Reinforcement schedules are frequently used in behavior therapy. Children with autism
have been taught to speak by using positive reinforcement, shaping, prompting, and fading.
Token systems and other reinforcement techniques have been used in some psychiatric
hospitals, schools, and businesses. In behavior therapy for couples, contingency contracts help
partners increase the exchange of positive reinforcers.

Review Questions

1. For each of the four basic reinforcement schedules, describe the rule for rein-
forcement, the typical response pattern, and the rate of extinction.

2. From your own experience, describe a situation that resembles one of the four sim-
ple reinforcement schedules. How is the reinforcement schedule in your example
similar to the laboratory example? Are there any important differences between the
two? Is the behavior pattern in real life similar to behavior in the laboratory?

3. What are some factors that can affect performance on a reinforcement sched-
ule? Illustrate using concrete examples.

4. What is the difference between the molecular and molar theories of behavior?
Describe a molecular theory and a molar theory of why responding is usually
faster on VR schedules than on VI schedules.

5. Give a few examples of how the principles of operant conditioning have been
used in behavior therapy. In describing the methods, identify as many different
terms and principles of operant conditioning as you can.



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interresponse times. Journal of Experimental Psychology, 52, 145–161.

Baum, W.M. (1973). The correlation-based law of effect. Journal of the Experimental Analysis of Behav-
ior, 20, 137–153.

Baxter, G.A., & Schlinger, H. (1990). Performance of children under a multiple random-ratio
random-interval schedule of reinforcement. Journal of the Experimental Analysis of Behavior, 54,

Bentall, R.P., & Lowe, C.F. (1987). The role of verbal behavior in human learning: III. Instructional
effects in children. Journal of the Experimental Analysis of Behavior, 47, 177–190.

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Behavior, 50, 65–73.

Bullock, C.E., & Hackenberg, T.D. (2006). Second-order schedules of token reinforcement with
pigeons: Implications for unit price. Journal of the Experimental Analysis of Behavior, 85, 95–106.

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Learning Objectives

After reading this chapter, you should be able to

• identify different procedures for increasing or decreasing behavior
• describe three theories of avoidance and explain their strengths and weaknesses
• discuss the phenomenon of learned helplessness as it occurs in animals and

in people
• describe factors that determine whether punishment will be effective
• explain the disadvantages of using punishment as a method of controlling

• describe different types of behavior decelerators and how they are used in

behavior therapy

C H A P T E R 7

Avoidance and Punishment

Chapters 5 and 6 were devoted to the topic of positive reinforcement, in which a
response is followed by a reinforcer and as a result the response is strengthened. How-
ever, positive reinforcement is only one of four possible relationships between a behav-
ior and its consequences. Figure 7.1 presents these four possibilities in the form of a
two-by-two matrix. First, after a behavior occurs, a stimulus can be presented, or a
stimulus can be removed or omitted. In each of these cases, the result could be either
an increase or a decrease in the behavior, depending on the nature of the stimulus.
Since we have already examined positive reinforcement, this chapter will focus on the
other three cases.

We can begin with some definitions. With negative reinforcement (cell 3), a behavior
increases if some stimulus is removed after the behavior occurs. For example, suppose a


person with a headache takes some ibuprofen, and the headache promptly goes away. In this
case, the individual escapes from the pain of the headache by performing some behavior.
As a result, this behavior should be strengthened in the future: The next time the person has
a headache, he is likely to take ibuprofen again. Another type of negative reinforcement is
avoidance, in which a response prevents an unpleasant stimulus from occurring in the first
place. For example, paying your income tax avoids the unpleasant consequences of failing
to do so.

Cell 2 represents the procedure of punishment, in which a behavior is followed
by an unpleasant stimulus, and the behavior then decreases. Cell 4 represents negative
punishment (also called omission) in which a pleasant stimulus is removed or omit-
ted if a behavior occurs. If a parent refuses to give a child her usual weekly allowance
after some bad behavior (such as staying out too late), this is an example of negative
punishment. To help you remember these terms, Figure 7.2 gives a pictorial example
of each.

The first part of this chapter surveys a number of experiments on negative reinforcement,
and it discusses some of the theoretical issues about avoidance that psychologists have debated
over the years. Next, we will look at the two types of punishment procedures. Although
punishment is, in theory, the opposite of reinforcement, some psychologists have concluded
that punishment is not an effective form of behavioral control. We will consider the evi-
dence and attempt to draw some conclusions. Finally, we will examine some of the ways
that punishment has been used in behavior modification.

Figure 7.1 A two-by-two matrix depicting two types of reinforcement and two types of punishment.



1 2

3 4












r o






A Representative Experiment

Solomon and Wynne (1953) conducted an experiment that illustrates many of the properties
of negative reinforcement. Dogs were tested in a shuttle box—a chamber with two rectan-
gular compartments separated by a barrier several inches high. A dog could move from one
compartment to the other simply by jumping over the barrier. There were two overhead lights,
one for each compartment. Every few minutes, the light above the dog was turned off (but
the light in the other compartment remained on). If the dog remained in the dark compart-
ment, after 10 seconds the dog received a shock from the floor of the chamber until it hopped
over the barrier to the other compartment. Thus the dog could escape from the shock by
jumping over the barrier. However, the dog could also avoid the shock completely by jumping
over the barrier before the 10 seconds of darkness had elapsed. The next trial was the same,
except that the dog had to jump back into the first compartment to escape or avoid the shock.

For the first few trials a typical dog’s responses were escape responses—it did not jump
over the barrier until the shock had started. After a few trials, a dog would start making
avoidance responses—it would jump over the barrier soon after the light went out, and if it
jumped in less than 10 seconds it did not receive the shock. After a few dozen trials, a typical
dog would almost always jump over the barrier just 2 or 3 seconds after the light went out.
Many dogs never again received a shock after their first successful avoidance response because
they always jumped in less than 10 seconds after the light went out.

Figure 7.2 Two types of reinforcement and two types of punishment. (1) Rewarding a dog for a new
trick is positive reinforcement. (2) Getting burned from touching a hot skillet is punishment. (3)
Driving around a pothole is negative reinforcement (avoidance). (4) Time-out for bad behavior is
negative punishment (omission).


Results such as these had led earlier psychologists (e.g., Mowrer, 1947) to ponder a ques-
tion that is sometimes called the avoidance paradox: How can the nonoccurrence of an
event (shock) serve as a reinforcer for the avoidance response? These psychologists had no
problem explaining escape responses because there the response produced an obvious stimu-
lus change: The shock stopped when the escape response was made. But with avoidance
responses, there was no such change: There was no shock before the avoidance response and
no shock after it. Some theorists felt it did not make sense to say that no change in the
stimulus conditions (no shock before the jump and no shock after) could act as a reinforcer
for jumping. It was this puzzle about avoidance responses that led to the development of an
influential theory of avoidance called two-factor theory, or two-process theory.

Two-Factor Theory

The two factors, or processes, of this theory are classical conditioning and operant condition-
ing, and according to the theory both are necessary for avoidance responses to occur. These
two factors can be illustrated in the experiment of Solomon and Wynne. An unconditioned
response to shock is fear. Through classical conditioning, this fear response is transferred
from the unconditioned stimulus (shock) to a conditioned stimulus (the 10 seconds of dark-
ness that preceded each shock). The second factor, based on operant conditioning, is escape
from a fear-provoking CS. A dog could escape from a dark compartment to an illuminated
compartment by jumping over the barrier. The crucial point is that in two-factor theory,
what we have been calling avoidance responses is redefined as escape responses. The theory
says that the reinforcer for jumping is not the avoidance of the shock but rather the escape
from a fear-eliciting CS. Removing the fear-evoking CS (darkness) is an observable change
in the stimulus environment that could certainly act as a negative reinforcer. This is two-
factor theory’s solution to the avoidance paradox.

Although two-factor theory became a popular explanation of avoidance behavior, it had
some problems. One problem concerned the relation between fear and avoidance responses.
If the theory is correct, we should observe an increase in fear when the signal for shock is
presented and a decrease in fear once the avoidance response is made. However, observable
signs of fear frequently disappear as animals become more experienced in avoidance tasks.
Solomon and Wynne (1953) noted that early in their experiment a dog would show various
signs of fear (whining, urination, shaking) when the light was turned off. Later, once the
dog became proficient in making the avoidance response, these observable signs of emotion
disappeared. But according to two-factor theory, fear should be greatest when avoidance
responses are the strongest, since fear is supposedly what motivates the avoidance response.

To deal with this problem, some versions of two-factor theory have downplayed the role
of fear in avoidance learning. For example, Dinsmoor (2001) has maintained that it is not
necessary to assume that the CS in avoidance learning produces fear (as measured by heart
rate or other physical signs). We only need to assume that the CS has become aversive
(meaning that it has become a stimulus the animal will try to remove).

A second serious problem for two-factor theory is that avoidance responses are often very
slow to extinguish. According to the principles of classical conditioning, the CR of fear (or
aversion, if we use Dinsmoor’s approach) should gradually weaken on every trial without
shock. If the CS (lights off in the Solomon and Wynne experiment) no longer elicits fear


or aversion, the avoidance response should not occur either. Therefore, two-factor theory
predicts that avoidance responding should gradually deteriorate after a series of trials without
shock. However, in the experiments of Solomon and Wynne, many dogs responded for
several hundred trials without receiving a shock. In addition, their response latencies con-
tinued to decrease during these trials even though no shock was received. This suggests that
the strength of the avoidance response was increasing, not decreasing, during these shock-
free trials.

These findings were troublesome for two-factor theory, and many psychologists viewed
the slow extinction of avoidance behavior as a major problem for the theory. To try to deal
with these problems, two other theories of avoidance were developed.

One-Factor Theory

To put it simply, one-factor theory states that the classical conditioning component of
two-factor theory is not necessary. There is no need to assume that escape from a fear-
eliciting CS is the reinforcer for an avoidance response because, contrary to the assumptions
of two-factor theory, avoidance of a shock can in itself serve as a reinforcer. An experiment
by Murray Sidman (1953) illustrates this point.

In the Sidman avoidance task (or free-operant avoidance), there is no signal pre-
ceding shock, but if the subject makes no responses, shocks occur at perfectly regular
intervals. For instance, in one condition of Sidman’s experiment, a rat would receive a shock
every 5 seconds throughout the session if it made no avoidance response (Figure 7.3a).
However, if the rat made an avoidance response (pressing a lever), the next shock did not
occur until 30 seconds after the response. Each response postponed the next shock for 30
seconds (Figure 7.3b). By responding regularly (say, once every 20 to 25 seconds), a rat
could avoid all the shocks. In practice, Sidman’s rats did not avoid all the shocks, but they
did respond frequently enough to avoid many of them.

Figure 7.3 The procedure in one condition of Sidman’s (1953) avoidance task. (a) If the subject makes no
responses, a shock is delivered every 5 seconds. (b) Each response postpones the next shock for 30 seconds.




Time (seconds)



Time (seconds)

0 20 40 60 80 100

0 20

30 Seconds 30 Seconds

40 60 80 100


On the surface, these results seem to pose a problem for two-factor theory because there
is no signal before a shock. If there is no fear-eliciting CS, why does an avoidance response
occur? Actually, two-factor theorists had a simple answer to this question. Although there
was no external CS in Sidman’s task, the passage of time might serve as a CS because the
shocks occurred at regular intervals. That is, once a rat was familiar with the procedure, its
fear might increase as more and more time elapsed without a response. The rat could associ-
ate fear with the stimulus “a long time since the last response,” and it could remove this
stimulus (and the associated fear) by making a response.

To make a better case for one-factor theory, we need an experiment in which neither an
external stimulus nor the passage of time could serve as a reliable signal that a shock was
approaching. To accomplish this, Herrnstein and Hineline (1966) developed a procedure in
which the passage of time was not a reliable signal that a shock was approaching. The basic
idea was that by pressing a lever, a rat could switch from a schedule that delivered shocks at
a rapid rate to one that delivered shocks at a slower rate. For example, in one condition there
was a 30% chance of shock if the rat had not recently pressed the lever but only a 10% chance
if the rat had recently pressed the lever. Obviously, to reduce the number of shocks, the
animal should remain on the 10% schedule as much as possible. However, the key feature of
this procedure was that pressing the lever did not ensure any amount of shock-free time.
Sometimes, just by chance, a rat would press the lever and get a shock almost immediately.
This is because lever pressing in this procedure only produced a lower rate of shocks on
average; it did not guarantee any fixed shock-free time.

Herrnstein and Hineline (1966) found that 17 of their 18 rats eventually acquired the
avoidance response. They concluded (1) that animals can learn an avoidance response when
neither an external CS nor the passage of time is a reliable signal for shock and (2) that to
master this task, animals must be sensitive to the average shock frequencies when they
respond and when they do not respond. They reasoned that the fear-conditioning factor in
two-factor theory is a needless complication: Why not simply assume that a reduction in
shock frequency is the reinforcer for the avoidance response? For this reason, one-factor
theory of avoidance is sometimes called the shock-frequency reduction theory (Hineline, 2001).

One-factor theory offers a simple explanation for the slow extinction of avoidance
responses. We have seen that once an avoidance response is acquired, the animal may avoid
every scheduled shock by making the appropriate response. Now suppose that at some
point the experimenter turns off the shock generator. From the animal’s perspective, the
following trials will appear no different from the previous trials: The stimulus comes on,
the subject responds, the stimulus goes off, no shock occurs. Since the animal can discrimi-
nate no change in the conditions, there is no change in behavior either, according to this

Cognitive Theory

Seligman and Johnston (1973) developed a cognitive theory of avoidance that they felt
was superior to both two-factor and one-factor theories. They proposed that in a typical
avoidance task an animal gradually develops two expectations: (1) no shock will occur if it
makes an avoidance response and (2) shock will occur if it does not make the response.
Because the animal prefers the first option over the second option, it makes the response.


Once these two expectations have been formed, Seligman and Johnston assumed that the
animal’s behavior will not change until one or both of the expectations are violated. This
can explain the slow extinction of avoidance behavior. As long as the animal responds on
each extinction trial, all it can observe is that a response is followed by no shock. This obser-
vation is consistent with the animal’s expectation, so there is no change in its behavior.
Presumably, extinction will only begin to occur if the animal eventually fails to make a
response on some trial (perhaps by mistake, or because it is distracted, or for some such
reason). Only on a trial without an avoidance response can the animal observe an outcome
(no response leads to no shock) that is inconsistent with its expectations.

A variation of cognitive theory proposed by Lovibond (2006) maintains that individuals
can learn more detailed expectations that include information about the three parts of the
three-term contingency (discriminative stimulus, operant response, and consequence). For
instance, an individual might learn that in the presence of one warning signal, a specific
response will avoid one type of aversive event, but if another warning signal occurs, a dif-
ferent avoidance response is required to avoid a different aversive event. Research with col-
lege students has found that they can and do develop these more elaborate three-part
expectations in avoidance tasks (Declercq, De Houwer, & Baeyens, 2008).

Biological Constraints in Avoidance Learning

As if the theoretical analysis of avoidance was not confusing enough, the picture is further
complicated by evidence that biological constraints can also play an important role in avoid-
ance learning, just as they can in classical conditioning and with the use of positive reinforce-
ment. Robert Bolles (1970) proposed that animals exhibit a type of preparedness in avoidance
learning. In this case, the preparedness consists of a propensity to perform certain behaviors
in a potentially dangerous situation. Bolles was critical of the traditional theories of avoid-
ance learning. He stated:

What keeps our little friends alive in the forest has nothing to do with avoidance learn-
ing as we ordinarily conceive of it or investigate it in the laboratory. . . . What keeps
animals alive in the wild is that they have very effective innate defensive reactions which
occur when they encounter any kind of new or sudden stimulus.

(pp. 32–33)

Bolles called these innate behavior patterns species-specific defense reactions (SSDRs).
As the name implies, SSDRs may be different for different animals, but Bolles suggested that
they usually fall into one of three categories: freezing, fleeing, and fighting (adopting an aggres-
sive posture and/or behaviors). In laboratory studies of avoidance, an avoidance response will
be quickly learned if it is identical with or at least similar to one of the subject’s SSDRs. If the
required avoidance response is not similar to an SSDR, the response will be learned slowly or
not at all. To support this hypothesis, Bolles noted that rats can learn to avoid a shock by jump-
ing or running out of a compartment in one or only a few trials. The rapid acquisition pre-
sumably reflects the fact that for rats, fleeing is a highly probable response to danger. However,
it is very difficult to train a rat to avoid shock by pressing a lever, presumably because this
response is unlike any of the creature’s typical responses to danger (Figure 7.4).


The important point here is that the difficulty in learning new responses such as lever
pressing depends on the nature of the reinforcer. When the reinforcer is avoidance of
shock, lever pressing is a difficult response for rats to acquire, and some rats never learn it.
Yet when the reinforcer is food or water, lever pressing is a relatively easy response for rats
to learn. As another example, we know it is quite easy to shape a pigeon to peck a key
when food is the reinforcer. In comparison, it is very difficult to train a pigeon to peck a
key to avoid a shock. The problem is apparently that a pigeon’s most usual response to an
aversive stimulus is to fly away, a response that has almost nothing in common with stand-
ing in place and pecking. Because of examples like this, Fanselow (1997) has argued that
the basic principle of negative reinforcement (which states that any response that helps to
avoid an aversive event will be strengthened) is not especially useful when SSDRs take
over: Even a simple response such as pressing a lever or pecking a key may be difficult for
the animal to learn.

A few studies have shown that it is possible to train animals to make an arbitrary operant
response in an avoidance situation by somehow making the desired response more compat-
ible with the SSDRs of that species. For example, in response to a mild shock, a pigeon may
exhibit SSDRs from the “fighting” category, including flapping its wings. Beginning with
this response of wing flapping, Rachlin (1969) trained pigeons to operate a “key” that pro-
truded into the chamber in order to avoid the shock. With rats, Modaresi (1990) found that
lever pressing was much easier to train as an avoidance response if the lever was higher on
the wall, and especially if lever presses not only avoided the shocks but produced a “safe

Figure 7.4 Running or fleeing, a species-specific defense reaction for many animals, is obviously
incompatible with making an operant response such as pressing a lever. (ziggy_mars/Shutterstock)


area” (a platform) on which the rats could stand. Modaresi showed that these two features
coincided with the rats’ natural tendencies to stretch upward and to seek a safe area when
facing a potentially painful stimulus. Both of these studies are consistent with Bolles’s claim
that the ease of learning an avoidance response depends on the similarity between that
response and one of the animal’s SSDRs.

Conclusions About the Theories of Avoidance

Over the years, two-factor theory has been a popular theory of avoidance behavior, but it
has several problems. Avoidance learning can occur when there is no external signal for
shock (Herrnstein & Hineline, 1966). In addition, two-factor theory has difficulty explaining
the slowness of extinction in avoidance tasks. Both one-factor theory and cognitive theory
avoid these problems by assuming that a fear-eliciting CS is not an indispensable requirement
for avoidance behavior. However, we have seen evidence that fear does play a role in some
avoidance situations, and for this and other reasons some learning theorists have continued
to favor two-factor theory over the other two theories. After several decades of research and
debate, the question of which theory of avoidance is best has not been settled to everyone’s
satisfaction. This may be a sign that each theory is partially correct. Perhaps fear does play
an important role in some avoidance situations, but it is not a necessary role; avoidance
responding may sometimes occur in the absence of fear, as the one-factor and cognitive
theories propose.


The Procedure of Response Blocking (Flooding)

The slow extinction of avoidance responses is not inevitable: Extinction can be speeded
up by using a procedure called response blocking (also called flooding). As its name
suggests, response blocking involves presenting the signal that precedes shock but
preventing the subject from making the avoidance response. In one experiment, rats
learned to avoid a shock by running from one compartment to another (Page & Hall,
1953). After the response was learned, one group of rats received normal extinction
trials. A second group had the extinction trials preceded by five trials in which a rat was
retained in the first compartment for 15 seconds, with the door to the second compart-
ment closed. Thus these rats were prevented from making the avoidance response,
but unlike in the acquisition phase, they received no shocks in the first compartment.
Extinction proceeded much more rapidly in the response-blocking group. There is con-
siderable evidence that response blocking is an effective way to accelerate the extinc-
tion of avoidance responses.

This procedure has been adopted by some behavior therapists as a treatment
for phobias. The major difference between flooding and systematic desensitization


(Chapter 3) is that the hierarchy of fearful events or stimuli is eliminated. Instead of
beginning with a stimulus that elicits only a small amount of fear, a therapist using a
flooding procedure starts immediately with a highly feared stimulus and forces the
patient to remain in the presence of this stimulus until the patient’s external signs
of fear subside. For example, an 11-year-old boy with a fear of loud noises was
exposed to the noise of many bursting balloons in a small room (with the boy’s full
consent and that of his parents). He was encouraged by the therapist to break bal-
loons himself, and within two sessions his fear of the noises had disappeared (Yule,
Sacks, & Hersov, 1974).

Studies comparing the effectiveness of flooding and systematic desensitization have
found that they are about equally effective (Morganstern, 1973), but flooding can some-
times succeed in eliminating a phobia when systematic desensitization has failed. Yule
et al. (1974) cautioned that flooding should be used with care and that long-duration
sessions are essential: The therapist should first observe the onset of fear, and then
continue with the procedure until a definite reduction in fear is seen. If the session is
terminated too soon, the patient’s phobia might actually increase. Despite these draw-
backs, flooding can be an effective form of treatment for phobias when used carefully
(Zoellner, Abramowitz, Moore, & Slagle, 2009).

Other behavioral treatments also rely on prolonged stimulus exposure to eliminate
unwanted behaviors. For example, patients with obsessive-compulsive disorders
(which involve repeatedly and excessively engaging in rituals such as hand washing,
checking to make sure doors are locked, etc.) can be treated by exposing them to the
stimuli that trigger these reactions while preventing them from performing the ritualistic
behaviors (Abramowitz & Foa, 2000). Research has shown that this approach can be
effective in reducing compulsive behaviors.


Aversive stimuli can do more than produce fear and avoidance responses. Abundant research
with both animals and people has shown that repeated exposure to aversive events that are
unpredictable and out of the individual’s control can have long-term debilitating effects.
Seligman and his colleagues (Maier & Seligman, 1976) have proposed that in such circum-
stances, both animals and people may develop the expectation that their behavior has little
effect on their environment, and this expectation may generalize to a wide range of situa-
tions. Seligman calls this general expectation learned helplessness. Consider the following
experiment. A dog is first placed in a harness where it receives a series inescapable shocks.
On the next day, the dog is placed in a shuttle box where it receives escape/avoidance trials
similar to those administered by Solomon and Wynne (1953): A 10-second period of dark-
ness is followed by shock unless the dog jumps into the other compartment. Whereas Solo-
mon and Wynne’s dogs learned the task within a few trials, about two thirds of Seligman’s


dogs never learned either to escape or avoid the shock. Seligman concluded that in the initial
training with inescapable shock, the dogs developed an expectation that its behavior has no
effect on the aversive consequences it experiences, and this expectation of helplessness car-
ried over to the shuttle box.

Similar experiments have been conducted with humans. For instance, in one study
(Hiroto & Seligman, 1975) college students were first presented with a series of loud
noises that they could not avoid. They were then asked to solve a series of anagrams.
These students had much greater difficulty solving the anagrams than students who were
not exposed to the unavoidable noises. A typical control participant solved all the ana-
grams and got faster and faster as the trials proceeded. A typical participant in the noise
group would fail on most of the problems, apparently giving up on a problem before the
allotted time had expired. Seligman’s explanation was the same for both the animal and
human cases: Early experience with uncontrollable aversive events produces a sense of
helplessness that carries over into other situations, leading to learning and performance

Many psychologists believe that learned helplessness can contribute to the severe
and prolonged periods of depression that some people experience. Hundreds of studies
have been published on learned helplessness in humans, and this research has branched
in many directions. Psychologists have applied the concept of learned helplessness to
women who have been the victims of domestic violence (Walker, 2009), to the ability
of the elderly to cope with their problems (Flannery, 2002), to new employees who
experience failures in the workplace (Boichuk et al., 2014), and to many other situa-
tions where people might feel that they have little control over important events in
their lives.

Seligman suggested that learned helplessness can be prevented through what he called
immunization. If an animal’s first exposure to shock is one where it can control the shock,
later exposure to uncontrollable shocks is less likely to produce learned helplessness. At the
human level, Seligman suggested that feelings of helplessness in a classroom environment
may be prevented by making sure that a child’s earliest classroom experiences are ones where
the child succeeds (ones where the child demonstrates a mastery over the task at hand).
McKean (1994) has made similar suggestions for helping college students who exhibit signs
of helplessness in academic settings. Such students tend to view course work as uncontrol-
lable, aversive, and inescapable. They assume that they are going to do poorly and give up
easily whenever they experience difficulty with course assignments or other setbacks. To
assist such students, McKean suggests that professors should make their courses as predictable
and controllable as possible (e.g., by clearly listing all course requirements on the syllabus,
by explaining the skills students will need to succeed in the course, and by suggesting how
to develop these skills). Initial course assignments should be ones that students are likely to
complete successfully, so they gain confidence that they have the ability to master the
requirements of the course.

Seligman (2006) has also proposed that a method for combating learned helplessness
and depression is to train people in learned optimism. The training involves a type
of cognitive therapy in which people practice thinking about potentially bad situations
in more positive ways. For instance, a middle-aged woman taking a college course might
be disappointed with her exam grade and think, “I am too old for this. I bet everyone
else did better than I did. It was a mistake for me to return to college now.” Seligman


proposes that this type of helpless think-
ing can be changed if a person learns to
recognize and dispute such negative
thoughts. For instance, the woman could
think, “A grade of B- is not that bad. I
am working full time and did not have as
much time to prepare as I would like.
Now that I know what to expect, I will
do better on the next exam.” Seligman
argues that by regularly practicing the
technique of disputing one’s thoughts of
helplessness and dejection, a person can
learn to avoid them. Some writers have
questioned the effectiveness of Seligman’s
techniques to teach optimism (e.g., Kel-
ley, 2004), but others have found that
they can be beneficial (Gilboy, 2005).
Perhaps it should not be too surprising
that just as learned helplessness can result
from experiences with uncontrollable
aversive events, there may be other learn-
ing experiences that can result in learned


Is Punishment the Opposite of Reinforcement?

According to Figure 7.1, punishment should have the opposite effect on behavior as positive
reinforcement: Reinforcement should increase behavior, and punishment should decrease
behavior. Whether this is actually the case is an empirical question, however, and such illus-
trious psychologists as Thorndike and Skinner concluded that it was not. Based his own
research, Skinner concluded that punishment produces only a “temporary suppression” of

Are the effects of punishment merely temporary? In some cases they can be, and animals
can habituate to a relatively mild punisher. In an experiment by Azrin (1960), pigeons
were responding steadily for food on a VI schedule, and then punishment was introduced—
each response produced a mild shock. Response rates decreased immediately, but over the
course of several sessions, they returned to their preshock levels. However, when Azrin used
more intense shocks, there was little or no recovery in responding over the course of the
experiment. Based on these and other similar results, there is no doubt that suitably
intense punishment can produce a permanent decrease or disappearance of the punished

Although Skinner did not define suppression, later writers took it to mean a general
decrease in behavior that is not limited to the particular behavior that is being punished.

Practice Quiz 1: chaPter 7
1. Two types of negative reinforcement

are ______ and ______.
2. According to the two-factor theory

of avoidance, a ______ develops to
an initially neutral stimulus that pre-
cedes an aversive event.

3. Extinguishing an avoidance response
by physically preventing the individual
from making the response is called

4. ______ are behaviors such as fleeing,
freezing, or fighting that animals tend
to make in dangerous situations.

5. According to Seligman, teaching
people to think about potentially bad
situations in more positive ways can
lead to ______.


1. escape, avoidance 2. fear response 3. response
blocking or flooding 4. SSDRs 5. learned optimism


Does the use of punishment lead to a general reduction in all behavior, or does only the
punished behavior decrease? An experiment by Schuster and Rachlin (1968) investigated
this question. Pigeons could sometimes peck at the left key in a test chamber, and at other
times they could peck at the right key. Both keys offered identical VI schedules of food
reinforcement, but then different schedules of shock were introduced on the two keys.
When the left key was lit (signaling that the VI schedule was available on this key), some of
the pigeon’s key pecks were followed by shock. However, when the right key was lit, shocks
were presented regardless of whether the pigeon pecked at the key. Under these conditions,
responding on the left key decreased markedly, but there was little change in response rate
on the right key.

Studies like this have established that punishment does more than simply cause a gen-
eral decrease in activity. When a particular behavior is punished, that behavior will
exhibit a large decrease in frequency while other, unpunished behaviors usually show no
substantial change. To summarize, contrary to the predictions of Thorndike and Skinner,
research results suggest that the effects of punishment are directly opposite to those of
reinforcement: Reinforcement produces an increase in whatever specific behavior is fol-
lowed by the hedonically positive stimulus, and punishment produces a decrease in the
specific behavior that is followed by the aversive stimulus. In both cases, we can expect
these changes in behavior to persist as long as the reinforcement or punishment contin-
gency remains in effect.

Factors Influencing the Effectiveness of Punishment

Many years ago, Azrin and Holz (1966) examined a number of variables that determine
what effects a punishment contingency will have. To their credit, all of their major points
appear as valid now as when their findings were published. Several of their points are
described in this section.

Manner of Introduction

If one’s goal is to obtain a large, permanent decrease in some behavior, then Azrin and
Holz (1966) recommended that the punisher be immediately introduced at its full inten-
sity. We have already seen that subjects can habituate to a mild punisher. The end result
is that a given intensity of punishment may completely eliminate a behavior if it is intro-
duced suddenly, but it may have little or no effect on behavior if it is approached gradually.
Azrin, Holz, and Hake (1963) reported that a shock of 80 volts following each response
completely stopped pigeons’ key-peck responses if the 80-volt intensity was used from the
outset. However, if the punishment began at lower intensities and then slowly increased,
the pigeons continued to respond even when the intensity was raised to as much as 130
volts. Since the goal when using punishment is to eliminate an undesirable behavior, not
to shape a tolerance of the aversive stimulus, the punisher should be at its maximum
intensity the first time it is presented.


Immediacy of Punishment

Just as the most effective reinforcer is one that is delivered immediately after the operant
response, a punisher that immediately follows a response is most effective in decreasing the
frequency of the response. The importance of delivering punishment immediately may
explain why many common forms of punishment are ineffective. For example, the mother
who tries to decrease a child’s misbehavior with the warning, “Just wait until your father
gets home,” is describing a very long delay between a behavior and its punishment. It would
not be surprising if this contingency had little effect on the child’s behavior. The same
principle applies in the classroom where a scolding from the teacher is most effective if the
teacher scolds a child immediately after the child has misbehaved, not after some time has
passed (Abramowitz & O’Leary, 1990). It has also been suggested that one reason some
people engage in crimes even though they are likely to get caught eventually is that they
receive the rewards immediately but the punishment is delayed. A large-scale study of ado-
lescents in the United States concluded that those who were involved in crimes such as
burglary or car theft tended to be less sensitive to delayed consequences than those who
never engaged in these criminal activities (Nagin & Pogarsky, 2004).

Schedule of Punishment

Like positive reinforcers, punishers need not be delivered after every occurrence of a behav-
ior. Azrin and Holz concluded, however, that the most effective way to eliminate a behavior
is to punish every response rather than to use some intermittent schedule of punishment. In
an experiment with rats where lever pressing for food also produced shocks on an FR sched-
ule, the effects of this punishment decreased as the size of the FR increased (Azrin et al.,
1963). The same general rule applies to human behavior: The most powerful way to reduce
behavior is to punish every occurrence (Hare, 2006). The schedule of punishment can also
affect the response patterns over time, and they are often the opposite of those obtained with
positive reinforcement. For example, whereas an FI schedule of reinforcement produces an
accelerating pattern of responding, an FI schedule can produce a deceleration—declining
response rates as the next punisher approaches (Azrin, 1956). FR schedules of reinforcement
produce a pause-then-respond pattern, but FR schedules of punishment produce a respond-
then-pause pattern (Hendry & Van-Toller, 1964). These and other studies on schedules of
punishment strengthen the view that punishment is the opposite of reinforcement in its
effects on behavior.

Motivation to Respond

Azrin and Holz noted that the effectiveness of a punishment procedure is inversely related
to the intensity of the individual’s motivation to respond. Azrin et al. (1963) demonstrated
this point by observing the effects of punishment on pigeons’ food-reinforced responses
when the birds were maintained at different levels of food deprivation. Punishment had little
effect on response rates when the pigeons were very hungry, but when these animals were
only slightly food deprived, the same intensity of punishment produced a complete cessation


of responding. This finding is not surprising, and its implications for human behavior should
be clear: If a behavior is highly motivated (e.g., parents stealing food because their children
are starving), the threat of punishment is not likely to have much effect.

Reinforcement of Alternative Behaviors

Based on their research with animals, Azrin and Holz concluded that punishment is much
more effective when the individual is provided with an alternative way to obtain the rein-
forcer. For instance, it is much easier to use punishment to stop a pigeon from pecking at a
response key that delivers food if another key is available that also produces food (without
punishment). For this reason, when behavior therapists decide that it is necessary to use
punishment to eliminate some unwanted behavior (e.g., fighting among children), they
almost always pair this punishment with reinforcement for an alternative behavior that is
incompatible with the unwanted behavior (e.g., cooperative play). A study with four chil-
dren who engaged in frequent self-injurious behaviors (hitting themselves, head banging)
showed how the mere availability of an alternative source of reinforcement can increase the
effectiveness of a punishment procedure (Thompson, Iwata, Conners, & Roscoe, 1999).
Before treatment began, a suitable reinforcer was found for each child, such as a toy, a game,
or a string of beads. During treatment, every instance of a self-injurious behavior was fol-
lowed by mild punishment (such as brief physical restraint, or a reprimand—“Don’t do
that!”). On some days, the alternative reinforcer preferred by each child was available,
whereas on other days the alternative reinforcer was not available. Figure 7.5 shows that for
each child, the punishment was more effective in reducing self-injurious behavior when the
alternative reinforcer was available.

Figure 7.5 Frequency of self-injurious behavior (plotted as a percentage of the frequency before treat-
ment began) is shown for four children under conditions with punishment alone and with punish-
ment plus the availability of an alternative reinforcer. (Based on Thompson et al., 1999)


Punishment as a Discriminative Stimulus

Imagine an experiment in which a pigeon’s responses go unpunished during some portions
of the session but are followed by shock during other parts of the session. Each time the
shock begins, the pigeon’s response rate increases! This behavior seems paradoxical until we
learn that the pigeon can obtain food only during those periods when its responses are
punished; an extinction schedule is in effect during the periods when responses are not
shocked (Holz & Azrin, 1961). In other words, the shocks following responses served as
discriminative stimuli for the availability of food reinforcement because they were the only
stimuli that differentiated between the periods of reinforcement and extinction. Azrin and
Holz suggested that similar explanations may account for some instances of self-injurious
behaviors that appear equally paradoxical at first glance. Because self-injurious behaviors
often bring to the individual the reinforcers of sympathy and attention, the aversive aspects
of this type of behavior (pain) may serve as discriminative stimuli that reinforcement is

Disadvantages of Using Punishment

Although Azrin and Holz (1966) concluded that punishment can be a method of behavior
change that is at least as effective as reinforcement, they warned that it can produce a number
of undesirable side effects. First, they noted that punishment can elicit several emotional
effects, such as fear and anger, which are generally disruptive of learning and performance.
A study on guard dogs that had been trained through the use of a shock collar found that
these dogs exhibited signs of fear and stress whenever their owner was present, even when
they were not in the training situation (Schilder & van der Borg, 2004). Similarly, many
studies have found that the children of parents who use corporal punishment have a higher
risk of developing anxiety disorders (Graham & Weems, 2015).

Second, punishment can sometimes lead to a general suppression of all behaviors, not only
the behavior being punished. Imagine that a child in a classroom raised his hand, asked a
question, and the teacher replied, “Well, that’s a very stupid question.” The teacher’s remark
might be intended to try to reduce the number of stupid questions that children ask, but the
likely result would be a decrease in all questions, good or bad, both from that child and from
everyone else in the class.

A third disadvantage is that in real-world situations the use of punishment demands the
continual monitoring of the individual’s behavior. In contrast, use of reinforcement does not
necessarily demand such monitoring because it is in the individual’s interest to point out
instances of a behavior that is followed by a reinforcer. If a child receives a reinforcer for
cleaning up her room, she will probably make sure her parents see the room after it is
cleaned. On the other hand, if the child is punished for a messy room, she is unlikely to call
her parents to see the messy room so that she can be punished.

Along the same lines, a practical problem with the use of punishment is that individuals
may try to circumvent the rules or escape from the situation entirely. Azrin and Holz (1966)
described the behavior of a clever rat that was scheduled to receive shocks for some of its
lever presses while working for food reinforcement. The rat learned to avoid the shocks by
lying on its back while pressing the lever, thereby using its fur as insulation from the shocks
delivered via the metal floor of the chamber. We might expect people to be even more


ingenious in their tricks to circumvent a punishment contingency. If a teacher’s primary
method of behavioral control in the classroom is punishment, the children will surely try to
hide evidence of any misbehavior. They may also try to avoid school altogether by pretend-
ing to be sick or by playing hooky.

Another problem with using punishment is that it can lead to aggression against either
the punisher or whoever happens to be around. The constant risk of bodily harm faced by
prison guards (and by prisoners) attests to this fact. Aggression as a response to aversive
stimulation is not unique to humans. Ulrich and Azrin (1962) reported a study in which
two rats were placed in an experimental chamber. The animals behaved peaceably until they
began to receive shocks, at which point they began to fight. Similar results have been
obtained with pigeons, mice, hamsters, cats, and monkeys.

A final problem with using punishment is that in institutional settings, the people who
must actually implement a behavior modification program may be reluctant to use punish-
ment. Various studies have examined the attitudes of personnel who work with institutional-
ized patients, such as individuals with a developmental handicap. The staff in such institutions
preferred other techniques for changing behavior, such as instruction, modeling, and rein-
forcement, over punishment (Davis & Russell, 1990). Perhaps these individuals have learned,
through their daily work experiences, about some of the disadvantages of punishment
described in the preceding paragraphs.

Given the numerous disadvantages of punishment, Azrin and Holz suggested that it
should be used reluctantly and with great care. However, they pointed out that punishment
will always be a part of our environment. It might be possible to legislate punishment out
of existence in institutions such as prisons, schools, and psychiatric hospitals. It would be
much more difficult, however, to eliminate punishment in everyday interpersonal interac-
tions (between parent and child, between spouses, etc.). Finally, the physical environment is
full of potential punishers that are impossible to eliminate. Just think of the possible punish-
ing consequences that might follow the wrong behavior while one is driving a car, walking
through a forest, swimming, skiing, cooking, or performing almost any behavior. As Vollmer
(2002, p. 469) put it, “Punishment happens.” Since punishment cannot be eliminated from
our environment, it is important for behavioral psychologists to continue to study this phe-
nomenon in order to increase our understanding of how it influences behavior.

Negative Punishment (Omission)

Cell 4 in Figure 7.1 represents the procedure of negative punishment or omission in which
some stimulus is removed if a response occurs, resulting in a decrease in responding. The
possibility of losing a reinforcer can have strong effects on behavior. In one study, college
students played a game in which they could win or lose money by clicking on moving
targets on a computer screen. Whenever money could be lost by choosing a particular target,
the students showed a strong tendency to avoid that target. Based on a quantitative analysis
of the students’ choices, the researchers estimated that the punishing effect of losing money
was about three times as powerful as the reinforcing effect of winning the same amount of
money (Rasmussen & Newland, 2008). Omission procedures are most effective if the omis-
sion occurs immediately after the undesired behavior, every time the behavior occurs. In
one case, therapists used time-outs to discourage an adult with developmental disabilities


from putting his hands in his mouth (which caused his hands to become red and swollen).
Time-outs reduced hand-mouthing action to near-zero levels if they occurred on a schedule
of continuous punishment, but the time-outs had much less effect if they were delivered on
FI schedules (Lerman, Iwata, Shore, & DeLeon, 1997). With both positive and negative
punishment, immediacy and consistency are important.


The term behavior decelerator is sometimes used to refer to any technique that can lead
to a slowing, reduction, or elimination of unwanted behaviors. Punishment and omission
are two of the most obvious methods for reducing undesired behaviors, but they are by no
means the only ones. Behavior therapists have developed a variety of other useful behavior
deceleration techniques, and we will examine some of the most common ones.


Wherever possible, behavior therapists avoid using punishment because the comfort and
happiness of the patient is one of their major concerns. However, if a behavior is dangerous
or otherwise undesirable, and if other techniques are impractical or unsuccessful, the use of
punishment may be deemed preferable to doing nothing at all.

One nonphysical form of punishment that is frequently used by parents and teachers is
scolding or reprimanding a child for bad behavior. This tactic can certainly influence a
child’s behavior, but not always in the way the adult wants. The problem is that a reprimand
is a form of attention, and we have already seen that attention can be a powerful reinforcer.
O’Leary, Kaufman, Kass, and Drabman (1970) found that the manner in which a reprimand
is given is a major factor determining its effectiveness. Most teachers use loud or public
reprimands that are heard not only by the child involved but by all others in the classroom.
However, when second-grade teachers were instructed to use “soft” or private reprimands
wherever possible (i.e., to walk up to the child and speak quietly, so that no other children
could hear), they observed a 50% decrease in disruptive behavior.

Stronger forms of punishment are sometimes necessary when a child’s behavior is a more
serious problem than a mere classroom disturbance. For example, some children with autism
or developmental disabilities engage in self-injurious behaviors such as repeatedly slapping
themselves in the face, biting deep into their skin, or banging their heads against any solid
object. Because of the risk of severe injury, these children are sometimes kept in physical
restraints around the clock, except when a therapist is in the immediate vicinity. Prochaska,
Smith, Marzilli, Colby, and Donovan (1974) described the treatment of a 9-year-old girl
who would hit her nose and chin with her fist at a rate of about 200 blows per hour if she
was not restrained. After nonaversive procedures were tried unsuccessfully, the therapists
decided to use a shock to her leg as a punisher for head banging. After receiving several
half-second shocks, the girl’s head banging stopped completely, and there was an overall
improvement in her behavior. The use of shock as a punisher with children is a controversial
matter (see Box 7.2), but to be fair, the aversive features of this procedure must be weighed
against the consequences of doing nothing.


One promising development in the treatment of self-injurious behaviors is the finding that
sometimes relatively mild punishers can be effective. For example, Fehr and Beckwith (1989)
found that head hitting by a 10-year-old boy with a handicap could be reduced by spraying
a water mist in the child’s face. This treatment was especially effective when used in combina-
tion with reinforcement for other, better behavior. Water mist has also been successfully used
to reduce aggression and other unwanted behaviors (Matson & Duncan, 1997).


Punishment Can Be Effective, but Should It Be Used in Therapy?

Showing that punishment can be used successfully is not the same as showing that it
should be used as a technique of behavioral control. In recent years, the controversy
over whether behavior therapists should be allowed to use aversive stimuli to control
the behavior of their patients has intensified. Much of the controversy has focused on
the treatment of children or adults with severe developmental and behavioral disorders.
With these individuals, aversive stimuli are sometimes used to eliminate self-destructive
or other dangerous behaviors.

One line of argument against the use of aversive stimuli is based on legal principles. In
the United States, an important principle is the “right to refuse treatment.” This principle
states that even if a treatment is known to be effective, and even if the treatment is clearly in
the best interests of the individual, that individual has the right to refuse the treatment. For
example, a person could refuse to have an infected tooth extracted even if failure to remove
the tooth could cause a life-threatening spread of the infection. It is easy to imagine a person
refusing a behavioral treatment that involved aversive stimuli, even if the treatment would be
beneficial in the long run. In the case of people with developmental disabilities, the issue is
even more complicated because these people are usually classified as “incompetent” to
make their own decisions, and treatment decisions must be made by their legal guardians.

Those who work with the developmentally disabled are divided on this issue. Some thera-
pists are against the use of aversive stimuli under any circumstances for ethical reasons, while
others argue that it would be unethical to restrict the use of the most effective treatments
available, even if they involve aversives. Psychologists also disagree about the effectiveness
of aversive treatments and how they compare to nonaversive procedures. Some claim that
nonaversive alternatives (e.g., reinforcement of alternative behaviors, shaping, extinction,
etc.) can be just as effective, but others disagree, arguing that the data have not yet shown
that nonaversive techniques can be equally effective for severe behavior problems.

No one advocates the unrestricted and indiscriminate use of aversive stimuli as a means
of behavior control. The debate is about whether aversive procedures should only be used
as a last resort or whether they should never be used at all (Vollmer, Peters, & Slocum,
2015). Perhaps, as time passes, a combination of ethical debate, court decisions, and
more data about the effectiveness of alternative techniques will help to settle this issue. For
now, the future of aversive stimuli in behavioral treatments remains unclear.


Negative Punishment: Response Cost and Time-Out

It is easy to incorporate a negative punishment contingency in any token system: Whereas
tokens can be earned by performing desirable behaviors, some tokens are lost if the indi-
vidual performs an undesirable behavior. The loss of tokens, money, or other conditioned
reinforcers following the occurrence of undesirable behaviors is called response cost.
Behavioral interventions that include a response-cost arrangement have been used with
children, people with developmental disabilities, prison inmates, and patients in psychiatric
hospitals (Maffei-Almodovar & Sturmey, 2013). A study with a group of disruptive first
graders used response cost as part of a token system in which they could earn tokens (simple
check marks on a sheet of paper) for on-task behavior and lose tokens for disruptive or
inappropriate behaviors. The tokens could later be exchanged for small snacks. The response
cost contingency was effective in reducing disruptive behaviors, and for many of the children
they dropped to zero (Donaldson, DeLeon, Fisher, & Kahng, 2014).

Probably the most common form of negative punishment is the time-out, in which one
or more desirable stimuli are temporarily removed if the individual performs some unwanted
behavior. In one case study, time-out was combined with reinforcement for alternative
behaviors to eliminate the hoarding behavior of a patient in a psychiatric hospital (Lane,
Wesolowski, & Burke, 1989). This case study illustrates what researchers call an ABAB
design. Each “A” phase is a baseline phase in which the patient’s behavior is recorded, but
no treatment is given. Each “B” phase is a treatment phase. Stan was an adult with a brain
injury, and he frequently hoarded such items as cigarette butts, pieces of dust and paper, food,
and small stones by hiding them in his pockets, socks, or underwear. In the initial 5-day
baseline phase, the researchers observed an average of about 10 hoarding episodes per day.
This was followed by a treatment phase (Days 6 through 15) in which Stan was rewarded
for two alternative behaviors—collecting baseball cards and picking up trash and throwing
it away properly. During this phase, any episodes of hoarding were punished with a time-out
period in which Stan was taken to a quiet area for 10 seconds. The number of hoarding
episodes decreased during this treatment phase (see Figure 7.6). In the second baseline phase,

Figure 7.6 Number of hoarding episodes by a man with brain injury in the last two days of four
phases: two baseline phases and two treatment phases in which hoarding was punished with time-outs
and alternative behaviors were reinforced. (Based on Lane et al., 1989)


the treatment was discontinued, and during this phase Stan’s hoarding behavior increased.
Finally, in the second treatment phase, the time-outs and reinforcement for alternative behav-
iors resumed, and Stan’s hoarding gradually declined and eventually stopped completely. In
a follow-up 1 year later, no hoarding was observed. This ABAB design demonstrated the
effectiveness of the treatment procedures because Stan’s hoarding occurred frequently in the
two baseline phases and decreased dramatically in the two treatment phases.

Time-outs are often used with children, as when a parent tells a child to go to his or her
room for misbehaving, and this can be a simple but effective means of reducing behavior
problems. In classroom situations, time-outs in which a child is sent to an isolated room can
reduce aggressive or disruptive behaviors. Time-outs can also be effective if teachers simply
remove a child from some ongoing activity. For example, because fourth-grade children in
one elementary school were constantly unruly and disruptive during gym class, their teach-
ers set up a time-out contingency. Any child who behaved in a disruptive way was imme-
diately told to stop playing and to go sit on the side of the room, where he or she had to
remain until all the sand had flowed through a large hourglass (which took about 3 minutes).
Children who repeatedly misbehaved also lost free play time and other desirable activities (a
response cost contingency). This omission procedure was very effective, and disruptive
behavior during gym class soon dropped by 95% (White & Bailey, 1990). Using time-out
techniques is not always easy, however. They can be difficult to implement for a teacher who
also has a room full of other children to teach (Warzak, Floress, Kellen, Kazmerski, & Chopko,
2012). Nevertheless, both time-out and response cost deserve consideration as methods of
behavior deceleration because they can reduce unwanted behaviors without presenting any
aversive stimulus.


In some cases, if an individual performs an undesired behavior, the parent, therapist, or
teacher requires several repetitions of an alternate, more desirable behavior. This technique
is called overcorrection, and it often involves two elements: restitution (making up for the
wrongdoing) and positive practice (practicing a better behavior). The corrective behavior is
usually designed to require more time and effort than the original bad behavior. For example,
Adams and Kelley (1992) taught parents how to use an overcorrection procedure to reduce
aggression against siblings. After an instance of physical or verbal aggression against a sibling,
restitution might consist of an apology, and the positive practice might involve sharing a toy,
touching the sibling gently, or saying something nice. This positive practice was repeated
several times. If the child did not practice these behaviors appropriately, the practice trials
started over from the beginning. This procedure produced a significant reduction in aggres-
sion between siblings.

Overcorrection has frequently been used with individuals who have mental disabilities
to reduce aggression and other undesirable behaviors. For example, Sisson, Hersen, and Van
Hasselt (1993) used overcorrection as part of a treatment package to teach adolescents with
profound disabilities to package items and sort them by zip code. Maladaptive behaviors
included stereotyped motions such as flapping hands, rocking back and forth, and twirling
and flipping the items. After each occurrence of such a behavior, the therapist guided the
patient through three repetitions of the correct sequence of behaviors.


Overcorrection meets the technical definition of a punishment procedure because a
sequence of events (the correction procedure) is contingent on the occurrence of an
undesired behavior, and the behavior decreases as a result. A difference from other pun-
ishment techniques, however, is that during the corrective exercises, the learner is given
repeated practice performing a more desirable behavior. This may be the most beneficial
component of the overcorrection procedure because providing the learner with a more
desirable alternative behavior is an important ingredient in many behavior reduction


If an undesired behavior occurs because it is followed by some positive reinforcer, and if
it is possible to remove that reinforcer, the behavior should eventually disappear through
simple extinction. One of the most common reinforcers to maintain unwanted behaviors
is attention. In the home, the classroom, or the psychiatric hospital, disruptive or maladap-
tive behavior may occur because of the attention it attracts from parents, peers, teachers,
or hospital staff. These behaviors will sometimes disappear if they are ignored by those
who previously provided their attention. For example, a woman had a skin rash that did
not go away because she continually scratched herself in the infected areas. The therapist
suspected that this scratching behavior was maintained by the attention the woman
received concerning her rash from her family and fiancé (who applied skin cream to the
rash for her). The therapist asked her family and fiancé to avoid all discussion of the rash
and not to help her treat it. The scratching behavior soon extinguished and the rash disap-
peared (Walton, 1960).

Extinction is sometimes slow, especially if the unwanted behavior has been intermittently
reinforced in the past. In addition, the unwanted behaviors sometimes increase rather than
decrease at the beginning of the extinction process. (Parents who decide to ignore tantrums
in an effort to extinguish them may initially witness one of the worst tantrums they have
ever seen.) As with any extinguished behavior, episodes of spontaneous recovery may occur.
Nevertheless, when used properly, extinction can be a very useful method of eliminating
unwanted behaviors. One of the most effective ways to use extinction is to combine it with
the reinforcement of other, more desirable behaviors.

Escape Extinction

This procedure can be used when an undesired behavior is maintained by escape from some
situation the individual does not like. For instance, some children with developmental dis-
abilities exhibit food refusal: They will not eat, nor will they swallow food put in their
mouths. Of course, the longer this behavior continues, the greater the risks to the child’s
health. Why this behavior occurs is not clear, but researchers have observed that food refusal
often leads to escape from the situation—the caregiver does not force the child to eat, and
the attempt to feed the child eventually ends. In escape extinction, the caregiver does
not allow the child to escape from the situation until the child eats. This may involve keep-
ing a spoonful of food in the child’s mouth until the child swallows the food. Although
some might question such a forceful technique, keep in mind how serious a problem


refusing to eat can be. This method is very effective in reducing food refusal behaviors
(Tarbox, Schiff, & Najdowski, 2010).

As another example, therapists at one institution found that a few children with devel-
opmental disabilities would engage in self-injurious behavior (head banging, hand biting,
etc.) whenever they were instructed to work on educational tasks, and by doing so they
escaped from their lessons. The therapists therefore began an extinction procedure in which
the child’s tutor would ignore the self-injurious behavior, tell the child to continue with the
lesson, and manually guide the child through the task if necessary. In this way, the reinforcer
(escape from the lesson) was eliminated, and episodes of self-injurious behavior decreased
dramatically (Pace, Iwata, Cowdery, Andree, & McIntyre, 1993).

Response Blocking

For behaviors that are too dangerous or destructive to wait for extinction to occur, an alter-
native is response blocking, which is physically restraining the individual to prevent the
inappropriate behavior. Most parents of young children probably use response blocking
quite often to prevent their youngsters from doing something that would be harmful to
themselves or to others (Figure 7.7). Behavior therapists have used response blocking to
reduce or eliminate such behaviors as self-injury, aggression, and destruction of property by
children or adults with developmental disabilities (Smith, Russo, & Le, 1999).

Figure 7.7 Sometimes the best and fastest way to deal with a dangerous behavior is response
blocking—physically preventing the behavior. (Luis Echeverri Urrea/


Response blocking can have both short-term and long-term benefits. First, by pre-
venting the unwanted behavior, immediate damage or injury can be avoided. Second,
as the individual learns that the behavior will be blocked, attempts to initiate this behav-
ior usually decline. For example, to prevent a girl with developmental disabilities from
poking her fingers in her eyes, Lalli, Livezey, and Kates (1996) had the girl wear safety
goggles. Unlike cases of response blocking in which the therapist manually restrains the
patient, this use of goggles had the advantage of blocking the unwanted behaviors even
when the girl was alone. After she stopped trying to poke at her eyes, the goggles were
gradually replaced with her normal eyeglasses, and her eye-poking behavior did not

Differential Reinforcement of Alternative Behavior

A classic study by Ayllon and Haughton (1964) offers a good illustration of how extinc-
tion of inappropriate behaviors can be combined with reinforcement of more appropriate
behaviors—a procedure known as differential reinforcement of alternative behavior
(DRA). Ayllon and Haughton worked with patients in a psychiatric hospital who engaged
in psychotic or delusional speech. They found that this inappropriate speech was often
reinforced by the psychiatric nurses through their attention, sympathy, and conversation.
Ayllon and Haughton conducted a two-part study. In the first part, the nurses were explic-
itly instructed to reinforce psychotic speech with attention and tangible items (gum, candy,
etc.). Psychotic speech increased steadily during this part of the study. In the second phase,
the nurses were told to ignore psychotic speech, but to reinforce normal speech (e.g.,
conversations about the weather, ward activities, or other everyday topics). This study
demonstrated both the power of attention as a reinforcer and how attention can be with-
held from inappropriate behaviors and delivered for more desirable alternative

This chapter has already mentioned several other cases in which reinforcement of
alternative behaviors has been successfully combined with other behavior deceleration
techniques. In modern behavior therapy, DRA is a common part of treatment packages
for behavior reduction. Petscher, Rey, and Bailey (2009) reviewed over 100 studies in
which DRA was used with successful results. It has been used effectively for such prob-
lems as food refusal, aggression, disruptive classroom behavior, and self-injurious behav-
ior. The logic is that most behavior reduction techniques teach a person what not to do,
but they do not teach the patient what to do. DRA remedies this deficiency, and it
provides more acceptable behaviors to fill the “behavioral vacuum” that is created when
one behavior is reduced.

Stimulus Satiation

If it is not feasible to remove the reinforcer that is maintaining an undesired behavior, it
is sometimes possible to present so much of the reinforcer that it loses its effectiveness due
to stimulus satiation. Ayllon (1963) described a female psychiatric patient who hoarded
towels in her room. Despite the nurses’ efforts to remove them, she usually had more than
20 towels in the room. A program of stimulus satiation was begun in which the nurses


brought her many towels each day. At
first, the woman seemed to enjoy touch-
ing, folding, and stacking them, but soon
she started to complain that she had
enough and that the towels were in her
way. Once the number of towels in her
room reached about 600, she started
removing them on her own. The nurses
then stopped bringing her towels, and
afterward, no further instances of hoard-
ing were observed.

One unusual example of stimulus satia-
tion involved no physical objects at all. A
psychiatric patient who complained of
hearing voices was given ample time to lis-
ten to these voices. For 85 half-hour ses-
sions, the patient was instructed to sit in a
quiet place and record when the voices
were heard, what they said, and how
demanding the tone of voice was. By the
end of these sessions, the rate of these hal-
lucinations was close to zero (Glaister,
1985). This version of stimulus satiation has
also been used to treat obsessive thoughts.


In negative reinforcement, an aversive stimulus is removed or eliminated if a response occurs.
Two variations of negative reinforcement are escape and avoidance. The two-factor theory
of avoidance states that avoidance involves (1) learning to fear a previously neutral stimulus
and (2) responding to escape from this stimulus. A number of studies have supported the
two-factor theory, but some findings pose problems for the theory: Well-practiced subjects
continue to make avoidance responses while showing no measurable signs of fear, and
extinction of avoidance responses is very slow.

The one-factor theory of avoidance states that removing a fear-provoking CS is not
necessary for avoidance responding and that avoidance of the aversive event is in itself the
reinforcer. Studies supporting one-factor theory have shown that animals can learn avoid-
ance responses when there is no CS to signal an upcoming shock. The cognitive theory of
avoidance states that subjects learn to expect that (1) if they respond, no aversive event will
occur and (2) if they do not respond, an aversive event will occur. To teach a subject that
the second expectation is no longer correct, response blocking (or flooding) can be used.

Seligman showed that if animals are presented with aversive stimuli that they cannot avoid,
they may develop learned helplessness. He suggested that unavoidable aversive events can
lead to helplessness and depression in people, and this theory has been applied to many
aspects of human behavior.

Practice Quiz 2: chaPter 7
1. To minimize the chance that the

learner will habituate to a punishing
stimulus, it should be introduced

2. In terms of timing, the most effective
punisher is one that is delivered

3. In practice, it is always best to couple
punishment of an undesired behavior
with reinforcement of ______.

4. In an ABAB design, each “A” repre-
sents a ______ period, and each “B”
represents a ______ period.

5. If an undesired behavior is being
maintained by the attention it
receives, it can usually be decreased
by using ______.


1. at full intensity 2. immediately 3. an alternative
behavior, or a more desirable behavior 4. baseline,
treatment 5. extinction


In punishment, an aversive stimulus is presented if a response occurs, and the response is
weakened. Many factors influence the effectiveness of punishment, including its intensity,
immediacy, and schedule of presentation, and the availability of alternative behaviors. There
are disadvantages to using punishment: It requires continual monitoring of the subject, and
it can lead to undesirable side effects, such as aggression, a decrease in other behaviors, or
attempts to escape from the situation.

Behavior therapists usually do not use punishment unless there is no feasible alternative;
nevertheless, punishment can be an effective way of reducing a variety of unwanted behav-
iors in both children and adults. Other methods for reducing unwanted behaviors include
response cost, time-out, overcorrection, extinction, escape extinction, response blocking,
reinforcement of alternative behavior, and stimulus satiation.

Review Questions

1. What factors comprise the two-factor theory of avoidance? What types of evi-
dence pose problems for the theory?

2. Considering the research on how learned helplessness develops, explain what
types of experiences could lead to learned helplessness in (a) a freshman in col-
lege, (b) a baseball pitcher traded to a new team, or (c) an elderly resident in a
nursing home.

3. Name several factors that determine the effectiveness of a punishment proce-
dure. Give a concrete example to illustrate each factor. What are some potential
disadvantages of using punishment?

4. Imagine a toddler who has developed the habit of disrupting any games his older
brothers and sisters are playing. Describe at least two different techniques of
behavior deceleration that a parent might use in this situation.

5. Describe some examples of how punishment has been successfully used in
behavior therapy, and discuss some details that probably helped ensure the suc-
cess of the procedures.


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Learning Objectives

After reading this chapter, you should be able to

• discuss whether performing a response and receiving a reinforcer are essential
in the learning and in the performance of a new behavior

• describe studies on how reinforcement can be used to control visceral responses,
and explain how these techniques have been used in biofeedback

• list five different theories about how we can predict what will serve as a rein-
forcer, and discuss their strengths and weaknesses

• explain how a functional analysis of reinforcers can be used to determine the
causes of unusual or puzzling behaviors

• give examples of how the field of behavioral economics has been applied to
animal and human behaviors

C H A P T E R 8

Theories and Research on
Operant Conditioning

The theoretical issues examined in this chapter are very broad, and they deal with matters
of importance to the entire field of learning. They concern such basic issues as what ingre-
dients, if any, are essential for learning to take place and under what conditions a supposed
reinforcer will strengthen the behavior it follows. The topics can be divided into four general
categories. First, we will consider whether both the performance of a response and the
reinforcement of that response are necessary for learning to take place. Second, we will
examine attempts to use reinforcement to control “visceral” responses—responses of the
body’s glands and organs that usually occur without our awareness. Third, we will trace the
history of attempts to develop a method for predicting which stimuli will be effective rein-
forcers for a given individual and which will not. Being able to predict what will be a


reinforcer is clearly important in applied behavior analysis, and we will see that it is also
important from a scientific standpoint. Finally, we will survey recent efforts to link principles
from behavioral psychology with those of economists in a growing field of research known
as behavioral economics.


Operant conditioning might be described as “learning by doing”: An animal performs some
response and experiences the consequences, and the future likelihood of that response is
changed. For Thorndike, the performance of the response was a necessary part of the learn-
ing process. After all, if a response does not occur, how can it be strengthened by reinforce-
ment? Convinced that a pairing of response and reinforcer is essential for learning, Thorndike
(1946) proposed the following experiment:

Put the rat, in a little wire car, in the entrance chamber of a maze, run it through the
correct path of a simple maze and into the food compartment. Release it there and let
it eat the morsel provided. Repeat 10 to 100 times according to the difficulty of the
maze under ordinary conditions. . . . Then put it in the entrance chamber free to go
wherever it is inclined and observe what it does. Compare the behavior of such rats
with that of rats run in the customary manner.

(p. 278)

Thorndike predicted that a rat that was pulled passively through a maze would perform like
a naive subject in the later test since the animal had no opportunity to perform a response.
On this and other issues, Thorndike’s position was challenged by Edward C. Tolman (1932),
who might be characterized as an early cognitive psychologist. According to Tolman, oper-
ant conditioning involves not the simple strengthening of a response but the formation of
an expectation. In a maze, for example, a rat develops an expectation that a reinforcer will
be available in the goal box. In addition, Tolman proposed that the rat acquires a cognitive
map of the maze—a general understanding of the spatial layout of the maze. Tolman pro-
posed that both of these types of learning could be acquired by passive observation as well
as by active responding, so that animals should be able to learn something in the type of
experiment Thorndike described.

One study fashioned according to Thorndike’s specifications was conducted by McNa-
mara, Long, and Wike (1956), who used two groups of rats in an elevated T-maze. Rats in
the control group ran through the maze in the usual fashion, and a correct turn at the choice
point brought the animal to some food. Control rats received 16 trials in the maze, and by
the end of training they made the correct turn on 95% of the trials. Rats in the experimental
group received 16 trials in which they were transported through the maze in a wire basket.
Each experimental rat was paired with a control rat: It was transported to the correct or
incorrect arm of the maze in exactly the same sequence of turns that its counterpart in the
control group happened to choose. This training was followed by a series of extinction trials
in which all rats ran through the maze, but no food was available. During these extinction
tests, the experimental animals performed just as well as the control group even though they
had never been reinforced for running through the maze.


Similar findings of learning without the opportunity to practice the operant response
have been obtained in other studies (Dodwell & Bessant, 1960; Keith & McVety, 1988).
Dodwell and Bessant found that rats benefited substantially from riding in a cart through a
water maze with eight choice points. This shows that animals can learn not only a single
response but also a complex chain of responses without practice. These studies make it clear
that, contrary to Thorndike’s prediction, active responding is not essential for the acquisition
of an operant response.


Is Reinforcement Necessary for Operant Conditioning?

From a literal point of view, the answer to this question is obviously yes since by definition
operant conditioning consists of presenting a reinforcer after some response. But we have
seen that, loosely speaking, operant conditioning can be called a procedure for the learning
of new “voluntary,” or nonreflexive, behaviors. A better way to phrase this question might
be “Is reinforcement necessary for the learning of all new voluntary behaviors?” Thorndike
and other early behaviorists believed that it was, but again Tolman took the opposite posi-
tion. A famous experiment by Tolman and Honzik (1930), called the latent learning
experiment, provided evidence on this issue.

In the Tolman and Honzik experiment, rats received 17 trials in a maze with 14 choice
points, one trial per day. The rats were divided into three groups. Group 1 was never fed in
the maze; when the rats reached the goal box, they were simply removed from the maze.
Rats in Group 2 received a food reinforcer in the goal box on every trial. In Group 3, the
conditions were switched on Day 11: For the first 10 trials there was no food in the goal
box, but on Trials 11 through 17 food was available.

Figure 8.1 shows the average number of errors (wrong turns) from each group. Rats in
Group 2 (consistently reinforced) displayed a typical learning curve, with the number of
errors decreasing to about three per trial by the end of the experiment. Rats in Group 1
(never reinforced) showed much poorer performance. Their error rate dropped slightly but
leveled off at about seven errors per trial. The results from Group 3 are the most interesting.
On the first 11 trials, their results resembled those of Group 1. On Trial 12, however (right
after the first trial with food), the performance of Group 3 improved dramatically, and they
actually made slightly fewer errors than Group 2 for the remainder of the experiment. In
other words, as soon as rats in Group 3 learned that food was available in the goal box, their
performance became equal to that of rats that had been consistently reinforced since the
beginning of the experiment.

Tolman and Honzik concluded that although the rats in Group 3 received no food on
Trials 1 to 10, they learned just as much about the maze as rats in Group 2. However, because
at first they received no food in the maze, Group 3 rats were not motivated to display what
they had learned. Only after food was available did the rats in Group 3 translate their learn-
ing into performance. These findings tell us that reinforcement is not necessary for the
learning of a new response, but it is necessary for the performance of that response. Several
dozen experiments on latent learning were conducted between the 1920s and 1950s, and
most of them found evidence that learning can occur when the experimenter provides no


obvious reinforcer such as food (MacCorquodale & Meehl, 1954). All learning theorists are
now acutely aware of the distinction between learning and performance, largely because of
Tolman’s influential work.

Can Reinforcement Control Visceral Responses?

In a theoretical debate that began before theories of avoidance learning were developed
(Chapter 7), two-factor theorists were those who believed that classical conditioning and
operant conditioning are two distinctly different types of learning. Konorski and Miller
(1937), who favored two-factor theory, proposed that although operant responses are clearly
controlled by their consequences, classically conditioned responses are not. They hypothe-
sized that reinforcement can control the behavior of the skeletal muscles (those involved in
movement of the limbs) but not visceral responses (the behavior of the glands, organs, and
the smooth muscles of the stomach and intestines). On the other hand, one-factor theorists
believed that reinforcement and punishment are universal principles of learning that can be
used to control all types of behavior, including the responses of an individual’s glands, organs,
and smooth muscles.

For many years it was impossible to perform a meaningful experiment about this matter
because scientists had no way to separate skeletal and visceral responses. Suppose a misguided
one-factor theorist offered to deliver a reinforcer, a $20 bill, if you increased your heart rate

1 2 3 4 5 6 7 8 9





10 11 12 13 14

No food reward

Regularly rewarded

No food reward until day ll

15 16 17







Figure 8.1 Mean number of errors on each trial for the three groups in the Tolman and Honzik (1930)
experiment on latent learning.


by at least 10 beats per minute. You could easily accomplish this by running up a flight of
stairs or by doing a few push-ups. This demonstration of the control of heart rate through
reinforcement would not convince any two-factor theorist, who would simply point out
that what the reinforcer increased was the activity of the skeletal muscles, and the increase
in heart rate was an automatic, unlearned response to the body’s increase in activity. That is,
the increase in heart rate was not a direct result of the reinforcement; rather, it was a by-
product of skeletal activity. To perform a convincing study, it is necessary to eliminate any
possible influence of the body’s skeletal muscles.

During the 1960s, Neal Miller and his colleagues devised a procedure that met this
requirement. Rats were given an injection of the drug curare, which causes a temporary
paralysis of all skeletal muscles. However, the normal activity of the glands and organs is
not affected by curare, so it might be possible to observe the direct control of visceral
responses by reinforcement. But what could serve as an effective reinforcer for a para-
lyzed rat? To solve this problem, Miller made use of a finding by Olds and Milner (1954)
that a mild, pulsating electrical current delivered via an electrode to certain structures in
the brain acts as a powerful reinforcer. Rats will press a lever at high rates for many hours
if this type of electrical stimulation of the brain (ESB) is made contingent on this

In one set of experiments, Miller and DiCara (1967) attempted to increase or decrease
the heart rates of different rats, using ESB as reinforcement. After measuring a rat’s baseline
heart rate (which averaged about 400 beats per minute), the experimenters began a shap-
ing procedure. If the goal was an increase in heart rate, reinforcement would be provided
for some small (e.g., 2%) increase. The criterion for reinforcement was then gradually
raised. With other rats, Miller and DiCara used a similar procedure to try to shape
decreases in heart rate. They obtained substantial changes in heart rate in both directions:
By the end of a session, the average heart rate was over 500 beats per minute for subjects
reinforced for a rapid heart rate and about 330 beats per minute for subjects reinforced
for a slow heart rate.

Miller’s research group also found that reinforcement could control many visceral
responses besides heart rate (DiCara, 1970). They found that curarized rats could either
dilate or constrict the blood vessels of the skin, increase or decrease the activity of the
intestines, and increase or decrease the rate of urine production by the kidneys. The speci-
ficity of some of these responses was quite impressive. Unfortunately, later studies by both
Miller and others had difficulties in replicating the early results on the control of heart
rate by ESB reinforcement. Sometimes such control was demonstrated, but often it was
not, and there was no obvious pattern in the successes and failures (Miller & Dworkin,
1974). If we must try to draw some conclusions from these conflicting data, it seems that
reinforcement can exert direct control over some visceral responses when the activity of
the skeletal muscles has been eliminated, but this control is not as easy to obtain as the
early studies seemed to suggest.

From a practical standpoint, however, an important question is whether reinforcement
techniques can be used to control internal bodily processes under any circumstances, not just
in the extreme case where the body is temporarily paralyzed by a drug. If people can learn
to control such bodily processes as heart rate, blood pressure, muscle tension, intestinal activ-
ity, etc., there could be substantial medical benefits. The next section describes some attempts
to train people to control their internal bodily processes to obtain health benefits.



Some psychologists have speculated that one reason we have so little control over many of
our bodily functions is that feedback from our organs and glands is weak or nonexistent.
The term biofeedback refers to any procedure designed to supply the individual with
amplified feedback about some bodily process. The reasoning is that improved feedback may
lead to the possibility of better control.

The procedures of biofeedback can be illustrated by examining one study on the control
of muscle tension in the forehead. Excessive tension in the forehead muscles is the cause of
muscle-contraction headaches, which some people experience at a high frequency. Budzyn-
ski, Stoyva, Adler, and Mullaney (1973) attempted to train adults who suffered from frequent
muscle-contraction headaches to relax these muscles. During therapy sessions, each patient
received electromyogram (EMG) biofeedback: Electrodes attached to the patient’s forehead
monitored muscle tension, and the level of tension was translated into a continuous train of
clicks the patient could hear. The patient was instructed to slow down the rate of clicking,
thereby decreasing the tension in these muscles. Patients learned to accomplish this task
almost immediately, and their average muscle tension levels were about 50% lower in the
first biofeedback session than in the preceding baseline sessions. After biofeedback training,
patients could produce low-forehead tension without the biofeedback equipment, and they
were instructed to practice this muscle relaxation at home. There was a marked reduction
in headaches for about 75% of the patients, and these improvements were maintained in a
3-month follow-up. On average, patients reported a decrease of about 80% in the frequency
and severity of their headaches, and many were able to decrease or eliminate medication
they had been taking. A review of over 100 studies concluded that biofeedback can be quite
effective for tension and migraine headaches both in the short term and in follow-ups of a
year or more (Nestoriuc, Martin, Rief, & Andrasik, 2008).

Using EMG biofeedback in the opposite way—to increase muscle tension—can also have
therapeutic benefits. Johnson and Garton (1973) used biofeedback to treat 10 patients with
hemiplegia (paralysis on one side of the body) who had failed to improve with traditional
muscular-rehabilitation training. With electrodes connected to the paralyzed muscles of the
leg, a patient received auditory feedback on the level of muscle tension (which was initially
very low, of course). Any increase in muscle tension would produce a louder sound, and a
patient’s task was to increase the loudness of the signal. All patients rapidly learned how to
do this, and after a number of sessions, all showed some improvement in muscle functioning.
Five improved to the point where they could walk without a leg brace. This study and others
have demonstrated quite convincingly that EMG biofeedback can be a useful supplement
to traditional rehabilitation therapy for certain muscular disorders, producing improvements
that would not be obtained without the biofeedback.

Feedback from an EMG device is only one of many types of biofeedback; some other
examples include feedback on heart rate, cardiac irregularities, blood pressure, skin tempera-
ture, electrical activity of the brain, stomach acidity, and intestinal activity. Biofeedback has
been tried as a treatment for many different problems with varying degrees of success. For
instance, training patients to increase the temperature of their hands has been found to be
an effective treatment for migraine headaches in both children and adults (Nestoriuc &
Martin, 2007; Scharff, Marcus, & Masek, 2002). In one study, a combination of skin tem-
perature biofeedback and training in other skills (including progressive relaxation


techniques) produced substantial improvement in patients suffering from irritable bowel
syndrome, a disorder with symptoms that include frequent intestinal pain, gas, and diarrhea
(Schwartz, Taylor, Scharff, & Blanchard, 1990). In treating patients who complained of short-
ness of breath and other breathing difficulties during panic attacks, therapists found that
these symptoms could be reduced by providing respiratory biofeedback, including feedback
on the depth and regularity of their breathing (Meuret, Wilhelm, & Roth, 2004).

Not all attempts to treat medical problems with biofeedback have been successful. For
instance, some attempts to use biofeedback as a treatment for high blood pressure have not
obtained good results, whereas others have found substantial decreases in blood pressure
levels in most patients (Nakao, Nomura, Shimosawa, Fujita, & Kuboki, 2000). As with other
treatments for medical problems, there are often large individual differences in how well
patients respond to biofeedback. There has been some modest progress in predicting which
individuals will benefit from biofeedback treatments and which will not (Weber, Köberl,
Frank, & Doppelmayr, 2011).

Research on biofeedback has grown substantially over the years, and biofeedback tech-
niques have been applied to an increasingly diverse array of medical disorders. The effective-
ness of biofeedback must be judged on a problem-by-problem basis. For some medical
problems, biofeedback may be ineffective. For other problems, it may be only as effective as
other, less expensive treatments. For still others, it may produce health improvements that
are superior to those of any other known treatment.


Neurofeedback: Controlling Your Brain Waves

Neurofeedback is a type of biofeedback designed to help people control the elec-
trical activity of their brains. It has been used in attempts to treat a variety of medi-
cal problems, including epilepsy, chronic pain, anxiety, depression, and addictions
(Jensen et al., 2013; Sterman & Thompson, 2014). One area where neurofeed-
back has been extensively studied is in the treatment of children diagnosed with
attention-deficit hyperactivity disorder (ADHD). For example, in one study (Linden,
Habib, & Radojevic, 1996) children with ADHD were given 40 sessions of neuro-
feedback in which each child received feedback on the electrical activity of his or
her brain. The purpose of the training was to increase a particular brain wave pat-
tern called beta waves, which are thought to be associated with an attentive and
alert mental state. A child received feedback whenever beta waves were present in
the electroencephalogram (EEG) recording. After their training sessions were com-
pleted, these children obtained higher scores on an IQ test and exhibited greater

Many other studies have examined neurofeedback for ADHD in both children and
young adults. Some have found significant benefits from this treatment, but others have


not. As a result, there is still a debate among practitioners about what role neurofeed-
back should play in the treatment of ADHD. Some maintain that it can be an effective
alternative to medication. Others have included neurofeedback as one part of a larger
treatment package for ADHD (Little, Lubar, & Cannon, 2010).

Another area where neurofeedback shows promise is in the treatment of epilepsy.
Because epileptic seizures are caused by abnormal brain activity, it seems reason-
able to suggest that controlling brain waves might help to prevent seizure episodes.
There is evidence that training patients to produce specific types of brain wave pat-
terns can reduce the frequency of seizures. One study found that the reductions in
seizures continued 10 years after neurofeedback treatment (Strehl, Birkle, Wörz, &
Kotchoubey, 2014).

Other psychologists have examined whether neurofeedback can be used to
enhance the cognitive performance of normal adults. Zoefel, Huster, and Herrmann
(2011) gave college students five sessions of neurofeedback training to increase alpha
waves—brain waves that have a distinct cyclical pattern occurring at a frequency of
about 10 cycles per second. By the fifth session, their EEGs showed a clear increase
in alpha waves (Figure 8.2). As a measure of cognitive functioning, the students were
given a mental rotation test (in which they had to decide which of two objects pre-
sented in different orientations in a visual display were identical). Their performance on
this task was better than before the neurofeedback training and better than that of a
control group that did not receive the training. There are also some intriguing studies
showing that neurofeedback training can enhance the artistic, musical, and creative
performances of healthy adults (Gruzelier, 2014). These are preliminary findings, but
they suggest that learning to control one’s brain waves may be beneficial in a variety
of different ways.

Figure 8.2 Sample brain wave patterns from a college student before and after five sessions of
neurofeedback training for alpha waves. (From Zoefel, B., Huster, R.J., & Herrmann, C.S., 2011,
Neurofeedback training of the upper alpha frequency band in EEG improves cognitive perfor-
mance, NeuroImage, 54, 1427–1431. Adapted by permission of Elsevier.)



The past several chapters should leave no
doubt that the principle of reinforcement
is one of the most central concepts in the
behavioral approach to learning. How-
ever, critics of the behavioral approach
have argued that the definition of rein-
forcement is circular and, therefore, that
the concept is not scientifically valid. This
is a serious criticism, so we need to take a
look at what the term circular means and
whether it applies to the concept of

A simple definition of a reinforcer is “a
stimulus that increases a behavior that it
follows.” As a concrete example, suppose a
mother has found that she can get her son
to wash the dishes every evening (which
he would normally try to avoid) if she lets
him watch television only after the dishes
are done. If asked, “Why did the boy wash
the dishes?” a behavioral psychologist
might say, “Because television is a rein-
forcer.” If asked, “How do you know tele-
vision is a reinforcer?” the reply might be
“Because it increased the behavior of
doing the dishes.” The circularity in this sort of reasoning should be clear: A stimulus is
called a reinforcer because it increases some behavior, and it is said to increase the behavior
because it is a reinforcer (Figure 8.3). As stated, this simple definition of a reinforcer makes

Practice Quiz 1: chaPter 8
1. Tolman claimed that rats could still

learn a maze if they were carried
through it because they developed
a ______.

2. Experiments on latent learning have
shown that reinforcement is neces-
sary for the ______ on an operant
response but not for the ______ of
the response.

3. In experiments on the control of heart
rate by reinforcement, ______ was
used as a reinforcer for rats that were
temporarily paralyzed with curare.

4. In using EMG biofeedback for ten-
sion headaches, patients listen to
clicks that indicate ______, and they
are told to try to reduce the rate of
the clicks.

5. The technique of reinforcing particu-
lar types of brain waves is called


1. cognitive map 2. performance, learning 3. ESB
4. tension in their forehead muscles 5. neurofeedback

Figure 8.3 Critics have said that the concept of reinforcement is circular. The circularity can be
avoided by finding an independent way to predict in advance what will serve as a reinforcer.

A reinforcer is
a stimulus that strengthens behavior

A stimulus that strengthens behavior
is a reinforcer


no specific predictions whatsoever. If the boy did not do the dishes, this would not be a
problem for the behavioral psychologist, who could simply conclude, “Television is not a
reinforcer for this boy.”

If there were nothing more to the concept of a reinforcer than this, then critics would be
correct in saying that the term is circular and not predictive. To deal with this issue, behav-
ioral psychologists have tried to find a way to predict which stimuli will be reinforcers and
which will not. The problem boils down to finding some rule that will tell us in advance
whether a stimulus will act as a reinforcer. If we can find such a rule, one that makes new,
testable predictions, then the circularity of the term reinforcer will be broken. Several attempts
to develop this sort of rule are described below.

Need Reduction

Clark Hull (1943) proposed that all primary reinforcers are stimuli that reduce some bio-
logical need and that all stimuli that reduce a biological need will act as reinforcers. The
simplicity of this need reduction theory is appealing, and it is certainly true that many
primary reinforcers serve important biological functions. We know that food, water,
warmth, and avoidance of pain are all primary reinforcers, and each also plays an important
role in the continued survival of an organism. Unfortunately, it does not take much thought
to come up with exceptions to this rule. For example, sexual stimulation is a powerful
reinforcer, but despite what you may hear some people claim, no one will die if deprived
of sex indefinitely. Another example of a reinforcer that serves no biological function is
saccharin (or any other artificial sweetener). Saccharin has no nutritional value, but because
of its sweet taste it is a reinforcer for both humans and nonhumans. People purchase sac-
charin and add it to their coffee or tea, and rats choose to drink water flavored with sac-
charin over plain water.

Besides reinforcers that satisfy no biological needs, there are also examples of biological
necessities for which there is no corresponding reinforcer. One such example is vitamin B1
(thiamine). Although intake of thiamine is essential for maintaining good health, animals
such as rats apparently cannot detect the presence or absence of thiamine in their food by
smell or taste. As a result, rats suffering from a thiamine deficiency will not immediately
select a food that contains thiamine over one that does not.

It makes sense that most biological necessities will function as reinforcers because a crea-
ture could not survive if it were not strongly motivated to obtain these reinforcers. As a
predictor of reinforcing capacity, however, the need-reduction hypothesis is inadequate
because there are many exceptions to this principle—reinforcers that satisfy no biological
needs and biological needs that are not translated into reinforcers.

Drive Reduction

Recognizing the problems with the need-reduction hypothesis, Hull and his student Neal
Miller (1948, 1951) proposed the drive-reduction theory of reinforcement. This theory
states that strong stimulation of any sort is aversive to an organism, and any reduction in
this stimulation acts as a reinforcer for the immediately preceding behavior. The term drive


reduction was chosen because many of the strong stimuli an animal experiences are fre-
quently called drives (the hunger drive, the sex drive, etc.). In addition, the theory asserts
that other strong stimuli (e.g., loud noise, intense heat, fear) will also provide reinforce-
ment when their intensity is reduced. A reduction in stimulation of any sort should serve
as a reinforcer.

There are at least two major problems with the drive-reduction theory. First, if we mea-
sure the intensity of stimulation using an objective, physical scale of measurement, not all
reductions in stimulation act as reinforcers. For example, reducing the room temperature
from 100°F to 75°F (which is the reduction of a stimulus, heat) would probably serve as a
reinforcer for most animals, but reducing the room temperature from 25°F to 0°F (an equally
large reduction in heat) would not. Common sense tells us that 100°F is “too hot” and 0°F
is “too cold,” but that is beside the point; one reduction in heat serves as a reinforcer and
the other does not.

Second, there are many examples of reinforcers that either produce no decrease in
stimulation or actually produce an increase in stimulation. Sheffield, Wulff, and Backer
(1951) found that male rats would repeatedly run down an alley when the reinforcer was
a female rat in heat. This reinforcer produced no decrease in the male’s sex drive because
the rats were always separated before they could have sex, yet the male rat’s high speed
of running continued trial after trial. Similarly, we know that sexual foreplay is reinforc-
ing for human beings even when it does not culminate in intercourse. The popularity
of pornographic magazines, movies, and Internet sites provides further evidence on this

There are countless examples, from many different species, where an increase in stimula-
tion acts as a primary reinforcer. Human infants, kittens, and other young animals spend
long periods of time playing with toys and other objects that produce ever-changing visual,
auditory, and tactile stimulation. The opportunity to run in a running wheel can serve as a
reinforcer for rats (Belke & Pierce, 2009). Photographs presented as a slide show can serve
as reinforcers for monkeys, and motion pictures are even stronger reinforcers (Blatter &
Schultz, 2006). A great variety of stimuli and activities that increase sensory stimulation can
serve as reinforcers for adult humans: music, engaging in sports and exercise, mountain
climbing, skydiving, horror films, and the like. There seems to be no way to reconcile these
facts with the drive-reduction hypothesis.


Because of the problems with the need-reduction and drive-reduction theories, Paul
Meehl (1950) adopted a more modest theoretical position, but one that still offered the
possibility of making new predictions and thereby avoiding the circularity of the term
reinforcer. Meehl invoked the concept of trans-situationality, which simply means that
a stimulus that acts as a reinforcer in one situation will also be a reinforcer in other situ-
ations. For example, suppose that through a simple experiment we determine that water
sweetened with saccharin can reinforce wheel running by a mouse: Running in the activ-
ity wheel increases if every few revolutions of the wheel are followed by access to the
saccharin solution. Having established that saccharin is a reinforcer for wheel running,
the principle of trans-situationality states that we can make new predictions. For instance,


we should be able to use saccharin as a reinforcer for lever pressing, climbing a ladder,
learning the correct sequence of turns in a maze, and so on. In the same way, a mother
can use the principle of trans-situationality to predict what will be a reinforcer for her
child (Figure 8.4).

In reality, the principle of trans-situationality works quite well in many cases. Parents and
teachers know that reinforcers such as snacks, beverages, toys, games, recess, and so on can
be used to strengthen a multitude of different behaviors. There is, however, one problem
with this principle: In some cases, a reinforcer in one situation does not act as a reinforcer
in another situation. The first person to document clear exceptions to the principle of trans-
situationality was David Premack, whose influential experiments and writings changed the
way many psychologists think about reinforcement.

Premack’s Principle

The procedure of reinforcement can be described as a contingency between a behavior
(the operant response) and a stimulus (the reinforcer). This description suggests that when
using reinforcement, we are dealing with two distinct classes of events: reinforceable
behaviors on one hand and reinforcing stimuli on the other. One of Premack’s contribu-
tions was to show that there is no clear boundary between these two classes of events and
that it may be counterproductive to talk about two separate classes at all. He pointed out
that nearly all reinforcers involve both a stimulus (such as food) and a behavior (such as
eating), and it may be the latter that actually strengthens the operant response. Is it water
or the act of drinking that is a reinforcer for a thirsty animal? Is a toy a reinforcer for a
child or is it the behavior of playing with the toy? Is a window with a view a reinforcer
for a monkey or is it the behavior of looking? Premack proposed that it is more accurate
to characterize the reinforcement procedure as a contingency between one behavior and
another than as a contingency between a behavior and a stimulus. For example, he would

Figure 8.4 The principle of trans-situationality can be used to predict what will be an effective rein-
forcer in new situations.


state that in many operant conditioning experiments with rats, the contingency is between
the behavior of lever pressing and the behavior of eating—eating can occur if and only if
a lever press occurs.

How does Premack’s idea about behaviors as reinforcers relate to the principle of
trans-situationality? If trans-situationality is correct, then there must be one subset of
behaviors that we might call reinforcing behaviors (e.g., eating, drinking, playing) and
another subset of behaviors that are reinforceable behaviors (e.g., doing homework, house-
cleaning, going to work). According to the principle of trans-situationality, any behavior
selected from the first subset should serve as a reinforcer for any behavior in the second
subset. However, Premack’s experiments showed several ways in which trans-situationality
can be violated.

To replace the principle of trans-situationality, Premack (1959, 1965) proposed an
alternative theory, now called Premack’s principle, which provides a straightforward
method for determining whether one behavior will act as a reinforcer for another. The
key is to measure the durations of the behaviors in a baseline situation, where all behaviors
can occur at any time without restriction. Premack’s principle states that more probable
behaviors will reinforce less probable behaviors. “More probable” simply means the behavior
that the individual spends more time doing when there are no restrictions on what the
individual can do. Premack suggested that instead of talking about two categories of
behaviors—reinforceable behaviors and reinforcing behaviors—we should rank behaviors
on a scale of probability that ranges from behaviors of high probability to those of zero
probability. Behaviors higher on the probability scale will serve as reinforcers for behaviors
that are lower on the probability scale.

A study Premack (1963) conducted with Cebus monkeys highlights the advantages
of Premack’s principle and the weaknesses of the trans-situationality principle. These
monkeys are inquisitive animals that will explore and manipulate any objects placed in
their environment. Premack allowed the monkeys to play with different mechanical
objects. Figure 8.5 shows that for one monkey, Chicko, operating a lever had the highest
probability, operating a plunger had the lowest, and opening a small door had an inter-
mediate probability.

Later, Premack arranged different contingencies in which one item served as the “operant
response” and the other as the potential “reinforcer”—the reinforcer was locked and could
not be operated until the monkey first played with the other object. In six different phases,
every possible combination of operant response and reinforcer was tested, and Figure 8.5
shows the results. The lever served as a reinforcer for both door opening and plunger pulling.
Door opening reinforced plunger pulling but it did not reinforce lever pressing. Plunger
pulling did not reinforce either of the other behaviors. You should see that each of these six
results is in agreement with the principle that more probable behaviors will reinforce less
probable behaviors.

Notice that door opening, the behavior of intermediate probability, violated the prin-
ciple of trans-situationality. When it was contingent on plunger pulling, door opening
was a reinforcer. When it led to the availability of lever pressing, it played the role of a
reinforceable response. Which was door opening, then, a reinforcer or a reinforceable
response? Premack’s answer is that it can be either, depending on the behavior’s relative
position on the scale of probabilities. A behavior will act as a reinforcer for behaviors that
are lower on the probability scale, and it will be a reinforceable response for behaviors


higher on the probability scale. For this reason, Premack’s principle is sometimes called a
principle of reinforcement relativity: There are no absolute categories of reinforcers and
reinforceable responses, and which role a behavior plays depends on its relative location
on the probability scale.

Premack (1971) also proposed a principle of punishment that is complementary to his
reinforcement principle: Less probable behaviors will punish more probable behaviors. Since an
individual may not perform a low-probability behavior if given a choice, the experimenter
can arrange a reciprocal contingency, which requires that two behaviors occur in a
fixed proportion. For example, in one condition of an experiment I conducted (Mazur,
1975), rats were required to engage in 15 seconds of wheel running for every 5 seconds
of drinking. The results from one typical rat show how this experiment simultaneously
verified Premack’s reinforcement and punishment rules. In baseline sessions, this rat spent
about 17% of the session drinking and about 10% of the session running (Figure 8.6). But

Figure 8.5 The procedure used in Premack’s (1963) experiment and the results from one monkey,
Chicko. The notation D→L means that Chicko was required to open the door before being allowed
to operate the lever.

L = Lever Pressing
D = Door Opening
P = Plunger Pulling





Contingency Conditions Result Conclusion

1. D L D Increases L Reinforces D

P Increases L Reinforces P

L Does Not Increase D Does Not
Reinforce L

L Does Not Increase P Does Not
Reinforce L

D Does Not Increase P Does Not
Reinforce D

P Increases D Reinforces P

2. P L

3. L D

4. P D

5. L P

6. D P


when the rat was required to spend 15 seconds running for every 5 seconds of drinking,
the percentage of time spent running increased compared to baseline, while drinking time
decreased compared to baseline. In other words, the higher probability behavior, drinking,
reinforced running, and at the same time, the running requirement punished drinking.
All the other rats in this experiment showed similar results. Other studies have also found
support for Premack’s rules (Amari, Grace, & Fisher, 1995; Hanley, Iwata, Roscoe, Thomp-
son, & Lindberg, 2003).

Premack’s Principle in Behavior Modification

Although we have focused on the theoretical implications of Premack’s principle, it has had
a large impact on the applied field of behavior modification in several ways. First, it has
stressed that behaviors themselves can serve as reinforcers, thereby encouraging behavior
therapists to use such reinforcers in their work. Therapists now frequently instruct clients
to use “Premackian reinforcers,” such as reading, playing cards, phoning a friend, or watching
television, as reinforcers for desired behaviors such as exercising, studying, or avoiding smok-
ing. Premackian reinforcers have also been widely adopted in classroom settings. Imagine
the difficulties teachers would face in setting up a token system if they relied only on tangible
reinforcers such as snacks, beverages, toys, and prizes. The costs of using such items as rein-
forcers would be prohibitive, and problems of satiation would be commonplace. However,
by making certain activities contingent on good behavior, teachers gain access to a wide
variety of inexpensive reinforcers.

Figure 8.6 The performance of one rat in Mazur’s (1975) experiment. In the first reciprocal contingency,
running time increased and drinking time decreased compared to their baseline levels. In the second recip-
rocal contingency, running time decreased and drinking time increased compared to their baseline levels.


Premack’s principle was used by the parents of a 7-year-old boy who refused to eat all
but a few very specific foods and who would become aggressive if they tried to feed him
anything else. His parents were concerned about his health on such a restricted diet, so
behavior therapists devised the following plan. At mealtimes, the parents would tell the boy
that if he ate a small amount of a new food, he could then eat one of his favorite foods. If
he refused to eat the new food, he would not be allowed to eat his favorite food (but he was
given a less preferred food so he would not go hungry). As a result of this simple strategy,
the boy gradually began to eat a wider variety of foods, and he was calmer when presented
with new foods (Brown, Spencer, & Swift, 2002).

Homme, deBaca, Devine, Steinhorst, and Rickert (1963) used Premack’s principle to
control the behavior of a class of nursery-school children. Among the most probable behav-
iors of these children were running around the room, screaming, pushing chairs about, and
so on. A program was then established in which these high-probability behaviors were made
contingent on low-probability behaviors, such as sitting quietly and listening to the teacher.
After a few minutes of such a low-probability behavior, the teacher would ring a bell and
give the instructions “run and scream,” at which point the children could perform these
high-probability behaviors for a few minutes. Then the bell would ring again and the
teacher would give instructions for another behavior, which might be one of high or low
probability. After a few days, the children’s obedience of the teacher’s instructions was nearly

A similar procedure was used by Azrin, Vinas, and Ehle (2007) with two 13-year-old
boys diagnosed with ADHD. They were so active and disruptive in the classroom that
it was a problem for the whole class. The researchers observed that when they were
allowed in the school’s recreation room, the boys spent most of their time engaged in
vigorous physical activity with the play equipment. This high-probability behavior was
therefore used as a reinforcer for sitting quietly and attentively during class. After several
minutes of appropriate behavior, the teacher would say, “You can now play because you
have been so calm and attentive,” and the boys were allowed to play in the recreation
room for a few minutes. Under this arrangement, the boys’ behaviors during class
improved dramatically.

These examples illustrate just a few of the many ways that Premack’s principle has been
used in applied settings. Although the next section shows that the principle has limitations,
it has proven to be a successful rule of thumb for deciding which events will be reinforcers
and which will not.

Response Deprivation Theory

Research has shown that Premack’s principle usually makes very good predictions about
what will serve as reinforcers or punishers. There are, however, certain cases where, con-
trary to Premack’s principle, a low-probability behavior can actually be used as a reinforcer
for a behavior of higher probability. My experiment with rats running and drinking
(Mazur, 1975) illustrates how this can happen. Recall that one rat spent about 17% of the
time drinking and 10% running in baseline sessions (Figure 8.6). This animal’s ratio of
drinking time to running time was therefore about 1.7 to 1. In one of the reciprocal
contingencies, 45 seconds of drinking were required for every 5 seconds of running. This


is a 9:1 ratio of drinking to running, which is higher than the rat exhibited in baseline.
Figure 8.6 shows that in this reciprocal contingency, running time decreased to about 2%
of the session time while drinking time actually increased to 21%. Therefore, contrary to
Premack’s principle, in this case a low-probability behavior actually reinforced a high-
probability behavior.

To handle results of this type, Timberlake and Allison (1974; Allison, 1993) proposed
the response deprivation theory of reinforcement, which is actually a refinement of
Premack’s principle. The essence of this theory is that whenever a contingency restricts
an individual’s access to some behavior compared to baseline (when there are no restric-
tions on any behavior), the restricted behavior will serve as a reinforcer, regardless of
whether it is a high-probability or a low-probability behavior. To understand how this
theory works, imagine that a man typically spends 30 minutes a day working out with
his home exercise equipment, and he spends 60 minutes a day studying for a difficult
graduate course. He decides that he should be spending more time on this course, but
he has trouble making himself study any longer. To use response deprivation theory, the
man makes an agreement with his wife (who will act to enforce the rule) that for every
20 minutes he spends studying, he earns 5 minutes of exercise time (see Figure 8.7).
Notice that if the man continued to study just 60 minutes a day, he would earn only 15
minutes of exercise time, so this would deprive him of the 30 minutes of exercise that he
used to have. Therefore, according to response deprivation theory, this contingency pro-
duces a relative deprivation of exercise. Because of this, the theory predicts that the man
will strike some compromise between studying and exercising—for example, he might
increase his studying to 100 minutes a day and earn 25 minutes of exercise time (which
is closer to his baseline level of 30 minutes). Seeing this increase in studying compared
to baseline, we would say that exercising (the lower-probability behavior) has served as
a reinforcer for studying.

In summary, response deprivation theory states that in any schedule where the propor-
tion of two behaviors is controlled, the more restricted behavior will act as a reinforcer
for the less restricted behavior, regardless of whether it is the high- or low-probability

Figure 8.7 A hypothetical example of response deprivation theory. Because the contingency deprives
the man of his usual amount of exercise time, exercising should serve as a reinforcer for studying.

60 min studying

30 min exercising

100 min studying

25 min exercising

Every 20 min studying earns 5 min exercising.

Exercising has served as a reinforcer for studying.






behavior. Although it may be a little more difficult to understand than Premack’s principle,
response deprivation theory is the most reliable predictor of reinforcer effectiveness of all
the theories we have examined. It allows us to predict whether an activity will serve as a
reinforcer by observing the probability of that behavior (and of the behavior to be rein-
forced) in a baseline situation. This theory has been tested both in laboratory experiments
with animals and in applied settings with people, and it has proven to be an accurate rule
for predicting when a contingency will produce an increase in a desired behavior and
when it will not (Klatt & Morris, 2001). For example, Konarski (1987) set up different
contingencies between two behaviors in a population of adults with developmental dis-
abilities, and this allowed him to make a direct comparison of the predictions of Premack’s
principle and response deprivation theory. The predictions of Premack’s principle suc-
ceeded in some cases and failed in others, but the predictions of response deprivation
theory proved to be correct almost 100% of the time. Because response deprivation theory
allows us to predict in advance what will serve as a reinforcer, the definition of a reinforcer
is no longer circular.

The Functional Analysis of Behaviors and Reinforcers

Response deprivation theory offers a good way to predict when an activity will serve as
an effective reinforcer. However, a different problem that often challenges behavior thera-
pists is to determine what reinforcer is maintaining some undesired behavior. Those who
work with children or adults who have autism or developmental disabilities often see
bizarre or inappropriate behaviors that seem to occur for no obvious reasons. Examples
include the destruction of toys or other objects, aggression against peers or caregivers,
screaming, self-injurious behaviors (SIBs), and chewing on inedible objects. One useful
first step toward eliminating these behaviors is to conduct a functional analysis, which
is a method that allows the therapist to determine what reinforcer is maintaining the
unwanted behavior.

These maladaptive behaviors may occur for many possible reasons. An aggressive act may
allow a child to seize a desired toy (a positive reinforcer). Destroying objects may lead to
attention from the caregiver (another positive reinforcer). Screaming or disruptive behavior
may produce an interruption in an unwanted lesson or activity (a negative reinforcer). In
addition, some behaviors (e.g., chewing on inedible objects, repetitive motions, or SIBs) may
produce what is called automatic reinforcement; that is, sensory stimulation from the
behavior may serve as its own reinforcer (Fisher, Adelinis, Thompson, Worsdell, & Zarcone,

How can the cause of a particular maladaptive behavior be determined? Using the method
of functional analysis, the patient’s environment is systematically changed in ways that allow
the therapist to test different explanations of the inappropriate behavior. For example, Wat-
son, Ray, Turner, and Logan (1999) used functional analysis to evaluate the SIB of a 10-year-
old boy who had a mental disability. In his classroom, the boy would frequently bang his
head on the table, slap his face, and scratch at his face with his fingernails. On different days,
the boy’s teacher reacted to episodes of SIB in different ways. On some days, the teacher
immediately said, “Don’t do that” after each instance of SIB to see if the behavior was being
reinforced by the teacher’s attention. On other days, the boy was given a toy or other item


after each instance of SIB to see if tangible reinforcers might be strengthening this behavior.
To assess the possibility that the SIB might be producing automatic reinforcement, the boy
was sometimes placed in a room by himself, where he could receive no attention or tangible
reinforcers if he engaged in SIB. Finally, on some days, whatever task the boy was working
on was terminated after an instance of SIB to determine whether the behavior might be
reinforced by escape from unpleasant tasks.

Figure 8.8 shows the results from these tests. Look at these results, and try to decide
what was causing the SIB. Compared to the normal classroom situation (labeled “Base-
line”), the rate of SIB was much lower in the situations testing the effects of attention,
tangible reinforcers, and automatic reinforcement, but it was higher when it allowed the
boy to escape from the ongoing task. The researchers therefore concluded that the SIB
was actually escape behavior. As a treatment, they instructed the boy’s teacher to allow
him to end a nonpreferred task and switch to a more preferred task if he completed it
without any instance of SIB. After this approach was adopted, the boy’s SIB virtually

Functional analysis must be done on a case-by-case basis because the same behaviors
may occur for different reasons for different people. In one survey of more than 100
individuals who engaged in SIB, functional analysis found that for about a third of them,

Figure 8.8 An example of functional analysis (Watson et al., 1999). The rates of SIB exhibited by a
boy with a mental disability are shown for five different experimental conditions.


the behavior was being maintained by attention from the caregiver. For these individuals,
the SIB was greatly reduced by having the caregiver ignore instances of SIB but give the
patients attention when they were engaged in other behaviors (Fischer, Iwata, & Worsdell,
1997). In another example of functional analysis, researchers found that finger sucking
by two children was being maintained not by attention or by escape from unpleasant
tasks but by automatic reinforcement (the sensory stimulation of the fingers). When
bandages or rubber gloves were put on the children’s fingers, their finger sucking
decreased (Ellingson et al., 2000). Functional analysis can also be used for adults with
psychological disorders who display unusual or disturbing behaviors (Strohmeier, Pace, &
Luiselli, 2014).

The power of functional analysis is that the therapist need not simply watch helplessly
and wonder why a maladaptive behavior is occurring. By the appropriate manipulation of
the environment, possible sources of reinforcement can be evaluated, and based on this
information, an appropriate treatment plan can be tailored to the needs of each individual.


This chapter has described several different theories about reinforcement. To achieve a
better understanding of how reinforcement works in everyday settings, some psycholo-
gists have turned to theories from the field of economics. Microeconomics, which is
concerned with the behavior of individual consumers, and the study of operant condi-
tioning, which is concerned with the behavior of individual organisms, have several
common features. Both disciplines examine how the individual works to obtain relatively
scarce and precious commodities (consumer goods in economics, reinforcers in operant
conditioning). In both cases the resources of the individual (money in economics, time
or behavior in operant conditioning) are limited. Both disciplines attempt to predict how
individuals will allocate their limited resources to obtain scarce commodities. Because of
these common interests, some psychologists and economists have begun to share theoreti-
cal ideas and research techniques. The field of behavioral economics is a product of
these cooperative efforts. This section describes a few of the ways in which economic
concepts have been applied to human and animal behaviors, both inside and outside the

Optimization: Theory and Research

A basic question for microeconomists is how individual consumers will distribute their
incomes among all the possible ways it can be spent, saved, or invested. Suppose a woman
brings home $800 a week after taxes. How much of this will she spend on food, on rent, on
household items, on clothing, on entertainment, on charitable contributions, and so on?
Optimization theory provides a straightforward and reasonable answer: She will distribute
her income in whatever way maximizes her “subjective value” (or loosely speaking, in
whatever way gives her the most satisfaction). Although this principle is easy to state, putting
it into practice can be extremely difficult. How can we know whether buying a new pair
of shoes or giving that same amount of money to a worthy charity will give the woman
greater satisfaction? For that matter, how does the woman know? Despite these difficulties,


optimization theory maintains that people can and do make such judgments and then dis-
tribute their income accordingly.

Because the “subjective value” of any reinforcer will vary from one person to another,
testing the principle of optimization in a rigorous way is not easy. Nevertheless, by making
some reasonable assumptions about which reinforcers have greater or lesser value, research-
ers have been able to obtain concrete evidence that supports optimization theory. As
shown in the next section, some of this evidence has come from studies with nonhuman

Optimization and Behavioral Ecology

Behavioral ecologists study the behaviors of animals in their natural habitats or in semi-
naturalistic settings, and they attempt to determine how the behavior patterns of different
species are shaped by environmental factors and the pressures of survival. It is easy to see
why the concept of optimization is appealing to behavioral ecologists, with their interest
in the relationship between evolution and behavior: Animals whose behaviors are more
nearly optimal should increase their chances of surviving and of breeding offspring that
will have similar behavioral tendencies. Behavioral ecologists have documented many
cases where an animal’s behaviors are close to optimal; these cases involve such varied
pursuits as foraging for food, searching for a mate, and choosing group size (Krebs &
Davies, 1978).

Here is one example of how the principle of optimization can be applied to animal
behavior. When searching for its prey, any predator must make decisions. If a large prey is
encountered, it should of course be captured. On the other hand, if a small prey is encoun-
tered, the predator’s decision is trickier. If a long time is required to chase, capture, and eat
the small prey, it may not be worthwhile to go after it because during this time the predator
will miss the opportunity to capture any larger prey that might come along. A general rule
is that if the density of large prey is low (so that encounters with large prey are rare), the
predator should go after any prey, large or small. If the density of large prey is high, however,
the predator should ignore small prey because in chasing them it would lose valuable time
during which a large prey might come along.

Werner and Hall (1974) tested these predictions by placing 10 bluegill sunfish in a large
aquarium with three sizes of prey (smaller fish). When prey density was low (20 of each
type), the sunfish ate all three types of prey as often as they were encountered. When prey
density was high (350 of each type), the sunfish ate only the largest prey. When prey density
was intermediate (200 of each type), the sunfish ate only the two largest prey types. By
measuring the time the sunfish required to capture and eat prey of each type, Werner and
Hall were able to calculate that the behaviors of the sunfish were exactly what optimization
theory predicted for all three situations.

This example shows how scientists have applied optimization theory to the behaviors
of animals in naturalistic settings. Operant conditioning experiments have also provided
some support for the theory (Silberberg, Bauman, & Hursh, 1993). In the psychological
laboratory, optimization theory can be put to a more rigorous test, and its predictions can
be compared to those of alternative theories. Some of this research will be described in
Chapter 12.


Elasticity and Inelasticity of Demand

In operant research, many studies have been done to see how behavior changes as the
requirements of a reinforcement schedule become more severe, such as when a ratio require-
ment is increased from FR 10 to FR 100. This question is similar to the economic question
of how the demand for a commodity changes as its price increases. Economists use the term
elastic demand if the amount of a commodity purchased decreases markedly when its
price increases. Demand is typically elastic when close substitutes for the product are readily
available. For example, the demand for a specific brand of cola would probably drop dramati-
cally if its price increased by 50% because people would switch to other brands that taste
about the same. Conversely, the term inelastic demand means changes in price of a prod-
uct have relatively little effect on the amount purchased. This is generally the case for prod-
ucts with no close substitutes. In modern society, the demand for gasoline is fairly inelastic
because many people have no alternative to driving their cars to work, school, shopping
centers, and so on.

One way behavioral economists can measure demand with people is simply by using a
questionnaire format. In one study, college students were asked to estimate how much
alcohol they would consume during an evening at a bar, depending on the prices of the
drinks (which ranged from free drinks to $9 per drink). Figure 8.9 shows that the students’
answers conformed to a typical demand curve—they estimated that they would drink
a lot if the drinks were free or inexpensive, but their estimated consumption decreased
steadily as the prices of the drinks increased (Murphy, MacKillop, Skidmore, & Pederson,

Figure 8.9 A demand curve obtained by asking college students about how much alcohol they would
consume in an evening at different prices per drink. (From Murphy, J.G., MacKillop, J., Skidmore,
J.R., Pederson, A.A., 2009, Reliability and validity of a demand curve measure of alcohol reinforce-
ment. Experimental and Clinical Psychopharmacology, 17, 396–404. © American Psychological Associa-
tion. Reprinted with permission.)



$0.01 $0.10








$1.00 $10.00


Figure 8.10 Demand curves for food pellets and for fat, obtained by having rats work for these two
reinforcers on different FR schedules. (From Madden, G.J., Smethells, J.R., Ewan, E.E., & Hursh, S.R.,
Tests of behavioral-economic assessments of relative reinforcer efficacy: Economic substitutes. Journal
of the Experimental Analysis of Behavior, 87, 219–240. Copyright 2007 by the Society for the Experi-
mental Analysis of Behavior.)


1 10




Fixed ratio








Demand curves can be obtained from animals by measuring how much they respond
for a particular type of reinforcer while increasing the “price” by requiring more and
more responses per reinforcer. For example, Madden, Smethells, Ewan, and Hursh (2007)
had rats press a lever for food pellets on schedules that ranged from FR 1 to FR 200 or
higher. The food pellets were formulated to satisfy all the rats’ dietary needs. The data are
shown as triangles in Figure 8.10. As the size of the FR schedule increased, the rats’
demand for food decreased, but only slightly. In another phase of this experiment, the
researchers used the same procedure to obtain demand curves when the reinforcer was fat
(a liquid consisting of corn oil mixed in water, which provided calories but was not a
complete diet). As shown by the open circles in Figure 8.10, the demand for fat was more
elastic than for food pellets—as the size of the FR schedules increased, the rats’ consump-
tion of fat decreased much more sharply.

Besides providing examples of two reinforcers with different elasticities of demand, this
experiment shows that deciding which of two reinforcers is “stronger” is a complex question
with no simple answer. Notice that with very small FR schedules, the rats earned more fat
reinforcers than food pellets, but with larger FR schedules, they earned more food pellets
than fat reinforcers. Which, then, is the more effective reinforcer? One possible answer to
this question is to determine which reinforcer has the higher peak output (the reinforcement
schedule at which the individual makes the most total responses, which can be calculated
by multiplying the number of reinforcers earned times the size of the ratio schedule). In
Figure 8.10, these points are marked by the vertical lines, and they show that food pellets
had a higher peak output than fat. However, other researchers have proposed other ways to
compare the strengths of two different reinforcers, such as by measuring which is preferred
in a choice situation, which can sustain the highest response ratio before an animal stops
responding, and other measures (Hursh & Silberberg, 2008). Unfortunately, these different
measures of reinforcer strength do not always agree. At least for now, a seemingly simple
question, “Which of two reinforcers is stronger?” does not appear to have a simple answer.



Behavioral Economics and Drug Abuse

Animal experiments can often provide valuable information about matters that are of
great importance to human behavior. One such area involves the effects of addictive
drugs on an individual’s behavior. Many laboratory experiments have examined how
animals respond when given the opportunity to work to obtain drugs such as alcohol,
heroin, or cocaine. These drugs can serve as powerful reinforcers for animals ranging
from rats to monkeys, and economic concepts can be used to analyze the effects of a
drug more precisely. For example, some studies have used animal subjects to measure
the elasticity of different drugs. Animals may be allowed to work for drugs on FR sched-
ules of different sizes to determine how the “price” of the drug affects consumption. Sur-
prisingly, some drugs considered to be highly addictive have relatively elastic demand.
One experiment with rats found that demand for cocaine was much more elastic than
demand for food (Christensen, Silberberg, Hursh, Huntsberry, & Riley, 2008).

Research with animals has also found that other factors besides price can affect demand
for a drug, such as the availability of substitutes and competition from other reinforcers.
In one study, baboons had to choose between food and intravenous injections of heroin.
When both were plentiful (a choice was available every 2 minutes), the baboons chose
the two alternatives about equally often, and as a result they consumed a good deal of
heroin. But when the two reinforcers were less plentiful (a choice was available only every
12 minutes), the baboons chose food most of the time, and their consumption of heroin
decreased dramatically (Elsmore, Fletcher, Conrad, & Sodetz, 1980). Studies like this show
that even addictive drugs conform to standard economic principles of supply and demand
and that drug consumption will decrease if the cost gets high enough. Furthermore, it does
not always take a manipulation as extreme as decreasing the availability of food to reduce
drug consumption. Carroll (1993) showed that rhesus monkeys’ demand for the drug PCP
could be substantially reduced simply by giving them access to saccharin as an alternative
reinforcer. Similar results have been obtained with other addictive drugs.

Research using the behavioral economic approach to drug addiction has also been
conducted with human participants, involving such drugs as nicotine, caffeine, alcohol,
and heroin. As with the animal studies, this research has shown that economic principles
can be applied to drugs just as well as to other commodities (Bickel, Johnson, Koffarnus,
MacKillop, & Murphy, 2014). For instance, as the price of a drug increases, or as substitute
reinforcers become more available, drug consumption declines (Bickel, DeGrandpre, &
Higgins, 1995). This research can help to analyze the effectiveness of different treatments
for drug addictions. Consider the strategy of treating heroin addicts by giving them metha-
done as a substitute. In economic terms, methadone is an imperfect substitute for heroin
because it delivers some but not all of the reinforcing properties of heroin. More specifically,


methadone prevents the withdrawal symptoms associated with heroin abstinence, but it
does not provide the euphoria, or “high,” that heroin does. In addition, for a drug user, the
clinical setting in which methadone is administered may not be as reinforcing as the social
environment in which heroin is typically used (Hursh, 1991). For these reasons, it would be
a mistake to expect the availability of methadone treatment to eliminate heroin use, even if
the treatment were freely and easily available to all those who currently use heroin.

Vuchinich (1999) has argued that to reduce drug abuse in our society, a multifaceted
approach is best. First, the cost of using drugs should be increased through stricter drug
enforcement policies that reduce the supply. Second, the community must make sure that
reinforcers are available for other, nondrug activities. For young people who may be tempted
to experiment with drugs, sports and recreational programs that require participants to
avoid drugs may be effective. For recovering addicts, the alternative reinforcers can be pro-
vided by supportive family and friends and a job that demands a drug-free employee. Third,
Vuchinich emphasizes that the reinforcers for nondrug activities should be ones that can be
delivered promptly because delayed reinforcers are notoriously ineffective.

Other Applications

Behavioral economic principles have been
applied to other behavior problems, includ-
ing smoking, overeating, and compulsive
gambling (Buscemi, Murphy, Berlin, &
Raynor, 2014; Cherukupalli, 2010). One
important theme of the behavioral economic
approach is that although it can sometimes
be difficult to change such behaviors, it is not
impossible. Behavioral economists and psy-
chologists argue that these problem behav-
iors should not be viewed as incurable
diseases but rather as economic behaviors
that follow the same principles as do other
behaviors (Heyman, 2009). Whether one
uses the terminology of economics (supply,
demand, elasticity) or of learning theory
(reinforcement, punishment, stimulus con-
trol), these behaviors can be changed by
appropriate modifications in the individual’s

As the field of behavioral economics
has grown, researchers have examined an

Practice Quiz 2: chaPter 8
1. The fact that such things as sex and

artificial sweeteners are reinforcers is
a problem for ______ theory.

2. The fact that visual stimulation, exer-
cise, and horror films can be reinforc-
ers is a problem for ______ theory.

3. According to Premack’s principle,
______ behaviors will reinforce
______ behaviors.

4. The procedure of using a series of
test conditions to determine what is
maintaining a person’s maladaptive
behavior is called ______.

5. If the demand for a product decreases
sharply when its price increases,
demand for the product is called


1. need-reduction 2. drive-reduction 3. more probable,
less probable 4. functional analysis 5. elastic


increasing variety of topics, such as how much time supermarket shoppers take to make
decisions on high-priced versus low-priced items (Oliveira-Castro, 2003), when customers
do and do not use a maximization strategy when choosing between different brands of
products (Foxall & Schrezenmaier, 2003), and what factors affect how much money
employees save for retirement (Howard & Yazdipour, 2014). Combining principles from
psychology and from economics has become a fruitful way to analyze a wide range of
consumer behaviors.


Thorndike predicted that an individual must actively respond for learning to occur, but
experiments in which animals were passively transported through mazes showed that they
learned without active responding. In the latent learning experiment of Tolman and Hon-
zik, rats showed immediate improvement in their performance once food was presented at
the end of a maze. Tolman and Honzik concluded that the rats had learned the maze
without reinforcement but that reinforcement was necessary before they would perform
the correct responses.

Studies with animals found that reinforcement can control visceral responses such as
heart rate and stomach activity, but some of these findings have been difficult to replicate.
Nevertheless, research with human patients has found many useful medical applications
of biofeedback, in which a person is given continuous feedback about some bodily pro-
cess and attempts to control it. Biofeedback has been used successfully for headaches,
some types of muscular paralysis, stomach and intestinal disorders, and a variety of other

How can we predict what will be a reinforcer? The need-reduction and drive-
reduction theories have obvious shortcomings. The principle of trans-situationality
states that a reinforcer in one situation will be a reinforcer in other situations. Premack’s
principle states that more probable behaviors will reinforce less probable behaviors.
But the best general rule for predicting what will be a reinforcer seems to be response
deprivation theory, which states that whenever a contingency is arranged between two
behaviors, the more restricted behavior should act as a reinforcer for the less restricted

The field of behavioral economics combines the techniques of operant research and the
principles of economics. Optimization theory, which states that individuals will distribute
their money, time, or responses in a way that optimizes subjective value, has been applied to
many cases of animal behavior in natural settings. Other research has tested economic prin-
ciples about supply and demand, elasticity, and substitutability among reinforcers using
animals and humans in controlled environments.



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Analysis of Behavior, 60, 129–140.

Amari, A., Grace, N.C., & Fisher, W.W. (1995). Achieving and maintaining compliance with the
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Azrin, N.H., Vinas, V., & Ehle, C.T. (2007). Physical activity as reinforcement for classroom calmness
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Learning Objectives

After reading this chapter, you should be able to

• discuss the debate over whether generalization gradients are innate or learned,
and evaluate the evidence for each position

• discuss the debate over whether stimulus control is absolute or relational, and
evaluate the evidence for each position

• define behavioral contrast and discuss different theories of why it occurs
• define errorless discrimination learning and give examples of its use in behavior

• explain what is known about the structure of natural concepts, and describe

the research on natural concept learning by animals
• describe some of the ways that stimulus control techniques are used in behavior


C H A P T E R 9

Stimulus Control and
Concept Learning

The relationship between stimuli and the behaviors that follow them is the topic of this
chapter, a topic called stimulus control. As we have seen throughout this book, predict-
ing what response will occur in the presence of a given stimulus is a challenging task,
even when the same stimulus is presented again and again in a controlled laboratory
environment. But in the real world, all creatures are repeatedly confronted with stimuli
and events they have never experienced before, and their survival may depend on an
adaptive response. The topic of stimulus control includes research on how creatures
respond to such novel stimuli. In previous chapters we used the term generalization to


describe the transfer of responding from trained to untrained stimuli. In this chapter, we
will examine the process of generalization more closely. We will also explore the topic
of concept learning, which involves the classification of different objects into a single
category (e.g., “trees”), even though their visual appearances may sometimes have little
in common.


Measuring Generalization Gradients

Suppose that we have trained a pigeon to peck at a yellow key by reinforcing pecks with
food on a VI schedule. Now we want to determine how much generalization there will
be to other colors, such as blue, green, orange, and red. How can we collect this infor-
mation? One way is to use probe trials, in which the other colors are briefly presented
to measure the pigeon’s responding but no reinforcer is given. The probe trials are
occasionally inserted among reinforced trials with the training stimulus. For instance,
90% of the trials might involve the yellow key light and the VI schedule, but 10% of the
trials would include the other colors and an extinction schedule. Another method for
obtaining generalization gradients is to follow training with the yellow light with a
continuous set of extinction trials with both the yellow light and other colors. In this
method, the trick is to obtain enough trials with each stimulus before responding extin-
guishes. Often this can be accomplished by keeping the durations of the extinction trials

With human subjects, other techniques for measuring generalization are available. For
example, Droit-Volet (2002) first asked young children to listen to several presentations of
a 4-second tone. The children were then given test trials with tones of different durations;
they were told to respond by saying “yes” if it was the same 4-second tone and “no” if the
tone was longer or shorter in duration. As Figure 9.1 shows, Droit-Volet obtained a fairly
symmetrical generalization gradient, with the most “yes” responses to the 4-second tone
and fewer “yes” responses to shorter or longer tones.

What Causes Generalization Gradients?

Why should reinforcement of a behavior in the presence of one stimulus cause this behav-
ior to occur to similar stimuli that have never been used in training? Pavlov’s (1927) answer
was that generalization is an automatic by-product of the conditioning process. His basic
idea was that the effects of conditioning somehow spread across to nearby neurons in the
cerebral cortex. Although the neural details of Pavlov’s theory are not accurate, his more
general view that generalization is an inherent property of the nervous system seems quite

A very different hypothesis was proposed by Lashley and Wade (1946). They theorized
that some explicit discrimination training along the dimension in question (such as wave-
length of light or frequency of tone) is necessary before the typical peaked generalization
gradient is obtained. For instance, if the dimension of interest is color, they would claim that


the learner must receive experience in which reinforcers are delivered when a particular
color is present but not when the color is absent. Without such discrimination training,
Lashley and Wade proposed that the generalization gradient would be flat; that is, the indi-
vidual would respond just as strongly to all colors—there would be no discrimination among
them. In short, whereas Pavlov proposed that generalization gradients are innate, Lashley and
Wade proposed that they depend on learning experiences.

How Experience Affects the Shape of Generalization Gradients

A nice set of experiments by Jenkins and Harrison (1960, 1962) provided support for
the position of Lashley and Wade by showing that an animal’s experience can have a
major effect on the shape of its generalization gradient. Three groups of pigeons
responded on a VI schedule for food reinforcement in the presence of a 1,000-Hz tone.
One group received nondifferential training, in which every trial was the same—the key
light was lit, the 1,000-Hz tone was on, and the VI schedule was in effect. Once the
pigeons were responding steadily, they received a series of extinction trials with different
tone frequencies, and some trials had no tone at all. The results are presented in the top
panel of Figure 9.2. As Lashley and Wade predicted, the pigeons in this group produced
generalization gradients that were basically flat: Response rates were roughly the same at
all tone frequencies!








0 1 2 3 4


Stimulus Duration (seconds)








5 6 7 8

Figure 9.1 A generalization gradient obtained when children were trained to identify a 4-second tone
and then were tested with tones of longer and shorter durations. (From Droit-Volet, 2002, Scalar
timing in temporal generalization in children with short and long stimulus durations, Quarterly Journal
of Experimental Psychology, 55A, 1193–1209. Copyright The Experimental Psychology Society,
reprinted by permission of Taylor & Francis Ltd, on behalf of the Experimental
Psychology Society.)

Figure 9.2 Generalization gradients for tone frequency after nondifferential training with a 1,000-Hz
tone (top panel), presence–absence training with a 1,000-Hz tone (center panel), and intradimensional
training with a 1,000-Hz tone as S+ and a 950-Hz tone as S– (bottom panel). (Based on data from
Jenkins & Harrison, 1960, 1962)






300 450 670 1,000 1,500

Nondifferential Training

2,250 3,500 No, Tone






300 450 670 1,000 1,500

Presence–Absence Training



f T





2,250 3,500 No, Tone






300 450 670 1,000 1,500

Intradimensional Training

2,250 3,500
Tone Frequency (Hz)


Pigeons in the second group received presence–absence training, which included two
types of trials: (1) trials with the 1,000-Hz tone and the VI schedule for food, exactly as
in the first group, and (2) trials without the tone, during which the key light was lit as
usual but no food was ever delivered. The 1,000-Hz tone would be called an S+ (a dis-
criminative stimulus for reinforcement) and the absence of the tone would be called an
S– (a discriminative stimulus for the absence of reinforcement). When these pigeons were
later tested with other tone frequencies, they produced typical generalization gradients
with sharp peaks at 1,000 Hz, as shown in the center panel in Figure 9.2. Notice that in
this condition, the tone was the only stimulus reliably correlated with reinforcement
(because the key light and the other sights and smells of the chamber were present both
on reinforced trials and on extinction trials). Because it was the best signal for the avail-
ability of reinforcement, the tone came to exert control over the pigeons’ responding, as
can be seen in the declines in response rate that occurred when the tone’s frequency was

A third group tested by Jenkins and Harrison (1962) received discrimination training in
which the 1,000-Hz tone was the S+ and a 950-Hz tone was an S–. In other words, food
was available on trials with the 1,000-Hz tone but not on trials with the 950-Hz tone. This
type of training is called intradimensional training because S+ and S– came from the same
stimulus dimension (tone frequency). When tested with different tones in extinction, these
pigeons produced much narrower generalization gradients, as shown in the bottom panel of
Figure 9.2. These very sharply peaked gradients showed that an animal’s experience can have
major effects on the shape of its generalization gradients. In summary, these experiments
support Lashley and Wade’s hypothesis that the shapes of generalization gradients depend
on an individual’s experience.

The story is not so simple, however. Other studies have shown that peaked generalization
gradients can sometimes be obtained with nondifferential training. For example, Guttman
and Kalish (1956) found peaked gradients with different key colors after pigeons received
nondifferential training with a yellow key light. These results seem to support Pavlov’s theory
that no special training is necessary for generalization gradients to appear. In defense of their
theory, Lashley and Wade suggested that although animals might receive only nondifferential
training within an experiment, they may have learned from their everyday experiences prior
to the experiment that different stimuli along the dimension in question can signal different
consequences. The pigeons in the Guttman and Kalish experiment might have learned from
their everyday experiences that color is frequently an informative characteristic of a stimulus;
as a result, they were predisposed to “pay attention” to the color of the key in the experi-
mental chamber.

How Sensory Deprivation Affects the Shape of Generalization Gradients

Once the possibility of preexperiment learning is entertained, the Lashley and Wade
theory becomes quite difficult to test. It becomes necessary to prevent the possibility of
discrimination learning along the dimension in question from the moment an animal is
born. Rudolph, Honig, and Gerry (1969) conducted such an experiment by raising chick-
ens and quail in an environment that was illuminated with a monochromatic green light
of 530 nanometers (nm). Because this special light emitted only a single wavelength, all
objects appeared green regardless of their actual color in white light. (Imagine watching


a black-and-white movie while wearing green-tinted glasses: Everything on the screen
would appear as a mixture of green and black.) The birds were also trained to peck a green
key for food. When tested with other key colors, the birds displayed typical generalization
gradients, with peaks at 530 nm. Other experiments of this type found similar results.
These results clearly contradict the theory of Lashley and Wade, because normal general-
ization gradients were found with birds that had absolutely no prior experience with
different colors.

To summarize, the research on the relationship between experience and generalization
has shown that Pavlov’s theory and Lashley and Wade’s theory are both partially right and
partially wrong. The experiments of Jenkins and Harrison found flat generalization gra-
dients for the pigeons that had no discrimination training, and the type of training the
pigeons received had major effects on the shapes of their gradients. In contrast, the experi-
ments on sensory deprivation showed that peaked generalization gradients can sometimes
be observed even when animals have no prior experience with a particular stimulus dimen-
sion. The results suggest a compromise position: In some cases, discrimination learning may
be necessary before stimulus control is obtained; in other cases, no experience may be
necessary. The evidence that, for birds, such experience is necessary for tones but not for
colors is consistent with the idea that vision is a dominant sensory modality for these crea-
tures. Perhaps we might say that birds are “prepared” to associate the color of a stimulus
with the consequences that follow, but they are not prepared to associate the pitch of a tone
with subsequent events.


Imagine a simple experiment on discrimination learning in which a chicken is presented
with two discriminative stimuli, a medium gray card and a dark gray card. Approaching
the medium gray card is reinforced, but approaching the dark gray card is not. With
enough training, the chicken will learn to choose the medium gray card. But exactly
what has the animal learned? According to the absolute theory of stimulus control,
the animal has simply learned about the two stimuli separately: It has learned that choos-
ing the medium gray color produces food and choosing the dark gray color produces no

On the other hand, according to the relational theory of stimulus control, the animal
has learned something about the relationship between the two stimuli: It has learned that
the lighter gray is associated with food. The absolute position assumes that the animal responds
to each stimulus without reference to the other; the relational position assumes that the
animal responds to the relationship between the two. C. Lloyd Morgan (1894), an early
writer on animal behavior, favored the absolute position because he believed that nonhumans
are simply not capable of understanding relationships such as lighter, darker, larger, or redder.
These relationships are abstract concepts that are not part of any single stimulus, and he felt
that animals do not have the capacity to form such abstractions. An early advocate of the
relational position was the German psychologist Wolfgang Kohler (1939). The question of
whether animals can learn about relationships continues to intrigue modern psychologists
(Wright & Lickteig, 2010). Let us look at the evidence on both sides of this debate and
attempt to come to some resolution.


Transposition and Peak Shift

In support of the relational position, Kohler (1939) presented evidence for a phenomenon
called transposition. After training several chickens on the task just described, Kohler gave
them several trials on which the two stimuli were (1) the medium gray card that had previ-
ously served as the S+ and (2) a card with a lighter gray. Which stimulus would the chickens
choose? If the absolute theory is correct, the chickens should choose the medium gray,
because choosing that particular shade of gray had been reinforced in the past. However, if
the chickens had learned to respond to the relation between the two training stimuli (choos-
ing the lighter gray), they should choose the novel, light gray card. Across several extinction
trials, all of the chickens showed a preference for the light gray card over the previously
reinforced medium gray card, supporting the relational theory (Figure 9.3). The term trans-
position is meant to convey the idea that the animal has transferred the relational rule
(“Choose the lighter gray”) to a new pair of stimuli.

Kohler also found evidence for transposition with chimpanzees, and similar results have
been obtained with several other species, including human children (Alberts & Ehrenfreund,
1951), penguins (Manabe, Murata, Kawashima, Asahina, & Okutsu, 2009), and even turtles
(Leighty, Grand, Pittman Courte, Maloney, & Bettinger, 2013). These results constitute one
important piece of evidence for the relational theory.

In research on generalization gradients, Hanson (1959) discovered a phenomenon called
peak shift that is in some ways similar to transposition. Pigeons in a control group received
several sessions of training in which pecking at a 550-nm key light occasionally produced
food, and they had no training with any other key color. In an experimental group, pigeons
received intradimensional training with the 550-nm key light as S+ and a 555-nm key light

Figure 9.3 In Kohler’s (1939) experiment on transposition, chickens were first rewarded for approach-
ing the medium gray card (S+). In the test phase, they tended to approach the lighter of the two cards,
not the S+ used in training.


S+ S-



as S–. After this training, Hanson measured the birds’ responses to a range of different key
colors during extinction so as to obtain generalization gradients.

As shown in Figure 9.4, the control group produced a typical generalization gradient
with a peak at 550 nm, as expected. However, the experimental group produced a peak
around 530 to 540 nm rather than at the previously reinforced wavelength of 550 nm. The
term peak shift refers to this shift in the generalization gradient in a direction away from the
S–. Peak shift has been observed with many other stimuli besides colors and with many
different species, including humans (Derenne, 2010).

The absolute position would seem to predict a peak at 550 nm for both groups, since this
was the S+. However, the relational position can account for this peak shift as follows. Lights
of both 550 and 555 nm are greenish yellow, but the shorter wavelength is a bit greener.
The pigeons that received intradimensional training might have learned that the greener of
the two stimuli was a signal for reinforcement. This would explain why they responded
more to the 530- and 540-nm stimuli, which are greener still.

Figure 9.4 Generalization gradients for wavelength of light. The control group was trained only with
a 550-nm key light at S+. The experimental group shows a peak shift to the left after training with
a 550-nm key light as S+ and a 555-nm key light as S–. (From Hanson, H.M., 1959, Effects of dis-
crimination training on stimulus generalization, Journal of Experimental Psychology, 58, 321–334. ©
American Psychological Association. Reprinted with permission.)





480 520 560

S+ S-

Wavelength (nm)





Experimental Group

Control Group


Spence’s Theory of Excitatory and Inhibitory Gradients

Although the findings on transposition and peak shift seem to favor the relational theory,
a clever version of the absolute theory developed by Kenneth Spence (1937) can account
quite nicely for both of these phenomena. Spence proposed that in intradimensional
training, an excitatory generalization gradient develops around the S+ and an inhibitory
gradient develops around the S–. Here is how this reasoning might apply to Hanson’s
experiment. Figure 9.5a depicts an excitatory generalization gradient around 550 nm
and an inhibitory gradient centered around 555 nm. The term associative strength refers
to the ability of each stimulus to elicit a response. Spence proposed that the net associa-
tive strength of any stimulus can be determined by subtracting its inhibitory strength
from its excitatory strength. For each wavelength, the result of this subtraction is shown
in Figure 9.5b.

Figure 9.5 An analysis of peak shift based on Spence’s (1937) theory. (a) Intradimensional training is
assumed to produce an excitatory gradient around S+ (550 nm) and an inhibitory gradient around
S– (555 nm). (b) The net associative strength of each wavelength equals the difference between its
excitatory strength and inhibitory strength. Because of the inhibitory gradient around S–, the peak
of this gradient is shifted from S+ in a direction away from S–.






530 550
Wavelength (nm)

S+ S-












570 590



530 550

Wavelength (nm)
570 590

Excitatory Gradient
Around S+

Inhibitory Gradient
Around S-


Notice that the S+, at 550 nm, has the highest excitatory strength, but it also has a good
deal of inhibitory strength because of its proximity to the S–. On the other hand, a stimulus
in the vicinity of 530 to 540 nm has considerable excitatory strength but relatively little
inhibitory strength (because it is farther away from the S–). The result is that stimuli around
530 to 540 nm actually have a higher net associative strength than the S+ of 550 nm. By
comparing Figures 9.4 and 9.5, you can see that Spence’s theory predicts the type of peak
shift that Hanson actually obtained. His theory does a very good job of accounting for peak
shift, and it can also account for transposition using the same type of reasoning about excit-
atory and inhibitory gradients.

The Intermediate-Size Problem

Although Spence’s theory offers a reasonable explanation of both transposition and peak
shift, it does not predict the results on a test called the intermediate-size problem.
Gonzalez, Gentry, and Bitterman (1954) conducted an experiment on the intermediate-
size problem with chimpanzees. Their stimuli were nine squares of different sizes. Their
smallest square (Square 1) had an area of 9 square inches, and their largest square (Square
9) had an area of about 27 square inches. During training, the chimpanzees were always
presented with Squares 1, 5, and 9, and they were reinforced if they chose the intermedi-
ate square, Square 5. (Of course, the left-to-right locations of the squares varied randomly
from trial to trial so that the chimps could not use position as a discriminative

On test trials, the chimpanzees were presented with different sets of three squares, and
they were reinforced no matter which square they chose. For example, suppose the three
squares were Squares 4, 7, and 9. The predictions of the relational position are straightfor-
ward: If the chimps had learned to choose the square of intermediate size, they should choose
Square 7. Figure 9.6 helps to explain the predictions of Spence’s theory. The initial training
should have produced an excitatory gradient around Square 5 and inhibitory gradients
around Squares 1 and 9. Because Square 5 is flanked on each side by an inhibitory gradient,
there is no peak shift in this case; instead, the inhibitory gradients simply sharpen the gradi-
ent of net associative strength around Square 5. Therefore, a chimpanzee should choose
whichever stimulus is closer to Square 5 (Square 4 in this example). The actual results sup-
ported the relational theory and contradicted Spence’s theory: The chimps usually chose the
square of intermediate size on test trials regardless of which three squares were presented.
They behaved as though they were responding to the relationships among the stimuli, not
their absolute sizes.

Other Data, and Some Conclusions

Lazareva and her colleagues conducted a careful series of experiments with pigeons to
reexamine the debate over absolute versus relational stimulus control (Lazareva, Wasser-
man, & Young, 2005; Lazareva, Young, & Wasserman, 2014). Figure 9.7 gives one example
of the type of procedure they used. On some trials, the pigeons were trained with Circle 1
as S– and Circle 2 as S+. On other trials, they were trained with Circle 5 as S– and













2 3 4 5 6 7 8 9


Smallest LargestSquare size

Square size
2 3 4 5 6 7 8 9



b) +



Figure 9.6 An application of Spence’s (1937) theory to the intermediate-size problem. (a) In initial
training, an excitatory gradient develops around S+ (Square 5) and inhibitory gradients develop
around the two S–s (Squares 1 and 9). (b) Because of the two symmetrical inhibitory gradients, there
is no peak shift in the gradient of net associative strength.

Training Pair

Test Pair

1 2 3 4 5 6

S– S+

Training Pair

S– S+

Figure 9.7 Examples of the types of stimuli and tests used by Lazareva and colleagues to compare the
absolute and relational theories of stimulus control. After discrimination training with Circle 1 versus
Circle 2, and training with Circle 5 versus Circle 6, pigeons were tested with a new pair of stimuli—
Circle 3 versus Circle 4.


Circle 6 as S+. Therefore, in both cases, a choice of the larger circle was reinforced. Then
the pigeons were given a choice between two new stimuli, Circles 3 and 4. Notice that
Circle 3 is similar in size to Circle 2 (an S+) and Circle 4 is similar to Circle 5 (an S–).
Therefore, Spence’s theory predicts that through the process of generalization, the pigeons
should choose Circle 3 over Circle 4. However, if the pigeons learned the relational rule
of always picking the larger circle, they should choose Circle 4. The pigeons did show a
preference for Circle 4 over Circle 3, which supported the prediction of the relational

Lazareva and her colleagues concluded that although there may be some situations
where animals respond to the absolute properties of stimuli as Spence theorized, most of
the evidence now favors the relational approach to stimulus control. They also found that
relational responding was stronger when their animals were trained with more examples
(e.g., four different pairs of circles, with the larger circle serving as S+ in every pair). It
makes sense that giving animals more examples, all of them consistent with the same
relational rule, should help them learn the rule better. They concluded that there is
“strong support for the idea that animals are indeed capable of relational responding”
(Lazareva et al., 2005, p. 43).


Phenomena such as peak shift and transposition show that it is often impossible to predict
how one stimulus will affect an individual’s behavior unless we also take into account
other stimuli—either those currently present or encountered in the past. The phenome-
non of behavioral contrast (Reynolds, 1961) also shows that stimuli cannot be judged
in isolation.

An experiment by Gutman (1977) provides a good example of behavioral contrast.
Pigeons responded on a key in a chamber where there were two discriminative stimuli,
a noise and a light, which alternated every 3 minutes throughout a session (in what is
called a multiple schedule). In Phase 1, a VI 30-second schedule was in effect when
the noise was on, and a separate VI 30-second schedule was in effect when the light was
on. Not surprisingly, response rates during the noise and light were about the same in
this condition (Figure 9.8). In Phase 2, the schedule operating during the noise was
switched to extinction. Figure 9.8 shows that, as expected, responding became slower
and slower during the noise. What was more surprising, however, was that response rates
increased dramatically in the presence of the light, even though the reinforcement sched-
ule for the light was not changed. This change in responding to one stimulus that occurs
after a change in the reinforcement schedule for another stimulus is called behavioral

To be more specific, Gutman’s study provided an example of positive contrast, because
there was an increase in responding during the unchanged light component. The opposite
effect has also been observed. For example, suppose that instead of extinction the schedule
for the noise delivered three times as many reinforcers in Phase 2. The likely result would
be an increase in responding during the noise and a decrease in responding during the light.
This decrease in responding during the unchanged light component would be called nega-
tive contrast.


Behavioral contrast has been observed with many different types of reinforcers and with
many different species, from bumblebees to humans. There are several different theories
about why it occurs. According to the behavioral reallocation hypothesis, faster responding in
the unchanged component (positive contrast) is possible because of the slower responding
that occurs in the component that is changed to extinction. The slower responding in the
extinction component might allow the subject to recover from fatigue, so the “well-rested”
animal can respond faster in the unchanged component (Dougan, McSweeney, & Farmer-
Dougan, 1986).

Another theory of behavioral contrast is the reinforcer habituation/satiation hypothesis
(McSweeney & Weatherly, 1998). The basic idea behind this theory is the well-established
finding that the more frequently a reinforcer is presented over a short period of time, the
less effective it becomes because of habituation, satiation, or both. In Gutman’s experiment,
less food was delivered in Phase 2, so there was probably less habituation and satiation to the
food, which could explain why there was faster responding in the light component than in
Phase 1.

A third theory of behavioral contrast focuses on a comparison of the two reinforcement
rates (Herrnstein, 1970). According to this theory, rate of response in one component of a
multiple schedule depends not only on the reinforcement available during that component
but also on the rate of reinforcement in the other component. To speak loosely, it is as
though the animal judges the value of one component by comparing it to its neighbors.

Figure 9.8 Results from Gutman’s (1977) experiment on behavioral contrasts in rats. When both the
light and the noise signaled VI 30-second schedules (Phase 1), response rates were about the same for
both stimuli. When the noise signaled a period extinction (Phase 2), response rates declined toward
zero when the noise was present but increased substantially above those of Phase 1 when the light was





Last 5

1 2
Blocks of 3 Sessions







3 4


Phase 1 Phase 2


In the first phase of Gutman’s experiment,
the schedule during the light component
was “nothing special” since the same
schedule was available during the noise
component. The light therefore produced
only a moderate rate of response. During
the second phase of the experiment, the
light component was quite attractive com-
pared to the extinction schedule of the
noise component, so the light produced a
high response rate.

In a review of the many experiments
and theories about behavioral contrast,
Williams (2002) concluded that it is caused
by several different factors, and no single
theory can account for all of the data.
Habituation and satiation probably con-
tribute to the effect, as does the sort of
comparison process proposed by Herrn-
stein (1970). Williams also presented evi-
dence that behavioral contrast is largely
based on anticipation of the upcoming
component, rather than a reaction to the
preceding component. For example, if a
multiple schedule includes three compo-
nents—A, B, and C—that are repeatedly
presented in this order, responding in com-
ponent B is mostly affected by the schedule
in component C. More recently, however,
Killeen (2014) showed that the schedule in component A also has a short-lived effect on
responding in component B, and he developed a mathematical model that takes into account
the effects of both the preceding and upcoming components.

Although the phenomenon of behavioral contrast is easy to describe, explaining why it
occurs has turned out to be much harder, and it appears to be produced by several different
factors. Although its causes are complex, behavioral contrast demonstrates that it can be
dangerous to study reinforcement schedules as though they were isolated entities. An indi-
vidual’s behavior on one reinforcement schedule may be greatly influenced by events occur-
ring before and after the schedule is in effect.


Suppose that as a laboratory exercise for a course on learning, your assignment is to teach a
pigeon a strong discrimination between red and green key colors. The red key will signal a
VI 1-minute schedule, and you would like moderate, steady responding to this key color.

Practice Quiz 1: chaPter 9
1. Lashley and Wade proposed that

generalization gradients were the
result of experience, and without dis-
crimination training, animals would
show ______ generalization

2. In ______ training, one stimulus serves
as a S+ and another stimulus on the
same dimension serves as S–.

3. In the phenomenon of peak shift, the
peak of the generalization shifts from
the S+ in the direction ______ the S–.

4. Results from the intermediate-size
problem favor the ______ theory of
stimulus control.

5. Suppose that responding during
either blue or yellow stimuli is rein-
forced, but then the schedule for the
yellow stimulus switches to extinc-
tion. Responding during the blue
stimulus should ______, which is
called ______ behavioral contrast.


1. flat 2. intradimensional 3. away from 4. rela-
tional 5. increase, positive


The green key will signal extinction, so you would like no responding when the key is green.
You could begin by using food to shape pecking at the red key. At first, you would reinforce
every response and then gradually shift to longer and longer VI schedules. After several ses-
sions with a VI 1-minute schedule on the red key, the pigeon would probably respond
steadily throughout the session, and you could then introduce the green key color and its
extinction schedule. From now on, sessions might alternate between 3-minute red compo-
nents and 3-minute green components. At first, we would expect the pigeon to respond
when the key was green because of generalization, but eventually responses to green should
decrease to a low level.

This might sound like a sensible plan for developing a good red/green discrimination,
but Terrace (1966) listed several reasons why it is not ideal. This method takes a long
time, and along the way the animal makes many “errors” (unreinforced responses on
the green key). Because the training must continue for several sessions before a good
discrimination is achieved, there are likely to be many setbacks owing to the spontane-
ous recovery of responding on the green key at the start of each session. It also appears
that this type of discrimination training is aversive for the animal. The pigeon may
exhibit aggressive behavior, such as wing flapping. If another pigeon is present in an
adjacent compartment, the pigeon may engage in an aggressive display and eventually
attack the other animal. Such attacks typically occur soon after the transition from S+
to S–. A final problem with this procedure is that even after months of training, the
animal’s performance is usually not perfect—there are occasional bursts of responding
to the S–.

Terrace (1963) showed that there is a better method of discrimination training,
which he called errorless discrimination learning because the learner typically
makes few or no responses to the S–. The errorless discrimination procedure differs
from the traditional procedure in two main ways. First, rather than waiting for strong,
steady responding to the S+, the experimenter introduces the S– early in the training
procedure. Terrace introduced the S– within 30 seconds of the pigeon’s first peck at
the red key. Second, a fading procedure is used to make it unlikely that the learner will
respond to the S–. At first, the S– was presented for only 5 seconds at a time, which
gave the pigeon little chance to respond in its presence. In addition, Terrace knew that
pigeons usually do not peck at a dark key, so at the beginning of training, the S– was
not an illuminated green key but a dark key. The S– was then gradually changed from
a dark key to a dimly lit green key, and over trials the intensity of the green light was
increased. In summary, in Terrace’s procedure the S– was introduced early in training,
it was presented very briefly at first, and it was initially a stimulus that was unlikely to
elicit responding.

Terrace’s errorless discrimination procedure proved to be an effective way to decrease
the number of responses to the S– and improve the learner’s long-term performance. In
one experiment, pigeons trained with a conventional discrimination procedure made an
average of more than 3,000 responses to the S– during 28 sessions, but those trained with
the errorless procedure averaged only about 25 responses to the S–. Terrace also reported
that other disadvantages of the traditional discrimination training were reduced—there
were no setbacks at the beginning of a new session and no signs that the training was
aversive for the animals.



Errorless Learning in Education

B. F. Skinner (1958) maintained that classroom curricula should be designed so
that the student almost never makes a mistake. His reasoning is that if we do not
want children to avoid learning experiences, and if making an incorrect response
(and thereby failing to receive reinforcement) is aversive, then we should try to elimi-
nate these aversive episodes as much as possible. Because errorless discrimina-
tion training can accomplish this and produce good learning in a minimum amount
of time, variations of Terrace’s techniques have been used in many educational

In one example, Duffy and Wishart (1987) used a fading procedure to teach children
with Down syndrome to identify basic shapes such as ovals and rectangles. Some of
the children were taught using a conventional trial-and-error method using cards with
three shapes, such as the right-hand card in Figure 9.9. A child would be asked to
“point to the rectangle” and would be praised if he or she made a correct response. If
the child made an error (which happened frequently in the conventional procedure), the
teacher would say, “No, that is not right. Try again the next time.” The errorless learn-
ing procedure was exactly the same, except that at first the cards had only the correct
shape and two blank spaces, as on the left-hand card in Figure 9.9. Not surprisingly,
the children had little problem pointing to the correct shape. Then, very small incorrect
shapes were added, as on the center card in Figure 9.9; over trials, the sizes of the
incorrect shapes were gradually increased until they were the same size as the correct
shape. Duffy and Wishart found that with the errorless procedure, the children made
very few mistakes during training, and their performance remained slightly better at the
end of training. They also reported that the children’s attitudes toward the learning situ-
ation seemed to be better with the errorless procedure, perhaps because they did not
suffer many failures.

Because of these benefits, errorless learning procedures, along with other tech-
niques that gradually increase the difficulty of the discriminations, have frequently been
incorporated in teaching procedures for children with developmental disabilities (Muel-
ler & Palkovic, 2007). However, there can be both advantages and disadvantages to
using errorless discrimination procedures. After errorless training, the children may have
difficulty learning discrimination reversals, in which the roles of S+ and S– are reversed
(McIlvane, Kledaras, Iennaco, McDonald, & Stoddard, 1995). They may also have dif-
ficulty generalizing and maintaining their discrimination skills in new situations (Jones &
Eayrs, 1992). Educators must therefore carefully consider both the benefits and limi-
tations when deciding whether to use errorless discrimination training or alternative


Figure 9.9 Examples of the types of cards used by Duffy and Wishart (1987) to teach children
with Down syndrome the names of shapes. Errorless learning started with only the correct shape
(left), then small incorrect shapes were added (center), and the incorrect shapes gradually became
larger until they were the same size as the correct shape (right).

Adult learners can also benefit from errorless discrimination training. For example, it
has been widely used to reteach adults information they have lost as a result of Alzheim-
er’s disease or other brain disorders. In one study, 12 patients in the early stages of
Alzheimer’s disease were given errorless training to help them relearn names of people
they had forgotten. As a result of this training, the patients were significantly better at
remembering the names of these people when they saw their faces, and the improve-
ment in the memories persisted 6 months later. The improvement, however, was spe-
cific to those names and faces they had studied; when trying to remember the names
of other people, they were no better than before. In other words, the errorless training
techniques helped these patients relearn specific information they had lost; it did not
produce overall improvement in their memory functioning (Clare, Wilson, Carter, Roth, &
Hodges, 2002). As is the case with children, research with adults has shown that error-
less procedures can be beneficial for some types of learning but that making errors can
be advantageous in other learning tasks (Cyr & Anderson, 2015).


Many of the discrimination tasks described in this chapter might seem quite artificial for
three reasons: (1) The stimuli were simple, idealized images that an animal would be unlikely
to encounter in the natural environment (e.g., a perfect square, uniformly red, on a plain


white background); (2) only a small number of stimuli were used (sometimes just two
stimuli, S+ and S–); and (3) the difference between positive and negative instances was well
defined and unambiguous. For instance, the S+ might be a red square and the S– a green
square, and the animal would not be presented with any other shapes nor with any squares
that were a mixture of red and green.

In research on the topic of concept learning, or categorization, all three of these restrictions
are removed. This research is designed to resemble more closely the types of discriminations
an individual must learn in the natural environment. For example, when an animal learns
to discriminate between predators and nonpredators or between edible plants and poisonous
plants, (1) the stimuli will generally not be simple, idealized forms, (2) there may be countless
examples from each category, and (3) the distinction between positive and negative instances
may not be large or obvious. Research on concept learning has explored how both animals
and people learn to make such complex discriminations.

The Structure of Natural Categories

Eleanor Rosch (1973, 1975) conducted a series of experiments on how people respond to
different members of “natural” categories—categories of objects found in the real world,
such as birds, vegetables, or vehicles. Two of her most important conclusions were that the
boundaries of these categories are not distinct and that people tend to judge some members
of a category as “good” or “typical” examples of the category and others as “bad” or “atypi-
cal” examples. Rosch used the terms central instances and peripheral instances to refer
to typical and atypical examples, respectively.

In one experiment, Rosch (1973) simply asked people to estimate the typicality of
different examples of various categories on a 7-point rating scale, with 1 signifying a very
typical instance and 7 a very atypical example. Her participants found this an easy task,
and different instances received very different rankings. For example, in the category of
birds, robin received a mean ranking of 1.1, chicken a mean ranking of 3.8, and bat a
mean ranking of 5.8. Thus, robins were judged to be typical birds, chickens were rated
as much less typical, and bats were treated as very marginal examples of birds. The
example of bats illustrates how the boundaries of a natural category may be indistinct.
Bats are not really birds at all, but many people probably do not know this, and they may
consider bats as (atypical) members of the bird category. Conversely, whereas an olive is
a fruit, many people do not classify it as such, and in Rosch’s study it received a mean
rating of 6.2.

Rosch described three important characteristics of natural categories. First, people tend
to agree about which examples are central and which are peripheral. Second, if people are
asked to list the members of various categories, they list central instances more frequently.
For instance, when Battig and Montague (1969) asked adults to make lists of birds, robin
was listed by 377 people, chicken by 40 people, and bat by only 3 people. Third, in reaction-
time tests, people take longer to decide that peripheral examples are members of the

It is interesting to speculate about how children learn to identify members and nonmem-
bers of various natural categories. Language might play an important role: A parent may
point to a robin and say, “That is a bird.” Later, the parent may tell the child that it is a robin,


and that robins are one type of bird. Yet language alone cannot explain why natural catego-
ries have the structure they do (with central instances, peripheral instances, and ambiguous
boundaries). Although a child may be taught “A robin is a bird” and “A chicken is a bird,”
the child will still judge the chicken to be an atypical bird and will be a bit slower to agree
that a chicken is a bird. Why does this happen?

Cognitive psychologists have proposed many different theories of human concept learn-
ing, including exemplar theories, prototype theories, and feature theories. According to
exemplar theories (e.g., Jäkel, Schölkopf, & Wichmann, 2008), a category such as bird
consists of the memory of many individual examples of birds the person has seen. If a newly
encountered instance is similar to the examples in memory, it will be judged to be a member
of the bird category. According to prototype theories (e.g., Hampton, 2006), through
experience with many birds a person develops a prototype—an idea of what an ideal or
typical bird is like. If a new instance is very similar to the prototype, it will be considered a
central instance of a bird. If it is only moderately similar to the prototype, it will be consid-
ered a peripheral instance. If it is very unlike the prototype, it will not be considered a
member of the bird category. According to feature theories, a person judges whether a
given instance is a member of a category by checking for specific features (e.g., Spalding &
Ross, 2000). Members of the bird category might include the following features, among
others: It has wings, feathers, a beak, and two legs, it sings, it flies, it perches in trees. A robin
has all of these features, so it is judged to be a typical bird; a chicken does not, so it is judged
to be less typical. There has been extensive debate about which theory of concept learning
is best.

Regardless of how people manage to classify natural objects, the task is a complex
one. Consider the natural concept of tree. For many people, the ideal tree might be
something like a full-grown maple tree, with a sturdy brown trunk and a full canopy of
large green leaves. Yet people can correctly identify objects as trees even when they have
none of the characteristics of this ideal tree (e.g., a small sapling with no leaves, half
buried in snow). Recognizing the impressive concept-learning abilities that people pos-
sess, some psychologists wondered whether any other animals have the ability to learn
natural concepts.

Animal Studies on Natural Concept Learning

Quite a few experiments have examined natural concept learning by animals. Herrn-
stein and his colleagues had pigeons view slides of everyday objects or scenes. In one
experiment, Herrnstein (1979) used the natural concept of tree: If a slide contained one
tree, several trees, or any portion of a tree (e.g., a branch, a part of the trunk), it was a
positive instance, and pecking at the response key was reinforced on a VI schedule. If
the slide did not contain a tree or any portion of a tree, it was a negative instance—pecking
produced no food, and the slide remained on the screen until 2 seconds elapsed without
a peck.

In each session, a pigeon saw 80 different slides, half positive instances and half negative.
At first, the same 80 slides were presented each session. The pigeons quickly learned to
discriminate between positive and negative instances, and after only a few sessions they were
responding substantially faster to the positive slides than to negative slides. You might think


that the pigeons did not learn anything about the general category of tree, but simply
learned about the 80 slides individually. However, when presented with slides they had
never seen before, the pigeons responded about as rapidly to the positive slides and about
as slowly to the negative slides as they did to old positive and negative slides, respectively.
In other words, they were able to classify new slides as trees or nontrees about as well as the
old slides (Figure 9.10).

Similar concept-formation experiments with pigeons have used many other categories
besides trees. Among the concepts that pigeons have successfully learned are people (Her-
rnstein & Loveland, 1964), water (Herrnstein, Loveland, & Cable, 1976), fish (Herrnstein &
de Villiers, 1980), and artificial objects (Lubow, 1974). They have also been trained to dis-
tinguish among the different letters of the alphabet (Blough, 1982). The ability to learn
natural concepts has also been found in many other species, including monkeys, orangutans,
dogs, and mynahs.

One question that arises from this research is whether animals recognize that the
two-dimensional slides or pictures that they view are actually images of three-
dimensional objects. This is a difficult question to answer, but some research suggests
that they can. Delius (1992) presented pigeons with actual three-dimensional objects
that were either spherical (marbles, peas, ball bearings, etc.) or nonspherical (dice,
buttons, nuts, flowers, etc.), and each choice of a spherical object was reinforced with
food. The pigeons quickly learned to choose the spherical objects. They were then tested
with photographs or black-and-white drawings of spherical and nonspherical objects,
and they chose the pictures of spherical objects with a high level of accuracy. In a related
study, Honig and Stewart (1988) found that pigeons responded to photographs taken at

Figure 9.10 People have no difficulty classifying objects as “trees” even though they vary greatly in
their appearance, and neither do pigeons.


two distinctive locations in ways that suggested they had formed concepts of the actual
physical locations represented in the photographs. These studies show that, at least under
certain conditions, animals can learn the correspondence between pictures and three-
dimensional objects.

In a clever experiment by Watanabe, Sakamoto, and Wakita (1995), pigeons were taught
to discriminate between the paintings of two artists, the impressionist Monet and the abstract
painter Picasso. After they learned this discrimination with one set of paintings for each
artist, they were able to correctly categorize new paintings by Monet and Picasso that they
had not seen before. Furthermore, without additional training, they were also able to distin-
guish between the works of other impressionist painters (Renoir and Cezanne) and other
abstract painters (Matisse and Braque). The experimenters also tested the birds with some
familiar paintings that were presented upside down or reversed left to right. With the abstract
paintings of Picasso, this had little effect on the birds’ accuracy. However, with Monet’s paint-
ings, which depict more realistic three-dimensional objects to the human eye, the birds made
more errors with the upside-down or reversed images. This finding provides a bit more
evidence that pigeons can respond to two-dimensional images as representations of three-
dimensional objects.

Possibly the most basic question about animal concept learning is the same one that
is asked about human concept learning: How do they do it? The three classes of theo-
ries developed for human concept learning (prototype theories, exemplar theories, and
feature theories) have also been applied to animal concept learning, and as with human
concept learning, there is no agreement about which type of theory is best. However,
there are some interesting similarities between human and animal concept learning.
Like people, animals differentiate between central and peripheral instances of a cate-
gory. For example, they respond more slowly to instances that contain only a few
features of the positive category than instances that contain more positive features
(Jitsumori, 2006). In some cases, they may display a stronger response to a prototypical
example they have never seen before than to less central examples that they have seen
before (Pearce, 1989).

Another characteristic of concept learning that is shared by people and animals is
flexibility—animals can learn to classify stimuli according to a variety of different criteria,
depending on what the task demands. They can classify instances as positive or negative
either on the basis of the overall characteristics of the image or on small details. For instance,
pigeons in one experiment had to categorize computer-modified pictures of human faces
as male or female. The pigeons could successfully use small textural details (the smoothness
of the face) or large-scale features (the overall shape of the face), whichever was relevant for
the particular set of slides with which they were trained (Troje, Huber, Loidolt, Aust, &
Fieder, 1999).

As more evidence of flexibility, animals can also learn concepts that vary in their level of
generality. Vonk and MacDonald (2004) tested orangutans’ abilities to learn three classifica-
tion tasks. The first and most concrete task was to distinguish between orangutans and other
primates. The second task, which involved more general categories, was to distinguish
between primates and other animals. The third task, involving the broadest and most general
categories, was to distinguish between animals and nonanimals. Notice that in the most
concrete task, the positive instances (pictures of different orangutans) would have many
perceptual similarities, whereas in the most general task, the positive instances (pictures of


different animals) were perceptually much more varied. Nevertheless, the orangutans were
able to learn all three tasks quite well.

Others animals, including pigeons and monkeys, have also been able to learn more general
concepts similar to those used with the orangutans, with varying degrees of success (Roberts
& Mazmanian, 1988). Taken as a whole, the animal research has shown that even though
they do not have language to help them, animals have some impressive abilities when it
comes to concept learning. Whether animals can learn even more challenging and more
abstract concepts, such as analogies, will be examined in Chapter 10.


Stimulus Equivalence Training

One important ability of humans (and possibly other animals, though this is uncer-
tain) is that they can learn to categorize stimuli together even if the stimuli have
nothing in common. This ability is crucial in learning language, in learning to read,
and for other intellectual skills. Written and spoken words are arbitrary stimuli that
refer to objects or events in the world. For example, a child in elementary school
must learn that the spoken word “six,” the written word “six,” the number “6,”
and the Roman numeral “VI” all refer to the same quantity. Behavioral psycholo-
gists sometimes refer to this phenomenon as stimulus equivalence: The different
stimuli can be used interchangeably by a person who understands spoken and
written English.

Psychologists have conducted numerous experiments in laboratory settings to
investigate how and when people can learn such stimulus equivalence (e.g., Sid-
man & Tailby, 1982; Zinn, Newland, & Ritchie, 2015). These laboratory procedures
are now being used in a variety of applied settings. In some cases, stimulus equiva-
lence training can assist children who are having difficulty learning to read. For
example, one group of children was given practice in (1) matching written words to
spoken words and (2) writing printed words by copying them. After this practice, the
children were able to read the written words (which they could not do before), even
though the practice did not involve reading the written words out loud. Evidently,
this training helped the children learn equivalences between (1) hearing a spoken
word, (2) seeing the written word, and (3) reading the word out loud. Besides learn-
ing to read the words they had practiced, the children were also able to read other
words that used the same syllables in different combinations (Melchiori, de Souza, &
de Rose, 2000).

Similar procedures have been used to teach children with visual disabilities the braille
alphabet by training equivalence relations among printed letters, braille letters, and spo-
ken letters (Toussaint & Tiger, 2010). Stimulus equivalence training can also be used for


more advanced academic skills. In one study, equivalence-based training was given to
college students in introductory psychology in an attempt to teach them a difficult topic
(the concept of a statistical interaction). On a post-test, students who were given the
training obtained an average score of 92%, compared to 57% in a control group that
did not receive the training (Fields et al., 2009). If stimulus equivalence training continues
to produce encouraging results such as these, it will surely be used in more clinical and
educational settings in the future.


Almost every instance of behavior modification involves stimulus control in one way or
another. For instance, treatments of phobias are designed to eliminate a response (a fear reac-
tion) that is under the control of a certain class of stimuli (the phobic objects or situations).
What is special about the following examples, however, is that one of the main features of
the behavioral treatment is the development of appropriate stimulus control.

Study Habits and Health Habits

There are many different reasons why some students do poorly in school. One frequent
problem among students who do poorly is that no matter where they are, studying is a
low-probability behavior. The problem is that there are no stimuli that reliably elicit
study behavior. A student may go to her room after dinner, planning to study, but may
turn on the television or stereo instead. She may go to the library with her reading
assignments but may find herself socializing with friends or taking a nap instead of

Recognizing that poor study habits are frequently the result of ineffective stimulus
control, Fox (1962) devised the following program for a group of college students who
were having difficulty. The students were assigned a specific hour of the day, and they
were instructed to spend at least a part of this hour, every day, studying their most dif-
ficult course. This studying was to be done in the same place every day (usually in a small
room of a library or a classroom building). The student was told to take only materials
related to the course into that room and not to use that room on other occasions. A
student was not necessarily expected to spend the entire hour in that room: If the student
began to daydream or became bored or restless, he was to read one more page and then
leave immediately. The purpose of this procedure was to establish a particular time and
place as a strong stimulus for studying a particular subject by repeatedly pairing this time
and place with nothing but study behavior (Figure 9.11). At first, the students found it
difficult to study for long in this new setting, and they would leave the room well before
the hour was over. Gradually, however, their study periods grew longer, and eventually
they could spend the entire hour in productive study. At this point, the therapist chose
the student’s second-most difficult course, and the stimulus control procedure was


repeated. Before long, each student was studying each of his courses for 1 hour a day at
a specific time and place.

All of Fox’s students exhibited substantial improvement in their grades. It is not certain
how much of this improvement was due to better stimulus control because the students were
also given training in other techniques, including the SQ3R method (survey, question, read,
recite, and review). However, setting a time and place for studying is at least an important
first step. Other evidence suggests that combining stimulus control techniques with other
behavioral methods such as self-reinforcement can lead to improved academic performance
(Richards, 1981).

Stimulus control techniques have also been used to promote healthier lifestyles and com-
bat obesity. Some of the techniques are designed to reduce overeating. For instance, because
people often eat excessively while watching television, a simple but helpful strategy is never
to allow yourself to eat snacks in front of the television (Gore, Foster, DeiLillo, Kirk, & West,
2003). Other techniques are aimed at increasing physical activity and reducing sedentary
behaviors such as watching television and using computers. One group of researchers
worked with obese children and their parents to try to reduce sedentary behaviors. The
methods included having the children keep logs to record the amount of time they engaged
in sedentary behaviors, posting signs around the house encouraging more physical activity,

Figure 9.11 An effective strategy of stimulus control is to have a specific time and place for studying
where there are few opportunities for competing behaviors. (


and limiting the number of hours the television was on (a technique known as narrowing
because the opportunities to engage in an undesirable activity are restricted). These methods
proved effective—the children’s levels of daily physical activity increased, and they lost
weight (Epstein, Paluch, Kilanowski, & Raynor, 2004).


Most people have experienced occasional insomnia, but persistent, severe insomnia can be a
serious problem. A person who lies in bed awake most of the night is unlikely to function
well the next day. Although some cases of chronic insomnia are due to medical problems,
many are the result of inappropriate stimulus control. That is, the stimulus of one’s bed does
not reliably produce the behavior of sleeping. The role of stimulus control becomes apparent
if we compare the behavior of insomniacs with those of people without sleeping problems.
A normal person exhibits one sort of stimulus control: She is able to sleep well in her own
bed, but she may have some difficulty falling asleep in a different place, such as on a couch
or in a hotel room. An insomniac may exhibit exactly the opposite pattern: He may have
difficulty falling asleep in his own bed, but he may fall asleep on a couch, in front of the
television, or in a different bed. This pattern shows that insomnia is often not a general
inability to fall asleep but a failure to fall asleep in the presence of a particular stimulus, one’s
own bed.

The reason a person’s own bed may fail to serve as a stimulus for sleeping is fairly
clear: The bed may become associated with many activities that are incompatible with
sleeping, including reading, watching television, eating, and thinking about the day’s
events or one’s problems. To make one’s bed a more effective stimulus for sleeping, some
behavior therapists recommend that the client never do anything but sleep there. Bootzin
(1972) described the case of a man who would lie in bed for several hours each night
worrying about everyday problems before falling asleep with the television on. The man
was instructed to go to bed each night when he felt sleepy but not to watch television
or do anything else in bed. If he could not get to sleep after a few minutes, he was to
get out of bed and go into another room. He could then do whatever he liked, and he
was not to go back to bed until he felt sleepy. Each time he went to bed, the same
instructions were to be followed: Get up and leave the room if you do not fall asleep
within a few minutes. At first, he had to get up many times each night before falling
asleep, but after a few weeks he would usually fall asleep within a few minutes the first
time he got in bed.

The techniques first devised by Bootzin have been used with many insomniac patients
with good results (Taylor & Roane, 2010). The procedure is effective for at least two reasons.
First, since the clients are instructed to remain out of bed when they cannot sleep, their need
for sleep increases early in the program, when they spend a large part of the night out of
bed. Therefore, when they go to bed, their chances of falling asleep are greater. Second, since
the bed is used only for sleeping, its associations with other behaviors gradually decrease and
at the same time its association with sleep increases.

This type of behavioral intervention can now be delivered more precisely with the assis-
tance of modern computer technology. Riley, Mihm, Behar, and Morin (2010) gave adults
with insomnia small hand-held computers that recorded their sleeping and waking patterns.


The computers provided the patients with
customized instructions and prompts about
when to go to bed, when to get out of bed
if they were still awake, and so on. This
technology is still being tested and refined,
but preliminary results suggest that it can
improve the sleep quality of people with
chronic insomnia.

The usefulness of these procedures for
training stimulus control may hinge on the
reduction of incompatible behaviors. The
student in a quiet room of the library will
have little to do but study. In addition,
those few behaviors other than studying
that can occur (such as daydreaming) are
prevented because the student is instructed
to leave the room immediately if he or she
stops studying. Similarly, the therapy for
insomnia involves preventing the client
from engaging in any behavior other than
sleeping in one’s bed. In a sense, then, these
stimulus control techniques are the oppo-
site of the procedure of reinforcing incom-
patible behaviors so as to eliminate an
undesirable behavior. In the former,
incompatible behaviors are prevented, and
in the latter, they are reinforced.


Pavlov proposed that generalization is an automatic by-product of the conditioning process,
whereas Lashley and Wade proposed that experience is necessary for typical gradients to
occur. Each theory seems to be correct in some cases and wrong in others. Some experi-
ments found that discrimination training was necessary before typical generalization gradi-
ents appeared. However, experiments on sensory deprivation supported Pavlov’s position by
finding generalization gradients for color with birds that were raised in an environment with
only one color.

Another question is whether stimulus control is absolute or relational. Spence’s theory
of absolute stimulus control can account for peak shifts in generalization gradients by
assuming that an excitatory gradient develops around the S+ and an inhibitory gradient
develops around the S–. However, this theory cannot explain results from the intermediate-
size problem, which supports the position that animals can respond to relationships
between stimuli. Other evidence also suggests that animals are capable of learning rela-
tional rules.

Terrace developed an errorless discrimination training procedure, in which the S– is
introduced very early in training but under conditions in which the subject is not likely to

Practice Quiz 2: chaPter 9
1. In ______, the S– is introduced early

in training, and it is presented in a
way that makes it unlikely that the
learner will respond to it.

2. A robin would be called a ______
example of a bird, whereas an ostrich
would be called a ______ example.

3. According to ______ theories of con-
cept learning, people categorize new
instances by comparing them to their
memories of past examples of the
concept they have encountered.

4. To provide convincing evidence that
an animal has learned a natural con-
cept such as fish, it is essential to
include ______ as test stimuli.

5. In some cases, a person may have
difficulty studying in a particular loca-
tion because that location is associ-
ated with ______.


1. errorless discrimination learning 2. central,
peripheral 3. exemplar 4. examples never seen before
5. many behaviors other than studying


respond to this stimulus. Errorless discrimination training has been successfully used in a
variety of educational settings with both children and adults.

Concept learning occurs when individuals learn to treat one class of stimuli as positive
and another class as negative. For natural categories, people tend to differentiate between
central instances (typical examples) and peripheral instances (atypical examples). There are
several different theories of how concept learning takes place, including exemplar theories,
prototype theories, and feature theories. Studies with pigeons and other animals show that
they can readily learn such natural categories as tree, water, and people.

Stimulus control techniques are used in behavior modification when a desired response
seldom occurs in the presence of the appropriate stimulus. For students who have difficulty
studying, a special location can be trained as a strong discriminate stimulus for study behav-
ior. If a person’s insomnia is due to poor stimulus control, the person’s bed can be trained as
a strong discriminative stimulus for sleeping.

Review Questions

1. What was Pavlov’s theory about the cause of generalization gradients? What is
another theory about them? What do experiments on discrimination training and
on sensory deprivation tell us about this issue?

2. Describe the difference between the absolute and relational theories of stimulus
control. What do studies on transposition, peak shift, and the intermediate-size
problem indicate about these theories?

3. What is errorless discrimination learning? Describe how this technique could be
used to teach young children the names of different types of flowers.

4. Describe some findings about natural categories in humans and some findings
about natural category learning by pigeons. What do these studies demonstrate
about concept formation by animals?

5. Give one or two examples of how stimulus control techniques have been used
in behavior-modification programs. Describe some specific procedures that the
client must practice in order for the treatment to work.


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Learning Objectives

After reading this chapter, you should be able to

• describe how short-term memory and rehearsal have been studied with

• describe how long-term memory has been studied with animals
• explain what is known about animals’ abilities to measure time, to count, and

to learn serial patterns
• discuss different attempts to teach language to animals and evaluate their

• describe research on animals’ abilities in the areas of object permanence,

analogies, and metacognition

C H A P T E R 1 0

Comparative Cognition

In recent years there has been increasing interest in applying concepts from cognitive
psychology (which previously focused almost exclusively on people) to animals. Through
this interest a new field has emerged called animal cognition or comparative cogni-
tion. A major purpose of this field is to compare the cognitive processes of different
species, including humans. By making such comparisons, researchers hope to find com-
monalities in the ways different species receive, process, store, and use information about
their world. The comparative approach can give us a better perspective on those abilities
that we have in common with other species, and it can also help us understand what
makes the human species unique. This chapter will survey some of the major topics of
traditional cognitive psychology, including memory, problem solving, reasoning, and


language. We will try to determine how animals’ abilities in each of these domains com-
pare to those of people.


A prevalent view about human memory is that it is important to distinguish between long-
term memory, which can retain information for months or years, and short-term
memory, which can only hold information for a matter of seconds. The facts in your
long-term memory include such items as your birthday, the names of your friends, the fact
that 4 + 5 = 9, the meaning of the word rectangle, and thousands of other pieces of infor-
mation. An example of an item in short-term memory is a phone number you have just
looked up for the first time. If someone distracts you for a few seconds after you looked
up the number, you will probably forget the number and have to look it up again. The
following sections will survey animal research on both types of memory as well as rehearsal,
a process that is important for both types of memory.

Short-Term Memory, or Working Memory

Besides being short-lived, short-term memory is also said to have a very limited capac-
ity compared to the large capacity of long-term memory. Although your short-term
memory is large enough to hold a seven-digit phone number long enough to dial it,
you would probably have great difficulty remembering two new phone numbers at

In both human and animal research, the term working memory is now frequently
used instead of short-term memory (Baddeley, 2010). This change in terminology
reflects the view that the information in working memory is used to guide whatever
tasks the individual is currently performing. For example, suppose you are working on
a series of simple addition problems, without the aid of a calculator. At any given
moment, your working memory would contain several different pieces of information:
that you are adding the hundreds column, that the total so far is 26, that the next number
to be added is 8, and so on. Notice that the information must continually be updated:
Your answers would be incorrect if you remembered the previous total rather than the
present one or if you failed to add the hundreds column because you confused it with
the hundreds column of the previous problem. In many tasks like this, people need to
remember important details about their current task and to ignore similar details from
already completed tasks. In a similar way, a butterfly searching for nectar may need to
remember which patches of flowers it has already visited today, and it must not confuse
today’s visits with yesterday’s.

Research with animals has examined different properties of working memory, such
as its duration, its capacity, and factors that affect accuracy of performance. The follow-
ing sections describe two techniques that are frequently used to study working memory
in animals.


Delayed Matching to Sample

As an introduction to delayed matching to sample, Figure 10.1a diagrams the simpler task
of matching to sample as it might be used with pigeons in a chamber with three response
keys. Before each trial, the center key is lit with one of two colors (e.g., red or green).
This color is called the sample stimulus. Typically, the pigeon must peck at this key to light
the two side keys: The left key will then become green and the right key red, or vice
versa. These two colors are called the comparison stimuli. The pigeon’s task is to peck at
the side key that has the same color as the center key. A correct response produces a food
reinforcer; an incorrect response produces no food. Matching to sample is an easy task
for pigeons and other animals, and once they learn the task they can perform with nearly
100% accuracy.

Figure 10.1b diagrams the more complex procedure of delayed matching to
sample (DMTS). In this case, the sample is presented for a certain period of time,
then there is a delay during which the keys are dark, and finally the two side keys are
lit. Once again, the correct response is a peck at the comparison stimulus that matches
the sample, but because the sample is no longer present, the pigeon must remember its

Figure 10.1 (a) The procedure of simple matching to sample: The right key matches the center key,
so a peck at the right key is the correct response. (b) The procedure of delayed matching to sample:
A peck at the right key is again the correct response, but now the pigeon must remember the sample
color through the delay interval.

Green Red

a) Matching to sample

b) Delayed matching to sample



Presentation of






color through the delay if it is to perform better than chance. Since one of the two
keys is correct, chance performance is 50%. If the animal is correct more than 50% of
the time, this means it has remembered something about the sample through the delay

By using delays of different durations in the DMTS procedure, we can measure how
long information about the sample is retained in working memory. The answer is dif-
ferent for different species. For example, the circles in the upper panel of Figure 10.2
show the accuracy of pigeons in an experiment by Grant (1975). The average percent-
age of correct choices decreased steadily with longer delays, and with the 10-second
delay, the pigeons made the correct choice about 66% of the time. The results from a
similar study with monkeys and 4-year-old children are shown in the bottom panel of
Figure 10.2. The monkeys did better on this task than the young children. However,
by age 5 the children outperformed the monkeys, and their DMTS performance
steadily improved up to at least age 14.

Performance on this task can be affected by many factors, such as the presence of other
stimuli that can interfere with the memory of the sample. In human memory tasks, two
types of interference have long been recognized: retroactive interference and proactive inter-
ference. Retroactive interference occurs when the presentation of some new material
interferes with the memory of something that was learned earlier. For example, suppose that
in a list-learning task like the one used by Ebbinghaus (1885; see Chapter 1), a person
memorizes List A, then List B, and then is tested on List A. The memorization of List B will
impair the person’s memory of List A and lead to poorer performance than if the person
never had to learn List B. Proactive interference occurs when previously learned material
impairs the learning of new material. For example, it might be easy to memorize one list,
List D, in isolation, but this list may be much harder to learn if it is preceded by the memo-
rization of Lists A, B, and C.

Both types of interference have been found with animals in DMTS. Retroactive
interference can be demonstrated by presenting various sorts of stimuli during the delay
interval. Not surprisingly, when the sample and comparison stimuli are different colors,
matching performance is impaired if colored lights are presented during the delay inter-
val (Jarvik, Goldfarb, & Carley, 1969). In fact, any sort of surprising or unexpected
stimulus presented during the delay interval is likely to impair performance on the
matching task.

To demonstrate the existence of proactive interference in DMTS, studies have shown
that stimuli presented before the sample can impair performance (White, Parkinson,
Brown, & Wixted, 2004). Proactive interference can occur if a series of trials are pre-
sented in rapid succession because the memory of the preceding trials can interfere
with performance on later trials. For example, the triangles in the upper panel of Figure
10.2 show the results from a condition in which each DMTS trial was immediately
preceded by one or more interference trials in which the opposite color was correct.
As can be seen, performance was considerably worse when these interference trials
were added.

Variations of the DMTS task have also been used with humans. A participant may be
presented with one or more sample stimuli, such as nonsense syllables or unfamiliar shapes.
After a delay, a comparison stimulus is presented and the person must decide if it was one
of the sample stimuli. Using the DMTS task along with brain recording techniques such as

Figure 10.2 The upper panel shows the performance of pigeons in a delayed matching-to-sample task,
where the delay between sample and choice stimuli was varied. The lower panel shows the perfor-
mance of monkeys and 4-year-old children on a similar task. Note that the scale on the x-axis is
different in the two panels. (Top: From Grant, D.S., 1975, Proactive interference in pigeon short-term
memory, Journal of Experimental Psychology: Animal Behavior and Processes, 1, 207–220. © American
Psychological Association. Adapted with permission Bottom: Adapted from Behavioural Processes, Vol.
103, Chelonis, J.J., Cox, A.R., Karr, M.J., Prunty, P.K., Baldwin, R.L., & Paule, M.G., Comparison of
delayed matching-to-sample performance in monkeys and children, 261–268. Copyright 2014, with
permission from Elsevier.)







00 2 4 6
Delay (seconds)





8 10








500 5 10 15 20
Delay (seconds)





25 30 35

No Interference

Grant, 1975



Chelonis et al., 2014

4-Year-Old Children


functional magnetic resonance imaging, researchers can identify which parts of the brain are
involved in working memory, and this research can help to understand various brain disor-
ders. For instance, individuals with schizophrenia perform worse than normal adults on the
DMTS task, and they also show different patterns of brain activity when they perform this
task (Koychev, El-Deredy, Haenschel, & Deakin, 2010).

The Radial-Arm Maze

An apparatus frequently used in memory research with rodents is the radial-arm maze,
which simulates a situation in which an animal explores a territory in search of food.
Figure 10.3 shows the floor plan of a typical eight-arm maze used for rats. The entire
maze is a platform that rests a few feet above the floor; the maze has no walls, so the rat
can see any objects that may be in the room (windows, doors, desks, etc.). At the end of
each arm is a cup in which a bit of food can be stored. In a typical experiment, some
food is deposited at the end of each arm. The rat is placed in the center area to start a
trial and is given time to explore the maze and collect whatever food it can find. Once
the rat collects the food in one arm, it will find no more food in that arm if it returns
later during the same trial. The most efficient strategy for obtaining food is therefore to
visit each arm once and only once.

An easy way for a rat to perform this task would be simply to start at one arm and then
go around the maze in a clockwise (or counter-clockwise) pattern, but rats do not follow

Figure 10.3 The floor plan of an eight-arm maze for rats.


this type of strategy. Instead, they seem to select successive arms in a haphazard manner.
What they do use to orient their travels within the maze are visual landmarks in the room
surrounding the maze. The landmarks help the animals identify individual arms and
keep track of which ones they have already visited (Babb & Crystal, 2003; Mazmanian &
Roberts, 1983).

Perhaps the most remarkable feature of an average rat’s performance on this task is
its accuracy. The first visit to any arm is considered a correct response, and any repeat
visit is an error because there will be no food. If a trial is ended after the rat visits eight
arms (including any repeat visits), it will usually make seven or eight correct responses
(Olton, 1978). This performance means that the rat is very skillful at avoiding the arms
that it has already visited on the current trial. With larger, 17-arm mazes, rats still aver-
age about 15 correct responses out of 17 visits (Olton, Collison, & Werz, 1977), and
similar performance has been obtained from gerbils (Wilkie & Slobin, 1983). It is com-
monly said that human working memory can retain only about seven unrelated items
at once (e.g., seven words or seven random digits). With this number as a point of
comparison, the nearly flawless performance of rats in a 17-arm maze is especially
impressive. Equally impressive are the time intervals over which rats can remember
which arms they have visited. Beatty and Shavalia (1980) allowed rats to visit four arms
of an eight-arm maze, after which they were removed from the maze. If they were
returned to the maze as much as 4 hours later, the rats were almost perfect in their
selection of the four arms they had not previously visited. This finding shows why
working memory is probably a more appropriate term than short-term memory. In
research with people, short-term memory has generally referred to information that is
lost in a matter of seconds, but a rat’s memory for its travels in the radial-arm maze can
last 100 times longer. Compared to the very rapid forgetting typically found in DMTS,
this research also shows that how long information remains in working memory can
vary greatly depending on the nature of the task.


The concept of rehearsal is easy to understand when thinking about human learning. We
can rehearse a speech by reading it aloud or by reading it silently. It seems natural to think
of rehearsal as overt or silent speech in which we repeatedly recite whatever we wish to
remember. Theories of human memory state that rehearsal can keep information active in
short-term memory (which is called maintenance rehearsal), and it can also promote the
transfer of this information into long-term memory (sometimes called associative

Because we tend to equate rehearsal with speech, it may surprise you to learn that psy-
chologists have found good evidence for rehearsal in animals. Since animals do not use
language, what does it mean to say that they can engage in rehearsal? With animals, rehearsal
is more difficult to define, but it refers to an active processing of stimuli or events after they
have occurred. Rehearsal cannot be observed directly; its existence can only be inferred
from an animal’s performance on tasks that make use of short- or long-term memory. The
available data suggest that rehearsal seems to serve the same functions for animals as it does
for people.


Maintenance Rehearsal

Evidence for maintenance rehearsal in animals comes from a technique called directed
forgetting. When this technique is used with human participants, items such as pictures
or words are presented, and after each item the person is instructed either to remember it
or to forget it. The typical finding is that people recall more of the items they were
instructed to remember, presumably because they rehearsed them (e.g., Quinlan, Taylor,
& Fawcett, 2010). To examine directed forgetting with animals, a variation of DMTS can
be used. On each trial, first a sample stimulus is presented, and then either a “remember
cue” or a “forget cue” is presented during the delay that follows the sample stimulus. The
remember cue tells the animal that it is important to remember the sample because a test
is coming up (i.e., the comparison stimuli will soon follow). The forget cue tells the animal
that it is safe to forget the sample because there will be no test on this trial (Figure 10.4).
Therefore, the animal is “directed” either to remember or to forget the sample. If an ani-
mal can choose whether to engage in rehearsal, it should eventually learn to follow the
directions and rehearse the sample when it sees the remember cue but not when it sees
the forget cue. Once an animal is well trained on this task, occasional probe trials are
included—the forget cue is presented, but then (in what should be a surprise to the ani-
mal) the comparison stimuli are presented, and a correct choice is reinforced. The idea is
that if the animals had learned not to bother rehearsing on trials with the forget cue, they
should perform poorly on these occasional surprise quizzes. In one study with pigeons,

Figure 10.4 On “remember trials” of a directed forgetting task, the animal is given a signal to remem-
ber the sample stimulus and then receives a reinforcer if it makes the correct choice. On “forget trials”
there is no test except on occasional probe trials.








No Test
(excepton occasional

probe trials)


this is exactly what was found: On probe trials that followed the forget cues, the pigeons
averaged about 70% correct choices, compared to about 90% on trials with the remember
cue (Maki & Hegvik, 1980).

Evidence for directed forgetting has been obtained with other species, including mon-
keys and rats (Miller & Armus, 1999; Tu & Hampton, 2014). In another experiment with
pigeons, Milmine, Watanabe, and Colombo (2008) recorded the activity of individual
neurons in the prefrontal cortex (a part of the brain associated with working memory), and
they found significantly greater activity during the delay intervals after remember cues than
after forget cues. Taken together, these studies on directed forgetting make a strong case
that, like people, nonhuman animals can choose whether to rehearse information they have
recently received.

Associative Rehearsal

Research on human memory has shown that rehearsal increases the strength of long-
term memory. If people are given a list of items to remember and then given a distrac-
tion-free period (in which they presumably recite or rehearse the material in some
way), their ability to recall the list items at a later time will be improved. In a clever
series of experiments, Wagner, Rudy, and Whitlow (1973) demonstrated that rehearsal
also contributes to the strength of long-term learning in classical conditioning with
rabbits. They demonstrated that the acquisition of a CR proceeds more slowly if some
posttrial episode (PTE) that “distracts” the animal occurs shortly after each conditioning
trial. They also showed that surprising PTEs are more distracting (interfere more with
learning) than expected PTEs. Expected PTEs were sequences of stimuli that the rab-
bits had seen many times, whereas surprising PTEs were arrangements of stimuli that
the animals had not seen before. During classical conditioning, the rabbits received a
series of trials on which a CS was paired with a US (an electrical pulse in the vicinity
of the eye, which produced an eyeblink). For all rabbits, a PTE occurred 10 seconds
after each conditioning trial. However, for half of the rabbits, the PTE was an expected
sequence of stimuli, and for the other half, it was a surprising sequence of stimuli. The
eyeblink conditioning to the CS developed much more slowly in the rabbits that
received surprising PTEs.

The researchers reasoned that in order for a long-term CS–US association to develop, an
animal needs a distraction-free period after each conditioning trial during which rehearsal
takes place. The surprising PTEs distracted the rabbits and interrupted their rehearsal of the
events that had just occurred, so the rate of conditioning was slowed. The expected PTEs
caused less disruption of rehearsal because the rabbits had seen these PTEs before, so they
had more time to rehearse, and they learned faster.

If this reasoning is correct, then the sooner a surprising PTE occurs after the conditioning
trial, the greater should be the disruption of conditioning. To test this prediction, Wagner,
Rudy, and Whitlow varied the time between the trial and the surprising PTE from 3 to 300
seconds for different groups of subjects. Figure 10.5 shows the median percentages of CRs
to the new CS over the first 10 conditioning trials. As can be seen, the PTEs had their great-
est disruptive effects when they closely followed each conditioning trial and thereby kept
rehearsal to a minimum.


Long-Term Memory, Retrieval, and Forgetting

In contrast to the very limited size of short-term memory, the storage capacity of long-
term memory is very large. It is probably safe to say that no one has yet found a way
to measure and quantify this capacity for either animals or people, but some studies
have demonstrated impressive feats of learning and remembering. Vaughan and Greene
(1983, 1984) trained pigeons to classify slides of everyday scenes as either “positive”
(responses to these slides were reinforced with food) or “negative” (responses to these
slides were never reinforced). Each slide was randomly assigned to the positive or nega-
tive category, so the only way to know which was which was to remember each indi-
vidual slide. They started with 40 positive slides and 40 negative slides. After about 10
daily sessions, the pigeons were discriminating between positive and negative slides
with better than 90% accuracy. They were then trained with more slides, and with 320
slides their accuracy was still above 90%. Taking this method further, Cook, Levison,
Gillett, and Blaisdell (2005) trained pigeons with over 1,600 slides, and they found
accuracy levels above 75%. Equally impressive memory for pictures has been found
with humans (Shepard, 1967).

Studies with other species of birds have demonstrated similar feats of memory, often
involving memory for caches—sites where the birds have stored food. For example, a bird

Figure 10.5 The percentage of conditioned eyeblink responses in four different groups of rabbits in
the Wagner et al. (1973) experiment. For each group, the x-axis shows the amount of time that elapsed
between each conditioning trial and a surprising PTE. (From Wagner, A.R., Rudy, J.W., Whitlow,
J.W., 1973, Rehearsal in animal conditioning, Journal of Experimental Psychology, 97, 407–426, ©
American Psychological Association. Reprinted with permission.)













Trial–PTE Interval (Seconds)


known as Clark’s nutcracker gathers more than 20,000 pine seeds each fall and stores them
in the ground in several thousand different locations. To survive the winter, the bird must
recover a large portion of these seeds. Field observations and laboratory experiments have
shown that nutcrackers do not use random searching or olfactory cues in recovering their
caches. Although they may use certain characteristics of cache sites to aid their searches
(e.g., the appearance of the soil above a cache), the birds’ memories of specific visual
landmarks and spatial cues are much more important (Kelly, Kamil, & Cheng, 2010; Vander
Wall, 1982).

Other studies with animals have investigated the time course of forgetting from long-
term memory, just as Ebbinghaus (1885) tested his recall of nonsense syllables after different
intervals to construct a forgetting curve (see Chapter 1). The general shape of forgetting
curves for animals is similar to the pattern in Figure 1.3: Forgetting is rapid at first, with a
substantial loss during the first 24 hours, but subsequent forgetting proceeds at a much
slower rate (Gleitman, 1971).

What causes the forgetting of information in long-term memory? For humans, a
prevalent view is that interference from other stimuli and events, to which we are con-
stantly exposed in daily life, is a major cause of forgetting (Wixted, 2004), and this view
has substantial empirical support. Both proactive and retroactive interference have been
observed in studies of animal long-term memory (Amundson & Miller, 2008; Engel-
mann, 2009). As an example of proactive interference, suppose that a pigeon receives
several days of training on a discrimination task in which S+ is blue and S– is green.
Then the roles of S+ and S– are reversed for one session, and the bird learns to respond
to the green stimulus. If the bird is then tested on the following day, the early training
with blue as the S+ is likely to interfere with the bird’s memory of the more recent
training, and it may respond more to blue and less to green. This is an instance of pro-
active interference because the memory of the early training impairs the memory of the
later training.

If an individual forgets something that was learned long ago, is this because the
memory has been lost forever, or is the problem one of retrieval failure (the memory is
still there but it is difficult to find)? In research on human memory, there is evidence that
many instances of forgetting are really cases of retrieval failure. Although you may not
be able to recall some information on your first attempt (e.g., the Democratic candidate
elected president of the United States in 1980), you may succeed if you are given a hint
(e.g., peanuts).

One phenomenon that supports the concept of retrieval failure is the context-shift
effect: if you learn some new information in one context (such as a particular room), your
recall of the information will be better if you are tested in the same context than in a new
context (a different room). The context-shift effect has been found with both humans and
animals (Millin & Riccio, 2004; Smith & Vela, 2001), and it shows how specific cues can
help one remember things that would otherwise be forgotten.

Based on the idea that forgetting is often a problem of retrieval failure, many experiments
with animals have shown that “forgotten” memories can be recovered if the animal is given
an appropriate clue or reminder. For example, Gordon, Smith, and Katz (1979) trained rats
on an avoidance task in which a rat had to go from a white room to a black room to avoid
a shock. Three days after training, rats in one group were given a reminder of their previous
avoidance learning: They were simply confined in the white compartment for 15 seconds,


with no shock. Rats in a control group were not returned to the test chamber. Twenty-four
hours later, both groups were tested in extinction to see how quickly they would move into
the black chamber. The rats that had received the reminder treatment entered the black
room significantly faster, presumably because the reminder served to revive their memories
of their earlier avoidance training. The general conclusion from this line of research is that
any stimulus that is present during a learning experience (including the room or chamber
in which the learning takes place) can later serve as a reminder and make it more likely that
the experience will be remembered.


Chunking of Information by Animals

Many experiments with humans have shown that memorizing is easier if a long list
of information is divided into portions of more manageable size called chunks (Miller,
1956). For example, the telephone number 711–2468 consists of seven digits, which
is about all that human short-term memory can hold at once. However, the burden on
memory is lightened if “711” reminds you of the name of a chain of convenience stores,
and if you remember “2468” as the first four even numbers. In this way, the problem
of remembering seven pieces of information is reduced to remembering two chunks of

Experiments have shown that animals can also use chunking to help them learn
and remember a long list. In one experiment (Terrace, 1991), five stimuli were pre-
sented in random locations on a translucent screen, and pigeons had to peck the five
stimuli in the correct order to obtain food (see Figure 10.6a). Some of the stimuli were
different colors and others were white shapes on a black background. Terrace wanted
to see whether pigeons could learn the list of five stimuli faster if it were divided into
two chunks, with only colors in one chunk and only shapes in the other. Five groups
of pigeons learned a different list of colors and/or shapes. As Figure 10.6b shows,
the list for Group II was nicely divided into two chunks: The first three stimuli were
colors and the last two were shapes. The list for Group IV was divided into one large
chunk of four colors, followed by the diamond shape. The lists for the other three
groups were not organized into chunks. As Terrace expected, the two groups that
had lists divided into chunks required significantly less practice to learn the correct
pecking sequence. As more evidence that the pigeons in these two groups were
using chunks, Terrace found that the longest hesitation between pecks occurred at
the switch between colors and shapes. For instance, in Group II, the pigeons would
peck the three colors quickly, then hesitate briefly, and then peck the two shapes in
rapid succession.



a) b) Group Correct sequence

























Figure 10.6 (a) In Terrace’s (1991) experiment, five visual stimuli were arranged randomly in
any of eight locations on a rectangular screen, and a pigeon received food only if it pecked the
stimuli in exactly the correct sequence. (b) For the five groups of pigeons, the correct sequence
is shown (R = red, G = green, B = blue, Y = yellow, V = violet).

If a set of stimuli is not already organized into chunks, animals may develop their own
chunks. Dallal and Meck (1990) found evidence for chunking by rats in a 12-arm radial
maze. Four arms (in different parts of the maze) had sunflower seeds at the end, four
had food pellets, and four had rice puffs. For one group of rats, the locations of the dif-
ferent types of food were the same trial after trial. With practice, they tended to select
the arms in chunks based on the different food types. For example, a rat might first go
to the arms with sunflower seeds, then those with food pellets, and finally those with
rice puffs. A typical rat’s performance was usually not so perfectly organized, but there
was a strong tendency to group the arms by food type. As a result, their accuracy (i.e.,
not going down the same arm twice) was better than for rats in a second group where
the food locations were changed every trial (so they could not use a chunking strategy).
Dallal and Meck concluded that by chunking on the basis of food type, the rats were
able to decrease the burdens on their working memories and thereby perform more

Some animals may use chunking as a learning strategy in their natural environ-
ments. Suge and Okanoya (2010) found that when Bengalese finches listen to the
songs of others of their species, they perceive them as chunks, not as individual
notes. Williams and Staples (1992) studied how young male zebra finches learned
songs up to 15 notes long from older male finches. They found that the older finches
tended to divide their songs into chunks of about three notes; the younger finches
would copy these chunks, and eventually they could put the chunks together into a
complete song.



Experiments on an
“Internal Clock”

Try to imagine what would happen in the
following experiment. A rat is first trained
on an FI 40-second schedule. A light is
turned on to signal the start of each
40-second interval, and after the rein-
forcer, the light is turned off during an
intertrial interval, and then the next trial
begins. Training on this schedule contin-
ues until the animal’s response rate in each
interval consistently shows the accelerat-
ing pattern that is typical of FI perfor-
mance. Now the procedure is changed so
that on occasional trials no reinforcer is
delivered—the light remains on for about
80 seconds, and then the trial ends in
darkness. With further training, the ani-
mal will learn that a reinforcer is available
after 40 seconds on some trials but not on
others. How do you think the animal will
respond on nonreinforced trials?

Figure 10.7 presents the results from an experiment like the one just described (Rob-
erts, 1981). The open circles show that on trials without reinforcement, response rates
started low, increased for a while, reached a maximum at about 40 seconds, and then
declined. The location of the peak indicates that the rats were able to estimate the pas-
sage of time fairly accurately since they responded the fastest at just about the time a
response might be reinforced (around 40 seconds). On other trials, a tone was presented
instead of the light, and the tone usually meant that a reinforcer was available on an FI

Human beings are much better at learning lists than the animals in these experi-
ments. For instance, a child can memorize a list of five items without much effort,
but the pigeons in Terrace’s experiment needed over 100 sessions to do so. Still,
the research on chunking demonstrates more similarities between human and ani-
mal memory: If a list is already organized into chunks, both animals and people
can learn the list faster. If a set of items is not already organized, both animals and
people may group similar items together, and this will help to improve memory and
avoid mistakes.

Practice Quiz 1: chaPter 10
1. DMTS is a procedure used to study

______ memory.
2. When the presentation of new mate-

rial interferes with the memory of
something learned earlier, this is
called ______.

3. ______ rehearsal serves to keep
information in short-term memory.

4. If a surprising event occurs soon after
a classical conditioning trial, this will
result in ______ conditioning than
would have occurred without the
surprising event.

5. If an animal seems to have forgotten
some new learning, it is sometimes
possible for the animal to recover the
learning if given a ______.


1. short-term or working 2. retroactive interference
3. maintenance 4. less 5. reminder or clue


20-second schedule. The filled circles in Figure 10.7 show the results from nonrein-
forced test trials with the tone. Again, response rates first increased and then decreased,
but on these trials the peak response rate occurred at about 20 seconds. These results
show that the rats had learned that the tone signaled a 20-second interval and the light
signaled a 40-second interval, and in both cases they could estimate these intervals fairly
well. This procedure for studying animal timing abilities is called the peak procedure
because the peak of the response-rate function tells us how accurately the animals could
time the intervals.

How accurately can animals distinguish between two events that have different dura-
tions? Suppose a rat receives food for pressing the left lever after a 5-second tone and for
pressing the right lever after an 8-second tone. Experiments using this type of procedure
with both rats and pigeons have shown that they can discriminate between two stimuli if
their durations differ by roughly 25% (Church, Getty, & Lerner, 1976; Stubbs, 1968). This
finding illustrates a principle of perception called Weber’s law, which says that the
amount a stimulus must be changed before the change is detectable is proportional to the
size of the stimulus. Weber’s law was first applied to human perception, but it applies
equally well to animals. Thus, an animal may be able to discriminate between a 4-second
tone and a 5-second tone (which differ by 25%), but not between a 10-second tone and
an 11-second tone (which differ by only 10%), even though there is a 1-second difference
in both cases.

Figure 10.7 Rats’ response rates in an experiment using the peak procedure. The filled circles
show the results from trials with a tone that usually signaled an FI 20-second schedule. The
open circles show the results from trials with a light that usually signaled an FI 40-second
schedule. (From Roberts, S., 1981, Isolation of an internal clock, Journal of Experimental Psychol-
ogy: Animal Behavior Processes, 7, 1242–1268. ©American Psychological Association. Adapted
with permission.)



0 20 40

Time (seconds)

20 seconds
40 seconds







60 80


This research shows that animals are fairly good at judging durations, but it does not tell
us exactly how they measure the passage of time. Some psychologists have proposed that
every animal has an “internal clock” that it can use to time the duration of events in its
environment. Church (1984) and Roberts (1982) claimed that in some respects an animal’s
internal clock is analogous to a stopwatch. Like a stopwatch, the internal clock can be used
to time different types of stimuli. Roberts trained rats to press one lever after a 1-second tone
and another after a 4-second tone. When the stimuli were then changed to 1- and 4-second
lights, the rats continued to choose correctly without additional training. Like a stopwatch,
the internal clock can be stopped and then restarted (e.g., if a stimulus light is turned off for
5 or 10 seconds and then turned back on).

Other theories of animal timing have been developed over the years, including the behav-
ioral theory of timing (Killeen & Fetterman, 1988) and the learning-to-time theory (Machado &
Arantes, 2006). The details of these theories are complex, but in essence they state that
animals can use their own behaviors to measure durations. For example, if a reinforcement
schedule requires that the animal wait for 5 seconds and then make a response (a DRL
schedule, as described in Chapter 6), the animal might walk to all four corners of the experi-
mental chamber and then make the operant response. In this way, the animal could time the
5-second interval with reasonable accuracy.

The research on this topic has shown that animals have fairly versatile timing abilities.
They can discriminate between stimuli of slightly different durations, and they can trans-
fer this skill from a visual stimulus to an auditory stimulus. They can time the total
duration of a stimulus that is temporarily interrupted. They can time the total duration
of a compound stimulus that begins as a light and then changes to a tone. An animal’s
ability to time events is certainly far less accurate than an ordinary wristwatch, but then
so is a person’s.


Many of the techniques used to study animals’ counting abilities are similar to those used to
study timing, and the results are similar as well. Mechner (1958) used a variation of a FR
schedule in which a rat had to switch from one lever to another after completing the ratio
requirement. For example, if 16 responses were required, on half of the trials, the 16th con-
secutive response on lever A was reinforced. On the other half of the trials, the rat had to
make 16 or more consecutive responses on lever A and then 1 response on lever B to collect
the reinforcer. If the rat switched too early (say, after 14 responses), there was no reinforcer,
and the rat had to start from the beginning and make another 16 responses on key A before
a reinforcer was available. In four different conditions, either 4, 8, 12, or 16 consecutive
responses were required. For these four conditions, Figure 10.8 shows one rat’s probability
of switching to lever B after different run lengths (where a run is a string of consecutive
responses on lever A). We can see that as the ratio requirement increased, the average run
length also increased in a systematic way. When 4 responses were required, the most com-
mon run length was 5; when 16 responses were required, the most common run length was
18. Producing run lengths that were, on the average, slightly longer than required was a
sensible strategy because the penalty for switching too early was severe. More recent studies
with pigeons, using procedures similar to Mechner’s, obtained very similar results, and they


provided further evidence that number discrimination by animals follows Weber’s law (Fet-
terman & Killeen, 2010).

The counting abilities displayed by the rats and pigeons in these experiments were not
exact: On some trials they switched too early, and on others they made more responses than
necessary. This is actually quite similar to what human adults or children do when they do
not have the time or interest in getting an exact total—they estimate, using what has been
called an approximate number system (Bonny & Lourenco, 2013). Of course, when it is neces-
sary, humans can also count objects to get an exact number. Can animals learn to count
objects in an exact rather than an approximate way? A few studies suggest that they can, at
least with small numbers. In one experiment, rats were able to learn a discrimination in
which three bursts of noise served as the S+ and either two or four bursts served as S–
(Davis & Albert, 1986). Capaldi and Miller (1988) found evidence that the rats learned
abstract concepts of number that could transfer from one type of stimulus to another. Some
writers have proposed that counting is a skill that animals can learn only with difficulty,
but Capaldi and Miller concluded just the opposite, stating that “rats assign abstract number
tags to reinforcers readily, easily, and under most, if not all, circumstances” (1988, p. 16).

Figure 10.8 One rat’s probability of switching from lever A to lever B after different run lengths in
Mechner’s (1958) experiment. The required run length is the number of consecutive responses
required on lever A before a switch to lever B would be reinforced. (Adapted from Mechner, F.,
Probability relations within response sequences under ratio reinforcement, Journal of the Experimental
Analysis of Behavior, 1, 109–121. Copyright 1958 by the Society for the Experimental Analysis of
Behavior, Inc.)









4 6 8 10 12
Run Length








14 16 18 20

Required Run Length


22 24 26


Another study found evidence of a rudimentary counting ability in domestic dogs (West &
Young, 2002).

Other evidence for an exact counting ability was presented by Pepperberg (1987), who
trained a parrot, Alex, to respond to any number of objects from two through six by
actually saying the appropriate number. In training, a number of objects (e.g., keys, small
pieces of paper or wood, corks) would be placed on a tray, and Alex was reinforced if he
said the correct number. For instance, the experimenter might present three corks and
ask, “What’s this?” The correct response would be “Three corks.” Different objects were
used on different trials so that Alex would not simply learn to say “three” whenever he
saw corks. After a few months of training, Alex was responding correctly on about 80%
of the trials. To show that Alex’s counting ability was not limited to the training stimuli,
new objects were presented on test trials. In some cases, Alex did not even know the
names of the objects (e.g., wooden beads or small bottles), but he was able to give the
correct number of objects on about 75% of the test trials with new stimuli. Pepperberg
found that Alex could count up to six objects, whether familiar or novel, with a high
degree of accuracy.

Matsuzawa (1985) has reported a similar counting skill in a chimpanzee by having the
chimp press response keys with the numbers 1 through 6 on them. Brannon and Terrace
(2000) taught macaques to point to arrays of abstract shapes in order of increasing num-
ber: To receive a reward, the monkey had to first point to the array with one shape, then
to the arrays with two, three, and four shapes. After learning this task, the monkeys were
able to transfer this ability to arrays with between five and nine shapes, even though they
had received no training with these larger numbers. These studies, along with Pepper-
berg’s research with Alex, provide the best evidence available for accurate counting by


Communicating through language is one of the most impressive behaviors that people can
perform. Some scientists and philosophers have claimed that the ability to use language is
one skill that only human beings possess (e.g., Chomsky, 1972). For this reason, attempts to
teach language to chimpanzees and other animals have received tremendous attention. This
section describes some of the most important studies on this topic and examines what the
animals have been able to accomplish.

Research With Chimpanzees

In early attempts to teach language to chimpanzees, some researchers tried to get the
animals to speak (e.g., Kellogg & Kellogg, 1933). These studies were unsuccessful, mainly
because a chimpanzee’s vocal apparatus does not permit it to make many human speech
sounds. To avoid this problem, Gardner and Gardner (1969) decided to try to teach a
chimpanzee, Washoe, to use American Sign Language (ASL). Using a mixture of modeling,
manual guidance, and a good deal of patience, they were able to teach Washoe to produce


signs for quite a few different words, including nouns (e.g., flower, toothbrush, hat), verbs
(go, listen, tickle), adjectives (sweet, funny, more), pronouns (you, me), and prepositions (in, out).
After 4 years, Washoe had learned about 130 signs. This was quite an impressive vocabu-
lary (though still small compared to that of the average 4-year-old child, who knows
several thousand words).

After being taught a sign in a few contexts, Washoe sometimes used it in a new situation
without further training. For instance, she was taught the sign for more in combination with
a few different signs (including more tickle and more swinging), and she later began to use the
sign to ask for more food and for more of other activities. Although she frequently used
signs in various combinations, the order in which she used the signs in a “sentence” was
quite inconsistent. For example, she might sign the phrase food eat on some occasions and
eat food on others, with no apparent reason for the different word orders. In contrast, both
children and adults tend to use consistent word orders whether they are using spoken or
sign language. In short, Washoe had a good vocabulary but poor (perhaps nonexistent)

Instead of using ASL, David Premack (1971, 1983) constructed a language consisting
of different shapes that represented different words. Sentences were created by placing the
shapes on a magnetic board in a specific order. Premack’s pupil, a 6-year-old chimpanzee
named Sarah, learned to respond appropriately to many different configurations of these
symbols. The order of symbols was a critical part of the language Sarah learned, and she
demonstrated an impressive ability to respond on the basis of symbol order. For instance,
after Sarah learned the symbols for different colors and for the word on, she was taught to
respond appropriately to the sequences green on red versus red on green: In the first case she
would put a green card on top of a red card, and in the second case she would do the
opposite. This shows that her responses were controlled by the order of the symbols, not
just by the symbols themselves. Having succeeded at this task, Sarah was then able to
respond correctly to new symbol strings such as blue on yellow with no further training.
Sarah had learned not only that the order of symbols was important but that this same
order could be applied to other symbols as well. This example demonstrates an under-
standing of a grammatical rule, that is, an abstract rule about sentence structure that applies
to entire classes of words.

Sarah was able to learn many grammatical forms and concepts, including plurals,
yes-no questions, and quantifiers (all, some, none, and several). One disappointing fea-
ture of her performance, however, was that she seldom initiated a conversation. Her
use of the symbol language was almost exclusively confined to answering questions
posed by the experimenters. Furthermore, if one of her trainers placed a question on
the board and then left the room, Sarah would usually give either an incorrect response
or none at all. This behavior contrasts quite starkly with that of young children, who
spontaneously practice and use the words they have learned, even when no one is

One researcher who came to pessimistic conclusions about chimp language learning
was Herbert Terrace (1979), who taught ASL to a chimpanzee named Nim Chimpsky.
Nim learned about 125 signs for nouns, verbs, adjectives, pronouns, and prepositions. He
frequently used these signs in combinations of two or more, but like Washoe, Nim
showed very little consistency of word order. Based on his analyses of the behaviors of


Nim, Washoe, Sarah, and other chimps, Terrace concluded that they had learned only the
most primitive grammatical rules, and that frequently they would just string together
signs in a random order. They relied heavily on imitation and on prompting by their
trainers; they showed little spontaneous use of language. The complexity and length of
their sentences did not increase with additional training. Terrace (1979) concluded that
what these chimps had learned lacked many of the essential characteristics of human

Research With Other Species

One of the most accomplished animal language learners to date has been Kanzi, a
bonobo trained by Savage-Rumbaugh and her associates (Savage-Rumbaugh, 1986;
Segerdahl, Fields, & Savage-Rumbaugh, 2005). Kanzi was taught to use lexigrams—
pictorial symbols that represent words. He learned over 300 lexigrams and used them
in a relatively consistent order (e.g., referring first to an action and then an object),
which is evidence of a basic grammar. He exhibited an understanding of many spoken
English words in addition to the lexigrams. He discriminated among different word
orders in spoken sentences and responded appropriately. Language studies have also
been conducted with other primates, including gorillas and orangutans (e.g., Bonvil-
lian & Patterson, 1999; Miles, 1999). ASL has been used in some cases and pictorial
symbols in others. In many of these studies, the animals were able to learn well over
100 signs.

There have also been some studies with nonprimates. Herman, Richards, and Wolz
(1984) trained two bottle-nosed dolphins to respond to about two dozen manual gestures
by engaging in the appropriate activities. For example, a trainer might make the gestures
for frisbee fetch basket, and the dolphin would then find the frisbee and put it in the basket.
The dolphins could also answer questions about whether a particular object was or was
not present in the tank (Herman & Forestell, 1985). Similar work has been done with sea
lions (Schusterman & Krieger, 1984). And the parrot Alex, whose counting abilities have
already been described, learned to say about 50 English words and use them appropriately
to make requests (“Gimme tickle”) and answer questions (Trainer: “What’s this?” Alex:
“Clothespin”). Alex could also answer questions about the physical properties of objects,
describing either an object’s shape or color depending on what question his trainer asked
(Pepperberg, 2010).

Some studies have looked at dogs’ abilities to understand spoken language. Kaminski,
Call, and Fischer (2004) tested a pet collie that was trained by its owners to retrieve
different objects and found that it had learned the names of about 200 different objects.
In addition, if the collie was asked to retrieve an object with a name it had not learned,
the dog would go into the room with the objects, bypass familiar objects, and return
with the unfamiliar object. The dog seemed to be able to infer the names of new objects
using a process of elimination. With another collie, Pilley (2013) found that after exten-
sive training, the dog could respond to the grammatical structure of three-word sen-
tences in much the same way that this ability has been demonstrated with dolphins and
sea lions.


Some Conclusions

Terrace was almost surely correct in saying that the linguistic capacities that animals have
exhibited are quite limited compared to those of humans. On the positive side, however, this
research has shown that animals have at least some measure of language ability. They have
demonstrated many of the characteristics of human language:

1. Use of Abstract Symbols
Possibly the most fundamental characteristic of language is that any arbitrary symbol can

be used to represent an object or concept. It is also the characteristic that has been most
thoroughly demonstrated in animals. As we have seen, animals of several species have shown
the ability to use words, signs, or symbols to represent objects, actions, and descriptions.

2. Productivity
Much of the power of language stems from the ability to take a finite set of words

and combine them in new ways so that one can communicate and understand new
ideas. The ability to use words and symbols in new combinations has been observed
in the language of chimpanzees and other primates. The studies with dolphins and
with the parrot Alex have demonstrated an ability to understand new symbol com-
binations that they heard or saw for the very first time.

3. Grammar
The early work by Premack showed that the chimpanzee Sarah could respond not just

to individual symbols but to the order in which the symbols were presented. This was
also found by Herman in his work with dolphins. In terms of language production,
the evidence is not impressive. Chimps and bonobos have shown some degree of regu-
larity in word order, but their sentences are short, and the word order they use is not
always consistent. There is evidence that other species (pygmy chimpanzees, dolphins,
dogs, and parrots) can learn at least some basic principles of grammar. Nevertheless,
even those who are typically enthusiastic about animal language abilities admit that the
grammatical skills of nonhumans seem quite limited (Givón & Rumbaugh, 2009).

4. Displacement
The ability to use language to talk about the past or the future and about objects

and events not currently present is called displacement. Some studies found that chim-
panzees can use their signs to describe behaviors they have just performed or are
about to perform (Premack, 1986). In one case, researchers found that two pygmy
chimpanzees could use lexigrams to refer to objects and events not present (Savage-
Rumbaugh, McDonald, Sevcik, Hopkins, & Rubert, 1986). However, there is consid-
erable debate about this matter. One experiment found that whereas 12-month-old
human infants could gesture to communicate about a desired object that was not
present, chimpanzees did not do so. The researchers concluded that this may be a
uniquely human ability (Liszkowski, Schäfer, Carpenter, & Tomasello, 2009).

5. Use in Communication
For people, the purpose of language is to communicate with others. Terrace (1979)

claimed that the language-trained chimps used their language only to obtain reinforcers,


not to communicate information. However, later findings suggested that animals
do use their signs to communicate with other animals or with people. Fouts,
Fouts, and Schoenfield (1984) reported that five chimpanzees that had been taught
ASL signs would use these signs to communicate with one another, even when
no human beings were present to prompt or reinforce these behaviors. Greenfield
and Savage-Rumbaugh (1993) found that two different species of chimpanzees
used the symbols they were taught by humans to express a variety of different
functions, such as agreement, requests, and promises. These chimpanzees often
displayed the sort of turn taking in the use of symbols that is typical of human

In summary, some of the main characteristics of human language have been found, at
least at a rudimentary level, in other species. Future research will probably uncover other
linguistic abilities in animals. Although no other species has shown the level of language
capabilities that people have, it is not quite accurate to say that language is a uniquely
human ability.


Besides language, many other advanced cognitive skills have been studied in animals, includ-
ing abstract reasoning, problem solving, and the manufacture and use of tools. This section
reviews a few of the findings.

Object Permanence

Object permanence is an understanding that objects continue to exist even when they
are not visible. The developmental psychologist Jean Piaget (1926) proposed that during
the first 2 years of life, human infants proceed through six different stages in which their
understanding of object permanence becomes more and more complete. Piaget developed
a series of tests to determine which of the six stages an infant has reached, and these tests
can be adapted quite easily for use with animals. Research with different species, including
cats and dogs, has shown that they follow more or less the same sequence of stages as
human infants, eventually reaching stage six, in which they will correctly search for an
object after an “invisible displacement” (Dore & Dumas, 1987). For example, Figure 10.9
shows the procedure used by Miller, Rayburn-Reeves, and Zentall (2009). A dog watches
as a person places a snack in one of the two containers. The bar with the containers is
rotated 90 degrees (an “invisible displacement” because the snack cannot be seen), the
room is darkened for a few seconds, and then the dog is allowed to choose one container.
Most dogs were successful at this task as long as the period of darkness was not too long.
This level of competence has been found in several species of primates (Albiach-Serrano,
Call, & Barth, 2010) and birds (Pepperberg & Funk, 1990). However, not all species per-
form equally well on these tasks. A study with dolphins found that they were successful
with visible object displacements but not invisible displacements (Jaakkola, Guarino,
Rodriguez, Erb, & Trone, 2010).



An analogy is a statement of the form “A is to B as C is to D.” To test someone’s ability to
understand analogies, we can give the person two or more choices for D and ask which is
correct. For example, consider the analogy, “Lock is to key as can is to ______.” Is paintbrush
or can opener a more appropriate answer? On this type of problem, the ability to make judg-
ments about physical similarity is usually not enough. In physical terms, a can opener is not
especially similar to a key, a lock, or a can. To solve this analogy, one must understand (1)
the relation between lock and key, (2) the relation between can opener and can, and (3) the
similarity of the two relations (i.e., that the second item of each pair is used to open the
first). In other words, to understand an analogy one must be able to understand a relation
(similarity) between two relations.

Gillan, Premack, and Woodruff (1981) tested Sarah, the language-trained chimpanzee,
with analogies that involved either perceptual relations or functional relations between
objects. The analogy in the previous paragraph involves functional relations because it
requires an understanding of the functions the different objects serve, and it was one of the
analogies given to Sarah (Figure 10.10). An example of a perceptual analogy is the following:
“Large yellow triangle is to small yellow triangle as large red crescent is to (small red crescent
or small yellow crescent)?” This analogy also requires an understanding of the relations
between objects, but in this case the relations pertain only to the perceptual properties of
the objects (their relative sizes).

Sarah was fairly good at solving both types of analogies. There has not been much
research on this ability with other species, but one study found that baboons could success-
fully solve perceptual analogies (Fagot & Parron, 2010).

Figure 10.9 In this test of object permanence, a dog watches as a person puts a treat in one of the two
containers (which are aligned as in the left panel). The bar with the containers is rotated 90 degrees
(right panel), the room is darkened for a few seconds, and then the dog is allowed to choose one
container. (Reprinted from Behavioural Processes, Vol. 81, Miller, H.C., Rayburn-Reeves, R., & Zentall,
T., What do dogs know about hidden objects? 439–446. Copyright 2009, with permission from


Transitive Inference

If Adam is shorter than Bill, and if Bill is shorter than Carl, then it follows that Adam is
shorter than Carl. This conclusion is justified because inequalities of size are transitive; that
is, they conform to the following general rule: if A < B and B < C, then A < C. If we draw the correct conclusion about the heights of Adam and Carl without ever having seen them side by side, we are displaying the capacity for transitive inference. Gillan (1981) tested whether chimpanzees were capable of transitive inference by first training them with containers of different colors that had food in some situations but not others. For instance, one chimp was taught that blue was better than black, black was better than red, and so on. In the test for transitive inference, a chimp had to choose between two containers that had never been paired before. For instance, when given a choice between blue and red, would the chimp choose blue? Gillan found that the chimps were capable of making such inferences. Later studies have shown that Figure 10.10 Pictures presented to the chimpanzee Sarah by Gillan, Premack, and Woodruff (1981). The pictures represent the analogy, “Lock is to key as can is to what?” Two possible answers, can opener and paintbrush, were presented below the line, and Sarah chose the correct answer. (From Gillan, D.J., Premack, D., & Woodruff, G., 1981, Reasoning in the chimpanzee: I. Analogical reasoning. Journal of Experimental Psychology: Animal Behavior Processes, 7, 1–17. © American Psychological Asso- ciation. Reprinted with permission.) COMPARATIVE COGNITION 285 transitive inference can be found in numerous species, including rats (Davis, 1992), mice (DeVito, Kanter, & Eichenbaum, 2010), and pigeons (von Fersen, Wynne, Delius, & Staddon, 1991). Tool Use and Manufacture You might think that only human beings are capable of making and using tools, but this is not so. Several different species are known to use tools of various types (Figure 10.11). For example, sea otters hold rocks against their chests while floating in the water and use them to crack open the shells of mollusks. Several birds, including the woodpecker finch and the crow, use sticks or branches to fish out larvae or insects from holes where their beaks will not reach. Examples of tool use among primates are numerous. For instance, chimpanzees use leaves as towels to wipe themselves or as umbrellas in the rain, and they use sticks and rocks as weapons to defend themselves against predators. Even more impressive than examples of tool use are the rare instances in which animals have been observed to make a tool and then use it for some specific purpose. One example is a chimpanzee that was taught how to hit one stone against another to make a cutting tool and then to use the tool to cut a cord. Later, the chimp made such cutting tools on his own, learning by trial and error how to smash the stones effectively to get Figure 10.11 Many animals, like this gorilla, use sticks as tools to extract insects from logs or trees. (dean bertoncelj/ COMPARATIVE COGNITION286 sharp cutting edges (Toth, Schich, Savage-Rumbaugh, Sevcik, & Rumbaugh, 1993). This chimp first learned the skill through observation, but other animals have learned to manufacture tools by themselves. Weir, Chappell, and Kacelnik (2002) found that a female crow learned to bend a straight piece of wire into a hook and then use the hook to pull a container of food out of a vertical pipe. The crow used her beak and foot to bend the wire, and it was not an accidental behavior: Of 10 trials in which the crow was given a straight piece of wire, she bent the wire and successfully retrieved the food con- tainer 9 times. These examples of tool making have generated a great deal of interest because they suggest that the animals may have some basic understanding of the cause- and-effect relation between modifying an object and then using that object to accomplish some task. BOX 10.2 SPOTLIGHT ON RESEARCH Metacognition: Do Animals Know What They Know? Stated simply, metacognition is thinking about one’s thinking. To be more specific, it is the ability to reflect on one’s memories and thought processes and make judgments about them. For instance, people can state how sure they are about something they remember or about whether they know a particular piece of information. I may tell you that I am positive I know the name of a particular actor, even though I can’t think of it at the moment. I may say I think I remember that Sam was at last summer’s department picnic but that I am not really sure. People have the ability to make judgments about the accuracy of their own memories (along with other abilities that would also be classified as metacognition). In recent years, there have been many studies examining whether animals are also capable of metacognition (Kornell, 2009). Different techniques have been used to test for such abilities, and many of them have obtained positive results. For example, to determine whether rhesus monkeys could judge the accuracy of their memories, they were given a delayed matching-to-sample task in which they had the option of choos- ing an “uncertainty response,” which allowed them to skip a trial and move on to the next one. The monkeys frequently made the uncertainty response when the trial was a difficult one, but they seldom made the uncertainty response on easy trials. This shows that they could accurately judge when they were likely to make mistakes (Hampton, 2001). One study found that apes would seek more information when on trials where they were uncertain but not when they already knew the correct choice (Call, 2010). When rhesus monkeys performed a discrimination task with easy and difficult trials, they chose to take larger risks to obtain greater rewards on the easy trials, which suggests that they knew they would make the correct choice (Shields, Smith, Guttmannova, & Washburn, 2005). COMPARATIVE COGNITION 287 CONCLUSIONS Human beings cannot boast that they are the only species on earth capable of abstract thinking. Many lines of evidence suggest that other animals can learn a vari- ety of tasks that involve abstract reasoning. It seems likely that more examples of abstract reasoning will be found in other species in future research. Perhaps the moral is that it is always risky to claim, “Here is a problem in abstract reasoning that only humans (or only primates) can solve.” The danger is that some clever researcher will find a way to teach a bird or rodent to solve exactly that problem. Although no one would seriously question the vast differences between human and nonhuman intellectual abilities, some of the apparent limitations of animals’ reason- ing abilities might be due to limitations in our current training or testing procedures, not to the animals. SUMMARY Two procedures used to study short-term memory in animals are DMTS and the radial-arm maze. In DMTS, performance accuracy declines quickly as the delay between sample and comparison stimuli increases. Studies using radial-arm mazes have shown that rats can gener- ally avoid repeat visits to arms of a maze where they have already collected food, even when a maze has as many as 17 arms. Other studies have found evidence for maintenance rehearsal, associative rehearsal, and chunking in animals. Experiments on long-term memory have shown that pigeons can remember several hundred pictures with a high degree of accuracy. The topic of animal metacognition has received a great deal of attention from behav- ioral and cognitive psychologists because metacognition has been considered to be a sophisticated human capability. Some psychologists are still skeptical about whether these results are convincing demonstrations of metacognition. However, the evidence for animal metacognition is growing, and it may provide a compelling example of conti- nuity between humans and other animals and offer insights into the evolution of human mental abilities (Smith, Couchman, & Beran, 2014). Practice Quiz 2: chaPter 10 1. In the peak procedure, if an animal’s responses are sometimes reinforced 20 seconds after the start of a trial, the rate of responding peaks at about ______ from the start of the trial. 2. Two species that have demonstrated the ability to count by using words or symbols to represent numbers are the ______ and the ______. 3. Grouping similar objects together as a strategy for improving memory is called ______. 4. When the chimpanzee Washoe was taught sign language, she learned the signs for many words, but she showed little ability to use ______. 5. ______ is the understanding that objects continue to exist when they are not visible. Answers 1. 20 seconds 2. parrot, chimpanzee 3. chunking 4. grammar or consistent word order 5. object permanence COMPARATIVE COGNITION288 Other studies have demonstrated that long-term memory can be improved if an animal is given an appropriate stimulus as a reminder of a previous learning experience. Various experiments on timing have demonstrated that the duration of a stimulus can control an animal’s behavior with reasonable accuracy and so can the number of stimuli. When researchers have tried to teach language to animals, the responses resemble human language abilities in some respects but not in others. Some chimpanzees have learned to use more than 100 signs or symbols for words, but they seldom use any consistent word order or grammar. However, Premack’s research with a chimpanzee and some studies with dol- phins showed that the animals could learn the importance of word order. Other studies have found that several species (gorillas, parrots, dogs) can learn the meanings of gestures, symbols, or spoken words. Animals of various species have exhibited various types of abstract reasoning. For instance, rats, mice, and pigeons can solve problems of transitive inference. Cats, dogs, and birds can perform tasks involving object permanence. Baboons and chimpanzees have successfully solved tests of analogical reasoning. There is also some evidence that nonhuman primates have the capacity for metacognition. Review Questions 1. Describe how DMTS and the radial-arm maze can be used to study animal short-term memory. Discuss some of the main findings that have been obtained with these procedures. 2. What are maintenance rehearsal and associative rehearsal? Describe one exper- iment that appears to demonstrate each type of rehearsal in animals. 3. Describe two pieces of evidence that animals can use chunking as an aid to memory. 4. Discuss the strengths and the limitations of the language abilities of chimpanzees trained to use ASL. What other techniques have been used to teach language to animals, what other species have been used, and what has been found? 5. Describe some tasks that have been used to test animals’ reasoning abilities. Give examples of reasoning abilities that are found in many species and of abili- ties that have been found in just a few species. REFERENCES Albiach-Serrano, A., Call, J., & Barth, J. (2010). Great apes track hidden objects after changes in the objects’ position and in subject’s orientation. American Journal of Primatology, 72, 349–359. Amundson, J.C., & Miller, R.R. (2008). Associative interference in Pavlovian conditioning: A func- tion of similarity between the interfering and target associative structures. Quarterly Journal of Experimental Psychology, 61, 1340–1355. COMPARATIVE COGNITION 289 Babb, S.J., & Crystal, J.D. (2003). 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Learning Objectives After reading this chapter, you should be able to • describe several different theories of imitation and discuss their strengths and weaknesses • explain Bandura’s theory about the four factors necessary for successful imitation • describe several ways in which modeling has been used in behavior therapy • discuss the roles of reinforcement, knowledge of results, and knowledge of performance in motor-skill learning • describe Adams’s two-stage theory and Schmidt’s schema theory and explain how they differ • compare the response chain approach and the concept of motor programs, and present evidence for the existence of motor programs C H A P T E R 1 1 Observational Learning and Motor Skills Let there be no mistake about it: A large proportion of human learning occurs not through classical conditioning or as a result of reinforcement or punishment but through observation. In their classic book, Social Learning and Personality Development (1963), Bandura and Walters argued that traditional learning theory was grossly incomplete because it neglected the role of observational learning. As we have seen, traditional learning theory emphasizes the importance of individual experience: An individual performs some behavior and experiences the consequences that follow. The point of Bandura and Walters was that a good deal of learning occurs through vicarious rather than personal experience: We observe the behavior OBSERVATIONAL LEARNING AND MOTOR SKILLS 294 of others, we observe the consequences, and later we may imitate their behavior. In the first part of this chapter, we will survey different theories about how observational learning takes place, and we will examine the importance of observational learning in everyday life and in behavior therapy. Another topic that has been largely neglected by traditional learning theorists is motor-skill learning. Many everyday behaviors are examples of learned motor skills— walking, driving, writing, typing, playing a musical instrument, playing sports, etc. The second half of this chapter will examine some of the factors that affect our ability to learn and perform motor skills and some of the most popular theories about how motor skills are learned. THEORIES OF IMITATION Imitation as an Instinct Evidence that imitation may be an innate tendency comes from research on both human infants and animals. Meltzoff and Moore (1977) sought to determine whether 12- to 21-day-old infants would imitate any of four gestures made by an adult tester: lip protrusion, mouth opening, tongue protrusion, and sequential finger movement. Meltzoff and Moore found a reliable tendency for the infants to imitate the specific behavior that they had just seen. Because of the young ages of these infants, it seems very unlikely that such imitative behaviors were the result of prior learning. Although these results are fascinating, the ability of newborns to imitate may be lim- ited to just a few special behaviors. Other research has found little evidence for a general ability to imitate in young children. Children of ages 1 to 2 years were taught to imitate an adult in performing a specific set of gestures (the “baseline matching relations” in Figure 11.1). Once they learned to imitate these gestures, they were tested to see if they would imitate a new set of gestures (the “target matching relations” in Figure 11.1). The children showed very little tendency to imitate the new gestures (Horne & Erjavec, 2007). The researchers concluded that these young children were not yet capable of imitating arbitrary new behaviors, only those that they had been specifically trained to imitate. These findings suggest that a general ability to imitate new behaviors does not appear until later in childhood. When it comes to imitation by animals, hundreds of experiments have been conducted with such diverse subjects as primates, cats, dogs, rodents, birds, and fish (Robert, 1990). In some cases, animals may simply copy the behaviors of others that are nearby, as when one deer is startled and starts to run and other deer start running as well. This is imitation in its most primitive sense because nothing new is learned: The animals are simply imitating behaviors they already knew how to perform. A more advanced type of social learning, true imitation, occurs when an animal imitates a behavior that it has never performed before and probably would not learn on its own. While observing a troop of monkeys living on an island, Kawai (1965) reported several examples of true imitation—novel behaviors that spread quickly through the troop as a result of observational learning. These included Figure 11.1 In a test of generalized imitation, 1- to 2-year-old children who were first taught to imitate an adult making the gestures in the top set later showed little imitation of the new gestures in the bottom set. (From Horne, P.J., & M. Erjavec, M., Do infants show generalized imitation of ges- tures? Journal of the Experimental Analysis of Behavior, 87, 63–87. Copyright 2007 by the Society for the Experimental Analysis of Behavior, Inc. Reprinted with permission.) OBSERVATIONAL LEARNING AND MOTOR SKILLS 296 washing the sand off sweet potatoes and bathing in the ocean (which the monkeys had never done until one pioneer took up this activity). Examples of true imitation have also been seen in gorillas and orangutans. Orangutans in captivity have imitated many complex behaviors of their human caretakers, such as “sweeping and weeding paths, mixing ingredients for pancakes, tying up hammocks and riding in them, and washing dishes or laundry” (Byrne & Russon, 1998, p. 678). Researchers have reported examples of true imitation in rats, quail, and other species. In summary, the ability to learn through observation can be observed in many species, and this lends credence to the view that the tendency to imitate is instinctive. The problem with this account, however, is that it tells us nothing about when imitation will occur and when it will not. Other theories of imitation have tried to answer this question. Imitation as an Operant Response In an influential book, Social Learning and Imitation, Miller and Dollard (1941) claimed that observational learning is simply a special case of operant conditioning where the discrimina- tive stimulus is the behavior of another person, and the appropriate response is a similar behavior on the part of the observer. One of their many experiments will illustrate their approach. First-grade children participated in this experiment in pairs, with one child being the “leader” and the other the “learner.” On each of several trials, the two children would enter a room in which there were two chairs with a large box on top of each. The leader was instructed in advance to go to one of the two boxes, where there might be a piece of candy. The learner could see where the leader went, but not whether the leader obtained any candy. Next, it was the learner’s turn to go to one of the two boxes, where he or she might or might not find a piece of candy. Half of the learners were in an imitation group— they were reinforced for making the same choice as the leader. The other learners were in the nonimitation group—they obtained reinforcement if their choice was opposite that of the leader. The result of this simple experiment was not surprising: After a few trials, children in the imitation group always copied the response of the leader, and those in the non- imitation group always made the opposite response. Miller and Dollard concluded that, like any other operant response, imitation will occur if imitation is reinforced, and nonimitation will occur if nonimitation is reinforced. In both cases, the behavior of another person is the discriminative stimulus that indicates what response is appropriate. According to Miller and Dollard, then, imitative learning fits nicely into the three-term contingency of discriminative stimulus, response, and reinforcement. There is no need to claim that observational learning is a separate class of learning that is different from operant conditioning. Imitation as a Generalized Operant Response As Bandura (1969) pointed out, Miller and Dollard’s analysis of imitation applies only to those cases where a learner (1) observes the behavior of a model, (2) immediately copies the response, and (3) receives reinforcement. Many everyday examples of imitation do not follow OBSERVATIONAL LEARNING AND MOTOR SKILLS 297 this pattern. Suppose a little girl watches her mother make herself a bowl of cereal: The mother takes a bowl out of the cabinet, pours in the cereal, and then adds milk and sugar. The next day, alone in the kitchen, the girl may decide to make herself a bowl of cereal, and she may do so successfully. Here we have an example of learning by observation, but if the girl had never done this before, obviously these behaviors could not have been reinforced. This is a case of learning without prior practice of the response and without prior reinforcement. The principle of reinforcement can account for such novel behavior if we include the concept of generalization, however. If the young girl had been previously reinforced for imitating the behaviors of her parents, her imitation of the behaviors involved in making a bowl of cereal might be simply an example of generalization. This explanation seems plau- sible because most parents frequently reinforce imitation by their children (Figure 11.2). Imitating a parent’s behavior of speaking a word or phrase, of solving a puzzle, of holding a spoon correctly, etc., may be reinforced with smiles, hugs, and praise. It would not be surpris- ing if this history of reinforcement led to imitation in new situations—generalized imitation. Generalized imitation has been demonstrated in a various experiments. For example, chil- dren with severe developmental disabilities were reinforced for imitating a variety of behaviors performed by the teacher (standing up, nodding yes, opening a door). After establishing imita- tive responses (which required several sessions), the teacher occasionally performed various new behaviors, and the children would also imitate these behaviors although they never received Figure 11.2 Children frequently imitate the behaviors of their parents and are rewarded for doing so. (Brenda Delany/Shutterstock) OBSERVATIONAL LEARNING AND MOTOR SKILLS 298 reinforcers for doing so (Baer, Peterson, & Sherman, 1967). Many other studies have also found generalized imitative behavior in children (e.g., Camòes-Costa, Erjavec, & Horne, 2011). Bandura’s Theory of Imitation Bandura maintained that the theory of generalized imitation, like the other theories of imita- tion, is inadequate. His reasons can be illustrated by considering a famous experiment on the imitation of aggressive behaviors by 4-year-olds (Bandura, 1965). Each child first watched a short film in which an adult performed four distinctive aggressive behaviors against an inflated Bobo doll. Some of the children then saw the adult model being rein- forced by another adult: She was given a soft drink, candies, and other snacks and was called a “strong champion.” Other children saw the model being punished for his aggressive behavior: The model was scolded for “picking on that clown,” was spanked, and was warned not to act that way again. For children in a third group, the film contained no consequences for the model’s aggressive behavior. Immediately after viewing the film, a child was brought into a room that contained a Bobo doll and many other toys. The child was encouraged to play with the toys and was left alone in the room but was observed through a one-way mirror. Many instances of aggressive behaviors against the Bobo doll were recorded, and most of these resembled those of the adult model in the film (Figure 11.3). Figure 11.3 The top row shows frames from a film in which an adult model exhibits a number of different aggressive behaviors toward a Bobo doll. The two bottom rows show children imitating the model after having watched the film. (From Bandura, et al., Imitation of film-mediated aggressive models, Journal of Abnormal Psychology, 66, 1963, 3–11, © American Psychological Association. Reprinted with permission.) OBSERVATIONAL LEARNING AND MOTOR SKILLS 299 Bandura claimed that two specific findings from this experiment cannot be explained by the theory of generalized imitation. First, the consequences to the model made a dif- ference: Children who saw the model being punished displayed less imitation than chil- dren in the other two groups. Second, in the final phase of the study, the experimenter offered to reward the child if he or she would imitate the behavior of the model in the film. With this incentive, children in all three groups produced large and equal amounts of aggressive behavior. Bandura argued that the theory of generalized imitation cannot explain (1) why consequences to the model affect the behaviors of the learner or (2) why some children did not imitate until they were offered a reward for doing so. We can evalu- ate the validity of these two points later, but first let us examine the theory Bandura developed as an alternative. Bandura’s theory of imitation (1969) can definitely be classified as a cognitive theory, for it proposes several processes that cannot be observed in an individual’s behavior. It states that four factors determine whether imitative behavior will occur: 1. Attentional Processes The learner must pay attention to the appropriate features of the model’s behavior if imitation is to occur. A young girl may watch her mother make a bowl of cereal, but if she did not pay attention to where the sugar came from and how much to put in, she may be quite unsuccessful in her attempt at imitation. 2. Retentional Processes The learner must retain some of the information that is gained through observation if imitation is to occur at a later time. Bandura states that rehearsal can be important here. Thus the little girl may say to herself, “First the cereal, then the milk, then the sugar.” Notice that this information is stated in a fairly abstract way, and Bandura assumes that some abstraction of this type is all that is remembered. Thus the child may not remember exactly where in the refrigerator the milk was or exactly where on the table her mother placed the bowl, but such specific information is not usually necessary for successful imitation. 3. Motor Reproductive Processes The learner must have the appropriate motor skills in order to imitate a model. In other words, the learner must be able to translate general knowledge (“Put a bowl on the table”; “Pour in some cereal”) into a coordinated pattern of muscle movements. In the examples of children making cereal or hitting a Bobo doll, this translation of knowledge into action poses no problem because the children already possessed the required motor skills (handling objects, pouring, kicking, punching, etc.). In other cases of observational learning, however, motor abilities cannot be taken for granted. For example, a model may demonstrate slowly and in a step- by-step manner the sequence of movements involved in juggling three balls, and the learner may retain this information in an abstract form (i.e., he or she may be able to recite the necessary sequences), but the learner may still be unable to produce the appropriate movements without extensive practice. Similarly, imitating behaviors such as doing a cartwheel, landing an airplane, or smoothly plastering a wall may be impossible because the observer lacks the necessary motor skills. OBSERVATIONAL LEARNING AND MOTOR SKILLS 300 4. Incentive and Motivational Processes According to Bandura, the first three processes are all that are necessary for the learn- ing of a new behavior, but the learner will not actually perform the behavior without an appropriate incentive. The learner must have an expectation that performing the new behavior will produce some type of reinforcement. Bandura’s (1965) Bobo doll study is a good example. Children who saw the model being punished for aggressive play with the Bobo doll presumably developed the expectation that such behavior would lead to unpleasant consequences, so they were less likely to imitate the model. However, when the experimenter changed the children’s expectations by offering rewards for imitating the model, these children exhibited just as much imitation as the other two groups. Generalized Imitation Versus Bandura’s Theory Not everyone agrees with Bandura’s claims that the theory of generalized imitation is inad- equate. Kymissis and Poulson (1990) claimed that the theory can account for all types of imitative behaviors using only well-established principles of operant conditioning. Based on what we know about generalization, it seems reasonable to make the following, specific predictions: Imitation will most likely occur in situations that are similar to those where imitation was reinforced in the past. Conversely, imitation will be least likely occur in situ- ations that are similar to those where imitation was punished in the past. We can apply these two principles to Bandura’s (1965) experiment. Why did children frequently fail to imitate the adult model who was punished? According to the theory of generalized imitation, this is because the children had learned from past experience that it is not a good idea to imitate someone who has just been punished. Why did children in all groups display large amounts of imitation when they were offered rewards for doing so? This result is similar to the latent learning experiments in which rats displayed their ability to run through a maze without errors only after food became available in the goal box (as described in Chapter 8). Behavioral psychologists have long recognized the distinction between learning and performance, and most have concluded that reinforcement is not essential for learning, but it is essential for the performance of learned behaviors. In summary, both the theory of generalized imitation and Bandura’s theory can account for these results, but they do so in slightly different ways. Whereas Bandura’s theory uses concepts such as attention, retention, and expectation of reward, the theory of generalized imitation relies on behavioral principles such as stimulus discrimination, generalization, and the learning/performance distinction. As in other debates between the cognitive and behav- ioral approaches, the debate over explanations of imitative behavior is partly about terminol- ogy and partly about how much we should speculate about processes that we cannot observe