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Animal Learning &Cognition

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Animal Learning &Cognition,An IntroductionThird Edition

John M. Pearce

Cardiff University

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Published in 2008 by Psychology Press27 Church Road, Hove, East Sussex, BN3 2FA

Simultaneously published in the USA and Canada by Psychology Press270 Madison Ave, New York, NY 10016

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

© 2008 Psychology Press

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

The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legalresponsibility or liability for any errors or omissions that may be made.

British Library Cataloguing in Publication DataA catalogue record for this book is available from the British Library

Library of Congress Cataloging-in-Publication DataPearce, John M.

Animal learning and cognition: an introduction / John M. Pearce.p. cm.

Includes bibliographical references and index.ISBN 978–1–84169–655–3—ISBN 978–1–84169–656–01. Animal intelligence. I. Title.

QL785.P32 2008591.5'13—dc22 2007034019

ISBN: 978–1–84169–655–3 (hbk)ISBN: 978–1–84169–656–0 (pbk)

Typeset by Newgen Imaging Systems (P) Ltd, Chennai, IndiaPrinted and bound in Slovenia

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For Victoria

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ContentsPreface ix

1 The study of animal intelligence 2The distribution of intelligence 4Defining animal intelligence 12Why study animal intelligence? 16Methods for studying animal intelligence 20Historical background 22

2 Associative learning 34Conditioning techniques 36The nature of associative learning 42Stimulus–stimulus learning 49The nature of US representations 52The conditioned response 55Concluding comment: the reflexive nature of the conditioned response 60

3 The conditions for learning: Surprise and attention 62Part 1: Surprise and conditioning 64Conditioning with a single CS 64Conditioning with a compound CS 68Evaluation of the Rescorla–Wagner model 72Part 2: Attention and conditioning 74Wagner’s theory 76Stimulus significance 80The Pearce–Hall theory 86Concluding comments 91

4 Instrumental conditioning 92The nature of instrumental learning 93The conditions of learning 97

The performance of instrumental behavior 106The Law of Effect and problem solving 111

5 Extinction 122Extinction as generalization decrement 123The conditions for extinction 125Associative changes during extinction 134Are trials important for Pavlovianextinction? 142

6 Discrimination learning 148Theories of discrimination learning 149Connectionist models of discrimination learning 161Metacognition and discrimination learning 166

7 Category formation 170Examples of categorization 171Theories of categorization 173Abstract categories 179Relationships as categories 180The representation of knowledge 188

8 Short-term retention 190Methods of study 191Forgetting 199Theoretical interpretation 202Serial position effects 206Metamemory 207

9 Long-term retention 212Capacity 214Durability 215

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viii Contents

Theoretical interpretation 218Episodic memory 225

10 Time, number, and serial order 232Time 233Number 243Serial order 253Transitive inference 259Concluding comments 262

11 Navigation 264Part 1: Short-distance travel 265Methods of navigation 265Part 2: Long-distance travel 283Navigational cues 284Homing 286Migration 289Concluding comments 293

12 Social learning 296Diet selection and foraging 298Choosing a mate 301Fear of predators 301Copying behavior: mimicry 302

Copying behavior: imitation 304Theory of mind 312Self-recognition 319Concluding comments 324

13 Animal communication andlanguage 326Animal communication 327Communication and language 336Can an ape create a sentence? 339Language training with other species 350The requirements for learning a language 356

14 The distribution of intelligence 360Intelligence and brain size 361The null hypothesis 364Intelligence and evolution 369

References 373Author index 403Subject index 411

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Preface

In preparing the third edition of this book, my aim, as it was for the previous editions,has been to provide an overview of what has been learned by pursuing one particularapproach to the study of animal intelligence. It is my belief that the intelligence ofanimals is the product of a number of mental processes. I think the best way ofunderstanding these processes is by studying the behavior of animals in anexperimental setting. This book, therefore, presents what is known about animalintelligence by considering experimental findings from the laboratory and from morenaturalistic settings.

I do not attach any great importance to the distinction between animal learningand animal cognition. Research in both areas has the common goal of elucidating themechanisms of animal intelligence and, very often, this research is conducted usingsimilar procedures. If there is any significance to the distinction, then it is that thefields of animal learning and animal cognition are concerned with different aspectsof intelligence. Chapters 2 to 6 are concerned predominantly with issues that fallunder the traditional heading of animal learning theory. My main concern in thesechapters is to show how it is possible with a few simple principles of associativelearning to explain a surprisingly wide range of experimental findings. Readersfamiliar with the previous edition will notice that apart from a new chapter devotedto extinction, there are relatively few changes to this part of the book. This lack ofchange does not mean that researchers are no longer actively investigating the basiclearning processes in animals. Rather, it means that the fundamental principles of

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x Preface

learning are now reasonably well established and that current research is directedtowards issues that are too advanced to be considered in an introductory text book.

The second half of the book covers material that is generally treated under theheading of animal cognition. My overall aim in these chapters is to examine what hasbeen learned from studying animal behavior about such topics as memory, therepresentation of knowledge, navigation, social learning, communication, andlanguage. I also hope to show that the principles developed in the earlier chapters areof relevance to understanding research that is reviewed in the later chapters. It is inthis part of the book that the most changes have been made. Research on animalcognition during the last 10 years has headed in many new directions. I have tried topresent a clear summary of this research, as well as a balanced evaluation of itstheoretical implications.

Those who wish to study the intelligence of animals face a daunting task. Notonly are there numerous different species to study, but there is also an array ofintellectual skills to be explored, each posing a unique set of challenging theoreticalproblems. As a result, many of the topics that I discuss are still in their infancy. Somereaders may therefore be disappointed to discover that we are still trying to answermany of the interesting questions that can be asked about the intelligence of animals.On the other hand, it is just this lack of knowledge that makes the study of animallearning and cognition so exciting. Many fascinating discoveries remain to be madeonce the appropriate experiments have been conducted.

One of the rewards for writing a book is the opportunity it provides to thank themany friends and colleagues who have been so generous with the help they havegiven me. The way in which this book is organized and much of the material itcontains have been greatly influenced by numerous discussions with A. Dickinson,G. Hall, N. J. Mackintosh, and E. M. Macphail. Different chapters have benefitedgreatly from the critical comments on earlier versions by A. Aydin, N. Clayton,M. Haselgrove, C. Heyes, V. LoLordo, A. McGregor, E. Redhead, and P. Wilson. A special word of thanks is due to Dave Lieberman, whose thoughtful comments onan earlier draft of the present edition identified numerous errors and helped to clarifythe manner in which much of the material is presented. The present edition hasalso greatly benefited from the detailed comments on the two previous editions byN. J. Mackintosh.

I should also like to express my gratitude to the staff at Psychology Press.Without the cajoling and encouragement of the Assistant Editor, Tara Stebnicky, it isunlikely that I would have embarked on this revision. I am particularly grateful to theProduction Editor, Veronica Lyons, who, with generous amounts of enthusiasm andimagination, has done a wonderful job in trying to transform a sow’s ear into a silkpurse. Thanks are also due to the colleagues who were kind enough to send mephotographs of their subjects while they were being tested. Finally, there is thepleasure of expressing gratitude to Victoria, my wife, who once again patientlytolerated the demands made on her while this edition was being prepared. In previouseditions I offered similar thanks to my children, but there is no need on this occasionnow that they have left home. Even so, Jess, Alex, and Tim would never forgive meif I neglected to mention their names.

While preparing for this revision I read a little about Darwin’s visit to theGalapagos Islands. I was so intrigued by the influence they had on him that I feltcompelled to visit the islands myself. During the final stages of preparing thisedition, Veronica and Tania, somewhat reluctantly, allowed me a two-week break totravel to the Galapagos Islands. The holiday was one of the highlights of my life.

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Preface xi

The sheer number of animals, and their absolute indifference to the presence ofhumans, was overwhelming. The picture on the previous page shows me tryingunsuccessfully to engage a giant tortoise in conversation. This, and many otherphotographs, were taken without any elaborate equipment and thus reveal how theanimals allowed me to approach as close as I wished in order to photograph them. I came away from the islands having discovered little that is new about theintelligence of animals, but with a deeper appreciation of how the environmentshapes not only their form, but also their behavior.

John M. PearceOctober, 2007

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CH

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C O N T E N T S

The nature of instrumental learning 93

The conditions of learning 97

The performance of instrumental behavior 106

The Law of Effect and problem solving 111

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4InstrumentalconditioningBehavior is affected by its consequences. Responses that lead to reward are repeated,whereas those that lead to punishment are withheld. Instrumental conditioning refersto the method of using reward and punishment in order to modify an animal’sbehavior. The first laboratory demonstration of instrumental conditioning wasprovided by Thorndike (1898) who, as we saw in Chapter l, trained cats to make aresponse in order to escape from a puzzle box and earn a small amount of fish. Sincethis pioneering work, there have been many thousands of successful demonstrationsof instrumental conditioning, employing a wide range of species, and a variety ofexperimental designs. Skinner, for example, taught two pigeons, by means ofinstrumental conditioning, to play ping-pong with each other.

From the point of view of understanding the mechanisms of animal intelligence,three important issues are raised by a successful demonstration of instrumentalconditioning. We need to know what information an animal acquires as a result of itstraining. Pavlovian conditioning was shown to promote the growth ofstimulus–stimulus associations, but what sort of associations develop when a responseis followed by a reward or punishment? Once the nature of the associations formedduring instrumental conditioning has been identified, we then need to specify theconditions that promote their growth. Surprise, for example, is important for successfulPavlovian conditioning, but what are the necessary ingredients to ensure the success ofinstrumental conditioning? Finally, we need to understand the factors that determinewhen, and how vigorously, an instrumental response will be performed.

Before turning to a detailed discussion of these issues, we must be clear what ismeant by the term reinforcer. This term refers to the events that result in thestrengthening of an instrumental response. The events are classified as either positivereinforcers, when they consist of the delivery of a stimulus, or negative reinforcers,when it involves the removal of a stimulus.

THE NATURE OF INSTRUMENTAL LEARN ING

Historical backgroundThorndike (1898) was the first to propose that instrumental conditioning is based onlearning about responses. According to his Law of Effect, when a response isfollowed by a reinforcer, then a stimulus–response (S–R) connection is strengthened.In the case of a rat that must press a lever for food, the stimulus might be the leveritself and the response would be the action of pressing the lever. Each successfullever press would thus serve to strengthen a connection between the sight of the leverand the response of pressing it. As a result, whenever the rat came across the lever inthe future, it would be likely to press it and thus gain reward. This analysis ofinstrumental conditioning has formed the basis of a number of extremely influentialtheories of learning (e.g. Hull, 1943).

K E Y T E R M

ReinforcerAn event thatincreases theprobability of aresponse whenpresented after it. If the event is theoccurrence of astimulus, such asfood, it is referred to as a positivereinforcer; but if theevent is the removalof a stimulus, such asshock, it is referred to as a negativereinforcer.

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94 A N I M A L L E A R N I N G & C O G N I T I O N

A feature of the Law of Effect that has proved unacceptable to the intuitions ofmany psychologists is that it fails to allow the animal to anticipate the goal for whichit is responding. The only knowledge that an S–R connection permits an animal topossess is the knowledge that it must make a particular response in the presence of agiven stimulus. The delivery of food after the response will, according to the Law ofEffect, effectively come as a complete surprise to the animal. In addition to soundingimplausible, this proposal has for many years conflicted with a variety ofexperimental findings.

One early finding is reported by Tinkelpaugh (1928), who required monkeys toselect one of two food wells to obtain reward. On some trials the reward was abanana, which was greatly preferred to the other reward, a lettuce leaf. Once theanimals had been trained they were occasionally presented with a lettuce leaf whenthey should have received a banana. The following quote, which is cited inMackintosh (1974), provides a clear indication that the monkey expected a moreattractive reward for making the correct response (Tinkelpaugh, 1928, p. 224):

She extends her hand to seize the food. But her hand drops to the floor withouttouching it. She looks at the lettuce but (unless very hungry) does not touch it.She looks around the cup and behind the board. She stands up and looks underand around her. She picks the cup up and examines it thoroughly inside and out.She had on occasion turned toward the observers present in the room andshrieked at them in apparent anger.

A rather different type of finding that showsanimals anticipate the rewards for which they areresponding can be found in experiments in which ratsran down an alley, or through a maze, for food. If arat is trained first with one reward which is thenchanged in attractiveness, there is a remarkably rapidchange in its performance on subsequent trials. Elliott(1928) found that the number of errors in a multiple-unit maze increased dramatically when the quality ofreward in the goal box was reduced. Indeed, theanimals were so dejected by this change that theymade more errors than a control group that had beentrained throughout with the less attractive reward(Figure 4.1). According to S–R theory, the change inperformance by the experimental group should havetaken place more slowly, and should not have resulted

in less accurate responding than that shown by the control group. As an alternativeexplanation, these findings imply that the animals had some expectancy of thereward they would receive in the goal that allowed them to detect when it was madeless attractive.

Tolman (1932) argued that findings such as these indicate that rats formR–unconditioned stimulus (US) associations as a result of instrumental conditioning.They are assumed to learn that a response will be followed by a particular outcome.There is no doubt that the results are consistent with this proposal, but they do notforce us to accept it. Several S–R theorists have pointed out that the anticipation ofreward could have been based on conditioned stimulus (CS)–US, rather than R–US

FIGURE 4.1 The meannumber of errors madeby two groups of rats ina multiple-unit maze. Forthe first nine trials thereward for the controlgroup was moreattractive than for theexperimental group, butfor the remaining trialsboth groups received thesame reward (adaptedfrom Elliott, 1928).

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4 Instrumental conditioning 95

associations. In Elliott’s (1928) experiment, for example, the animal consumed thereward in the goal box. It is possible that the stimuli created by this part of theapparatus served as a CS that became associated with food. After a number oftraining trials, therefore, the sight of the goal box would activate a representation ofthe reward and thereby permit the animal to detect when its value was changed. BothHull (1943) and Spence (1956) seized on this possibility and proposed that thestrength of instrumental responding is influenced by the Pavlovian properties of thecontext in which the response is performed.

The debate between S–R theorists and what might be called the expectancy(R–US) theorists continued until the 1970s (see for example, Bolles, 1972). In thelast 20 years or so, however, experiments have provided new insights into thenature of the associations that are formed during instrumental conditioning. Toanticipate the following discussion, these experiments show that both the S–R andthe expectancy theorists were correct. The experiments also show that these theoristsunderestimated the complexity of the information that animals can acquire in evenquite simple instrumental conditioning tasks.

Evidence for R–US associationsTo demonstrate support for an expectancy theory of instrumental conditioning,Colwill and Rescorla (1985) adopted a reinforcer devaluation design (see alsoAdams & Dickinson, 1981). A single group of rats was trained in the mannersummarized in Table 4.1. In the first (training) stage of the experiment subjects wereable to make one response (R1) to earn one reinforcer (US1) and another response(R2) to earn a different reinforcer (US2). The two responses were lever pressing orpulling a small chain that was suspended from the ceiling, and the two reinforcerswere food pellets or sucrose solution. After a number of sessions of this training, anaversion was formed to US1 by allowing subjects free access to it and then injectingthem with a mild poison (lithium chloride; LiCl). This treatment was so effective thatsubjects completely rejected US1 when it was subsequently presented to them. Forthe test trials subjects were again allowed to make either of the two responses, butthis time neither response led to the delivery of a reinforcer. The results from theexperiment are shown in Figure 4.2, which indicates that R2 was performed morevigorously than R1. The figure also shows a gradual decline in the strength of R2,which reflects the fact that neither response was followed by reward. This pattern ofresults can be most readily explained by assuming that during their training ratsformed Rl–US1 and R2–US2 associations. They would then be reluctant to performR1 in the test phase because of their knowledge that this response produced areinforcer that was no longer attractive.

Training Devaluation Test

R1 → US1 US1 → LiCl R1 versus R2R2 → US2

LiCl, lithium chloride; R, response; US, unconditioned stimulus.

TABLE 4.1 Summary of the training given to a single group of rats in anexperiment by Colwill and Rescorla (1985)

K E Y T E R M

Reinforcer devaluationA technique in whichthe positive reinforcerfor an instrumentalresponse issubsequentlydevalued, normally by pairing itsconsumption withillness.

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Evidence for S–R associationsThe evidence that instrumental conditioning results inthe development of S–R associations is perhaps lessconvincing than that concerning the development ofR–US associations. A re-examination of Figure 4.2reveals that after the devaluation treatment thereremained a tendency to perform R1. This tendencywas sustained even though the response neverresulted in the delivery of a reinforcer and, moreimportantly, it was sustained even though thedevaluation training resulted in a complete rejectionof US1. The fact that an animal is willing to make aresponse, even though it will reject the reinforcer thatnormally follows the response, is just what would beexpected if the original training resulted in the growth

of an S–R connection. In other words, because an S–R connection does not allow ananimal to anticipate the reward it will receive for its responses, once such aconnection has formed the animal will respond for the reward even if it is no longerattractive. Thus the results of the experiment by Colwill and Rescorla (1985) indicatethat during the course of their training rats acquired both R–US and S–Rassociations.

Readers who are struck by the rather low rate at which Rl was performed mightconclude that the S–R connection is normally of little importance in determiningresponding. Note, however, that for the test trials there was the opportunity ofperforming either R1 or R2. Even a slight preference for R2 would then have asuppressive effect on the performance of R1. On the basis of the present results,therefore, it is difficult to draw precise conclusions concerning the relativecontribution S–R and R–US associations to instrumental responding.

To complicate matters even further, it seems that the relative contribution of S–Rand R–US associations to instrumental behavior is influenced by the training given.Adams and Dickinson (1981) conducted a series of experiments in which rats had topress a lever for food. An aversion to the food was then conditioned using a techniquesimilar to that adopted by Colwill and Rescorla (1985). If a small amount ofinstrumental training had been given initially, then subjects showed a markedreluctance to press the lever in a final test session. But if extensive instrumental traininghad been given initially, there was little evidence of any effect at all of the devaluationtreatment. Adams and Dickinson (1981) were thus led to conclude that R–USassociations underlie the acquisition and early stages of instrumental training, but withextended practice this learning is transformed into an S–R habit. There is some debateabout the reasons for this change in influence of the two associations, or whether italways takes place (see Dickinson & Balleine, 1994).

Evidence for S–(R–US) associationsAnimals can thus learn to perform a particular response in the presence of a givenstimulus (S–R learning), they can also learn that a certain reinforcer will follow aresponse (R–US learning). The next question to ask is whether this information canbe integrated to provide the knowledge that in the presence of a certain stimulus acertain response will be followed by a certain outcome. Table 4.2 summarizes

FIGURE 4.2 The meanrates at which a singlegroup of rats performedtwo responses, R1 andR2, that had previouslybeen associated withtwo different rewards.Before the test sessions,the reward for R1, butnot R2, had beendevalued. No rewardswere presented in thetest session (adaptedfrom Rescorla, 1991).

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the design of an experiment by Rescorla (1991) that was conducted to test thispossibility.

A group of rats first received discrimination training in which a light or a noise(S1 or S2) was presented for 30 seconds at a time. During each stimulus the rats weretrained to perform two responses (pulling a chain or pressing a lever), which eachresulted in a different reinforcer (food pellets or sucrose solution). The design ofconditioning experiments is rarely simple and, in this case, it was made moredifficult by reversing the response–reinforce relationships for the two stimuli. Thusin S1, R1 led to US1 and R2 led to US2; but in S2, R1 led to US2 and R2 led to US1.For the second stage of the experiment, the reinforcer devaluation technique wasused to condition an aversion to US2. Finally, test trials were conducted in extinctionin which subjects were provided with the opportunity of performing the tworesponses in the presence of each stimulus. The result from these test trials was quiteclear. There was a marked preference to perform R1, rather than R2, in the presenceof S1; but in the presence of S2 there was a preference to perform R2 rather than R1.These findings cannot be explained by assuming that the only associations acquiredduring the first stage were S–R, otherwise the devaluation technique would havebeen ineffective. Nor can the results be explained by assuming that only R–USassociations developed, otherwise devaluation treatment should have weakened R1and R2 to the same extent in both stimuli. Instead, the results can be most readilyexplained by assuming that the subjects were sensitive to the fact that the devaluedreinforcer followed R2 in S1, and followed R1 in S2. Rescorla (1991) has argued thatthis conclusion indicates the development of a hierarchical associative structure thathe characterizes as S–(R–US). Animals are first believed to acquire an R–USassociation, and this association in its entirety is then assumed to enter into a newassociation with S. Whether it is useful to propose that an association can itself enterinto an association remains to be seen. There are certainly problems with this type ofsuggestion (see, for example, Holland, 1992). In addition, as Dickinson (1994) pointsout, there are alternative ways of explaining the findings of Rescorla (1991). Despitethese words of caution, the experiment demonstrates clearly that animals are able toanticipate the reward they will receive for making a certain response in the presenceof a given stimulus.

THE COND IT IONS OF LEARN INGThere is, therefore, abundant evidence to show that animals are capable of learningabout the consequences of their actions. We turn now to consider the conditions thatenable this learning to take place.

Discrimination training Devaluation Test

Sl: R1 → US1 and R2 → US2 S1: R1 � R2US2 → LiCl

S2: R1 → US2 and R2 → US1 S2: R2 � Rl

LiCl, lithium chloride; R, response; S, stimulus; US, unconditioned stimulus.

TABLE 4.2 Summary of the training given to a single group of rats in anexperiment by Rescorla (1991)

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ContiguityA fundamental principle of the early theories oflearning was that instrumental conditioning is mosteffective when the response is contiguous with or, inother words, followed immediately by the reinforcer.An early demonstration of this influence ofcontiguity on instrumental conditioning was made byLogan (1960), who trained rats to run down an alleyfor food. He found that the speed of running wassubstantially faster if the rats received food as soon asthey reached the goal box, as opposed to waiting inthe goal box before food was made available. Thisdisruptive effect of waiting was found with delaysfrom as little as 3 seconds. Moreover, the speed ofrunning down the alley was directly related to theduration of the delay in the goal box. Thiseffect, which is referred to as the gradient ofdelay, has been reported on numerous occasions(e.g. Dickinson, Watt, & Griffiths, 1992).

It is apparent from Logan’s (1960) study thateven relatively short delays between a responseand a reinforcer disrupt instrumental conditioning.Once this finding has been established, it thenbecomes pertinent to consider by how much thereinforcer can be delayed before instrumentalconditioning is no longer possible. The preciseanswer to this question remains to be sought, but astudy by Lattal and Gleeson (1990) indicates that itmay be greater than 30 seconds. Rats wererequired to press a lever for food, which wasdelivered 30 seconds after the response. If anotherresponse was made before food was delivered thenthe timer was reset and the rat had to wait another30 seconds before receiving food. This scheduleensured that the delay between any response andfood was at least 30 seconds. Despite beingexposed to such a demanding method of training,

each of the three rats in the experiment showed an increase in the rate of leverpressing as training progressed. The results from one rat are shown in Figure 4.3.The remarkable finding from this experiment is that rats with no prior experienceof lever pressing can increase the rate of performing this response when the onlyresponse-produced stimulus change occurs 30 seconds after a response hasbeen made.

It should be emphasized that the rate of lever pressing by the three rats wasrelatively slow, and would have been considerably faster if food had beenpresented immediately after the response. Temporal contiguity is thus importantfor instrumental conditioning, but such conditioning is still effective, albeit to alesser extent, when there is a gap between the response and the deliveryof reward.

FIGURE 4.3 The mean rate of pressing a lever by asingle rat when food was presented 30 seconds after aresponse (adapted from Lattal & Gleeson, 1990).

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Temporal contiguity is an important factor in theeffectiveness of instrumental conditioning. This goldenretriever’s obedience training will be much more effectiveif the owner rewards his dog with a treat straight afterthe desired response.

K E Y T E R M

Gradient of delayThe progressiveweakening of aninstrumental responseas a result ofincreasing the delaybetween thecompletion of theresponse and thedelivery of thereinforcer.

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Response–reinforcer contingencyWe saw in Chapter 3 that the CS–US contingency isimportant for Pavlovian conditioning because learningis more effective when the US occurs only in thepresence of the CS than when the US also occurs bothin the presence and absence of the CS. An experimentby Hammond (1980) makes a similar point forinstrumental behavior, by demonstrating theimportance of the response–reinforcer contingencyfor effective conditioning. The training schedule wasquite complex and required that the experimentalsession was divided into 1-second intervals. If aresponse occurred in any interval then, for three groupsof thirsty rats, water was delivered at the end of theinterval with a probability of 0.12. The results from agroup that received only this training, and no water inthe absence of lever pressing (Group 0), are shown inthe left-hand histogram of Figure 4.4. By the end oftraining this group was responding at more than 50responses a minute. For the remaining two groups,water was delivered after some of the 1-secondintervals in which a response did not occur. For Group0.08, the probability of one of these intervals being followed by water was 0.08, whereasfor Group 0.12 this probability was 0.12. The remaining two histograms show the finalresponse rates for these two groups. Both groups responded more slowly than Group 0,but responding was weakest in the group for which water was just as likely to bedelivered whether or not a response had been made. The contingency between responseand reinforcer thus influences the rate at which the response will be performed. We nowneed to ask why this should be the case. In fact, there are two answers to this question.

One answer is based on a quite different view of instrumental conditioning to thatconsidered thus far. According to this account, instrumental conditioning will beeffective whenever a response results in an increase in the rate of reinforcement(e.g. Baum, 1973). Thus there is no need for a response to be followed closely byreward for successful conditioning, all that is necessary is for the overall probability ofreward being delivered to increase. In other words, the contingency between a responseand reward is regarded as the critical determinant for the outcome of instrumentalconditioning. This position is referred to as a molar theory of reinforcement becauseanimals are assumed to compute the rate at which they make a response over asubstantial period of time and, at the same time, compute the rate at which reward isdelivered over the same period. If they should detect that an increase in the rate ofresponding is correlated with an increase in the rate of reward delivery, then theresponse will be performed more vigorously in the future. Moreover, the closer thecorrelation between the two rates, the more rapidly will the response be performed.Group 0 of Hammond’s (1980) experiment demonstrated a high correlation betweenthe rate at which the lever was pressed and the rate at which reward was delivered, andthis molar analysis correctly predicts that rats will learn to respond rapidly on the lever.In the case of Group 0.12, however, the rate of lever pressing had some influence onthe rate at which reward was delivered, but this influence was slight because the rewardwould be delivered even if a rat refused to press the lever. In these circumstances,

FIGURE 4.4 The mean rates of lever pressing for waterby three groups of thirsty rats in their final session oftraining. The groups differed in the probability withwhich free water was delivered during the intervalsbetween responses. Group 0 received no water duringthese intervals, Group 0.08 and Group 0.12 receivedwater with a probability of 0.08 and 0.12, respectively, atthe end of each period of 1 second in which a responsedid not occur (adapted from Hammond, 1980).

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Response–reinforcercontingencyThe degree to which the occurrence of thereinforcer depends on the instrumentalresponse. Positivecontingency: thefrequency of thereinforcer is increasedby making the response.Negative contingency:the frequency of thereinforcer is reduced bymaking the response.Zero contingency: thefrequency of thereinforcer is unaffectedby making the response.

Molar theory ofreinforcementThe assumption thatthe rate ofinstrumentalresponding isdetermined by theresponse–reinforcercontingency.

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responding is predicted to be slow and again the theoryis supported by the findings.

The molar analysis of instrumental behavior hasreceived a considerable amount of attention andgenerated a considerable body of experimentalresearch, but there are good reasons for believing that itmay be incorrect. In an experiment by Thomas (1981),rats in a test chamber containing a lever were given afree pellet of food once every 20 seconds, even if theydid nothing. At the same time, if they pressed the leverduring any 20-seconds interval then the pellet wasdelivered immediately, and the pellet at the end of theinterval was cancelled. Subsequent responses duringthe remainder of the interval were without effect. Thistreatment ensured that rats received three pellets of

food a minute whether or not they pressed the lever. Thus lever pressing in thisexperiment did not result in an increase in the rate of food delivery and, according to themolar point of view, the rate of making this response should not increase. The mean rateof responding during successive sessions is shown for one rat in Figure 4.5. Althoughthe rat took some time to press the lever, it eventually pressed at a reasonably high rate.A similar pattern of results was observed with the other rats in the experiment, whichclearly contradicts the prediction drawn from a molar analysis of instrumental behavior.

Thomas (1981) reports a second experiment, the design of which was much thesame as for the first experiment, except that lever pressing not only resulted in theoccasional, immediate delivery of food but also in an overall reduction of food bypostponing the start of the next 20-second interval by 20 seconds. On this occasion,the effect of lever pressing was to reduce the rate at which food was delivered, yeteach of six new rats demonstrated an increase in the rate of lever pressing as trainingprogressed. The result is opposite to that predicted by a molar analysis ofinstrumental conditioning.

Although molar theories of instrumental behavior (e.g. Baum, 1973) are ideallysuited to explaining results such as those reported by Hammond (1980), it is difficultto see how they can overcome the problem posed by the findings of Thomas (1981). Itis therefore appropriate to seek an alternative explanation for the influence of theresponse–reinforcer contingency on instrumental conditioning. One alternative, whichby now should be familiar, is that instrumental conditioning depends on the formationof associations. This position is referred to as a molecular theory of reinforcementbecause it assumes that the effectiveness of instrumental conditioning depends onspecific episodes of the response being paired with a reinforcer. The results from theexperiment can be readily explained by a molecular analysis of instrumentalconditioning, because contiguity between a response and a reinforcer is regarded as theimportant condition for successful conditioning. Each lever press that resulted in foodwould allow an association involving the response to gain in strength, which wouldthen encourage more vigorous responding as training progressed.

At first glance, Hammond’s (1980) results appear to contradict a molecularanalysis because the response was paired with reward in all three groups and theywould therefore be expected to respond at a similar rate, which was not the case. Itis, however, possible to reconcile these results with a molecular analysis ofinstrumental conditioning by appealing to the effects of associative competition, asthe following section shows.

FIGURE 4.5 The totalnumber of lever pressesrecorded in eachsession for a rat in theexperiment by Thomas(1981).

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Molecular theory ofreinforcementThe assumption thatthe rate ofinstrumentalresponding isdetermined byresponse–reinforcercontiguity.

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Associative competitionIf two stimuli are presented together for Pavlovian conditioning, the strength of theconditioned response (CR) that each elicits when tested individually is often weakerthan if they are presented for conditioning separately. This overshadowing effect isexplained by assuming the two stimuli are in competition for associative strength sothat the more strength acquired by one the less is available for the other (Rescorla &Wagner, 1972). Overshadowing is normally assumed to take place between stimulibut, if it is accepted that overshadowing can also occur between stimuli andresponses that signal the same reinforcer, then it is possible for molecular theories ofinstrumental behavior to explain the contingency effects reported by Hammond(1980). In Group 0 of his study, each delivery of water would strengthen alever-press–water association and result eventually in rapid lever pressing. In theother groups, however, the delivery of free water would allow the context to enterinto an association with this reinforcer. The delivery of water after a response willthen mean that it is signaled by both the context and the response, and theories ofassociative learning predict that the context–water association will restrict, throughovershadowing, the growth of the response–water association. As the strength of theresponse–water association determines the rate at which the response is performed,responding will be slower when some free reinforcers accompany the instrumentaltraining than when all the reinforcers are earned. Furthermore, the more often thatwater is delivered free, the stronger will be thecontext–water association and the weaker will be theresponse–water association. Thus the pattern ofresults shown in Figure 4.4 can be explained by amolecular analysis of instrumental conditioning,providing it is assumed that responses and stimulicompete with each other for their associativestrength. The results from two different experimentslend support to this assumption.

The first experiment directly supports the claimthat overshadowing is possible between stimuli andresponses. Pearce and Hall (1978) required rats topress a lever for food on a variable interval schedule,in which only a few responses were followed byreward. For an experimental group, each rewardedresponse was followed by a brief burst of white noisebefore the food was delivered. The noise, whichaccompanied only rewarded responses, resulted in asubstantially lower rate of lever pressing by theexperimental than by control groups that received either similar exposure to the noise(but after nonrewarded responses) or no exposure to the noise at all (Figure 4.6).Geoffrey Hall and I argued that the most plausible explanation for these findings isthat instrumental learning involves the formation of R–US associations and that thesewere weakened through overshadowing by a noise–food association that developedin the experimental group.

The second source of support for a molecular analysis of the effect ofcontingency on instrumental responding can be found in contingency experiments inwhich a brief stimulus signals the delivery of each free reinforcer. The brief stimulusshould itself enter into an association with the reinforcer and thus overshadow the

FIGURE 4.6 The meanrates of lever pressingby three groups of ratsthat received a burst of noise after eachrewarded response(Corr), after somenonrewarded responses(Uncorr), or no noise at all (Food alone)(adapted from Pearce &Hall, 1978).

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development of an association between the context and the reinforcer. Whenever aresponse is followed by the reinforcer it will now be able to enter a normal R–USassociation, because of the lack of competition from the context. Responding in theseconditions should thus be more vigorous than if the free US is not signaled. Insupport of this argument, both Hammond and Weinberg (1984) and Dickinson andCharnock (1985) have shown that free reinforcers disrupt instrumental responding toa greater extent when they are unsignaled than when they are signaled. Thesefindings make a particularly convincing case for the belief that competition forassociative strength is an important influence on the strength of an instrumentalresponse. They also indicate that this competition is responsible for the influence ofthe response–reinforcer contingency on the rate of instrumental responding.

The nature of the reinforcerPerhaps the most important requirement for successful instrumental conditioning isthat the response is followed by a reinforcer. But what makes a reinforcer? In nearlyall the experiments that have been described thus far, the reinforcer has been food fora hungry animal, or water for a thirsty animal. As these stimuli are of obviousbiological importance, it is hardly surprising to discover that animals are prepared toengage in an activity such as lever pressing in order to earn them. However, this doesnot mean that a reinforcer is necessarily a stimulus that is of biological significanceto the animal. As Schwartz (1989) notes, animals will press a lever to turn on a light,and it is difficult to imagine the biological need that is satisfied on these occasions.

Thorndike (1911) was the first to appreciate the need to identify the definingcharacteristics of a reinforcer, and his solution was contained within the Law ofEffect. He maintained that a reinforcer was a stimulus that resulted in a satisfyingstate of affairs. A satisfying state of affairs was then defined as “ . . . one which theanimal does nothing to avoid, often doing things which maintain or renew it”(Thorndike, 1913, p. 2). In other words, Thorndike effectively proposed that astimulus would serve as a reinforcer (increase the likelihood of a response) if animalswere willing to respond in order to receive that stimulus. The circularity in thisdefinition should be obvious and has served as a valid source of criticism of the Lawof Effect on more than one occasion (e.g. Meehl, 1950). Thorndike was not alone inproviding a circular definition of a reinforcer. Skinner has been perhaps the mostblatant in this respect, as the following quotation reveals (Skinner, 1953, pp. 72–73):

The only way to tell whether or not a given event is reinforcing to a givenorganism under given conditions is to make a direct test. We observe thefrequency of a selected response, then make an event contingent upon it andobserve any change in frequency. If there is a change, we classify the event asreinforcing.

To be fair, for practical purposes this definition is quite adequate. It provides auseful and unambiguous terminology. At the same time, once we have decided thata stimulus, such as food, is a positive reinforcer, then we can turn to a study of anumber of issues that are important to the analysis of instrumental learning. Forinstance, we have been able to study the role of the reinforcer in the associations thatare formed during instrumental learning, without worrying unduly about what it isthat makes a stimulus a reinforcer. But the definitions offered by Thorndike and

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Skinner are not very helpful if a general statement isbeing sought about the characteristics of a stimulusthat dictate whether or not it will function as areinforcer. And the absence of such a generalstatement makes our understanding of the conditionsthat promote instrumental learning incomplete.

A particularly elegant solution to the problem ofdeciding whether a stimulus will function as areinforcer is provided by the work of Premack (1959,1962, 1965), who put forward what is now called thePremack principle. He proposed that reinforcerswere not stimuli but opportunities to engage inbehavior. Thus the activity of eating, not the stimulusof food, should be regarded as the reinforcer when ananimal has been trained to lever press for food. Todetermine if one activity will serve as the reinforcerfor another activity, Premack proposed that theanimal should be allowed to engage freely in bothactivities. For example, a rat might be placed into achamber containing a lever and some food pellets. Ifit shows a greater willingness to eat the food than to press the lever, then we canconclude that the opportunity to eat will reinforce lever pressing, but the opportunityto lever press will not reinforce eating.

It is perhaps natural to think of the properties of a reinforcer as being absolute.That is, if eating is an effective reinforcer for one response, such as lever pressing,then it might be expected to serve as a reinforcer for any response. But Premack(1965) has argued this assumption is unjustified. An activity will only be reinforcingif subjects would rather engage in it than in the activity that is to be reinforced. Todemonstrate this relative property of a reinforcer, Premack (1971a) placed rats into arunning wheel, similar to the one sketched in Figure 4.7, for 15 minutes a day.

When the rats were thirsty, they preferred to drink rather than to run in the wheel,but when they were not thirsty, they preferred to run rather than to drink. For the testphase of the experiment, the wheel was locked and the rats had to lick the drinkingtube to free it and so gain the opportunity to run for 5 seconds. Running is notnormally regarded as a reinforcing activity but because rats that are not thirsty preferto run rather than drink, it follows from Premack’s (1965) argument that they shouldincrease the amount they drink in the wheel in order to earn the opportunity to run.Conversely; running would not be expected to reinforce drinking for thirsty rats,because in this state of deprivation they prefer drinking to running. In clear supportof this analysis, Premack (1971a) found that running could serve as a reinforcer fordrinking, but only with rats that were not thirsty.

As Allison (1989) has pointed out, Premack’s proposals can be expressedsuccinctly by paraphrasing Thorndike’s Law of Effect. For instrumental conditioningto be effective it is necessary for a response to be followed not by a satisfying stateof affairs, but by a preferred response. Despite the improvement this change affordswith respect to the problem of defining a reinforcer, experiments have shown that itdoes not account adequately for all the circumstances where one activity will serveas a reinforcer for another.

Consider an experiment by Allison and Timberlake (1974) in which rats werefirst allowed to drink from two spouts that provided different concentrations of

FIGURE 4.7 A sketchof the apparatus usedby Premack (1971a) todetermine if being giventhe opportunity to runcould serve as areinforcer for drinking inrats that were notthirsty (adapted fromPremack, 1971a).

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Premack principleThe proposal thatactivity A will reinforceactivity B, if activity Ais more probable thanactivity B.

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saccharin solution. This baseline test session revealed a preference for the sweetersolution. According to Premack’s proposals, therefore, rats should be willing toincrease their consumption of the weaker solution if drinking it is the only meansby which they can gain access to the sweeter solution. By contrast, rats should notbe willing to increase their consumption of the sweeter solution to gain access to theweaker one. To test this second prediction, rats were allowed to drink from the spoutsupplying the sweeter solution and, after every 10 licks, they were permitted onelick at the spout offering the less-sweet solution. This 10 : 1 ratio meant that,relative to the amount of sweet solution consumed, the rats received less of theweaker solution than they chose to consume in the baseline test session. As aconsequence of this constraint imposed by the experiment, Allison and Timberlake(1974) found that rats increased their consumption of the stronger solution. It isimportant to emphasize that this increase occurred in order to allow the rats to gainaccess to the less preferred solution, which, according to Premack’s theory, shouldnot have taken place.

Timberlake and Allison (1974) explained their results in terms of an equilibriumtheory of behavior. They argued that when an animal is able to engage in a variety ofactivities, it will have a natural tendency to allocate more time to some than others.The ideal amount of time that would be devoted to an activity is referred to as its blisspoint, and each activity is assumed to have its own bliss point. By preventing ananimal from engaging in even its least preferred activity, it will be displaced from thebliss point and do its best to restore responding to this point.

In the experiment by Allison and Timberlake (1974), therefore, forcing thesubjects to drink much more of the strong than the weak solution meant that theywere effectively deprived of the weak solution. As the only way to overcome thisdeficit was to drink more of the sweet solution, this is what they did. Of course, asthe rats approached their bliss point for the consumption of the weak solution, theywould go beyond their bliss point for the consumption of the sweet solution. To copewith this type of conflict, animals are believed to seek a compromise, or state ofequilibrium, in which the amount of each activity they perform will lead them asclose as possible to the bliss points for all activities. Thus the rats completed theexperiment by drinking rather more than they would prefer of the strong solution,and rather less than they would prefer of the weak solution.

By referring to bliss points, we can thus predict when the opportunity to engagein one activity will serve as a reinforcer for another activity. But this does not meanthat we have now identified completely the circumstances in which the delivery of aparticular event will function as a reinforcer. Some reinforcers do not elicit responsesthat can be analyzed usefully by equilibrium theory. Rats will press a lever to receivestimulation to certain regions of the brain, or to turn on a light, or to turn off anelectric shock to the feet. I find it difficult to envisage how any measure of baselineactivity in the presence of these events would reveal that they will serve asreinforcers for lever pressing. In the next section we will find that a stimulus that hasbeen paired with food can reinforce lever pressing in hungry rats. Again, simply byobserving an animal’s behavior in the presence of the stimulus, it is hard to imaginehow one could predict that the stimulus will function as a reinforcer. Ourunderstanding of the nature of a reinforcer has advanced considerably sinceThorndike proposed the Law of Effect. However, if we wish to determine withconfidence if a certain event will act as a reinforcer for a particular response, at timesthere will be no better alternative than to adopt Skinner’s suggestion of testing forthis property directly.

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Conditioned reinforcementThe discussion has been concerned thus far with primary reinforcers, that is, withstimuli that do not need to be paired with another stimulus to function as reinforcersfor instrumental conditioning. There are, in addition, numerous studies that haveshown that even a neutral stimulus may serve as an instrumental reinforcer by virtueof being paired with a primary reinforcer. An experiment by Hyde (1976) provides agood example of a stimulus acting in this capacity as a conditioned reinforcer. Inthe first stage of the experiment, an experimental group of hungry rats had a numberof sessions in which the occasional delivery of food was signaled by a brief tone.A control group was treated in much the same way except that the tone and foodwere presented randomly in respect to each other. Both groups were then given theopportunity to press the lever to present the tone. The results from the eight sessionsof this testing are displayed in Figure 4.8. Even though no food was presented in thistest phase, the experimental group initially showed a considerable willingness topress the lever. The superior rate of pressing by the experimental compared to thecontrol group strongly suggests that pairing the tone with food resulted in itbecoming a conditioned reinforcer.

In the previous experiment, the effect of the conditioned reinforcer wasrelatively short lasting, which should not be surprising because it will lose itsproperties by virtue of being presented in the absence of food. The effects ofconditioned reinforcers can be considerably more robust if their relationship withthe primary reinforcer is maintained, albeit intermittently. Experiments using tokenreinforcers provide a particularly forceful demonstration of how the influence of aconditioned reinforcer may be sustained in this way. Token reinforcers are typicallysmall plastic discs that are earned by performing some response, and once earnedthey can be exchanged for food. In an experiment by Kelleher (1958), chimpanzeeshad to press a key 125 times to receive a single token. When they had collected 50tokens they were allowed to push them all into a slot to receive food. In thisexperiment, therefore, the effect of the token reinforcers was sufficiently strong thatthey were able to reinforce a sequence of more than 6000 responses.

FIGURE 4.8 The mean rates of lever pressing for a brief tone by two groups ofrats. For the experimental group the tone had previously been paired with food,whereas for the control group the tone and food had been presented randomly inrespect to each other (adapted from Hyde, 1976).

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A straightforward explanation for the results of the experiment by Hyde (1976)is that the tone became an appetitive Pavlovian CS and thus effectively served as asubstitute for food. The results from experiments such as that by Kelleher (1958)have led Schwartz (1989) to argue that there are additional ways in whichconditioned reinforcers can be effective (see also Golub, 1977):

• They provide feedback that the correct response has been made. Delivering a tokenafter the completion of 125 responses would provide a useful signal that the subjectis engaged in the correct activity.

• Conditioned reinforcers might act as a cue for the next response to be performed.Kelleher (1958) observed that his chimpanzees often waited for several hoursbefore making their first response in a session. This delay was virtually eliminatedby giving the subject some tokens at the start of the session, thus indicating that thetokens acted as a cue for key pressing.

• Conditioned reinforcers may be effective because they help to counteract thedisruptive effects of imposing a long delay between a response and the delivery ofa primary reinforcer. Interestingly, as far as tokens are concerned, this property ofthe token is seen only when the chimpanzee is allowed to hold it during the delay.

Taken together, these proposals imply that the properties of a conditionedreinforcer are considerably more complex than would be expected if they were basedsolely on its Pavlovian properties.

THE PERFORMANCE OF INSTRUMENTA LB E H AV I O RThe experiments considered so far have been concerned with revealing theknowledge that is acquired during the course of instrumental conditioning. Theyhave also indicated some of the factors that influence the acquisition of thisknowledge. We turn our attention now to examining the factors that determine thevigor with which an animal will perform an instrumental response. We have alreadyseen that certain devaluation treatments can influence instrumental responding, andso too can manipulations designed to modify the strength of the instrumentalassociation. But there remain a number of other factors that influence instrumentalbehavior. In the discussion that follows we shall consider two of these influences insome detail: deprivation state and the presence of Pavlovian CSs.

DeprivationThe level of food deprivation has been shown, up to a point, to be directly related tothe vigor with which an animal responds for food. This is true when the response isrunning down an alley (Cotton, 1953) or pressing a lever (Clark, 1958). To explain thisrelationship, Hull (1943) suggested that motivational effects are mediated by activityin a drive center. Drive is a central state that is excited by needs and energizesbehavior. It was proposed that the greater the level of drive, the more vigorous will bethe response that the animal is currently performing. Thus, if a rat is pressing a leverfor food, then hunger will excite drive, which, in turn, will invigorate this activity.

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A serious shortcoming of Hull’s (1943) account is the claim that drive isnonspecific, so that it can be enhanced by an increase in any need of the animal.A number of curious predictions follow from this basic aspect of his theorizing. Forexample, the pain produced by electric shock is assumed to increase drive, so that ifanimals are given shocks while lever pressing for food, they should respond morerapidly than in the absence of shock. By far the most frequent finding is that thismanipulation has the opposite effect of decreasing appetitive instrumentalresponding (e.g. Boe & Church, 1967). Conversely, the theory predicts thatenhancing drive by making animals hungrier should facilitate the rate at which theypress a lever to escape or avoid shock. Again, it should not be surprising to discoverthat generally this prediction is not confirmed. Increases in deprivation have beenfound, in this respect, to be either without effect (Misanin & Campbell, 1969) or toreduce the rate of such behavior (Meyer, Adams, & Worthen, 1969; Leander, 1973).

In response to this problem, more recent theorists have proposed that animalspossess two drive centers: One is concerned with energizing behavior that leads toreward, the other is responsible for invigorating activity that minimizes contactwith aversive stimuli. These can be referred to, respectively, as the positive andnegative motivational systems. A number of such dual-system theories ofmotivation have been proposed (Konorski, 1967; Rescorla & Solomon, 1967;Estes, 1969).

The assumption that there are two motivational systems rather than a single drivecenter allows these theories to overcome many of the problems encountered byHull’s (1943) theory. For example, it is believed that deprivation states like hungerand thirst will increase activity only in the positive system, so that a change indeprivation should not influence the vigor of behavior that minimizes contact withaversive stimuli such as shock. Conversely, electric shock should not invigorateresponding for food as it will excite only the negative system.

But even this characterization of the way in which deprivation states influencebehavior may be too simple. Suppose that an animal that has been trained to leverpress for food when it is hungry is satiated by being granted unrestricted access tofood before it is returned to the conditioning chamber. The account that has just beendeveloped predicts that satiating the animal will reduce the motivational support forlever pressing by lowering the activity in the positive system. The animal would thusbe expected to respond less vigorously than one that was still hungry. There is someevidence to support this prediction (e.g. Balleine, Garner, Gonzalez & Dickinson,1995), but additional findings by Balleine (1992) demonstrate that dual-systemtheories of motivation are in need of elaboration if they are to provide a completeaccount of the way in which deprivation states influence responding.

In one experiment by Balleine (1992), two groups of rats were trained to press abar for food while they were hungry (H). For reasons that will be made evidentshortly, it is important to note that the food pellets used as the instrumental reinforcerwere different to the food that was presented at all other times in this experiment.Group H–S was then satiated (S) by being allowed unrestricted access to their normalfood for 24 hours, whereas Group H–H remained on the deprivation schedule.Finally, both groups were again given the opportunity to press the bar, but respondingnever resulted in the delivery of the reinforcer. Because of their different deprivationstates, dual-system theories of motivation, as well as our intuitions, predict thatGroup H–H should respond more vigorously than Group H–S in this test session. Butit seems that our intuitions are wrong on this occasion. The mean number ofresponses made by each group in the test session are shown in the two gray

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Dual-system theoriesof motivationTheories that assumethat behavior ismotivated by activityin a positive system,which energizesapproach to an object,and a negativesystem, whichenergizes withdrawalfrom an object.

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histograms on the left-hand side of Figure 4.9, which reveal that both groupsresponded quite vigorously, and at a similar rate.

The equivalent histograms on the right-hand side of Figure 4.9 show the resultsof two further groups from this study, which were trained to lever press for foodwhile they were satiated by being fed unrestricted food in their home cages. Rats willlearn to respond for food in these conditions, provided that the pellets are of adifferent flavor to that of the unrestricted food presented in the home cages. GroupS–S was then tested while satiated, whereas Group S–H was tested while hungry.Once again, and contrary to our intuitions, both groups performed similarly in thetest session despite their different levels of deprivation. When the results of the fourgroups are compared, it is evident that the groups that were trained hungry respondedsomewhat more on the test trials than those that were trained while they weresatiated. But to labor the point, there is no indication that changing deprivation levelfor the test session had any influence on responding.

Balleine’s (1992) explanation for these findings is that the incentive value, orattractiveness, of the reinforcer is an important determinant of how willing animalswill be to press for it. If an animal consumes a reinforcer while it is hungry, then thatreinforcer may well be more attractive than if it is consumed while the animal issatiated. Thus Group H–H and Group H–S may have responded rapidly in the testsession because they anticipated a food that in the past had proved attractive, becausethey had only eaten it while they were hungry. By way of contrast, the slowerresponding by Groups S–S and S–H can be attributed to them anticipating food thatin the past had not been particularly attractive, because they had eaten it only whilethey were not hungry.

This explanation was tested with two additional groups. Prior to the experiment,animals in Group Pre(S) H–S were given reward pellets while they were satiated to

FIGURE 4.9 The mean number of responses made by six groups of rats in anextinction test session. The left-hand letter of each pair indicates the level ofdeprivation when subjects were trained to lever press for reward—either satiated (S)or hungry (H)—the right-hand letter indicates the deprivation level during test trials.Two of the groups were allowed to consume the reward either satiated, Pre(S), orhungry, Pre(H), prior to instrumental conditioning (adapted from Balleine, 1992).

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demonstrate that the pellets are not particularly attractive in this deprivation state.The group was then trained to lever press while hungry and received test trials whilesatiated. On the test trials, the subjects should know that because, of their low levelof deprivation, the reward pellets are no longer attractive and they should be reluctantto press the lever. The results, which are shown in the blue histogram in the left-handside of Figure 4.9, confirmed this prediction. The final group to be considered, GroupPre(H) S–H, was first allowed to eat reward pellets in the home cage while hungry,instrumental conditioning was then conducted while the group was satiated and thetest trials were conducted while the group was hungry. In contrast to Group S–H andGroup S–S, this group should appreciate that the reward pellets are attractive whilehungry and respond more rapidly than the other two groups during the test trials.Once again, the results confirmed this prediction—see the blue histogram on theright-hand side of Figure 4.9

By now it should be evident that no simple conclusion can be drawn concerningthe way in which deprivation states influence the vigor of instrumental responding.On some occasions a change in deprivation state is able to modify directly the rateof responding, as dual-systems theories of motivation predict. On other occasions,this influence is more indirect by modifying the attractiveness of the reinforcer. Aninformative account of the way in which these findings may be integrated can befound in Balleine et al. (1995).

Pavlovian–instrumental interactionsFor a long time, theorists have been interested in the way in which Pavlovian CSsinfluence the strength of instrumental responses that are performed in theirpresence. One reason for this interest is that Pavlovian and instrumentalconditioning are regarded as two fundamental learning processes, and it isimportant to appreciate the way in which they work together to determine how ananimal behaves. A second reason was mentioned at the end of Chapter 2, where wesaw that Pavlovian CSs tend to elicit reflexive responses that may not always be inthe best interests of the animal. If a Pavlovian CS was also able to modulate thevigor of instrumental responding, then this would allow it to have a more general,and more flexible, influence on behavior than has so far been implied. For example,if a CS for food were to invigorate instrumental responses that normally lead tofood, then such responses would be strongest at a time when they are most needed,that is, in a context where food is likely to occur. The experiments described in thissection show that Pavlovian stimuli can modulate the strength of instrumentalresponding. They also show that there are at least two ways in which this influencetakes place.

Motivational influencesKonorski (1967), it should be recalled from Chapter 2, believed that a CS can excitean affective representation of the US that was responsible for arousing a preparatoryCR. He further believed that a component of this CR consists of a change in the levelof activity in a motivational system. A CS for food, say, was said to increase activityin the positive motivational system, whereas a CS for shock should excite thenegative system. If these proposals are correct, then it should be possible to alter thestrength of instrumental responding by presenting the appropriate Pavlovian CS(see also Rescorla & Solomon, 1967).

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An experiment by Lovibond (1983), using Pavlovian–instrumental transferdesign, provides good support for this prediction. Hungry rabbits were first trainedto operate a lever with their snouts to receive a squirt of sucrose into the mouth. Thelevers were then withdrawn for a number of sessions of Pavlovian conditioning inwhich a clicker that lasted for 10 seconds signaled the delivery of sucrose. In a finaltest stage, subjects were again able to press the lever and, as they were doing so, theclicker was occasionally operated. The effect of this appetitive CS was to increasethe rate of lever pressing both during its presence and for a short while after it wasturned off. A similar effect has also been reported in a study using an aversive US.Rescorla and LoLordo (1965) found that the presentation of a CS previously pairedwith shock enhanced the rate at which dogs responded to avoid shock.

In addition to explaining the findings that have just been described, a furtheradvantage of dual-system theories of motivation is that they are able to account formany of the effects of exposing animals simultaneously to both appetitive and aversivestimuli. For example, an animal may be exposed to one stimulus that signals rewardand another indicating danger. In these circumstances, instead of the two systemsworking independently, they are assumed to be connected by mutually inhibitory links,so that activity in one will inhibit the other (Dickinson & Pearce, 1977).

To understand this relationship, consider the effect of presenting a signal forshock to a rat while it is lever pressing for food. Prior to the signal, the level ofactivity in the positive system will be solely responsible for the rate of pressing.When the aversive CS is presented, it will arouse the negative system. The existenceof the inhibitory link will then allow the negative system to suppress activity in thepositive system and weaken instrumental responding. As soon as the aversive CS isturned off, the inhibition will be removed and the original response rate restored. Byassuming the existence of inhibitory links, dual-system theories can provide a verysimple explanation for conditioned suppression. It occurs because the aversive CSreduces the positive motivational support for the instrumental response.

Response-cueing properties of Pavlovian CRsIn addition to modulating activity in motivational systems, Pavlovian stimuli caninfluence instrumental responding through a response-cueing process (Trapold &Overmier, 1972). To demonstrate this point we shall consider an experiment byColwill and Rescorla (1988), which is very similar in design to an earlier study byKruse, Overmier, Konz, and Rokke (1983).

In the first stage of the experiment, hungry rats received Pavlovian conditioningin which US1 was occasionally delivered during a 30-second CS. Training was thengiven, in separate sessions, in which R1 produced US1 and R2 produced US2. Thetwo responses were chain pulling and lever pressing, and the two reinforcers werefood pellets and sucrose solution. For the test stage, animals had the opportunity forthe first time to perform R1 and R2 in the presence of the CS, but neither response ledto a reinforcer. As Figure 4.10 shows, R1 was performed more vigorously than R2.

The first point to note is that it is not possible to explain these findings byappealing to the motivational properties of the CS. The CS should, of course,enhance the level of activity in the positive system. But this increase in activityshould then invigorate R1 to exactly the same extent as R2 because the motivationalsupport for both responses will be provided by the same, positive, system.

In developing an alternative explanation for the findings by Colwill and Rescorla(1988), note that instrumental conditioning with the two responses was conducted in

K E Y T E R M

Pavlovian–instrumentaltransferTraining in which a CSis paired with a USand then the CS ispresented while thesubject is performingan instrumentalresponse.

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separate sessions. Thus R1 was acquired against abackground of presentations of US1 and, likewise,R2 was acquired against a background of US2presentations. If we now accept that the trainingresulted in the development of S-R associations, it isconceivable that certain properties of the two rewardscontributed towards the S component of theseassociations. For example, a memory of US1 mightcontribute to the set of stimuli that are responsible foreliciting R1. When the CS was presented for testing,it should activate a memory of US1, which in turnshould elicit R1 rather than R2. In other words, thePavlovian CS was able to invigorate the instrumentalresponse by providing cues that had previouslybecome associated with the instrumental response.

Concluding commentsThe research reviewed so far in this chapter shows that we have discovered aconsiderable amount about the associations that are formed during instrumentalconditioning. We have also discovered a great deal about the factors that influencethe strength of instrumental responding. In Chapter 2 a simple memory model wasdeveloped to show how the associations formed during Pavlovian conditioninginfluence responding. It would be helpful if a similar model could be developed forinstrumental conditioning, but this may not be an easy task. We would need to takeaccount of three different associations that have been shown to be involved ininstrumental behavior, S–R, R–US, S–(R–US). We would also need to take accountof the motivational and response-cueing properties of any Pavlovian CS–USassociations that may develop. Finally, the model would need to explain how changesin deprivation can influence responding. It hardly needs to be said that any modelthat is able to take account of all these factors satisfactorily will be complex andwould not fit comfortably into an introductory text. The interested reader is, however,referred to Dickinson (1994) who shows how much of our knowledge aboutinstrumental behavior can be explained by what he calls an associative-cyberneticmodel. In essence, this model is a more complex version of the dual-system theoriesof motivation that we have considered. The reader might also wish to consultBalleine (2001) for a more recent account of the influence of motivational processeson instrumental behavior.

Our discussion of the basic processes of instrumental conditioning is nowcomplete, but there is one final topic to consider in this chapter. That is, whether theprinciples we have considered can provide a satisfactory account for the problemsolving abilities of animals.

T H E L AW O F E F F E C T A N D P R O B L E MSOLV INGAnimals can be said to have solved a problem whenever they overcome an obstacleto attain a goal. The problem may be artificial, such as having to press a lever for

FIGURE 4.10 The meanrates of performing tworesponses, R1 and R2,in the presence of anestablished Pavlovianconditioned stimulus(CS). Prior to testing,instrumentalconditioning had beengiven in which thereinforcer for R1 wasthe same as thePavlovian unconditionedstimulus (US), and thereinforcer for R2 wasdifferent to thePavlovian US. Testingwas conducted in theabsence of anyreinforcers in a singlesession (adapted fromColwill & Rescorla,1988).

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reward, or it might be one that occurs naturally, such as having to locate a new sourceof food. Early studies of problem solving in animals were conducted by means ofcollecting anecdotes, but this unsatisfactory method was soon replaced byexperimental tests in the laboratory (see Chapter 1). As a result of his experiments,Thorndike (1911) argued that despite the range of potential problems that canconfront an animal, they are all solved in the same manner. Animals are assumed tobehave randomly until by trial and error the correct response is made and reward isforthcoming. To capture this idea, Thorndike (1911) proposed the Law of Effect,which stipulates that one effect of reward is to strengthen the accidentally occurringresponse and to make its occurrence more likely in the future. This account mayexplain adequately the way cats learn to escape from puzzle boxes, but is it suitablefor all aspects of problem solving? A number of researchers have argued that animalsare more sophisticated at solving problems than is implied by the Law of Effect. It hasbeen suggested that they are able to solve problems through insight. It has also beensuggested that animals can solve problems because they have an understanding of thecausal properties of the objects in their environment or, as it is sometimes described,an understanding of folk physics. We shall consider each of these possibilities.

InsightAn early objector to Thorndike’s (1911) account of problem solving was Kohler(1925). Thorndike’s experiments were so restrictive, he argued, that they preventedanimals from revealing their capacity to solve problems by any means other than the

most simple. Kohler spent the First World War on theCanary Islands, where he conducted a number ofstudies that were meant to reveal sophisticatedintellectual processes in animals. He is best knownfor experiments that, he claimed, demonstrate theimportance of insight in problem solving. Many ofhis findings are described in his book The mentalityof apes, which documents some remarkable feats ofproblem solving by chimpanzees and other animals.Two examples should be sufficient to give anindication of his methodology. These exampleinvolve Sultan (Figure 4.11), whom Kohler (1925)regarded as the brightest of his chimpanzees. On oneoccasion Sultan, was in a cage in which there wasalso a small stick. Outside the cage was a longerstick, which was beyond Sultan’s reach, and evenfurther away was a reward of fruit (p. 151):

Sultan tries to reach the fruit with the smaller ofthe sticks. Not succeeding, he tries a piece ofwire that projects from the netting in his cage,but that, too, is in vain. Then he gazes about him(there are always in the course of these testssome long pauses, during which the animalscrutinizes the whole visible area). He suddenlypicks up the little stick once more,

K E Y T E R M

InsightAn abrupt change inbehavior that leads toa problem beingsolved. The change inbehavior is sometimesattributed to a periodof thought followed bya flash of inspiration.

FIGURE 4.11 Sultan stacking boxes in an attempt toreach a banana (drawing based on Kohler, 1956).

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goes to the bars directly opposite to the long stick, scratches it towards himwith the auxiliary, seizes it and goes with it to the point opposite the objectivewhich he secures. From the moment that his eyes fell upon the long stick, hisprocedure forms one consecutive whole.

In the other study, Kohler (1925) hung a piece of fruit from the ceiling of a cagehousing six apes, including Sultan. There was a wooden box in the cage (p. 41):

All six apes vainly endeavored to reach the fruit by leaping up from the ground.Sultan soon relinquished this attempt, paced restlessly up and down, suddenlystood still in front of the box, seized it, tipped it hastily straight towards theobjective, but began to climb upon it at a (horizontal) distance of 1⁄2 meter andspringing upwards with all his force, tore down the banana.

In both examples there is a period when the animal responds incorrectly; this isthen followed by activity that, as it is reported, suggests that the solution to theproblem has suddenly occurred to the subject. There is certainly no hint in thesereports that the problem was solved by trial and error. Does this mean, then, thatKohler (1925) was correct in his criticism of Thorndike’s (1911) theorizing?

A problem with interpreting Kohler’s (1925) findings is that all of the apes hadplayed with boxes and sticks prior to the studies just described. The absence oftrial-and-error responding may thus have been due to the previous experience of theanimals. Sultan may, by accident, have learned about the consequences of jumpingfrom boxes in earlier sessions, and he was perhaps doing no more than acting on thebasis of his previous trial-and-error learning. This criticism of Kohler’s (1925) workis by no means original. Birch (1945) and Schiller (1952) have both suggested thatwithout prior experience with sticks and so forth, there is very little reason forbelieving that apes can solve Kohler’s problems in the manner just described.

The absence of trial-and-error responsesin Kohler’s (1925)findings might havebeen due to the factthat most apes wouldhave had priorexperience of playingwith sticks.

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An amusing experiment by Epstein, Kirshnit, Lanza, and Rubin (1984) alsoshows the importance of past experience in problem solving and, at the same time,raises some important issues concerning the intellectual abilities of animals. Pigeonswere given two different types of training. They were rewarded with food for pushinga box towards a spot randomly located at the base of a wall of the test chamber.Pushing in the absence of the spot was never rewarded. They were also trained tostand on the box when it was fixed to the floor and peck for food at a plastic bananasuspended from the ceiling. Attempts to peck the banana when not standing on thebox were never rewarded. Finally, on a test session they were confronted with a novelsituation in which the banana was suspended from the ceiling and the box was placedsome distance from beneath it. Epstein et al. (1984) report that (p. 61):

At first each pigeon appeared to be “confused”; it stretched and turned beneaththe banana, looked back and forth from banana to box, and so on. Then eachsubject began rather suddenly to push the box in what was clearly the directionof the banana. Each subject sighted the banana as it pushed and readjusted thebox as necessary to move it towards the banana. Each subject stopped pushingit in the appropriate place, climbed and pecked the banana. This quiteremarkable performance was achieved by one bird in 49 sec, which comparesvery favorably with the 5 min it took Sultan to solve his similar problem.

There can be no doubt from this study that the prior training of the pigeon playedan important role in helping it solve the problem. Even so, the study clearly revealsthat the pigeons performed on the test session in a manner that extends beyondtrial-and-error responding. The act of pecking the banana might have been acquiredby trial-and-error learning, and so, too, might the act of moving the box around. Butthe way in which the box was moved to below the banana does not seem to becompatible with this analysis.

The description by Epstein et al. (1984) of the pigeons’ behavior bears a strikingsimilarity to Kohler’s (1925) account of Sultan’s reaction to the similar problem. Itmight be thought, therefore, that it would be appropriate to account for the pigeons’success in terms of insight. In truth, this would not be a particularly useful approachas it really does not offer an account of the way in which the problem was solved.Other than indicating that the problem was solved suddenly, and not by trial anderror, the term “insight” adds little else to our understanding of these results.

I regret that I find it impossible to offer, with confidence, any explanation for thefindings by Epstein et al. (1984). But one possibility is that during their training with theblue spot, pigeons learned that certain responses moved the box towards the spot, andthat the box by the spot was a signal for food. The combination of these associationswould then result in them pushing the box towards the spot. During their training withthe banana, one of the things the pigeons may have learned is that the banana isassociated with food. Then, for the test session, although they would be unable to pushthe box towards the blue spot, generalization from their previous training might result inthem pushing the box in the direction of another signal for food, the banana.

Causal inference and folk physicsThe term “insight” is now rarely used in discussions of problem solving by animals.As an alternative, it has been proposed that animals have some understanding of

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causal relationships and that they can draw inferences based on this understanding tosolve problems. When a problem is encountered, therefore, animals are believed tosolve it through reasoning based on their understanding of the physical and causalproperties of the objects at their disposal. To take the example of Sultan joining twosticks together to reach food, if he understood that this action would create a longerstick that would allow him to reach further, he would then be able to solve theproblem in a manner that is considerably more sophisticated than relying on trial anderror. Of course, we have just seen that the studies by Kohler (1925) do not provideevidence that animals can solve problems in this way, but the results from otherexperiments have been taken as evidence that animals are capable of making causalinferences. The following discussion will focus separately on this work withprimates and birds.

PrimatesPremack (1976, pp. 249–261) describes an experiment with chimpanzees in whicha single subject would be shown an array of objects similar to the one in Figure 4.12.To gain reward, the chimpanzee was required to replace the strange shape inthe upper row with the knife from the lower row. The choice of the knife wasintended to reveal that the ape understood this object causes an apple to be cut inhalf. Two of the four subjects that were tested performed consistently well on thistask. They received a novel problem on each trial, thus their success could notdepend on them solving the problem by associating a given choice with a particulararray of objects. An alternative explanation for the problem shown in Figure 4.12 isthat the apes had repeatedly seen an apple being cut with a knife and they may havelearned to select the object from the lower row that was most strongly associatedwith the one from the upper row. Although this explanation will work for many ofthe test trials, Premack (1976) argues there were certain occasions where it providesan implausible explanation for the successful choice. For instance, one trial wassimilar to that shown in Figure 4.12 except the apple was replaced by a whole balland a ball cut into pieces. Even though the subjects had rarely seen a knife and aball together, they still made the correct choice (see also Premack & Premack, 1994,pp. 354–357).

FIGURE 4.12 Sketch of an array of objectsused by Premack(1976) to test forcausal inference inchimpanzees (adaptedfrom Premack, 1976).

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The findings from the experiment are encouraging, but there is some doubt abouthow they should be interpreted. The two apes who performed well on the task hadreceived extensive training on other tasks, and one of them, Sarah, had even been taughtan artificial language (see Chapter 13). Perhaps the extensive training given to thechimpanzees resulted in them acquiring a rich array of associations that helped themperform correctly on the tests, without the need to understand the relevant causalrelationships. It is also possible that because of the similarity between the shapes of anapple and a ball, stimulus generalization rather than a causal inference was responsiblefor the selection of the knife during the test with the ball. To properly evaluate the resultsfrom the experiment it would be necessary to have a complete account of the trainingthat was given before it started. It would also be important to have a full description ofthe method and results from the experiment itself. Unfortunately, the information that isavailable is rather brief and the reader is left in some doubt as to how Premack’s findingsshould be interpreted. Another possibility is that because of their extensive training, thetwo chimpanzees were able to appreciate causal relationships and to draw inferencesfrom them in a way that is not open to relatively naïve chimpanzees. Once again, thereis insufficient information available for this possibility to be assessed. There is nodenying that the experiment by Premack has revealed some intriguing findings but,before its full significance can be appreciated, additional experiments are needed inorder to pursue the issues that have just been raised.

Rather than study causal inference, some researchers have investigated what theycall “folk physics”, which refers to a common-sense appreciation of the causalproperties of objects in the environment. Problems could then be solved by drawinginferences from the understanding about these properties. Povinelli (2000) hasconducted a thorough series of experiments to explore whether chimpanzees makeuse of folk physics in problem solving, and they point to rather different conclusionsto those drawn by Premack (1976).

In one of Povinelli’s experiments, chimpanzees were confronted with a cleartube that contained a peanut. To retrieve the food, they were required to push it outof the tube with a stick. Once they had mastered this skill they were given the sametask but this time there was a trap in the tube. Pushing the stick in one directioncaused the peanut to fall out of the tube, pushing it in the other direction caused thepeanut to fall in the trap where it was inaccessible. A sketch of this simple apparatuscan be seen in Figure 4.13. Three of the four chimpanzees that were given this tasknever mastered it, and the fourth chimpanzee came to terms with it only after manypractice trials. The conclusion to be drawn from this study is that chimpanzees didnot have any appreciation of the problem created by the presence of the trap, that is,they lacked an understanding provided by folk physics concerning the properties oftraps. Instead, the eventual success of the single chimpanzee can be explained byassuming that she learned through trial and error how to avoid pushing the food intothe trap by inserting the stick into the end of the tube that was furthest from thepeanut. A similar failure to find evidence of successful performance on the sameproblem has been found with capuchin monkeys (Visalberghi & Limongelli, 1994).Povinelli (2000) cites a total of 27 experiments, using a variety of tests, all of whichshow that chimpanzees have a complete lack of understanding of the physicalproperties of the problems that confront them. The interested reader might also referto an article by Nissani (2006), which reports a failure by elephants to display causalreasoning in a tool-use task.

The negative results that have just been cited make it all the more important todetermine whether Premack (1976) was correct in claiming that chimpanzees are

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capable of causal inference. For the present, it is perhaps wisest to keep an open mindabout the capacity of primates to refer to folk physics when solving problems, butwhat about other species? Clayton and Dickinson (2006) have suggested that themost compelling evidence that animals have an appreciation of folk physics can befound in certain species of birds.

BirdsIn one study, Seed, Tebbich, Emery, and Clayton (2006) presented a group of naïverooks a version of the trap problem used by Povinelli (2000; described above). Whenfirst confronted with this problem, the direction in which the birds pushed the foodwas determined by chance but, as training progressed, they showed a markedimprovement in avoiding the trap. To test whether this improvement reflectedanything more than learning through trial and error, the birds were given a newproblem where performance was not expected to be influenced by the effects of anyprior trial-and-error learning. Six out of seven birds performed poorly on thenew problem, but one bird performed extremely well from the outset. As the authorspoint out, it is hard to know what conclusions to draw when one bird passes a testthat six others have failed, but the performance of the one successful bird encouragesthe view that future research with rooks might reveal promising results.

Another species of bird that might possess an understanding of folk physics isthe raven. Heinrich (2000; see also Heinrich and Bugnyar, 2005) describes a seriesof experiments with hand-reared ravens in which the birds were presented with apiece of meat hanging from a perch (see the left-hand side of Figure 4.14). The meatcould not be obtained by flying towards it and clasping it in the beak. Instead, toreach the meat some birds settled on the perch where the string was attached andgrasped the string below the perch with their beak and pulled it upwards. To stop themeat falling back they placed a foot on the string and then let it drop from their beakwhereupon they bent down to grasp again the string below the perch. This operationwas repeated until the meat was near enough to be grasped directly with the beak. Inanother test, ravens were confronted with the arrangement shown in the right-handside of Figure 4.14. On this occasion, meat could be retrieved by standing on the

Failure

Success

FIGURE 4.13 Right: Diagram of the apparatus used by Povinelli (2000) and by Visalberghi and Limongelli(1994) to test whether an animal will push a peanut in the direction that ensures it does not fall into a trap.From Visalberghi and Limongelli, 1994. Copyright © 1994 American Psychological Association. Reproducedwith permission. Left: A monkey about to attempt to retrieve a nut from the apparatus. Photograph byElisabetta Visalberghi.

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perch and pulling the string downwards. Birds who had mastered the original taskwere also adept at mastering this new task, but birds without prior experience ofpulling string never mastered this second task.

Heinrich and Bugnyar (2005) believe these results show that ravens have “somekind of understanding of means–end relationships, i.e. an apprehension of a cause-effect relation between string, food, and certain body parts” (p. 973). In other words,they have an appreciation of folk physics. This conclusion is based on the findingthat birds spontaneously solved the first problem but not the second one. It wasassumed that an understanding of cause–effect relations would allow the birds toappreciate that pulling food in one direction would result in the food moving in thesame direction (Figure 4.15). Such knowledge would be beneficial when the birdshad to pull the string upwards to make the meat rise upwards, but it would be ahindrance in the second problem in which the birds were required to pull the stringdownwards to make meat rise upwards.

Although the performance of the ravens is impressive, it does not necessarilydemonstrate that the birds relied on folk physics to solve the problem that initiallyconfronted them. As Heinrich and Bugnyar (2005) acknowledge, the solution to thefirst problem might have been a product of trial-and-error learning in which the sightof food being drawn ever closer served as the reward for the sequence of steppingand pulling that the birds engaged in. According to this analysis, the initial contactwith the string would have to occur by chance, which seems plausible because the bird’s

FIGURE 4.14 Diagramof the apparatus usedby Heinrich and Bugnyar(2005). A raven stoodon the perch and wasexpected to retrievefood by pulling thestring upwards (left-hand side) ordownwards (right-handside).

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beak may have been close to the string as it peered down from the perch at the food.It is also worth noting that the birds had experience of eating road-kill carcasses,which may have allowed them to refine their skills of pulling and stepping to retrieveedible constituents. In the case of the second problem, the naïve birds would beunlikely to make contact with the string as they looked down on the food, and theywould therefore be unlikely to initiate a response that would be rewarded by the sightof food being drawn upwards. In support of this claim, it is noteworthy that theauthors observed the naïve birds make rather few contacts with the string in the secondproblem. The success on the second problem by birds with experience of the firstproblem can also be readily explained by assuming that the original experienceincreased the likelihood that they would pull on string attached to the perch in thenew problem. A similar experiment has been conducted with elephants by Nissani(2004), who concluded that even their successful performance was a consequence ofnothing more than learning through trial and error.

Before describing one final laboratory study, it is worth considering an exampleof tool use by birds in their natural environment. Woodpecker finches live on theGalapagos Islands, where many of them use twigs or cactus spines held in their beaksto extract insects from holes in trees. They will even modify these tools by shorteningthem if they are too long, and removing twiglets if they prevent the twig from beinginserted into a hole. Although it might be thought that this behavior reflects anunderstanding of how sticks can be used as tools to extend the reach of the beak, andan understanding of how such tools can be modified to make them more effective, acareful study by Tebbich, Taborsky, Fessl, and Blomqvist (2001) provides a moremundane explanation for this behavior. It seems that juvenile woodpecker fincheshave a natural tendency to pick up twigs and cactus spines and to insert them in holesin trees. If this activity should result in food, then the particular action that has beenperformed will be repeated in other holes. Not all adult woodpecker finches displaythis skill, which has led Tebbich et al. (2001) to argue that tool use can be acquiredonly when the bird is young, and only if it is exposed to the appropriate environment.In other words, the skill of inserting twigs into holes is no more than a consequenceof the interaction between learning through trial and error and the maturation of aspecies-typical behavior.

Perhaps the most dramatic example of tool use in birds has been shown in NewCaledonian crows (Weir, Chappell and Kacelnik, 2002). These birds live onNew Caledonia, an island about 1600 km east of the north-east coast of Australia.

FIGURE 4.15 A raven solving the problem set by Heinrich and Bugnyar (2005). Photographs by BerndHeinrich and Thomas Bugnyar. Reprinted with permission.

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They use long thin strips of leaf with barbs running down one edge to draw prey fromcracks and holes in trees. It is not known if this ability is learned or inherited, but ifthe conclusions drawn from the study by Tebbich et al. (2001) have any generality,then it will be a mixture of the two.

In the experiment by Weir et al. (2002), a male and a female crow were expectedto retrieve a piece of food from a bucket with a handle that was placed in a clear,vertical tube (Figure 4.16). The tube was so deep that it was impossible for the birdsto reach the handle of the bucket with their beaks. A piece of straight wire and a pieceof wire with a hook at one end were placed near the tube and the birds were expectedto use the hooked wire to lift the bucket out of the tube by its handle. On oneoccasion, the male crow selected the hooked wire, which left the female with thestraight wire. She picked up one end in her beak inserted the other end in a smallopening and then bent the wire to create a hook which was of a suitable size to enableher to lift the bucket from the tube. A video clip of this sequence can be seen bygoing to the following web address http://www.sciencemag.org/cgi/content/full/297/5583/981/DC1. As one watches the female crow bend the wire it is hard not toagree with the authors that she was deliberately modifying the wire to create a tool,and that this modification relied on an understanding of “folk physics” and causality.However, appearances can be deceptive, and it would be a mistake to ignore thepossibility that the bird’s behavior was a consequence of less sophisticated processes.As with the woodpecker finches, it is possible that the skill displayed by the femalecrow was a consequence of the interaction between inherited tendencies and learningbased on prior experience with sticks, twigs, and so on. Before this explanation canbe rejected with complete confidence, more needs to be known about thedevelopment of tool use in New Caledonian crows in their natural habitat. It is alsoa pity that rather little is known about the prior experiences of the bird in question,which was captured in the wild.

The results from these tests for an understanding of folk physics in birds canperhaps most fairly be described as ambiguous in their theoretical significance.On the one hand, it is possible to explain most, if not all, of them in terms ofthe trial-and-error principles advocated by Thorndike (1911) almost a century ago.

FIGURE 4.16 A NewCaledonian crow liftinga bucket out of a tubein order to retrieve foodin an experiment byWeir, et al., (2002).Photograph by AlexWeir © BehaviouralEcology ResearchGroup, University ofOxford.

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However, a critic of this type of explanation would argue that it is so versatile that itcan explain almost any result that is obtained. Moreover, although the trial-and-errorexplanations we have considered may be plausible, there is no evidence to confirmthat they are necessarily correct. On the other hand, some of the behavior that hasbeen discovered with birds is so impressive to watch that many researchers find ithard to believe they lack any understanding of the problem that confronts them.

For myself, my sympathies rest with an analysis of problem solving in terms oftrial-and-error learning. The great advantage of this explanation is that it is based onfirmly established principles of associative learning. Problem solving relies on thecapacity for rewards to strengthen associations involving responses, and the transferof the solution from one problem to another is explained through stimulusgeneralization. By contrast, much less is known about the mental processes thatwould permit animals to make causal inferences, or to reason using folk physics.Seed et al. (2006) note briefly that these processes may involve the capacity foracquiring abstract rules about simple, physical properties of the environment. Givensuch a proposal two questions arise: first, how is such knowledge about theenvironment acquired, and second, are animals capable of abstract thought? As far asI am aware, no-one has offered an answer to the first question and, as for the secondquestion, we shall see in later chapters that whether or not animals are capable ofabstract thought is a contentious issue that has yet to be fully resolved.

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Subject index

abstract categories 179–80, 189, 358abstract mental codes 325, 359,

368, 371abstract thought 121acquired distinctiveness 157–8,

157, 158acquired equivalence 157–8, 157, 158active memory 220, 221adaptability 12–13addition 249–50, 250African claw-toed frog 216, 216African grey parrot

and category formation 172,182, 182

and communication 351–3and mimicry 304and mirror-use 322, 323number skills of 248–50, 248–9,

251, 252aggression, displays of 337–8, 337AI see artificial intelligenceair pressure 285Akeakamai (dolphin) 353–5alarm calls 331, 332–5, 336, 337albatross 286Alex (parrot) 248–50, 248–9, 251,

252, 351–3, 368alley running 94, 98, 106

and extinction 131–3, 132and navigation 267–8and number skills 245–6, 246

alpha conditioning 44American goldfinch 331American sign language 341–2,

349–50, 349amnesia, drug-induced 224–5amodal representation 240–1, 241amygdala 21, 224analogical reasoning 187, 188, 368, 371anecdotes 23, 112animal intelligence 2–33

and brain size 10–12, 10, 361–4definition 12–16, 362–3distribution 4–12, 4, 360–71ecological view 228, 229, 231and evolution 369–71general process view 230–1historical background 22–33null hypothesis of 261, 364–9reasons to study 16–20research methods 20–2

animal kingdom 4–5animal welfare 19–20anisomycin 224–5anthropomorphism 23–4, 168–9,

262–3ants 265–8, 267apes 16

and category formation 182and communication 329–50, 335–6,

340, 356–7and language 116, 329–50, 356–7and self-recognition 369vocalizations 339–40, 340see also chimpanzee; gorilla;

orang-utanAplysia californica (marine snail) 43,

43–5, 46, 47, 149, 263Arctic tern 289artificial intelligence (AI) 19associability/conditionability 67–8,

74–5, 91associative competition 101–2, 101associative learning 35–61, 123, 143

and attention 365–6conditioning techniques 26–42CRs 55–61and deception studies 313, 315definition 35and evolution 370–1memory model of 46–9, 46–9, 59nature of 42–9and the null hypothesis 364–6and problem solving 121and the reflexive nature of the CR

60–1and stimulus-stimulus learning

49–52and surprise 365–6and time 233and US representations 52–5see also instrumental (operant)

conditioning; Pavlovian(classical) conditioning

associative strength 64–71, 66, 68, 83,88–9, 91, 101, 102

and category formation 175–7, 179and discrimination learning 152–3equations 65–70, 83, 87, 88, 89and extinction 125–31, 134, 142negative 127–8, 130, 176and stimulus significance 83–4

associative-cybernetic model 111asymptote 36attention 365–6, 371

automatic 86and conditioning 63, 74–91, 86controlled/deliberate 86and the CS 63, 76, 77, 78–80dual 86multiple modes of 86

audience effects 335–6auditory templates 332Austin (chimpanzee) 345, 346, 357autoshaping 37, 37, 38, 60, 126, 127

omission schedules 60

baboonand deception 314, 314, 315predatory nature 333, 336

Bach, J.S. 172–3back-propagation networks 164–5

two-layer 164bees

light detection 285navigation 269–71, 271, 280see also bumble-bee; honey-bee

behaviorists 28bicoordinate navigation 288bidirectional control 308–9, 308–9,

310biological significance 80–1,

84–5, 102birds

evolution 6and imitation 304–5, 305and navigation 275and problem solving 117–21,

118–20short-term memory of 228,

229–30song birds 331–2, 363see also specific species

black and white images 173–4black-capped chickadee 305, 363, 363blackbird 302bliss points 104blocking 53, 63–4, 63, 69–70, 78,

365–6LePelley on 91and Mackintosh’s theory 83–4and navigation 293, 294and the Pearce–Hall model 88–9

Note: Page numbers in italic refer to information contained in tables and diagrams.

Pearm-S-Index.qxp 12/3/07 8:47 PM Page 411


412 Subject index

blocking (Continued)and the Rescorla–Wagner model

72–3, 73blue tit 230, 304–5body concept 323–4bombykol 265bonobo, and communication 343–5,

344, 346, 347–8, 348, 349, 357bottle-nosed dolphin

and communication 338, 353–6,354–5

and learning set studies 362brain 20–1

human 19size 10–12, 10, 11, 361–4

brain size-body ratio 10–11, 11, 361brown bear 266budgerigar 309–10, 367bumble-bee 298Burmese jungle fowl 298

cache 226canary 363capuchin monkey

and category formation 184and diet selection 300and problem solving 116, 117see also cebus monkey

carp 173cat

and communication 328and the puzzle box escape task

26–7, 26–7, 28category formation 170–89, 358

abstract categories 179–80, 189,358

categories as concepts 179–80examples of 171–3exemplar theories of 177–8, 178feature theory of 173–9and knowledge representation

188–9prototype theory of 178–9relationships as categories 180–7theories of 173–9

causal inference 114–21, 115cebus monkey

and imitation 312, 312and serial order 256–7, 256–7see also capuchin monkey

cephalization index (K) 11–12, 12,13, 361

chaffinch 331chain pulling tasks 95, 97, 110, 141, 253cheremes 341chick, and discrimination learning

159–61, 159–61chicken

and communication 331and discrimination learning 150

chimpanzee 4, 368, 371and abstract representation 189and category formation 179–81,

180, 186–7, 188, 189and communication 24, 116, 327,

336, 339–50, 342–4, 357, 359dominant 317–18and emulation learning 310and imitation 306, 307, 310,

311, 367and instrumental conditioning

105, 106lexigram use 343–5, 343–4, 346and mimicry 303and number skills 245, 245, 248,

250, 250, 252and plastic token use 342–3, 343and problem solving 112–17,

112–13, 115, 117and self-recognition 319–24,

320, 322short-term memory of 228and social learning 367subordinate 317–18and theory of mind 312–13,

314–18, 316, 317, 324and transitive inference

259–60, 262vocal tracts 340, 340welfare issues 19see also bonobo

chinchilla 172, 172chunking 255–6circadian rhythms 233–4circannual rhythms (internal

calendars) 291Clark’s nutcracker 214, 228, 230,

272–3, 294, 370Clever Hans 16, 243, 243clock-shift experiment 288coal tit 230cockroach 233–4, 262–3cognition, and language 358–9cognitive maps 276–80, 330, 369–70color perception 173–4communication 326–59, 369

definition 327development of 330–2honey-bees and 328–30, 328–30,

331, 338–9as innate process 330–3, 336, 356and intention 335–6and interpretation of the signal

330–1as learnt process 330–3, 336, 337and representation 334–5and the significance of signals

334–5vervet monkeys and 332–7, 333–4see also language

compass bearings 269–70, 271, 272compound stimuli 68–73, 83–4, 88–9,

126–9, 151–3, 160, 176–7concepts, categories as 179–80concrete codes 188, 371conditioned emotional response (CER)

see conditioned suppressionconditioned inhibition see inhibitory

conditioningconditioned reinforcement 105–6, 105conditioned response (CR) 29, 35,

39–40, 44, 55–60, 285air pressure as 285and autoshaping 37and blocking 63compensatory 56–8, 58consummatory 55–6and discrimination learning 149,

155, 156, 164, 165and eye-blink conditioning 36–7and fear of predators 302influence of the CS on 58–9and inhibitory conditioning 42and the memory model of

conditioning 47preparatory 56reflexive nature of 60–1and representation of the US 109response-cueing properties

110–11, 111and stimulus-stimulus

conditioning 50–2strength of 64, 65, 70, 71, 73

conditioned stimulus (CS) 29, 30,32–3, 123

associability/conditionability of67–8, 75

associative strength 125–31,134, 142

and attention 63, 76, 77, 78–80aversive 110and compensatory CRs 57, 58compound 68–73, 83–4, 88–9,

126–9, 151–3, 160and conditioned suppression 38and discrimination learning 162and excitatory conditioning 35,

36–7, 38, 39, 40and extinction 124, 125–30, 134–5,

136–46and eye-blink conditioning 36–7and fear of predators 302and imitation 305influence on the CR 58–9inhibitory 40, 41–2, 217and instrumental conditioning 106,

109–11intensity 67–8, 68and long-term memory

studies 216, 217



Subject index 413

memory model of 46–8, 59and the nature of US representations

52–4neural mechanisms of 43–5protection from extinction 126–8and the reflexive nature of the CR

60–1and the renewal effect 136–8single 64–8spontaneous recovery 134–6and stimulus-stimulus conditioning

50–2and taste aversion conditioning 39and timing 242and trace conditioning 194see also CS–no–US association;

CS–R association; CS–USassociation/contingency

conditioned suppression (conditionedemotional response) 37–8, 38,41, 42, 63, 67, 67–8, 71–2, 72,85, 85, 89–90, 89

conditioningalpha 44and attention 63, 74–91, 86compound 68–73, 83–4, 88–9,

126–9, 151–3, 160excitatory 35–40, 42–5, 44–5,

52–4, 72–3eye-blink 36–7, 36–7, 53–4, 64,

64, 68–9, 69, 124, 125,135, 135

information-processing model of46, 46

instrumental 92–121observational 302, 308, 309second-order (higher-order) 50–2,

51, 185–7, 186serial 49–50single CS 64–8and surprise 62–74trace 194–6, 195–6see also inhibitory conditioning;

instrumental (operant)conditioning; Pavlovian(classical) conditioning; tasteaversion conditioning

conditioning techniques 26–42autoshaping 37, 37, 38conditioned suppression 37–8, 38control groups 39–40, 40excitatory conditioning 35, 36–40inhibitory conditioning 35, 40–2

configural cues 153configural theory of discrimination

learning 155–7, 156connectionist models

of discrimination learning 161–6,162–6

exemplar-based networks 165–6, 165

hidden layers 165multi-layer networks 164–5, 165single-layer networks 162–4, 162–4of time 241

conscious thought 210–11consolidation theory (rehearsal theory)

of long-term memory 218–20,218, 225

conspecifics 300, 324, 335context 72, 138, 229context-stimulus associations

78–80, 90–1contextual variables 15contiguity 31, 63, 98, 98continuous reinforcement 130–1,

131–2, 134, 134, 145–6, 145control groups 39–40, 40

truly random control 40cooperation, and language 338–9copying behavior 297, 298, 301,

302–12, 324mimicry 303–4, 312, 324see also imitation

coriolis force 288corvids 181, 182, 368counting 233, 243, 245–9, 251–2

cardinal principle 252one–one principle 252stable-ordering principle 252

cow 229coyote 58–9CS–no–US association 135–6CS–R (response) association 364–5

inhibitory 140, 142CS–US association/contingency 71–2,

88–90, 91, 94–5, 99, 111,346, 364

associative strength 64–71, 83, 88and discrimination learning 162and extinction 123, 136–8, 138–40and inhibitory connections

136–8, 142and learned irrelevance 85–6, 90negative 71–2positive 71and stimulus significance 83–4, 85–7and the strength of the CR 64zero 71

dark-eyed juncos 363dead reckoning/path integration 266–9,

269–70, 278, 280, 293, 367decay theory 202–3deception 313–15, 314delay, gradient of 98, 98delay conditioning 194delayed matching to sample (DMTS)

197–9, 228and decay theory 202–3and forgetting 200, 201–3, 204, 205

density discrimination 167–9,167, 168

deprivation 106–109, 108, 111detours 276–8, 277diana monkeys 335diet selection 298–301, 299

see also fooddiscrimination learning 148–69,

171, 178, 368configural theory of 155–7, 156connectionist models of 161–6,

162–6elemental theories of 155, 156and learning sets 361–2, 362and metacognition 166–9,

167–8and relational learning 149–50Rescorla–Wagner theory of

152–5, 153–4Spence on 150–2, 155, 156stimulus preexposure 157–61theories of 149–61and theory of mind studies

317, 318and transposition 149–51

dishabituation 79, 193–4displacement (language) 337, 345,

354–6distance effect 257, 259distractors 193–4, 201–2, 203–4

surprising 203DMTS see delayed matching to

sample“Do as I do” test 307–8, 310,

311, 367dog 22–3

and classical conditioning29–30, 29

and communication 337, 337,350–1

and trial and error learning 25–6dolphin 16, 16, 124

and category formation 181and communication 338–9, 353–6,

354–5and learning set studies 362and self-recognition 322, 324,

369short-term retention of 198,

201, 206, 206, 207, 228see also bottle-nosed dolphin

drive centers 106, 107drives 30–1, 106–7drug tolerance 56–8, 58

eagle 80, 333, 334, 335ecological niches 369–70ecological view of animal intelligence

228, 229, 231EDS see extradimensional shift



414 Subject index

electric shocks 14, 31–2, 43–4, 50, 53,63–4, 67, 69, 71–3, 84–6, 89–90,107, 124–5, 129–30, 135

and conditioned suppression 38and eye-blink conditioning 36and inhibitory conditioning 41, 42and long-term memory studies 216,

216, 217, 219–21, 219and navigation experiments 285and preparatory CRs 56and stimulus-stimulus learning

51–2electroconvulsive shock (ECS) 218,

218–19, 220, 224–5, 228elemental theories of discrimination

learning 155, 156elephant 36

long-term memory of 213, 213and problem solving 116and self-recognition 322, 324, 369and theory of mind 317

emulation learning 310endogenous control, of migration

290–2, 290English language, spoken 339–40,

344–5environment 7–8episodic memory 225–31episodic-like memories 226–8equilibrium theories of behavior 104European robin 291–2evolutionary development 3, 4–10,

22, 23and intelligence 369–71tree of evolution 6, 7

excitatory conditioning 35–40, 72–3and the nature of US representations

52–4of neural processes 42–5, 44–5

exemplar effect 177exemplars 175, 177–9, 178expectancy (R–US) theorists 94–5

see also R–US associationsexperience 113–14extinction 36, 51–2, 57–8, 65–6, 66,

108, 109, 122–47, 224associative changes during 134–42of conditioned inhibition 129–30conditions for 125–42enhanced 128–9, 128–9as generalization decrement 123–5,

125, 128, 132–4, 144, 146and an inhibitory S–R connection

140–2, 141not affecting CS–US associations

138–40, 139partial reinforcement 130–4, 131,

132, 134, 145–7, 145and Pavlovian conditioning 142–7,

143–6

protection from 126–8, 126–7and the renewal effect 136–8,

136, 137and surprise 125–30trial-by-trial basis 142–7, 143–6as unlearning 123with a US 129

extradimensional shift (EDS) 81–3, 82eye-blink conditioning 36–7, 36–7,

53–4, 64, 64, 68–9, 69extinction 124, 125, 135, 135

face recognition 173–4, 176, 176, 178family trees 6, 8–9fear, of predators 301–2fear conditioning see electric shocks;

electroconvulsive shockfeature theory of category formation

173–9feature-positive discrimination 151–2,

155–6Fellow (dog) 350–1fish

and category formation 173and learning 13, 15and navigation 274short-term memory of 228see also specific species

folk physics 116–21food 8, 9

food-pulling tasks 117–19, 118–19hiding/storing behavior 191, 214,

226–7, 229–30, 363, 370rewards 13–14, 14, 15, 27–8see also diet selection

foraging behavior 300–1, 301forgetting 216–25, 228

decay theory of 202–3deliberate 204–5and proactive interference 200–1,

201, 203, 204and retroactive interference 163,

200, 201–2, 203and short-term retention 199–205

fossil record 6fruit-fly 327frustration theory 132–3, 135

Galapagos Islands 5, 6, 119geese 268–9, 268–9, 270general process view of animal

intelligence 230–1generalization

and category formation 174, 177,178

gradients 150, 150, 156mediated 180numerosity 244, 244temporal 236–8, 237, 240–1see also stimulus generalization

generalization decrement 37as extinction 123–5, 125, 128,

132–4, 144, 146geometric modules 274–6geometric relations 272–4,

273–4, 367geraniol 234gerbils 268, 269–72, 270, 272ghost control 310goal directed learning 32goldfish 364–5gorilla

and communication 339and self-recognition 319, 321, 322

gradient of delay 98, 98grain selection 80–1, 81grammar 337–8, 346–50, 353–6,

355, 358of American sign language 341and lexigrams 343using plastic tokens 342

gravity 288great spotted woodpecker 191great tit 304–5, 305, 363green turtle 283, 284, 291grief 341guppy 301

habituation 75, 77, 79, 80, 91definition 192and retroactive interference 201,

203and short-term retention 192–4,

193, 201, 203see also dishabituation

hamsterand navigation 268, 277–8, 277social behavior of 59–60

harbor seal 303heading vectors 272hedgehog 229higher vocal centre 363hippocampal place cells 280–3hippocampus 21, 363–4homing 284, 286–9

and bicoordinate navigation 288and landmark use 286map and compass hypothesis of

287–8and olfaction 289by retracing the outward route

287, 287Homo sapiens 5

see also human beingshoney-bee

communication (waggle dance) of328–30, 328–30, 331, 338–9

and navigation 268short-term memory of 228sounds made by 329–30



Subject index 415

and time perception 234, 235, 236,262

Hooker, John Lee 173Horizon (TV programme) 338horse 16, 243, 243human beings 371

brain 19and category formation 177, 178episodic memory of 225and the exemplar effect 177and language 327, 338, 339–40,

344–5, 356and learning 74, 86long-term memory of 213metamemory of 207–8navigation skills 266, 275and number 248and serial position effects 207see also Homo sapiens

hunger 106–9, 110“hunting by search image”

80–1, 80–1hyena 370

IDS see intradimensional shiftiguana 5, 6imaginal codes 188imitation 303, 304–12, 324, 367–8

and bidirectional control 308–9,308–9, 310

chimpanzees and 306, 307, 310,311, 367

“Do as I do’’ test 307–8, 310, 311,367

and emulation learning 310laboratory studies of 306–10mechanisms of 311–12monkeys and 311–12, 312, 367,

368naturalistic evidence 304–6and two-action control 309–10,

309inactive memory 220indigo bunting 292information-processing 15–16, 21information-processing model

of conditioning 46, 46of time 237–8, 237, 239, 239,

240, 241infrasound 285, 288inheritance, of problem solving

abilities 119–20inhibitory conditioning 35, 40–2,

41–3, 70–1, 73, 127–8detection 41–2extinction 129–30and latent inhibition 76and long-term memory 217and the nature of US representations

54–5, 55

innate processescommunication 330–3, 336, 356fear 302

insight 112–14instrumental (operant) conditioning

28, 30, 32–3, 92–121conditions of 97–106and discriminatory learning 153extinction 123–8, 131–4, 141historical background 93–5and long-term memory studies

216–17and the memory model 111and mimicry 303nature of 93–7and the null hypothesis 364, 365–6Pavlovian interactions 106, 109–11and problem solving 111–21and serial order 253and vigor of performance 106–11

intellectual curiosity 16intelligence see animal intelligenceintention, and communication 335–6internal clocks 233–6, 262,

287–8, 292alternatives to 241–2

interneurons 45intertrial interval 197interval scales 250interval timing 233, 236–41, 237–41,

242, 263midpoints 238, 239–40pacemaker 241, 263

intradimensional shift (IDS) 81–3, 82

Japanese macaque monkey 300, 306Japanese quail

and imitation 309, 309and mate choice 301

jaw movement 39–40, 40

Kanzi (bonobo) 344–5, 344, 346,347–8, 348, 349, 357

kinesthetic self concept 323–4knowledge attribution 315–19knowledge representation 188–9,

358–9, 368–9abstract mental code 325, 359,

368, 371concrete mental code 188, 371self-recognition 319–24, 320–2,

368–9see also number; serial order; time

Koko (gorilla) 319, 321

Lana (chimpanzee) 343, 346, 352landmarks 293–4

and cognitive maps 278–80and compass bearings 269–70,

271, 272

in distinctively shaped environments274–6, 274–5

and geometric relations 272–4,273–4, 367

and heading vectors 272and hippocampal place

cells 281–3and homing 286piloting with multiple 271–2,

271–2, 367piloting with single 269–71, 367retinal snapshots of 270, 271

language 327, 336–59, 371American sign language 341–2,

349–50, 349apes and 116, 329–50, 356–7arbitrariness of units 337, 338and cognition 358–9and cooperation 338–9definition 336–8discreteness of 337, 338, 345and displacement 337, 345, 354–6grammar 337–8, 341–3, 346–50,

353–6, 355, 358human 327, 338, 339–40, 344–5,

356as innate process 356and lexigrams 343–5, 343–4, 346and motivation 357and the null hypothesis 369and plastic tokens 342–3and problem solving 19productivity of 337–8, 346–7requirements for learning 356–79and semanticity 337, 346sentences 342–3, 346–50, 352–4,

355, 356, 358spoken English 339–40, 344–5spontaneous 357training assessment (apes) 345–50training methods (apes) 339–40training methods (non-primates)

350–6language acquisition device 356–7latent inhibition 76, 79–80, 88–91,

365–6Law of Effect 27–8, 30–1, 93–4,

102–4and problem solving 111–21

learned irrelevance 84, 85–6, 85, 90learning 8–9, 13–15

communication and 330–3,336, 337

definition 13emulation learning 310goal directed 32human beings and 74, 86and migration 292–3perceptual 158–61, 159–61relational 149–50



416 Subject index

learning (Continued)speed of 13–15, 14stimulus-stimulus 49–52and surprise 62–74trial and error 25–7, 112–14,

116–17, 119–21, 169, 211,297, 323, 336

see also associative learning;discrimination learning; sociallearning

learning curve 64, 64, 65learning sets 361–2, 362leopard 335lever pressing 93, 95–6, 98–102,

105–8, 110and conditioned suppression 37–8and CS–US contingency 71and discrimination learning

151–2, 153and extinction 124, 141and imitation studies 306–7, 307and interval timing 236–8, 237,

239–41, 240–1and number skills 245, 246, 251and preparatory CRs 56and serial order 253

lexigrams 343–5, 343–4, 346light

polarized 285ultraviolet 285

light-dark cycle 287–8, 292artificial 288, 291

limited capacity theory 202, 203–4loggerhead turtle 290–1, 290long-distance travel 265, 283–93

and air pressure 285homing 284, 286–9and magnetic fields 284, 288, 291migration 284, 289–93and navigational cues 284–5, 293–5and polarized light 285and ultraviolet light 285

long-term memory 191, 212–31capacity 214–15, 214, 215comparative studies of 228–31consolidation theory of 218–20,

218, 225durability 215–17, 216, 217episodic 225–31neural circuitry 218, 224–5retrieval theory of 218, 220–5,

222, 223loris 266Loulis (chimpanzee) 341–2, 342, 350

magnetic fields 269–70, 284,288, 291

magnitude effect 256, 259mammalian evolution 6Manx shearwater 286

map and compass hypothesis 287–8maps, cognitive 276–80, 330, 369–70marmoset monkey 83

and imitation 310, 367short-term memory of 366–7, 366

marsh tit 363marsh warbler 332Matata (bonobo) 344matching to sample 180–3, 181, 184,

186–7, 186, 189, 215, 368see also delayed matching to sample

matingand deception 314–15mate choice 301

maze running 94, 94and foraging behaviour 300–1, 301and long-term memory 222–4, 223and navigation skills 276–7, 277,

281–3, 281–2, 294and trace conditioning 195–6,

195, 196see also radial maze; T-mazes

mean length of utterances (mlu) 349, 349meaning 337, 346Melton (baboon) 314, 315memory 188–9

active 220, 221episodic 225–31episodic-like 226–8and evolution 370human 371inactive 220and the null hypothesis 366–7, 366reference 237–8retrieval 21spatial 363–4, 366, 366–7and standard operating procedures

76–9and stimulus significance 81visual 304see also long-term memory;

short-term retentionmemory model of associative learning

46–9, 46–9, 59, 111memory traces 202, 211, 241–2mental states 28, 312, 324–5metacognition

definition 166and discrimination learning 166–9,

167–8metamemory

definition 208and short-term retention 207–11, 209

metamphetamine 241Mexican jay 230migration 284, 289–93

definition 284, 289endogenous control of 290–2, 290and learning 292–3and the null hypothesis 367

mimicry 303–4, 312, 324vocal 303–4

mind 12mirror neurons 311–12, 324mirrors 319–24, 320, 321, 368–9Missouri cave bat 286mlu see mean length of utterancesmolar theory of reinforcement 99–100molecular theory of reinforcement

100, 101–2monkeys 368

and category formation 179, 187and discrimination learning 166–9,

167, 168episodic-like memory of 227fear of predators 301–2and imitation 311–12, 312, 367, 368and instrumental conditioning 93and metacognition 166–9, 167, 168mirror-use 322, 323number skills of 246–8, 247,

251, 252and self-recognition 322and serial order 253, 256–9,

256–8, 358short-term retention of 198, 201–2,

204, 228and trace conditioning 195and transitive inference 260–1,

260, 262see also specific species

morphine 56–8, 58motivation

dual-system theories of 107–8,109–10

and language 357motor neurons 44–5, 45, 46mouse 233music 172–3mynah bird 303

natural selection 5navigation 21, 264–95

bicoordinate 288and communication in honey-bees

328–30, 328–30, 331long-distance travel 265, 283–93methods of 265–83and the null hypothesis 367and the Rescorla-Wagner theory 293short-distance travel 265–83, 293and the sun 267

navigational cues 284–5, 293–5need 30, 106–7negative patterning 153, 156, 162neocortex 364neophobia 298–9, 300neural circuitry

of associative learning 42–5, 44, 45of long-term memory 218, 224–5



Subject index 417

neural net theories see connectionistmodels

New Caledonian crow 119–20, 120Nim (Neam Chimpsky) (chimpanzee)

341, 346, 349–50, 357no–US center 134–6, 135no–US representation 55nominal scales 247nous (mind) 12null hypothesis of intelligence 12,

261, 364–9and associative learning 364–6as impossible to refute 369and language 369and memory 366–7, 366and navigation 367and the representation of knowledge

368–9and social learning 367–8

number 233, 243–52, 263, 368absolute number 245–7, 245–7addition 249–50, 250interval scales 250nominal scales 247numerical symbols 248–50, 248–9,

252ordinal scales 247–8and perceptual matching 251–2relative numerosity 243–4, 244representation 247–8subitizing 251

number-identification task 86numerons 252numerosity generalizations 244, 244

oak leaf silhouette experiment 171,172, 173

observational conditioning 302, 308, 309octopus 298odour cues 309olfaction 289omission schedules 60operant conditioning see instrumental

(operant) conditioningorang-utan

and communication 339–40, 340and mimicry 303and self-recognition 323–4

ordinal scales 247–8orienting response (OR) 74–6, 75,

87–8, 87oscillators 236, 263overshadowing 70, 101–2, 293

Pacific sea anemone 192paramecium 149, 149, 192, 193parrot

and communication 351–3and mimicry 303see also African grey parrot

partial reinforcement 130–4, 131, 132,134, 145–7, 145

partial reinforcement effect (PRE)130, 132, 133, 145–7

path integration see deadreckoning/path integration

Pavlovian (classical) conditioning 20,28–30, 29, 32–3, 35–61,93, 99

using air pressure 285and autoshaping 37and the conditioned response 55–61and conditioned suppression 38and diet selection 299and discrimination learning

149–50, 162and dual attention 86and evolution 370–1and extinction 123–6, 128–31,

133–4, 138–47eye-blink conditioning 36–7and fear of predators 302and imitation 305using infrasound 285instrumental interactions 106,

109–11and long-term memory studies 217using magnetic fields 284memory model of 46–9, 46neural mechanisms of 43–5,

44, 45and the null hypothesis 364–6and overshadowing 101and stimulus-stimulus learning

49–52taste aversion conditioning 39and timing 242using ultraviolet and polarized

light 285Pavlovian-instrumental transfer

design 110peak procedure 239, 239peak shift 151, 153, 178Pearce–Hall theory 86–91pedometers 267penguin 24, 24–5pentobarbitol 224perception, colour 173–4perceptual learning 158–61, 159–61perceptual processing 21periodic timing 233–6, 263

oscillators 236, 263pheromone trails 265–6Phoenix (dolphin) 353phonemes 173, 341phylogenetic scale 5physiological techniques 20–1pigeon 16, 17–18, 55–6, 365–6, 368

and autoshaping 37, 37, 38, 60and category formation 171–2, 172,

173–5, 174–5, 176, 177,182–5, 182, 183–5, 358

and communication 339and discrimination learning 151,

151, 154episodic-like memory of 227–8and extinction 124, 126–8and homing 286–9, 293and imitation 310, 311and inhibitory conditioning 40and long-term memory 214–17,

214, 215, 227–8metamemory of 211navigation skills of 264, 272,

284–9, 293number skills of 243–4, 244, 251and problem solving 114and selective association 84and self-recognition 322and serial order 253–6, 254–5, 259short-term retention of 197–8, 200,

201–2, 204, 205, 206, 211, 228and stimulus significance 80–3, 84and stimulus-stimulus

conditioning 50and time 241–2and transitive inference 261–2, 261

pilotingwith multiple landmarks 271–2,

271–2with single landmarks 269–71

pinyon jay 363place cells 280–3plastic tokens 342–3, 343population-specific behavioral

traditions 305–6PRE see partial reinforcement effectpredators

and alarm calls 331, 332–5, 334,337

fear of 301–2prefrontal lobe 21Premack principle 103–4primacy effect 206–7primates 6, 8–9

and imitation 305–6and navigation 275and social learning 367see also specific species

proactive interference 200–1, 201,203, 204

problem solving 26–7and causal inference 114–21, 115and folk physics 116–21and insight 112–14and language 19and the Law of Effect 111–21

prospective code 198“protection from extinction” 126–8,

126–7



418 Subject index

prototype theory 178–9protozoan 327punishment 14, 31–2, 93

see also electric shocks;electroconvulsive shock

puzzle boxes 26–7, 26–7, 28

quail 367

R–US associations 94–5, 95, 97,101–2, 110–11, 311, 365

rabbit 64, 64, 68–9, 69, 77–80, 110and extinction 124–5, 135and habituation 192–4, 193and Pavlovian conditioning 36–7,

39–40, 53–4raccoon 60–1, 191radial maze 228–9, 366

and forgetting 200–1, 201, 202,203, 204–5

and navigation 294and serial position effects 206–7,

208and short-term retention 196–7,

196–7, 198, 199rat 14, 31–2, 32, 59

and attention and conditioning75–6, 75, 78, 83–8, 86, 89–90

and compensatory CRs 56–8and conditioned suppression 37–8and CS–US contingency 71and diet selection 298–301,

299, 301and discrimination learning 151–2,

153, 153, 157–8, 163episodic-like memory of 227, 228and extinction 128, 128, 130–4,

139, 140–2, 146and imitation 306–7, 307, 308–9,

308, 367–8and inhibitory conditioning 42and instrumental conditioning

93–101, 94–101, 103–8, 103,105, 108, 110, 365

and knowledge representation 188and learned irrelevance 85–6, 86long-term memory of 216–25,

218–19, 222, 227–8and the memory model of

conditioning 47–9, 47and method of learning 14–15navigation skills of 268, 273–9,

273–5, 277–9, 281–3, 281,293, 294

number skills of 245–6, 246, 251–2and orienting responses 87–8and preparatory CRs 56and selective association 84, 85and serial order 253short-term retention of 194–7,

195–7, 199–207, 201, 204,208, 228–9

and stimulus significance 83, 84, 85and stimulus-response theorists

31–2, 32and stimulus-stimulus learning

49–50and time 235–8, 237, 239–41,

240, 241and trace conditioning 194–6,

195–6raven 117–19, 118–19reactivation effects 221–5, 222, 223reasoning, analogical 187, 188,

368, 371recency effect 206–7reference memory 237–8reinforcement

conditioned 105–6, 105continuous 130–1, 131–2, 134,

134, 145–6, 145molar theory of 99–100molecular theory of 100, 101–2

reinforcer devaluation design 95–6,95, 97

reinforcers 93and associative competition 101–2conditioned 105–6definition 102–4incentive value of the 108, 109nature of the 102–4negative 93positive 93response contiguity 98token 105–6

relational learning 149–50relationships between objects 180–7,

188–9second-order 185–7, 186

renewal effect 136–8, 136, 137representation 21–2

A1 76–8, 76, 81A2 76–80, 76, 81amodal 240–1, 241and communication 334–5inactive 76, 76of number 247–8retrieval-generated A2

representations 78–9self-generated A2 representations

77–8, 79spatial 258–9, 262standard operating procedures

model 76, 76US 109see also knowledge representation

reptiles 6Rescorla–Wagner model 64–5, 68–71,

72–4, 83, 84, 91and category formation 175, 176

and discrimination learning 152–5,153–4, 156, 162, 163, 164–5

and extinction 123, 125–6,127–31, 142

and navigation 293research methods 20–2response-chaining 253–7, 254–7response-cueing 110–11, 111response-reinforcer contingency

99–100, 99, 100, 101–2retardation test 41–2retention interval 197–8, 216retinal snapshots 270, 271retrieval theory 218, 220–5,

222, 223retroactive interference 163, 200,

201–2, 203retrospective code 198reward 93

anticipation of 94–5, 96, 108and category formation 175–6and gradient of delay 98, 98and the Law of Effect 112and response-reinforcer contingency

99–100and short-term memory

studies 199and stimulus-response theorists

31, 32rhesus monkey

and category formation 181, 184and metamemory 208–11, 210and serial order 257–8, 257–8and short-term retention 206,

208–11Rico (dog) 351rook 117running wheels 103, 103

S–R associations 365and imitation 311and inhibition in extinction 140–2,

141and instrumental conditioning 93–4,

96and serial order 253and theory of mind studies 318

S–R theorists 46, 48–9, 52, 94–5, 96,111

S–(R–US) associations 96–7, 111sage grouse 301sameness 180–5, 181–5, 188–9Sarah (chimpanzee) 116, 187, 188,

260, 342–3, 346, 347, 349, 359,368, 371

satiety 107, 108–9satisfaction 28, 30scala naturae 4–5scrub jay 318–19, 324sea lion 181, 181, 215, 216



Subject index 419

Second World War 266second-order (higher-order)

conditioning 50–2, 51,185–7, 186

selective association 84–6self-awareness 323self-recognition 319–24, 320–2,

368–9semanticity 337, 346sensory neurons 44, 45, 46sensory preconditioning 50sentences 342–3, 346–50, 352–4, 355,

356, 358comprehension 347–8, 352,

353–4, 355production 348–50, 352–3,

356, 358sequential stimuli 245–6, 245sequential theory 132, 133serial conditioning 49–50serial delayed alternation 203serial order 233, 253–9, 254–8, 263,

358, 368and chunking 255–6and the distance effect 257, 259and the magnitude effect 256, 259spatial representation of 258–9and transitive inference 259–61

serial position effects 206–7, 208serial recognition tasks 253–7, 254–7serotonin 45Sheba (chimpanzee) 248, 250,

252, 368Sherman (chimpanzee) 345, 346, 357short-distance travel 265–83, 293

and cognitive maps 276–80and dead reckoning/path integration

266–9, 269, 270, 278, 280,293, 367

and detours 276–8, 277and distinctively shaped

environments 274–6, 274–5and geometric modules 274–6and geometric relations 272–4,

273–4and hippocampal place cells 280–3methods of navigation 265–83and novel release sites 278–80, 279and pheromone trails 265–6piloting with multiple landmarks

271–2, 271–2piloting with single landmarks

269–71short-term retention 190–211,

366, 366comparative studies of 228–31and decay theory 202–3forgetting 199–205and habituation 192–4, 193limited capacity theory of 202

and metamemory 207–11, 209methods of study 191–9and serial position effects 206–7and trace conditioning 194–6

shuttle-boxes 220–1Siamese fighting fish 229sign language 24, 341–2, 349–50, 349signals 334–5silkworm moth 265simultaneous chaining 253–7, 254–7simultaneous stimuli 246–7, 247Slocum (sailor) 266snakes 301–2, 302, 333, 334social behavior 59–60social enhancement 305social groups 367social learning 296–325

and copying behavior 302–12and diet selection 298–301, 299and foraging behavior 300–1, 301and mate choice 301and the null hypothesis 367–8and predator fear 301–2and self-recognition 319–24, 320–2and theory of mind 312–19, 324–5

song birds 331–2, 363SOP model see standard operating

procedures (SOP) modelspatial memory 363–4, 366, 366–7spatial representation 258–9, 262spontaneous recovery 134–6squirrel monkey 300

short-term retention of 206and transitive inference 260, 260

standard operating procedures (SOP)model 76–80, 76, 81, 83, 192

starling 292–3stars 292Stellar’s jay 328, 328, 337stickleback 192, 228, 328, 334stimulus 20

context-stimulus associations78–80, 90–1

and habituation 192–4preexposure 157–61significance 80–6see also conditioned stimulus; S–R

associations; S–(R–US)associations

stimulus enhancement 298, 306, 307,308, 309

stimulus generalizationdefinition 37and discrimination learning 150,

153, 155–6, 157–8neural basis of 45and problem solving 116, 121

stimulus-response theorists 30–3, 32see also S–R associations

stimulus-stimulus learning 49–52

Stravinsky, Igor 172–3strawberry finch 332subitizing 251Sultan (chimpanzee) 112–13, 112,

114, 115summation test 42, 43sun 267, 287–8sun compass 287–8surprise

and distractors 203and extinction 125–30and learning 62–74, 84,

86–7, 90symbolic distance effect 260–1

T-mazes 195, 203–4, 204tamarin monkey 229, 300

and self-recognition 322and short-term memory 366–7, 366

taste aversion conditioning 14, 39,47–9, 58–9, 78, 78, 95–6, 300

and discrimination learning 158,159–61

and trace conditioning 194–5, 195temporal generalization 236–8, 237,

240–1theory of mind 312–19, 324–5, 371

and communication 336and deception 313–15, 314and knowledge attribution 315–19

three-spined stickleback 192tiger 297time 233–42, 262–3, 368

circadian rhythms 233–4connectionist model of 241information-processing model of

237–8, 237, 239, 239, 240,241

interval timing 233, 236–41,237–41, 242, 263

and memory traces 241–2periodic timing 233–6, 263scalar timing theory 238, 238,

239–40, 240tool use 116–17, 117, 119–20trace conditioning 194–6, 195–6trace intervals 194transitive inference 259–62, 260–1

and spatial representation 262and the symbolic distance effect

260–1and value transfer 261–2

transposition 149–51transposition test 150trial and error learning 25–7, 112–14,

116–17, 119–21, 169, 211, 297,323, 336

trial-based theories 142truly random control 40two-action control 309–10, 309



420 Subject index

ultraviolet (UV) light 285unconditioned stimulus (US) 29, 30,

32–3, 35, 40, 123and attention 78–9, 80aversive 110and compensatory CRs 58and diet selection 299and discrimination learning 155,

156, 162, 165and extinction 125–6, 128–9, 135,

139–43, 145–6and eye-blink conditioning 36and fear of predators 302and imitation 305and inhibitory conditioning 40,

41, 42intensity 66–7and long-term memory studies 217memory model of 46–8, 59and the nature of representations

52–5, 54and the reflexive nature of the

CR 60

representation 109and stimulus-stimulus conditioning

49–50surprising 63–4, 69, 365–6and trace conditioning 194see also CS–US

association/contingency;no–US center; no–USrepresentation; R–USassociations; S–(R–US)associations

unobservable processes 21–2urine 266utterances, mean length of

(mlu) 349, 349

value transfer 261–2vasoconstriction 77–8,

192–3, 193vasopressin 241vervet monkey 332–7, 333–4vestibular system 268Vicki (chimpanzee) 307, 340

visual category formation 171–2,173–6

visual memory 304vocal mimicry 303–4vocal tract 340, 340

waggle dance (bees) 328–30, 328–30,331, 338–9

Washoe (chimpanzee) 24, 341–2,342, 344, 346, 348–9,350, 357

Western scrub jay 226–7, 226white-crowned sparrow 331–2, 370wind direction 289wolf 58–9, 59Woodpecker finch 119, 120words 337working memory 237–8wrasse 228

Yerkish 343

Zugunruhe 291, 292


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