Perception, 2006, volume 35, pages 659 ^ 670

DOI:10.1068/p5360

Simulating the `other-race' effect with autoassociative neuralnetworks: further evidence in favor of the face-space model

Roberto Caldara

Department of Psychology, University of Glasgow, 58 Hillhead Street, Glasgow G12 8QB, Scotland, UK;e-mail: r.caldara@psy.gla.ac.uk

Herveè Abdi

School of Brain and Behavioral Sciences, MS: GR 4.1, Program in Applied Cognition and Neuroscience,
University of Texas at Dallas, Richardson, TX 75083-0688, USA
Received 20 September 2004, in revised form 1 July 2005; published online 19 April 2006

Abstract. Other-race (OR) faces are less accurately recognized than same-race (SR) faces, butfaster classified by race. This phenomenon has often been reported as the `other-race' effect(ORE). Valentine (1991 Quarterly Journal of Experimental Psychology A: Human Experimental
Psychology 43 161 ^ 204) proposed a theoretical multidimensional face-space model that explained
both of these results, in terms of variations in exemplar density between races. According tothis model, SR faces are more widely distributed across the dimensions of the space than OR
faces. However, this model does not quantify nor state the dimensions coded within this face space.
The aim of the present study was to test the face-space explanation of the ORE with neuralnetwork simulations by quantifying its dimensions. We found the predicted density properties ofValentine's framework in the face-projection spaces of the autoassociative memories. This was
supported by an interaction for exemplar density between the race of the learned face set and therace of the faces. In addition, the elaborated face representations showed optimal responses for
SR but not for OR faces within SR face spaces when explored at the individual level, as gender
errors occurred significantly more often in OR than in SR face-space representations. Altogether,
our results add further evidence in favor of a statistical exemplar density explanation of the ORE
as suggested by Valentine, and question the plausibility of such coding for faces in the frameworkof recent neuroimaging studies.

1 Introduction
In social interactions, it is crucial to identify other people and to extract rapidly the
information relevant for communication. The human face is a particular visual object
composed of features arranged in definite spatial locations (eg two eyes placed abovea nose and a mouth) that readily provides this information. The uniqueness of the
facial features for a given individual, as well as their specific configuration, determines
face recognition and identification. At the same time, faces provide additional (semantic)
cues about the emotional state, age, sex, and race (Bruce and Young 1986). These cues
can be used to classify people in various categories such as young or old, male orfemale, Asian or Caucasian, etc. Interestingly, the latter semantic information, therace of a face, significantly modulates our proficiency in face recognition. Indeed, it isa well known phenomenon that people are less accurate in recognizing faces of adifferent race (Feingold 1914). This differential recognition ability is often referred toin the literature as the `other-race' effect (ORE) and has been well documented underlaboratory and field studies during the last thirty years (for a review, see Meissner andBrigham 2001; Valentine et al 1995). The robustness and the reliability of this phenomenon
across different racial groups has also been confirmed in meta-analytic studies of facerecognition tasks (Bothwell et al 1989; Meissner and Brigham 2001; Shapiro and Penrod1986), which established that same-race (SR) faces are recognized better and faster thanother-race (OR) faces. Nevertheless, even though several hypotheses have been proposed

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to explain the ORE, the underlying mechanisms of this phenomenon are still matterof an active debate (Meissner and Brigham 2001).
The impairment in recognizing OR faces is not the unique facet of the ORE. For
example, when faces are classified on the criterion of race, OR faces are classified fasterthan SR faces (Caldara et al 2004; Levin 1996; Valentine and Endo 1992). For instance,
Caucasian participants need less time to categorize a face as Asian than they need tocategorize a Caucasian face as Caucasian (the reverse pattern is observed for Asiansubjects). These results are provocative because they seem paradoxical: why are we fasterat classifying the faces which we find the hardest to recognize?
Valentine (1991; see also Valentine and Endo 1992), inspired by the categorization
literature (Medin and Shaffer 1978; Nosofsky 1986), proposed a theoretical framework
based on an exemplar model, which specifies the coding and representation of faces(and also facial distinctiveness and the face-inversion effect), in order to account for
the recognition and classification observations of the ORE. In this psychological model,a face is encoded and positioned as a point in an n-face-dimensional space which isconstructed through experience with the encountered faces. The dimensions represent
factors (physiognomic features, such as color of the hair, eye width, etc) that maximize
the discrimination between encountered faces. The origin of the face space is the mean on
all the dimensions and it is positioned at the place with the maximum exemplar density.
Typical faces are encountered more frequently, and therefore are located near the origin.
In contrast, distinctive faces are located far from the origin [but for a critical review
on this point see Burton and Vokey (1998)].
In the face-space model, the dimensions of the space are elaborated with visualexperience, and therefore a small number of OR face exemplars (due to the lack of experience with these faces) are located in a high-density cluster of faces compared to a large
number of exemplars of SR faces, which are widely distributed across the dimensions(see figure 1).

(b)(a)
Figure 1. A 2-D representation of the face-space model suggested by Valentine (1991). (a) A large
number of exemplars of same-race faces widely distributed across diagnostic information compared
to a smaller number of other-race face exemplars (b), that are located in a high-density space.

The high-density pattern for OR faces results from the small amount of variation
across dimensions that are optimal for discriminating SR faces. For instance, the colorof the hair and the eyes represent useful diagnostic (discriminative) visual information
in a Caucasian face space, but the same information is not diagnostic for Asian faces.
As a consequence, Caucasian faces will be more distributed across those dimensions
than Asian faces. This particular differential density, between the races in the face-space
representations, results in a high perceived similarity between OR faces, which is also
in turn reflected in the popular belief that OR faces ``all look alike'' (Feingold 1914).

Modeling the other-race effect661

More importantly, such patterns explain the poor face-recognition performance for OR
faces, because the higher density for OR faces increases the difficulty of the discrimination of different exemplars. At the same time, the classification advantage for OR
faces could be accounted for by a faster activation of nearby individuals, all leading
to a small, high-density cluster compared to a larger one (for SR faces). Thus, thismodel provides a useful and elegant account of the ORE and how faces might be
stored in human memory; it also posits the differential level of experience, or contact,with OR faces as the cause of the ORE (Valentine and Endo 1992). Nevertheless, thecritical limitation of the theoretical face-space model (Valentine 1991; Valentine andEndo 1992) is that it does not clearly state what is encoded in the dimensions of theface space. Indeed, in this model the dimensions are arbitrary and so are neither specified
nor quantified. Thus, such a model can explain different results by positing a variationin dimension weights or a shift in space location. For example, Valentine and Endo
(1992) discussed these changes in the multidimensional face space in terms of selective
attention. In addition, the projections of the faces on the face-space dimensions are
assumed to be normally distributed and having the same variance. As pointed out
by Burton and Vokey (1998), these last two assumptions are somewhat problematic,
because they imply that the distribution of the distances of the faces to their center
follows a w2 distribution (with the number of degrees of freedom being the numberof dimensions of the model). This means that, in such a model, most faces are far
from their common center when the number of dimensions is large. Burton and Vokey
suggested that one way of addressing this problem would be to specify the dimensions
by using the statistical properties of real faces. Such a procedure will also fix the valuesof the weights of the dimensions because these weights as well as the dimensions will
be dependent upon the sample of faces learned. An additional benefit of this approachis to suggest that perceptual learning can act as the psychological mechanism responsible for the acquisition of these dimensions.
Recently, the concept of perceptual learning has been proposed as an importantmechanism for understanding how differential experience affects the way we process
SR and OR faces (O'Toole et al 1995). Classically, this concept has been defined by
Gibson (1969, page 3) as ``an increase in the ability to extract information from theenvironment, as a result of practice and experience with stimulation coming from it''.
According to the differential-experience hypothesis, as face-recognition skills develop,
individuals learn to use the perceptual dimensions that are optimal for discriminating
among individual human faces. Evidence in favor of this hypothesis has been reported
by O'Toole and colleagues (O'Toole et al 1991, 1994, 1995) with computational models
based on face ^ pixel autoassociative neuronal networks (Abdi 1988; Abdi et al 1999).
When their neural networks were trained to recognize a majority of faces of one race
(Caucasian or Asian), they better reconstructed novel faces from the majority race thanfaces from the other race. More recently, Furl et al (2002) further investigated thishypothesis of the ORE by training 13 face-recognition algorithms with Caucasian faces
as the majority race. Overall, these authors refined and improved previous resultsfrom the ORE simulations by showing that this effect is present only in algorithms
that generate representations in the face space distorted to emphasize features that
permit the individuation of faces.
Taken together, the theoretical face-space model (Valentine 1991; Valentine andEndo 1992) and perceptual-learning theory have at least two major points in common.
First, as previously suggested in the literature (eg Brigham 1986; Brigham et al 1982;
Brigham and Malpass 1985; Platz and Hosch 1988), they both consider that differential
experience is a crucial factor in the ORE explanation. Second, they both support adifference at the level of internal representations as a theoretical explanation of the ORE.

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R Caldara, H Abdi

Surprisingly, despite such similarities, the relationship between these two theoretical
positions of the ORE has not yet been directly assessed. More precisely, none of theprevious studies based on perceptual-learning theory analyzed the structure of the internal
representation put forward by the neural network simulations of the ORE (Furl et al
2002; O'Toole et al 1991, 1994, 1995), in terms of the predicted density properties of
the Valentine model (Valentine 1991; Valentine and Endo 1992). The spatial distributionof face representations was never measured, and therefore the differential density hypothesis as an explanation of the ORE remains an open question. Moreover, to our knowledge,
the structure of the representations created by the neural network simulations was neverinvestigated at individual level (one face compared with the others).
From this logic, the purpose of the present study was to quantify the multidimensional theoretical face space proposed by Valentine, on the basis of statistical neuralnetwork computations, to measure the spatial distribution of face representations
and to verify its predictions of the ORE. We took advantage of the PCA approach,as this technique represents an optimal tool for analyzing the perceptual and statisticalinformation for faces (Abdi 1988), and clearly identifies the projections for each single
face. First, we hypothesized that the face space will be more scattered for SR than OR
faces. Obviously, these predictions will be sensitive to the set of learned faces (ie Caucasianas SR faces; Asian as OR faces), and the reverse pattern of results should be expected
for the converse learned set of faces (ie Asian as SR faces; Caucasian as OR faces).
Second, because the faces will be distributed in the face space as a function of
their similarity (Valentine 1991), the distance between any two points is analogous to
the similarity between two faces. Thus, for SR faces it should be expected that facesof the same gender would share a similar location in the space (Abdi et al 1995).
However, the neural network face memory space is shaped through the experience by
the race of the learned faces, and thus it is better constructed and tailored for the SR
faces than OR faces (Furl et al 2002; O'Toole et al 1991, 1994, 1995). As a consequence,
we hypothesized that SR face space would be more adapted on such perceptual dimensions (gender) for SR faces, but its representations would be defective for OR faces.
Indeed, behavioral studies reinforce such hypothesis, since it has been demonstrated
that we are better at classifying by gender (O'Toole et al 1996) or at estimating the age(Dehon and Bredart 2001) of SR faces than OR faces. These observations suggest SR
faces have a more adapted psychological representations than OR faces.
2 Methods
2.1 Stimuli
For use in our simulations, 316 gray-scale photographs of Caucasian (79 male, 79 female)and Asian faces (79 male, 79 female) were digitally scanned with 16 gray levels. This face
database was created by us from The University of Texas at Dallas and has beenpreviously used in various neural network simulations (eg Abdi et al 1999; O'Toole et al
1991, 1994, 1995). It is worth noting that the same stimuli have been used also inbehavioral studies that demonstrated an ORE in Asian and Caucasian participants fortheir respective OR faces (O'Toole et al 1994, 1996).
All the pictures were cropped around the face to remove clothing. Male faces wereclean-shaven and none had particularly distinctive features. All images showed a frontal
view, with the eyes being roughly aligned on the horizontal midline of the image.
2.2 Procedure
2.2.1 Principal-component analysis approach. We simulated a pure Caucasian and an
Asian developmental experience by training two neural networks with only their respective races. For the Caucasian learning, 158 gray-scale photographs of Caucasian faces
were coded in a pixel-based I-dimensional vector xk , concatenated from the columns

Modeling the other-race effect663

of the face image (with I representing the number of pixels in the images, and k indexing
the K  158 faces). The vectors are normalized so that xkT xk  1. The set of K faces isrepresented by an I6K matrix X in which the kth column is equal to xk . The faces
are stored in an autoassociative memory, in which each unit is connected to all theother units, and the intensity of the connections is represented by an I6I matrix W.
We used a Widrow ^ Hoff learning rule, which iteratively corrects the weights of the
network as a function of the quality of the response as follows:Wt1  Wt   Z X ÿ Wt  XX T ,where Z is a small positive constant (typically smaller than one). As previously demonstrated (Anderson et al 1977; Kohonen 1977), since the weight matrix W is a cross-productmatrix, it is positive semi-definite (ie all its eigenvalues are positive or zero, and all its
eigenvectors are real). As a consequence, W can be expressed as a weighted sum of
its eigenvectors:
LXW
ll ul ulT  UKU T , with U T U  I
l 1

where ul is the l th eigenvector of W, ll the l th eigenvalue, I represents the identity
matrix, K represents the L6L diagonal matrix of eigenvalues, U is the I6L matrix ofeigenvectors, and L is the rank of the matrix W. The eigenvectors in U are generallyordered according to their eigenvalues. In what follows, the eigenvector with the largest
eigenvalue is referred to as the first eigenvector; the eigenvector with the second
largest value is referred to as the second eigenvector, and so on. The eigenvectors andeigenvalues of the weight matrix W can be obtained directly by singular value decomposition (cf eg Horn and Johnson 1985) of the face matrix X. It is formally expressed as
X  UDV T ,where U represents the matrix of eigenvectors XX T, V represents the matrix of eigenvectors X T X, and D stands for the diagonal matrix of singular values, which are equal
to the square roots of the eigenvalues of XXT and X T X (they are the same).
The estimation of a face by the system can, thus, be represented as a weighted
sum of eigenvectors:
L
L
X
X
x^k 
ll ul ulT xk ll gl ul , with gl  ulT xk ,
l 1

l 1

where the weights ll are the projections of the faces onto the eigenvectors. These
weights can be interpreted as an indication of the extent to which a given eigenvector(or `macrofeature') characterizes a particular face. When the Widrow ^ Hoff learning isused, this process corresponds to
W  UU T ,and the estimation of a face is obtained by dropping the eigenvalues in
L
X
x^k 
gl ul , with gl  ulT xk .
l 1This is equivalent to giving the same importance to each eigenvector in the reconstructionof a face. More formally, Widrow ^ Hoff learning amounts to sphericizing the weights
matrix W.
We used a Widrow ^ Hoff learning rule followed by a jackknife procedure: we took
off one face from the set in an iterative way for the entire Caucasian-face database,and computed the values of its projections on the eigenvectors computed from all the664

R Caldara, H Abdi

other faces. Then, the entire set of Caucasian faces was memorized and a set of 158
pixel-based Asian faces was tested with the Caucasian autoassociative memory. In thisstep we computed the values of the projections of all Asian faces. We limited ouranalysis to a 3-D space with the axis defined by the first three eigenvectors. We decided
this, mainly, for three reasons. First, more than 90% of the variance is captured by
the first three eigenvectors. Second, it has already been demonstrated that the macrocharacteristics of the faces are captured in the first eigenvectors, the last eigenvectors
encoding characteristics of individual faces (O'Toole et al 1993; Valentin and Abdi
1996; Valentin et al 1994). Thus, the important information for the race of the faces lieson the first eigenvectors and taking into account all the space projections would shape
the face space into individual rather than global dimensions of the faces. Moreover,keeping the eigenvectors in a 3-D space offers also a greater convenience for the illustrations of the face space. Thus, the barycenters on the first three eigenvectors were
calculated across all the faces in their respective face spaces, by separately computing
the mean of the projections on each eigenvector. Finally, squared Euclidian distances(d 2 ) and the cosine values were computed as measures of similarity between each l
kth face projections of both races and their respective barycenters (b) in the face
spaces.
For the Asian learning, the same procedure was applied for the reverse set of faces.
The calculated squared Euclidian distances and cosine values were separately compared with an ANOVA with the learned face set (Caucasian/Asian) and race 3-D face
projection space (Caucasian/Asian) as factors for both race faces in both learningsets.
For all the face projections on the first three eigenvectors and for both face races
in both learning sets, we located the nearest point in the space and identified its
gender. A change between the selected face and its nearest neighbor was considered
as an error. The number of errors committed by the artificial neural networks wascompared by using an ANOVA with the learned face set (Caucasian/Asian) and race
3-D face projection space (Caucasian/Asian) as factors.
3 Results
Figure 2 illustrates the projections for each face of both races on the first three eigenvectors after the training with Caucasian or Asian faces. The Euclidian distances andcosine values were calculated between the positions of each face and their respective
barycenters. The observed means are reported in table 1. For the Euclidian distances
and the cosine values, the ANOVAs revealed a highly significant, full, crossover interaction between the race of the learned face set and the race of the 3-D face projection
space, respectively (F1, 628  94:50, MSE  0:142, p 5 0:0001) for the Euclidian distancesand (F1, 628  76:97, MSE  0:046, p 5 0:0001) for the cosine values. SR faces were
widely distributed and more dissimilar compared to OR faces.
In the face space, faces are represented as points. Hence, the distances between faces
can be computed and the nearest neighbor of each face can be identified. For example,
figure 3 illustrates this procedure and displays two selected targets of each race and theirneighbors in the SR or OR face space.
Both race faces were better represented in their SR face-space learning set than in the
OR one. The visual similarity between neighbors was higher (and the neighbors wereof the same gender) in the SR learning conditions than in the OR learning conditions(see table 2).
These observations were statistically confirmed by a significant interaction between
the race of the learned face set and the errors in the gender of the neighbor (F1, 628  10:18,
MSE  1:72, p 5 0:01).

Modeling the other-race effect

665

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Figure 2. 3-D face projection space on the three first eigenvectors after (a) a Caucasian learning,(b) an Asian learning. The cumulated distances (D) from the barycenter of each space are reported.
The face spaces are more widely distributed for same-race than for other-race faces.
Table 1. Means and standard deviations (SD) of the squared Euclidian distances and cosine
values calculated between each face position in the 3-D face projection spaces and the respective
barycenters.
Learning

Caucasian
Asian

Race (Euclidian distances)

Race (cosine values)

Caucasian face

Asian face

Caucasian face

Asian face

0.059 (0.043)
0.027 (0.028)

0.028 (0.027)
0.057 (0.050)

0.964 (0.027)
0.982 (0.018)

0.982 (0.018)
0.965 (0.031)

Table 2. Means and standard deviations (SD) of the percentage of gender errors in the 3-D
face projection spaces.
Learning

Caucasian
Asian

Race
Caucasian face

Asian face

19.6% (0.39%)
29.7% (0.45%)

24.6% (0.43%)
13.8% (0.34%)

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R Caldara, H Abdi

Nearestneighbor

Nearestneighbor(a)

CaucasianandAsiantargets(b)(c)
Figure 3. A Caucasian and an Asian target face (b) and their nearest neighbors found in the face-space
representations after a Caucasian (a) and an Asian (c) learning. Note the high visual similarity between
faces in the same-race learning conditions, and gender errors in the other-race learning conditions.

4 Discussion
The purpose of the present study was to quantify, on the basis of statistical neural network
computations, the multidimensional theoretical face space proposed by Valentine (1991)
as a possible explanation of the ORE. Our simulation results showed that the predicted
density properties of Valentine's model (see figure 1) are present on the 3-D
face projection space of the autoassociative memories for both race faces (see figure 2).
A significant interaction was found between the race of the learned faces and thedensity of the face projections on the three first eigenvectors: SR faces were widelydistributed and were less similar compared to OR faces. The face space created withthe experience of one particular race (SR) widely distributed representations for thelearned faces, by capturing diagnostic information and a larger amount of the varianceon learned faces compared to OR faces. As demonstrated by the cosine values, thisprocess leads to a larger difference in similarity between SR faces than between OR
faces. Altogether, these observations refine the state of knowledge of the role playedby these eigenvectors in terms of computational, statistical, perceptual learning(O'Toole et al 1994, 1995), and highlight their crucial role in the explanation of theORE. It is worth noting that these properties emerged spontaneously from the memory
representations of our neural network simulations, providing direct evidenceöas suggested by Valentine (1991)öthat a statistical exemplar density, derived from visualexperience, plays an important role in the explanation of the ORE.
At this stage, however, we uniquely focused on the distribution of each single facefrom the barycenter in order to estimate the density of the face spaces: the global
properties of face-space representations. Our findings suggest that OR faces are more
typical (owing to their higher similarity) than SR faces. Nevertheless, as pointed out
by Burton and Vokey (1998), for understanding the typicality of the faces, it is also
necessary to explore the local properties of their representations (one single facecompared to the others regardless of the barycenter). Indeed, it would be possible toobserve a modulation of density properties in function of the race of the faces uniquely

Modeling the other-race effect667

when such a measure is calculated by comparison with the barycenter. This result
would indicate that crucial factors in the explanation of the ORE rely only on global
dimensions. The exploration of local properties in the 3-D face projection space, however, leads to a comparable interaction between the Caucasian (mean cosine value for
Caucasian faces  0.93; for Asian faces  0.964) and the Asian learning sets (meancosine value for Caucasian faces  0.965; for Asian faces  0.932), and their respective
SR and OR face spaces. SR faces were more similar in the OR compared to the SR
face space. In a nutshell, we found that SR faces were widely distributed and wereless similar (and typical) at the global and the local level.
Our artificial models refined the model of Valentine by encoding and placing eachface in the space. Crucially, this quantification process offers the advantage to selectone face and identify its neighbors. As illustrated in figure 3, we selected one Caucasianand one Asian face and their nearest neighbors in the SR and OR face spaces. The
exploration of the face spaces at the individual level revealed a better gender discrimination for SR faces for both Caucasian and Asian faces. More precisely, the neighbors
looked like the target faces and were of the same gender as the target faces significantly more often in the SR than the OR learning conditions. Indeed, when the target
faces were projected in their respective OR faces space, their neighbors did not look
like the target face anymore, and also they were significantly more often from the
opposite gender than the target face. This pattern of results illustrates how the internal
representations created with experience are adapted for SR faces, but not for OR faces.
Like humans in gender classification (O'Toole et al 1996), the network created better
representations for SR than for OR faces. In other words, face spaces do not optimally
respond to OR faces (Furl et al 2002), but also they do not distribute OR faces foran optimal gender perception. Typically, because individuals have more experience
with SR faces, humans become more expert with the feature dimensions that are
diagnostic for distinguishing SR faces (Ellis et al 1975). As a consequence, perceptual
learning enhances individuals' ability to process and recognize SR faces, but it also
results in a decreased ability to process and recognize OR faces. These basic assumptions are reflected in the face-space representations created by our neural network
simulations. To conclude on this point, it is important to stress that this approach
can be used in future studies to determine the similarity of a set of faces and verify
the validity of some predictions issued from Valentine's theoretical framework (1991).
First, because neighbor faces share a high similarity in the face space, in a face-recognition task, for example, the nearest neighbor of one selected face would be more
confusable with this face compared to a distant neighbor. Such a pattern of results
would predict longer reaction times and larger errors (eg Davies et al 1979; Light et al1979). Second, the faces near to the barycenter will be more difficult to recognize thanthe distant ones, because [as defined by Valentine (1991)] they are typical. Behavioral
studies have shown that distinctive faces are better recognized than typical faces (egGoing and Read 1974; Light et al 1979; Valentine and Bruce 1986).
The overall pattern of results also leads to other considerations in the theoreticalframework of the ORE. First, we found that experience plays an important role in
the explanation of the ORE. These findings are in line with previous results fromneural network simulations (Furl et al 2002; O'Toole et al 1991, 1994, 1996) as well as
behavioral results (Brigham 1986; Brigham et al 1982; Brigham and Malpass 1985;
Platz and Hosch 1988). Second, our results demonstrated that visual experience with
one race modulates the similarity on the macro-dimensions (encoded in the first threeeigenvectors) encoding race information, by leading to an increased visual similarity
for OR faces in the face space. These findings confirm the predictions of Valentine's
model (1991). Incidentally, the adequacy of the face model has been recently questioned
by Levin (1996, 2000). This author suggested that a feature-selection process, driven

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R Caldara, H Abdi

by social cognitive mechanisms (eg social stereotypes applied to outgroup members),
causes race-specifying information to be coded as a visual feature (1) in OR faces but
not in SR faces. However, our findings show that a face-space model can explain theoccurrence of the ORE. It is, however, possible that social cognition factors interact
also with perceptual process and might contribute to a part of the ORE; but, crucially,
the unique visual experience is likely to be a critical factor in the explanation of theORE. Third, the more distributed representations of SR faces compared to OR faces
modulated the quality of the encoded diagnostic information in favour of SR faces, as
shown by gender errors in the OR face space. This modulation in the quality of theencoded representations might also account for an increased ability to encode configural information for SR faces (Michel et al, in press; Rhodes et al 1989; Tanaka et al2004). Finally, our simulation results suggest that the ORE occurs independently of therace of the faces. In our simulations we found a qualitatively comparable ORE effectfor the two sets of faces, which is the expression of a comparable heterogeneity within
the faces regardless of race. This observation is in line with an objective anthropometric investigation of facial dimensions conducted by Goldstein (1979a, 1979b) withindifferent races (Black, Caucasian, and Asian faces), that yielded no evidence for
racial differences in facial heterogeneity. Altogether, these findings do not support theinherent difficulty hypothesis as a possible explanation of the ORE (Malpass andKravitz 1969). This hypothesis postulates that discrimination difficulty for OR faces isdue to the lack of physiognomical variations for OR faces.(2) Our results add new
evidence to this question and show that OR faces in fact do look alike in the SR
learning situation (the Euclidian distances for OR faces are smaller and denser compared to those of the SR face space), but since this pattern of results is fully reversed
when exactly the same faces are used as SR faces, it can be definitely and objectively
stated that OR faces don't all look alike! As previously suggested by Valentine (1991),
this perceptual effect is largely due to our larger experience with SR faces.
Finally, in the field of cognitive neuroscience there is currently a debate about
the emergence of representations in the brain and the computational role played by theactivations identified with neuroimaging techniques. In the domain of face perception,
as revealed by functional magnetic resonance imaging (fMRI) studies, there is a regionof the brain that produces at least twice as many responses to faces than to othervisual objects (Kanwisher et al 1997; McCarthy et al 1997): the right middle fusiform
gyrus, the so-called `fusiform face area' (FFAöKanwisher et al 1997). Recently, theneural basis of the ORE has been investigated by using such techniques (Golby et al2001). The results showed a greater activation of the FFA for SR compared to OR
faces during the encoding stage of a face-recognition task. Because the visual expertisefor faces and non-face-objects plays an important role in the activity of the FFA(Gauthier et al 1999, 2000), the greater activity for SR faces was related to the greater
visual expertise and experience for those faces (Golby et al 2001). Of particularinterest, the interaction observed at the level of brain activations is in some way
comparable to the interaction observed at the level of the face-space representations
in our neural-network findings. Note that the encoding task used in the functional
neuroimaging study is also comparable to our neural network simulations procedure,
which consisted in the encoding of novel faces. Furthermore, recent neuroimaging studiesof the nature of the representations of faces at the brain level, in humans (Loffler et al2004) and monkeys (Giese et al 2004), revealed the existence of a prototype-referenced
encoding (Giese et al 2004; Loffler et al 2004), a pattern of results that is in line with
(1) Note that Levin (1996, 2000) does not precise the nature of this visual feature.

(2) If faces of a certain race were inherently more homogeneous, the individuals of this race must

present a difficulty in the recognition of the other members of their race. Currently, there is noevidence that supports this assumption.

Modeling the other-race effect669

the face-space model. Altogether, these observations suggest that the activity of the
FFA might be directly related to the recruitment of perceptual representations elaborated with experience, which are in turn modulated by the race of the faces. Further
studies combining neural-network simulations and neuroimaging techniques will help
to assess and better understand their nature and their exact relationship.
Acknowledgments. We thank two anonymous reviewers for helpful comments on a previous versionof the manuscript. Roberto Caldara is currently supported by a post-doctoral fellowship provided
by the Swiss National Science Foundation.
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