Culture Shapes How We Look at Faces
Caroline Blais1,2, Rachael E. Jack1, Christoph Scheepers1, Daniel Fiset1,2, Roberto Caldara1*
1 Department of Psychology, University of Glasgow, Glasgow, United Kingdom, 2 Département de Psychologie, Université de Montréal, Montréal, Canada

Abstract
Background: Face processing, amongst many basic visual skills, is thought to be invariant across all humans. From as early
as 1965, studies of eye movements have consistently revealed a systematic triangular sequence of fixations over the eyes
and the mouth, suggesting that faces elicit a universal, biologically-determined information extraction pattern.
Methodology/Principal Findings: Here we monitored the eye movements of Western Caucasian and East Asian observers
while they learned, recognized, and categorized by race Western Caucasian and East Asian faces. Western Caucasianobservers reproduced a scattered triangular pattern of fixations for faces of both races and across tasks. Contrary to
intuition, East Asian observers focused more on the central region of the face.
Conclusions/Significance: These results demonstrate that face processing can no longer be considered as arising from a
universal series of perceptual events. The strategy employed to extract visual information from faces differs across cultures.
Citation: Blais C, Jack RE, Scheepers C, Fiset D, Caldara R (2008) Culture Shapes How We Look at Faces. PLoS ONE 3(8): e3022. doi:10.1371/journal.pone.0003022
Editor: Alex O. Holcombe, University of Sydney, Australia
Received June 12, 2008; Accepted July 30, 2008; Published August 20, 2008
Copyright: ß 2008 Blais et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by The Economic and Social Research Council and Medical Research Council (ESRC) (RES-060-25-0010). REJ was supported by
a PhD studentship awarded by ESRC (PTA-031-2006-00192), CB by a PhD studentship provided by the Fonds Québécois de Recherche en Nature et Technologies(FQRNT) and DF by a FQRNT post-doctoral fellowship.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: r.caldara@psy.gla.ac.uk

square). Note that the term holistic here refers to the definition
used in the cultural framework and does not refer to the term andmechanisms relating to the holistic processing used in the faceperception literature. We will use the term holistic in its culturalcontext throughout the paper.
Convergent evidence supporting cultural diversity has beenfound in scene perception [10,11], description [10] and categorization [12], showing that Westerners focus analytically on salient
objects and use categorical rules when organizing their environment. By contrast, people from China, Korea and Japan - all East
Asian cultures - focus more holistically on relationships andsimilarities among objects when organizing the environment. All
these studies point to a similar pattern of results, revealing thatorthogonal mechanisms influence visual perception and categorization across cultures.
Until recently it was unknown whether these cultural perceptual
differences arise at the encoding, retrieval or more elaborate stagesof information processing. Tracking eye movements is anappropriate approach to address this issue, as sequences of eye
fixations describe the way in which overt visual attention is
directed and the intake of information is isolated [2]. Indeed,
recent eye-tracking data provided direct evidence that cultural
backgrounds shape visual environment affordance. Western
American observers had longer fixations on focal objects during
scene processing compared to Chinese observers, whereas Chinese
observers fixated more on the background compared to Western
American observers [13]. Cultural perceptual biases therefore
seem to arise from differences in what observers attend to in ascene, and what information is extracted to achieve perception.
Previous literature on culture has focused on visual scene
processing or simple visual categorization tasks. Natural scenes areheterogeneous visual inputs with complex statistical properties

Introduction
It is a widely held belief that many basic visual processes arecommon to all humans, independent of culture. Face recognition
is considered to be one such process, as this basic biological skill isnecessary for effective social interactions. Any approach aiming tounderstand face perception must recognize, however, that only asmall part of the visual information available on faces is actuallyused. Since the seminal work of Yarbus [1], we have known thathumans use a series of foveal fixations to extract visual information
to process faces, and that these sequences of eye fixations describe
the way in which overt visual attention is directed [2]. Studies ofeye movements have persistently revealed a systematic triangularsequence of fixations over the eye and the mouth, with dominanceto the eyes e.g., [3,4–7], suggesting that the presence of a face
triggers a universal, biologically-determined information extraction pattern. However, this literature is based on observations withadults from Western cultures only. Consequently, the universalityof these findings remains uncertain.
In the past decade, systematic differences between Westerners
and East Asians have been found in a variety of perceptual tasks
and paradigms for a recent review see [8]. Kitayama et al. [9]presented Western Caucasian and East Asian observers with asquare containing a line. Observers were then presented withsquares of various sizes and asked to draw a line that was identicalto the first line in either absolute or relative length to the previously
seen surrounding square. Western Caucasian observers were moreaccurate in absolute judgments, whereas East Asian observers weremore accurate in the relative task. These observations suggest that
Westerners have analytic strategies relating to focal information (i.e.
the line only), whereas East Asians have optimal holistic strategies
for encoding contextual information (i.e. the line within the
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movements of the same observers in the less demanding task offace categorization by race [18], which relies on using informationcommon to faces of the same race [19]. We observed a striking
cultural contrast across tasks between the fixation scanpaths of our
two cultural populations: Western Caucasian observers consistently fixated the eye region, and partially the mouth, whereas East
Asian observers fixated more on the central region of the face.

[14]. However, human faces are homogenous objects, roughly
symmetrical, that share similar salient shapes arranged in fixedlocations across exemplars (e.g. two eyes above a central nose andmouth). It is unclear whether these perceptual cultural differencesin scene processing would generalize to the biologically relevant
class of human faces, since faces are arguably the most importantand salient visual stimulus a human ever encounters. The ability toidentify conspecifics from the face is of primary interest for humansocial behavior and is routinely and effortlessly achieved in every
culture. For this reason, perhaps, literature on face processing, andin particular studies of eye movements, has so far largely ignored
the role of culture and generalized findings of Western Caucasianobservers to the entire human population.
Human beings have developed through social experience anatural expertise at extracting information from faces (identity, race,
gender, age, emotional state, etc.), with the exception of other-race/
ethnic group face recognition [15]; a well-known phenomenon often
reported in the literature as the other-race effect [16]. Despite
numerous studies having investigated the other-race effect for morethan thirty years for a review see [17], it is primarily still unknown (i)whether people from different cultures process faces using the same
perceptual strategies and (ii) whether they adapt visual information
extraction as a function of the race of the input face.
To address these issues, we monitored the eye movements offourteen Western Caucasian and fourteen East Asian observers
during the learning and recognition stages of a face recognition
task and a subsequent face categorization by race task, using
Western Caucasian and East Asian faces. The expression of thefamiliar faces was changed between learning and recognition toprevent trivial image matching strategies to memorize face
identities. Also, to prevent anticipatory strategies and to ensurethat the location of the first fixation on the faces is self-determinedby the observer, faces were pseudorandomly presented in one offour quadrants of a computer screen. To recognize faces, observers
must identify unique sets of facial features - a taxing constraint on
information extraction, which might modulate facial scanpaths.
To control for this factor, we subsequently recorded eye

Results
Behavioral
Face recognition. A two-way mixed design ANOVA
including Race of the face and Culture of the observer as respectively
within- and between-subjects variables revealed a significant
interaction: observers of both cultures were more accurate (d9) in
judging familiarity for same- than other-race faces (F(1,26) = 12.86, p = .001, g2 = 0.330) (Figure 1).
Post hoc two-tailed paired t-tests revealed that the advantage inrecognizing same-race faces was present in both groups ofobservers (Western Caucasians (p,.01); East Asians (p,.04)).
Neither the main effect of Race of the face (F(1, 26) = 1.93, p = .17,
g2 = .069) nor the Culture of the observer (F(1, 26) = .59, p = .44,
g2 = .022) reached significance.
As for correct response times (Table 1), neither the main effectof Race of the face (F(1, 26) = 2.96, p = .10, g2 = .102) nor Culture of theobserver (F(1, 26) = 1.04, p = .31, g2 = .038) reached significance.
The interaction between these factors also failed to reachsignificance (F(1, 26) = 2.36, p = .13, g2 = .083).
Categorization by race. Observers of both cultures showedceiling effects for accuracy (98% of correct answers). A two-way
mixed design ANOVA with Race of the face as within-subjects factor
and Culture of observer as between-subjects factor did not reveal any
significant differences for response times (Table 1). Neither themain effect of Race of the face (F(1, 26) = .78, p = .38g2 = .029), nor
Culture of the observer (F(1, 26) = 1.11, p = .30, g2 = .041) reachedsignificance. The interaction between these factors also failed toreach significance, (F(1, 26) = .021, p = .88. g2 = .0008).

Figure 1. d9 accuracy scores of the old/new face recognition paradigm, for Western Caucasian and East Asian observers. Error bars
report standard errors of the mean. Observers recognized same-race faces significantly better than other-race faces.
doi:10.1371/journal.pone.0003022.g001

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Table 1. Response times (RT) and number of fixations for Western Caucasian (WC) and East Asian (EA) observers (obs) during WC
and EA face learning, recognition and categorization by race.

Learning

Recognition

WC obsWC faces
RT—

Fixations 14.2 (0.6)

EA obsEA faces

WC faces

Classification

WC obsEA faces

WC faces

EA obsEA faces

WC faces

WC obsEA faces

WC faces

EA obsEA faces WC faces

EA faces———

1567 (122)1723 (134)1478 (112)1486 (100)751 (50)764 (52)692 (33)701 (27)

14.2 (0.6)

14.5 (0.5)

14.4 (0.6)

4.8 (0.4)

5.3 (0.5)

4.4 (0.4)

4.5 (0.3)

2.2 (0.2)

2.3 (0.2)

2 (0.1)2.1 (0.1)

Numbers in parenthesis report the (6) standard errors of the mean. Note that the presentation time was fixed during learning (5 seconds).
doi:10.1371/journal.pone.0003022.t001

design ANOVAs with Face regions as within-subject factor andCulture of the observer as a between-subjects factor revealed significant
interactions for those factors in all conditions (p,.001 - Fig. 3).
In general, Western Caucasian observers had significantly more
fixations landing in the eye region, while East Asian observers had
more fixations on the nose region, as revealed by independent twotailed t-tests carried out on the percentage of fixations in theseregions for each task separately (Fig. 3). Both cultural biases forthese facial features were reliable and robust, as highlighted by thelarge magnitude of Cohen’s d effect size values.
Notably, observers from a given culture fixated the same faceregions regardless of the race of the face. Western Caucasianobservers prominently fixated the eye region during learning andrecognition. In contrast, East Asians consistently fixated thecentral region of the face. The less demanding race categorization

Eye movements
On average, observers performed about 14 fixations per trial
during learning and 5 fixations during recognition (Table 1). Notethat faces were pseudorandomly presented in one of four
quadrants of a computer screen for 5 s during learning and until
response during recognition (M = 1563 ms) and categorization(M = 726 ms).
Western Caucasian and East Asian observers differed in how
they extracted facial information using eye movements: Figure 2
shows significant differences in fixation locations, as revealed by a
two-tailed Pixel test [20] (Zcrit.|4.25|, p,.05 – see Methods andFigure S1 for details).
To further investigate the magnitude of the fixation biases
across cultures, we calculated the percentage of fixations landing
within these face regions and the rest of the face. Two-way mixed

Figure 2. Fixation biases for Western Caucasian (WC - red) and East Asian (EA - blue) observers are highlighted by subtracting WC
and the EA Z-scored fixation distribution maps during WC and EA face learning, recognition and categorization by race. Areas
showing a significant fixation bias are delimited by white borders (Zcrit.|4.25|; p,.05); values near 0 indicate similar magnitude in fixation between
observers from different cultures.
doi:10.1371/journal.pone.0003022.g002

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Figure 3. Left y axis: Percentages of fixations landing in the eye, nose, mouth and rest of the facial regions across tasks(WC = Western Caucasian; EA = East Asian). Error bars report standard errors of the mean. Significant differences between observers fromdifferent cultures are reported at the bottom of the bars (* = p,.05; ** = p,.01). Right y axis: Cohen’s d (1988) effect size values. Note, that the absenceof fixations for the mouth region in the learning condition is due to the lack of significant fixation biases in this region as defined by the fixation map
analysis (see the Methods section for details).
doi:10.1371/journal.pone.0003022.g003

and applying a one-tailed Pixel test (Zcrit.4.64; p,.05 – see
Methods for details). The time slots in which fixations landing on
the eye, nose and mouth regions reached significance are reported
in Figure 4.
This analysis highlights the consistency of the bias towards theeyes (and partially the mouth) by Western Caucasian observers
and the nose, the central region of the face, by East Asianobservers across time and tasks (see also the supporting
QuickTimeTM movies for these tasks: Video S1: learning, VideoS2: recognition and Video S3: categorization).

task elicited eye movement patterns similar to the more taxing facerecognition, even with only 2 fixations on average (Table 1).
Statistical analyses on the number of fixations did not reveal any
significant effect, with the exception of a marginal main effect ofsmaller number of fixations for Western Caucasian faces(M = 4.56) compared to East Asian faces (M = 4.89) during facerecognition (F(1, 26) = 4.63, p = .04, g2 = .151).
The time course of fixation maps during learning, recognitionand categorization by race was tracked by computing fixation
maps on 20 ms time slots separately for each group of observers
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Figure 4. Time course of significant fixations on facial features (eyes, nose and mouth) during face learning, recognition andcategorization by race, for WC (red square - top rows) and EA (green circle - bottom rows) observers (Zcrit.4.64; p,.05). The
significant facial feature fixations are weighted by the surface area they covered within the region of interest; with darker outlines relating to wider
regions.
doi:10.1371/journal.pone.0003022.g004

was consistent when fixation scanpaths were compared acrossthree different face processing tasks: learning, recognition andcategorization by race.
These results demonstrate that people from different cultures
achieve human face processing by focusing on different face
information. Direct or excessive eye contact may be considered

Discussion
We report a striking cultural contrast: Western Caucasianobservers consistently fixated the eye region, and partially themouth, whereas East Asian observers fixated more on the central
region of the face to extract information from faces. This finding
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rude in East Asian cultures [21] and this social norm might havedetermined gaze avoidance in East Asian observers. To someextent our findings are consistent with observations that
Westerners tend to engage analytic perceptual strategies forprocessing the visual environment, whereas East Asians use holistic
perceptual strategies [8,9,11–13]. Westerners might allocate theirattention to single facial features (i.e. eyes and the mouth) with abias towards the eyes to effectively learn and recognize faces. This
triangular scan pattern strategy is fully consistent with previous eye
movements findings [1,4–7,22,23] and also in line with recent
neuroimaging results on Western Caucasian observers showing atuning in the neural face-sensitive regions for visual stimuli
containing more elements in the upper part [24]. East Asians on
the other hand would recognize faces by focusing in the region
that would be optimal and economical to integrate information
holistically: the center of the face (i.e. nose). Because retinal celldensity and visual resolution decrease steeply towards theperipheral visual field, the center of the face is likely to be themost advantageous spatial position to capture facial feature
information globally. Both strategies used by East Asian observers(social norm and holistic/global processing) could account for the
East Asian fixation bias towards the nose region. The present data
do not allow us to disentangle whether only one or both of these
explanations are valid and future studies are necessary to directly
address this issue. Nevertheless, both explanations reflect mechanisms shaped by culture and do not alter our main conclusions.
In addition, it is worth noting that to the best of our knowledge
these strategies cannot be explained by the differences in faces per
se. Both objective anthropometric measures [25,26] and computational models [19] have revealed comparable heterogeneity for
Caucasian, African and East Asian faces.
These findings are not a straightforward extension of previous
eye tracking results recorded during scene perception [13]. While
exploring visual scenes, East Asian observers made more(scattered) fixations to the background compared to Western
Caucasian observers, which instead focused more on central
objects present in the scene. The present findings on faceprocessing show the opposite pattern. East Asian observers focusedon a focal region (i.e. the center of the face), whereas Western
Caucasian observers sampled more largely the visual input space(i.e. scattered fixations across facial features). However, visual
natural scenes are complex heterogeneous stimuli engaging widesaccadic eye movements. In our experiment, a single face waspresented on a neutral background, constituting a salient narrow
stimulation for the visual system. This discrepancy in eye
movement patterns between faces and visual scenes highlights
the specificity of the mechanisms involved to resolve the visual task
(and categories) at hand. Nevertheless, importantly, although theprecise nature of the visual strategies used across these tasksremains to be clarified, both findings show a marked cultural
diversity in eye movements.
As expected, observers of both races were more accurate atrecognizing same- than other-race faces, as found in many studies
on the other-race effect [17]. Critically, however, previous studies
on the other-race effect did not isolate the information on which
the observers relied during face recognition and did not define theperceptual strategies they adopted. Therefore, the question ofwhether information used for face recognition is modulated by therace of the input faces is long standing within the cross-cultural
face literature [17]. We reveal that Westerners focus on the eyes,
whereas East Asians focus on the nose, regardless of the race of thefaces and the task at hand. Observers do not change perceptual
strategies as a function of the race of the face, highlighting therobustness of the perceptual mechanisms engaged during face
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processing. In addition, observers from different cultures reached acomparable behavioral performance by using different scanpaths,
which, at first sight, could appear puzzling. However, eyemovements do not provide direct evidence on the mental
representations elaborated and used by the observers to solve
the task. It remains possible that observers of both cultural groups
constructed comparable representations for processing faces
despite using different scanpaths. Future studies combining eye
movements with a rigorous control of the presented information
might help to clarify this issue.
Psychologists and philosophers have long assumed that while
culture impacts on the way we think about the world, basic
perceptual mechanisms are common among humans [27]. We
provide evidence that social experience and cultural factors shapehuman eye movements for processing faces, which contradicts theview that face processing is universally achieved.

Methods
Participants
Fourteen Western Caucasian (6 males, 9 females) and 14 East
Asian (7 males, 8 females) young adults (mean age 24.4 years and23.2 years respectively) participated in this study. All East Asianparticipants were newly enrolled international students attending the
University of Glasgow, having being born in East Asia and arriving
in a Western country (Glasgow, UK) for the first time. The average
duration of residence in the UK upon testing was 1 week within the
East Asian group. The East Asian group comprised 8 Chinese and 6
Japanese students. All participants had normal or corrected vision
and were paid £6 per hour for their participation. All theparticipants gave written informed consent and the protocol wasapproved by the departmental ethical committee.

Materials
Stimuli were obtained from the KDEF [28] and AFID [29]databases and consisted of 56 East Asian and 56 Western
Caucasian identities containing equal numbers of males andfemales. The images were 3826390 pixels in size, subtending 14u
degrees of visual angle vertically and 10u degrees of visual angle
horizontally. Images were viewed at a distance of 57 cm, which
represents the size of a real face (approximately 20 cm in height)
viewed from a distance of about 80 cm, reflecting a natural
distance during human interaction. All the pictures were cropped
around the face to remove clothing. Male faces were clean-shavenand none of the faces had particularly distinctive features (scarf,
jewelry, etc.). Faces were aligned on the eye and mouth positions,
their luminance was normalized and were presented on a10246768 pixel white background and displayed on a 210 Iiyama
HM204DTA monitor with a refresh rate of 120 Hz. Presentationof stimuli was controlled by the SR Research ExperimentBuilder
software, version 1.4.202.

Eye tracking
Eye movements were recorded at a sampling rate of 500 Hz
with the EyeLink II head-mounted eye-tracker (SR International),
which has an average gaze position error of ,0.5u, a resolution of
1 arc min and a linear output over the range of the monitor used.
Only the dominant eye of each participant was tracked althoughviewing was binocular. A manual calibration of eye fixations was
conducted at the beginning of each task (and once every maximum
28 trials thereafter) using a nine-point fixation procedure as
implemented in the EyeLink API software (see EyeLink Manualfor details). The calibration was then validated with the EyeLink
API software and repeated when necessary until the optimal
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calibration criterion was reached. At the beginning of each trial,
participants were instructed to fixate a dot at the center of thescreen to perform an automatic drift correction.

Observers were informed that they would be presented with aseries of faces to learn and subsequently recognize, and that there
would be two blocks of learning and recognition per race
condition. In each block, observers were instructed to learn 14
face identities randomly displaying either neutral, happy or disgust
expressions (7 females). After a 30 second pause, a series of 28
faces (14 faces from the learning phase – 14 new faces; 7 females)
were presented and observers were instructed to indicate as
quickly and as accurately as possible whether each face wasfamiliar or not by pressing on a two button control pad using their
dominant hand. Faces of the two races were presented in separate
blocks, with the order of presentation for same- and other-race
blocks being counterbalanced across observers. After the completion of the face recognition tasks, observers were instructed toperform a face categorization by race task on 56 Western
Caucasian (28 women) and 56 East Asian (28 women) faces.
Participants were required to indicate as quickly and as accurately
as possible the race of the presented faces (‘Caucasian’ or ‘Asian’)by pressing on a two button control pad using their dominanthand. Response buttons were counterbalanced across participants
for both tasks.
Each trial consisted of the presentation of a central fixation dot(which also served as an automatic drift correction) followed by aface presented pseudorandomly in one of four quadrants of thecomputer screen. Faces were presented for 5 seconds duration in
the learning phase and until the observer responded in therecognition and race categorization phase. Each face wassubsequently followed by the central fixation dot which preceded
the next face stimulus.

magnitude of the differences in fixation locations between observers
from different cultures, we used the significant regions revealed by
the Pixel test as a visual mask to define region of interest (e.g., roughly
landing on the eyes, nose, and mouth; we also included the rest of the
facial information in this analysis). We then calculated thepercentage of fixations landing on those regions. To control fordifferences in size of the different regions of interest, we normalized
the number of fixations by the area covered by the region of interest.
Finally, Cohen’s d effect size [30] was calculated within each region ofinterest, by pooling the standard deviations of both groups in theconditions of interest.
A similar procedure was applied to isolate the most fixated areas
throughout the time course. That is, we constructed 3-D fixation
maps (x coordinate, y coordinate and time) by dividing each trial
into 20 ms bins, and calculated for each of the bins the number oftimes a given location (x, y) was fixated across all the trials. We
then smoothed these 3-D fixation maps using a Gaussian kernel
with a sigma of 10 pixels in the spatial domain and of 20 ms in thetemporal domain. Finally, we transformed the 3D maps into Zscores, by using the mean and standard deviation across all thedimensions and applied a one-tailed Pixel test to isolate the areasthat elicited significant fixations (Zcrit.4.64; p,.05). The statisticalthreshold provided by the Pixel test corrects for multiple
comparisons across time and space while taking the spatial andtemporal correlation inherent to eye movement data sampling into
account [20]. We then defined three regions of interest: the eyes,
the nose and the mouth (see Fig. 4) in order to visualize thesignificant fixation effects over time on the 3-D maps (see also the
supporting QuickTimeTM movies). We highlighted the presence ofsignificant fixations in these regions by weighting the results as afunction of the number of pixels activated. Note that Figure 4
relies on the absolute (see group fixation maps in the supporting
Figure S1b for an example), rather than the differential fixation
maps (Fig. 2).

Data analyses

Supporting Information

Only correct trials were analyzed. Fixation distribution maps
were extracted individually for Western Caucasian and East Asianobservers and face race, for the learning, recognition andcategorization tasks separately. The fixation maps were computedby summing, across all (correct) trials, the fixation location
coordinates (x, y) across time. Since more than one pixel isprocessed during a fixation, we smoothed the resulting fixation
distributions with a Gaussian kernel with a sigma of 10 pixels(supporting Figure S1a). Fixation maps of all observers belonging
to the same cultural group were then summed together separately
for each face condition, resulting in group fixation maps (seesupporting Figure S1a - top right for an example of such a map).
We then Z-scored the resulting group fixation maps by assuming
identical Western Caucasian and East Asian eye movement
distributions for a particular face race as the null hypothesis.
Consequently, we pooled the fixation distributions of observers forboth groups and used the mean and the standard deviation forWestern Caucasian and East Asian faces to separately normalize thedata (supporting Figure S1b). Finally, to clearly reveal the differenceof fixation patterns across observers of different cultures, we
subtracted the group fixation maps of the East Asian observers
from the group Western Caucasian and we Z-scored the resulting
distribution (supporting Figure S1c). To establish significance, weused a robust statistical approach correcting for multiple comparisons in the fixation map space, by applying a two-tailed Pixel test [20]on the differential fixation maps with the following threshold(Zcrit.|4.25|; p,.05 – areas delimited with white borders insupporting Figure S1c). Thereafter, to further highlight the

Figure S1 Processing steps for the computation of fixation map
biases for Western Caucasian (WC - c: red) and East Asian (EA - c:blue) observers during the face learning, recognition andcategorization by race tasks. Please refer to the main text for
details.
Found at: doi:10.1371/journal.pone.0003022.s001 (15.14 MB
DOC)

Procedure

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Video S1 Learning. Time course of the Z-scored fixation maps

for Western Caucasian (WC) and East Asian (EA) observers during
the learning stage of the face recognition task, using Western
Caucasian and East Asian faces.
Found at: doi:10.1371/journal.pone.0003022.s002 (7.07 MB
MOV)
Video S2 Recognition. Time course of the Z-scored fixation
maps for Western Caucasian (WC) and East Asian (EA) observers
during the recognition stage of the face recognition task, using
Western Caucasian and East Asian faces.
Found at: doi:10.1371/journal.pone.0003022.s003 (1.27 MB
MOV)
Video S3 Categorization. Time course of the Z-scored fixation
maps for Western Caucasian (WC) and East Asian (EA) observers
during the face categorization by race task, using Western
Caucasian and East Asian faces. Note that on average observers
used only 2 fixations to solve this task.
Found at: doi:10.1371/journal.pone.0003022.s004 (1.27 MB
MOV)
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Acknowledgments

Author Contributions

The authors wish to thank Frédéric Gosselin for his helpful insights on theanalyses and David Kelly for insightful comments on a previous version ofthis paper.

Conceived and designed the experiments: RC. Performed the experiments:
CB REJ CS DF. Analyzed the data: CB. Contributed reagents/materials/
analysis tools: CB CS RC. Wrote the paper: RC. Implemented theexperiment and trained the authors to the eye movement technique: CS.

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August 2008 | Volume 3 | Issue 8 | e3022