PLEASE SCROLL DOWN FOR ARTICLE

This article was downloaded by: [Swets Content Distribution]On: 29 April 2010Access details: Access Details: [subscription number 912280237]Publisher Psychology PressInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

European Journal of Cognitive PsychologyPublication details, including instructions for authors and subscription information:http://www.informaworld.com/smpp/title~content=t713734596

Are Caricatures Special? Evidence of Peak Shift in Face RecognitionMichael B. Lewis

To cite this Article Lewis, Michael B.(1999) 'Are Caricatures Special? Evidence of Peak Shift in Face Recognition',European Journal of Cognitive Psychology, 11: 1, 105 — 117To link to this Article: DOI: 10.1080/713752302URL: http://dx.doi.org/10.1080/713752302

Full terms and conditions of use: http://www.informaworld.com/terms-and-conditions-of-access.pdf

This article may be used for research, teaching and private study purposes. Any substantial orsystematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply ordistribution in any form to anyone is expressly forbidden.

The publisher does not give any warranty express or implied or make any representation that the contentswill be complete or accurate or up to date. The accuracy of any instructions, formulae and drug dosesshould be independently verified with primary sources. The publisher shall not be liable for any loss,actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directlyor indirectly in connection with or arising out of the use of this material.

Are Caricatures Special? Evidence of Peak Shift in

Face Recognition

Michael B. LewisCardi� University, Cardi� , UK

Robert A . JohnstonUniversity of Birmingham, Birmingham, UK

It has been shown that it is possible to obtain faster and more accuraterecognition for a caricatured face than for a veridical face. This couldsuggest that there is something special about the transformations thatproduce caricatures. An experiment was conducted to investigate whetherthere are other circumstances in which improved recognition occurs awayfrom the veridical face. A peak-shif t paradigm was employed, using imagesgenerated from morphing between two faces, where participants learned torespond to a target and learned not to respond to a similar non-target.When tested on the whole range of morphed faces, the response pro® leshowed a shift in the peak of the responses away from the learned non-target face. From this it was concluded that the advantage seen with carica-tures is not special but a result of a shif t in the peak of responses awayfrom more typical faces.

INTRODUCTION

Caricatures have long been used to represent people . The imagesproduced by caricature artists often portray the subject’ s identity withrelatively limited detail. It has been considered that in understanding thecaricaturist ’ s art one may understand better the processes of face recogni-tion. The aim of the present study was to identify what it is that makes

Requests for reprints should be addressed to Michael B. Lewis, School of Psychology,Cardi� University, PO Box 901, Cardi� CF1 3YG, UK.

This research was supported by a University of Wales grant. The authors wish to thankMike Burton, Gillian Rhodes, Werner Sommer and an anonymous reviewer for their com-ments on a previous draf t of the manuscript.

� 1999 Psychology Press Ltd

EUROPEAN JOURNAL OF COGNITIVE PSYCHOLOGY, 1999, 11 (1) , 105± 117Downloaded By: [Swets Content Distribution] At: 15:13 29 April 2010

caricatures easier to recognise. We addressed the issue of whether it is aspecial property of just caricatures that leads to improved recognition orwhether other changes to a face can also have this e� ect.

It has been documented many times that recognition of faces can bemade to be faster or more accurate if the image presented is a computer-generated caricature of the face. Although this e� ect has been foundmany times, it is not consistent across experiments and is greatly in¯ u-enced by the types of stimuli and the measures of recognition used.Rhodes, Brennan and Carey (1987) found that recognition of line-drawncaricatures was faster if the images were presented as caricatures of theoriginals rather than the original line-drawn images. In their experiments,the line-drawn images were created by taking various points around theface. An average face was found by averaging the position of the pointsfor a range of faces. The caricatures were generated by exaggerating theline-drawn image by a certain percentage away from the average face.

A similar process of caricature generation has been used for textured(colour or grey-scale) faces. Benson and Perrett (1991) used the techniqueof exaggeration from an average to generate textured caricatures offamous faces. In their experiments, participants were required to indicatewhich image, from a range of caricatures and anti-caricatures, was thebest likeness of a particular person. Interpolation from their dataindicated that best likeness occurs for faces with a small degree of carica-ture (average of 4.4% caricature) .

Many other experiments have examined this advantage for caricaturedfaces (e.g. Benson & Perrett, 1991, 1994; Byatt & Rhodes, 1998; Ellis,1992; Mauro & Kubovy, 1992; Rhodes, 1994; Rhodes et al. , 1987;Rhodes, Carey, Byatt, & Pro� tt, 1998; Rhodes & McLean, 1990; Rhodes& Tremewan, 1994; Stevenage, 1995). The caricature e� ect has beenfound with children and adults, it has been found for whole faces andinternal features only, and it has been found in ornithologists ’ ability torecognise birds. Given the range of evidence, it is important that anymodel of face recognition should be able to account for the advantage forthe recognition of caricatures over veridical images.

A useful framework for considering face recognition is the face-spacemetaphor. Part of the explicit formulation of the face-space metaphor offace recognition , as proposed by Valentine (1991), is that faces are storedas points (or vectors) in a multidimensional Euclidean space: these pointsare de® ned by the facial dimensions that span the face space. This frame-work can help to account for a wide range of results seen in face recogni-tion, including the caricature advantage .

Rhodes et al. (1987) presented an account of face recognition that canexplain the caricature advantage . This account is based on the idea thatthere exists a psychologicall y meaningful norm (or average) face against

106 LEWIS AND JOHNSTON

Downloaded By: [Swets Content Distribution] At: 15:13 29 April 2010

which faces are encoded as norm-deviation vectors (i.e. faces are stored interms of how they deviate from a central norm face) . A caricature is animage that has been exaggerated away from an average face and so whenthis is encoded, it will be encoded as a vector with the same direction asthe veridical but its magnitude will be greater. It is this greater magnitudeof vector which Rhodes et al. used to explain the better recognition ofcaricatures compared to veridical faces.

The norm-based account described above, or variations of this theme,have been the main explanation for the caricature advantage ; however,there are alternative accounts that do not invoke a psychological lymeaningful norm face. These exemplar-based models make use of acommonly assumed feature of face space, namely the normal distributionof faces (an assumption that is common to both the norm-based andexemplar-based models) . This assumption has been supported byevidence from the e� ects of distinctiveness of faces: distinctive faces areeasier to recognise than typical faces (e.g. Going & Read, 1974;Johnston & Ellis, 1995a, 1995b; Valentine & Bruce, 1986). This kind ofaccount was ® rst suggested by Rhodes and Tremewan (1994), whoconsidered whether the caricature advantage is a direct consequence ofcaricatures being made more distinctive. Originally , this explanation wasdiscounted by Rhodes and Tremewan on the evidence of what they call`̀ lateral caricatures’ ’ ( images transformed laterally to the direction ofcaricaturing , which they report are harder to recognise than anti-carica-tures) . According to exemplar-based accounts, a lateral caricature shouldbe easier to recognise than an anti-caricature because an anti-caricaturewill move the representation closer to competing exemplars than thelateral caricature would.

More recent studies on lateral caricatures have reported resultscontrary to the original research (see Lewis, 1997; Lewis & Johnston,1998; Rhodes et al. , 1998) and have found that lateral caricatures areeasier to recognise than anti-caricatures. This research re-opens the issueof norm-based versus exemplar-based accounts of the caricature advan-tage.

The main, and possibly only, di� erence between norm-based accountsand exemplar-based accounts is the role played by the central norm face.For norm-based models, the norm face is important in that onlyexaggerations away from this point will lead to the advantage in recogni-tion seen with caricatures. For exemplar-based models, the normal distri-bution of faces means that the norm face is the location that has thehighest density of faces. Movement away from this norm face will leadinto areas of lower density, and hence allow recognition to be easier. Ifdensity is determined by just the local neighbours in face space (assuggested by Valentine, 1991, and the Voronoi model proposed by Lewis

CARICATURES AS PEAK SHIFT 107Downloaded By: [Swets Content Distribution] At: 15:13 29 April 2010

and Johnston, 1999), then it should be possible to ® nd directions to moveface representations that are not along the norm-deviat ion vector but stillreduce the local density of the representation. For this to happen, itwould be necessary to move the item away from its closest neighbours .This cannot be done with natural face spaces because it is impossible toknow what representations are present in any one person’ s face space.However, it is possible to introduce a number of new faces such thattheir relative relationship is known. If two very similar faces are intro-duced to a face space, then, assuming there are no other close neighbours ,an exemplar-based account would predict that moving the representationaway from its neighbour would lead to improved recognition . Therefore,with su� cient control over a new set of faces presented to participants , itshould be possible to manipulate the direction of change that leads toimproved recognition. All that is required is to generate a set of facessu� ciently similar to each other and whose relative structure in the facespace is known. In the experiment reported here, a set of faces wasemployed that meet these criteria.

The set of faces used were generated by morphing between two faces ina series of steps. It is possible to place all faces from this set in a unidi-mensional face subspace because, it is assumed, morphing tends to makelinear changes to an image. As long as the two original faces are distin-guishable by participants , and assuming a face space with Euclideanproperties, then the unidimensional face subspace will be a subspace ofparticipants ’ face spaces.

The following experiment aimed to train participants to respond to oneparticular face taken from the unidimensional face subspace formed bymorphing two faces together. By training the participants not to respondto another face from the same morph set, it was hoped to ® nd whate� ect this counter-item had on the distribution of responses along theunidimensional face subspace.

Using such a set of faces it is possible to investigate whether the advan-tage produced by caricaturing is due to exaggeration of the norm-devia-tion vector or due to moving the representation away from nearneighbours . If the advantage is due to exaggeration of the norm-deviationvector, then a symmetrical decrease in the responses would be expected asone moves away from the exact image that has been learned. If it is thedi� erence between faces that is encoded, then the decrease in theresponses should be less if the direction of change is not towards a closecounter-item than if it is towards one.

The results predicted by an exemplar-based model (e.g. the Voronoimodel) would also be consistent with studies conducted on other types ofunidimensional stimuli. Indeed, there is a large and detailed literature onthe phenomenon known as `̀ peak shift’ ’ . Peak shif t is a phenomenon that

108 LEWIS AND JOHNSTON

Downloaded By: [Swets Content Distribution] At: 15:13 29 April 2010

has been found for animal and human participants in experimentsconcerning unidimensional stimuli (see Thomas, 1993, for a review). Theessence of peak shift involves training an animal or human to respond toone stimulus and not to respond to another stimulus. These two stimulimust occur on some continuum. Examples of such continua are:wavelengths of light (Akins and Gouvier, 1982), geometric angles(Thomas, Mood, Morrison, & Wiertelak, 199) and pitches of tones(Galizio, 1985). Once the participants have been trained to respond to thetarget, they are then tested on a whole range of stimuli from along theunidimensional continuum. It has often been found that a peak shiftoccurs such that the peak of responses is away from the stimulus towhich participants were trained to respond. This shift in responses isaway from the stimulus to which they were trained not to respond.

Although peak shift has been found with many di� erent types ofstimuli, there is no evidence to suggest that it should occur with stimulias complex as faces. However, Rhodes (1996) has applied the principlesof peak shift to understand caricatures using the absolute-coding accountof face recognition (an exemplar-based model given Valentine’ s, 1991,terminology).

EXPERIMENT

This experiment employed a unidimensional set of faces generated usingmorphing software to test the regional nature of face-space representa-tions. The experiment is based on experiments conducted with animalsand humans that have demonstrated a peak shift. If faces are representedaccording to di� erences between exemplars, as required by the Voronoimodel, then it should be possible to demonstrate a peak shift using themorphed faces.

In the experiment, participants were taught to respond one way to aparticular target face and a di� erent way to a non-target face or anyother face. Both target and non-target faces came from the unidimen-sional morph set. Subsequently, the participants ’ response pro® le wastested over the whole range of morphed images to test for the presence ofa peak shift in responses.

Methods

Participants . Thirty-eight undergraduates from Cardi� Universityreceived course credit for their participation . Data from two participantswere not used because they failed to complete the training procedure tothe desired level of accuracy.

CARICATURES AS PEAK SHIFT 109Downloaded By: [Swets Content Distribution] At: 15:13 29 April 2010

Stimuli. A set of six, front-view, grey-scale faces with neutral expres-sions were used to generate the stimuli. Pairs of these faces were chosento generate the critical items. Morphing software (Morpher 1.5 forMacintosh by M. Fijimiya) was used on the pairs to generate a range ofintermediate morphs between the two faces. This technique involves theblending of facial features’ texture and shape. The technique employed tocreate the stimuli for this experiment was similar to that used in manyother experiments on facial caricatures (e.g. Benson & Perrett, 1991) andfeature extraction (e.g. Hancock, Burton, & Bruce, 1996; O’Toole, De� en-bacher, Valentin, & Abdi, 1994). A set of 56 anchor points was chosen:33 of these contained the internal features and were chosen on the basisof work conducted by Craw (1996); the others contained the best detailof the shape of the image (see Fig. 1) . From these anchor points , a trian-gularisation was performed on the images. The resulting triangles or ele-ments could be smoothly transformed from one image to the other.During this transformation, the shape of each triangle and the texturemap of the triangle were gradually blended between the two originalimages. A step size for the transformation was chosen so that 15 uniqueblended faces were produced (i.e. each step was 6.25%). The intervalbetween each blended face was equivalent in terms of the change in shapeand texture.

Only the nine centre-most images were used in the experiment ascritical items from each of the three sets of 15 morph images. These ninetest faces were referred to as f1, f2, and so on up to f9 (see Fig. 2) .Morphed ® ller items were also generated from each pair of faces. Eachmorphed ® ller face constituted one-third of one of the original faces andtwo-thirds of the other one in the pair. Each stimulus face subtended avertical visual angle of 12.6° and all stimuli were presented via acomputer monitor.

Procedure . Participants were assigned to one of six conditions . Thesesix conditions determined which faces they saw as their target face andtheir non-target face. The target face was always the one of the three cen-tral faces from the three ranges of test faces (i.e. f5) . The non-target face(i.e. f1) could be either of the two extreme test faces from the samemorph range as the target face. The ® ller faces used were determined bythe condition the participan t was in because the ® ller faces always camefrom the two pairs of faces not used to generate the target face.

Participants were introduced to two of the test faces by simultaneouspresentation. This introduction lasted for 1 min. During this presentation,it was explained to the participants that whenever they saw one of thesefaces (the target face) they were to respond with the ` t̀arget button’ ’ .Whenever they saw any other faces, including the other face they were

110 LEWIS AND JOHNSTON

Downloaded By: [Swets Content Distribution] At: 15:13 29 April 2010

FIG

.1

.T

wo

orig

inal

face

su

sed

toge

ner

ate

the

set

ofbl

ende

dfa

ces

used

.T

hepo

ints

and

lines

sho

wth

ean

chor

poin

tsan

dth

etr

iang

ular

elem

ents

use

db

yth

em

orph

soft

war

eto

gen

erat

eth

eb

lend

s.

111

Downloaded By: [Swets Content Distribution] At: 15:13 29 April 2010

FIG. 2. One set of morph faces used. The central face (f5) was always the target face andeither f1 or f9 was the non-target face.

112

Downloaded By: [Swets Content Distribution] At: 15:13 29 April 2010

currently being presented with (the non-target face) , they were to pressthe ``non-target’ ’ button.

The introductory stage was followed by a training phase. A series offaces was presented to the participants via a computer. Participants hadto make a decision as to whether each face was the target face or not.If they thought it was the target face, they pressed the `̀ target’ ’ button,otherwise they pressed the `̀ non-target’ ’ button. It was stressed to theparticipants that accuracy was more important than speed. Presentationof the faces was separated by 1000 msec and each face was presentedfor 300 msec. This training phase consisted of 80 trials made up of 20presentations of the target face (f5) , 20 presentations of the non-targetface (f1) and 10 presentations of each of the four ® ller faces. These wereall presented in a random order. The ® ller faces were included to ensurethat the required task was to respond one way to the target and adi� erent way to any other face. Without the ® ller faces, the task wouldhave been equivalent to having two target faces. No feedback was givenbecause the task was su� ciently easy so as not to require it in mostcases. Two participants did make more than 10 errors. These partici-pants did not proceed to the test phase and were replaced by new parti-cipants. All other participant s made less than four errors (mean numberof errors was 1.08) .

Following the training phase, there was a 30-sec interval before theparticipants were presented with the test phase. Participants were toldthat their task here was the same as in the training phase, but therewould be a few more faces added to make the task more di� cult. Thefaces presented in the test phase consisted of all nine faces from the unidi-mensional face-space set (i.e. f1 ± f9) , the four ® ller faces and the fourmorphed ® llers. Each face was presented 10 times in a random order,making 170 trials in all.

Design. The dependent variable was the proportion of times a testface produced a `̀ target’ ’ response. There was a within-participant inde-pendent variable of test face (nine levels: referring to the nine test faces)and a between-participant variable of which condition the participant wasin (i.e. which faces were the target and non-target faces) .

Results

The number of `̀ target’ ’ button responses for each test face was averagedover conditions and participants to give a curve of target responses. Thiscurve is shown in Fig. 3. The curve is asymmetrical about the target face,with the number of target responses dropping more sharply towards thenon-target face than in the opposite direction. Furthermore, the peak of

CARICATURES AS PEAK SHIFT 113Downloaded By: [Swets Content Distribution] At: 15:13 29 April 2010

the curve does not occur at the target face (f5: 0.744 ± 0.136; mean ±SD) but away from this in the opposite direction to the non-target face(f6: 0.825 ± 0.116) .

The movement of the peak away from the target face was analysedusing a two-way mixed-design analysis of variance, with condition andtest item as independent variables . This showed a signi® cant [F(1,30) =4.75, P < 0.05] increase in accuracy for the face away from the target(f6) over the target face (f5) . The e� ect of condition and the interactionwere not signi® cant [F(5,30) = 0.23 and F(5,30) = 0.09, respectively]. Asimilar analysis was conducted by items (i.e. by the six non-target faces) .This analysis also yielded a signi ® cant e� ect of test item when comparingf5 with f6 [F(1,5) = 54.36, P < 0.001].

The asymmetry of the curve was investigated with an analysis ofvariance on the six test faces (in all six conditions ) , which were nevertarget or non-target faces (i.e. f2, f3, f4, f6, f7 and f8) . The independentvariables were: whether the face was on the non-target side of the targetor not (two levels of direction); the number of faces between the test faceand the target face (three levels of distance) ; and the condition that theparticipant was in. This analysis found a signi® cant e� ect of direction[F(1,30) = 353.36 , P < 0.001] and a signi® cant e� ect of distance [F(2,60)= 196.92, P < 0.001]. The interaction between these two factors was

FIG. 3. The number of ``target’ ’ responses for the blended faces in the test phase. The targetface was f5 and the non-target face was f1. Error bars show 95% confidence intervals overthe 36 subjects.

Prob

abili

tyof

givi

nga

`̀tar

get

resp

onse

’’

114 LEWIS AND JOHNSTON

Downloaded By: [Swets Content Distribution] At: 15:13 29 April 2010

also signi ® cant [F(2,60) = 27.88, P < 0.001]. None of the e� ectscontaining condition were signi® cant [F(5,30) = 0.217 for the main e� ect,F(5,30) = 1.33 for the interaction with direction, F(10,60) = 1.11 for theinteraction with distance and F(10,60) = 1.635 for the three-way interac-tion]. Analysis of the simple main e� ects showed that the e� ect of direc-tion was signi® cant at one step from the target [F(1,30) = 318.40, P <0.05], two steps from the target [F(1,30) = 64.15, P < 0.05] and threesteps from the target [F(1,30) = 31.87, P < 0.05]. The e� ect of distancefrom the target was signi® cant both on the non-target ’ s side [F(2,60) =58.169, P < 0.05] and the opposite side of the target [F(2,60) = 206.36 ,P < 0.05].

Discussion

The results of the experiment suggest that participant s were more likelyto respond positively to a face which was slightly di� erent to the originalface learned if this takes the image further away from any competingidentities. Therefore, the point of optimal recognition in the face space iso� set from the veridical image and it would appear that this o� set can bea� ected by the close proximity of another similar stored face representa-tion.

The distortion of the representation which produces improved recogni-tion implies that face representations are a� ected by the presence of asimilar known exemplar. The e� ect of the similar exemplar is to push therepresentation which leads to optimal recognition away from the veridicalface in a direction which takes the representation further away from theother known exemplar. To account for this peak shift e� ect in recogni-tion, it is necessary to consider a model of face recognition in whichstorage or retrieval of faces is determined by the position, in the facespace, of the closest exemplars.

The present experiment has shown how peak shift can occur in variousdimensions of the face space. The exemplar-based accounts of the carica-ture advantage assume that this kind of peak shift is occurring all overthe face space but primarily in directions away from the densest parts ofthe face space (i.e. the central parts around the norm face) .

CONCLUSION

The recognition advantage for caricatures over veridical faces has beenused to suggest that there is something psychologica lly special abouttransformations to faces which increase the norm-deviation vector. Thisstudy has demonstrated that the recognition advantage for distorted faces

CARICATURES AS PEAK SHIFT 115Downloaded By: [Swets Content Distribution] At: 15:13 29 April 2010

is not unique to caricatures but may well occur in any direction in theface space. It would appear that an advantage for distorted faces can becaused by something as simple as a similar exemplar being learned. Thiswould suggest that there is not necessarily anything special aboutexaggeration along the norm-deviation vector (i.e. caricaturing). Thecaricature advantag e can be seen as a particular example of peak shiftbecause transformations in producing these images are likely to distortthe faces away from near neighbours , which, on average, will be moretypical faces.

Manuscript received October 1997Revised manuscript received May 1998

REFERENCES

Akins, F.R., & Gouvier, W.D. (1982). Stimulus alternation and the peak shift. Journal ofGeneral Psychology, 107, 299 ± 300.

Benson, P.J. , & Perrett, D.I. (1991) . Perception and recognition of photographic qualityfacial caricatures: Implication for the recognition of natural images. European Journal ofCognitive Psychology, 3, 105± 135.

Benson, P.J. , & Perrett, D.I. (1994). Visual processing of facial distinctiveness. Perception ,23, 75 ± 93.

Byatt, G., & Rhodes, G. (1998). Recognition of own-race and other-race caricatures: Impli-cations for models of face recognition. V ision Research, 38, 2455 ± 2468.

Craw, I. (1996). Machine coding of human faces. A report to the PSRDG at the HomeO� ce.

Ellis, H.D. (1992). The development of face processing skills. Philosophica l T ransactions ofthe Royal Society, B. 335 , 105± 111.

Galizio, M. (1985). Human peak shift: Analysis of the e� ects of three-stimulus discrimina-tion training. L earning and Motivation, 16, 478± 494.

Going, M., & Read, J.D. (1974). E� ects of uniqueness, sex of subject, and sex of photo-graph on facial recognition. Perceptua l and Motor Skills, 39, 109± 110.

Hancock, P.J.B. , Burton, A .M., & Bruce, V. (1996). Face processing: Human perceptionand principal components analysis. Memory and Cognition, 24, 26 ± 40.

Johnston, R.A ., & Ellis, H.D. (1995a). Age e� ects in the processing of typical and dis-tinctive faces. Quarterly Journal of Experimental Psychology, 48A , 447± 465.

Johnston, R .A., & Ellis, H.D. (1995b ). The development of face recognition. In T. Valen-tine (Ed.) , Cognitive and computation aspects of face recognition. London: Routledge.

Lewis, M.B. (1997). A computational and empirical investigation into the face-space. Unpub-lished PhD thesis, University of Wales, Cardi� .

Lewis, M.B., & Johnston, R.A . (1998). Understanding caricatures of faces. QuarterlyJournal of Experimental Psychology, 50A , 321 ± 346.

Lewis, M.B., & Johnston, R .A. (1999). A uni® ed account of the e� ects of caricaturingfaces. V isual Cognition, 6, 1± 42.

Mauro, R ., & Kubovy, M. (1992). Caricature and face recognition. Memory and Cognition,20, 433 ± 440.

O’Toole, A.J. , De� enbacher, K.A., Valentin, D., & Abdi, H. (1994). Structural aspects offace recognition and the other-race e� ect. Memory and Cognition, 22, 208 ± 224.

116 LEWIS AND JOHNSTON

Downloaded By: [Swets Content Distribution] At: 15:13 29 April 2010

Rhodes, G. (1994). Secrets of the face. New Zealand Journal of Psychology, 23, 3 ± 17.Rhodes, G. (1996). Superportraits : Caricatures and recognition. Hove: Psychology Press.Rhodes, G., Brennan, S., & Carey, S. (1987). Identi® cation and ratings of caricatures:

Implications for mental representations of faces. Cognitive Psychology, 19, 473± 497.Rhodes, G., Carey, S., Byatt, G., & Pro� tt, F. (1998). Coding spatial variations in faces

and simple shapes: A test of two models. V ision Research, 38, 2307 ± 2321.Rhodes, G., & McLean, I.G. (1990). Distinctiveness and expertise e� ect with homogeneous

stimuli: Towards a model of con® gural coding. Perception , 19, 773± 794.Rhodes, G., & Tremewan, T. (1994). Understanding face recognition: Caricature e� ects,

inversion, and the homogeneity problem. V isual Cognition, 1, 275± 311.Stevenage, S.V. (1995) . Can caricatures really produce distinctiveness e� ects? British Journal

of Psychology, 86, 127 ± 146.Thomas, D.R. (1993). A model for adaptation-level e� ect on stimulus generalization. Psy-

chologica l Review, 100 , 658 ± 673.Thomas, D.R ., Mood, K., Morrison, S., & Wiertelak, E. (1991) . Peak shift revisited: A test

of alternative interpretations. Journal of Experimental Psychology : Animal Behavior Pro-cesses , 17, 130 ± 140.

Valentine, T. (1991). A uni® ed account of the e� ects of distinctiveness, inversion and racein face recognition. Quarterly Journal of Experimental Psychology, 43A , 161± 204.

Valentine, T., & Bruce, V. (1986). The e� ects of distinctiveness in recognising and classify-ing faces. Perception , 15, 525± 535.

CARICATURES AS PEAK SHIFT 117Downloaded By: [Swets Content Distribution] At: 15:13 29 April 2010