Culture Shapes How We Look at Faces

Abstract

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.
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 Caucasian observers 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.
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.

Full text

Culture Shapes How We Look at Faces
Caroline Blais
1,2
, Rachael E. Jack
1
, Christoph Scheepers
1
, Daniel Fiset
1,2
, Roberto Caldara
1
*
1Department of Psychology, University of Glasgow, Glasgow, United Kingdom, 2De
´partement de Psychologie, Universite
´de Montre
´al, Montre
´al, 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 Caucasian
observers 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: ß2008 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 Que
´be
´cois 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
Introduction
It is a widely held belief that many basic visual processes are
common to all humans, independent of culture. Face recognition
is considered to be one such process, as this basic biological skill is
necessary for effective social interactions. Any approach aiming to
understand face perception must recognize, however, that only a
small part of the visual information available on faces is actually
used. Since the seminal work of Yarbus [1], we have known that
humans 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 of
eye movements have persistently revealed a systematic triangular
sequence of fixations over the eye and the mouth, with dominance
to the eyes e.g., [3,4–7], suggesting that the presence of a face
triggers a universal, biologically-determined information extrac-
tion pattern. However, this literature is based on observations with
adults from Western cultures only. Consequently, the universality
of 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 a
square containing a line. Observers were then presented with
squares of various sizes and asked to draw a line that was identical
to the first line in either absolute or relative length to the previously
seen surrounding square. Western Caucasian observers were more
accurate in absolute judgments, whereas East Asian observers were
more 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
square). Note that the term holistic here refers to the definition
used in the cultural framework and does not refer to the term and
mechanisms relating to the holistic processing used in the face
perception literature. We will use the term holistic in its cultural
context throughout the paper.
Convergent evidence supporting cultural diversity has been
found in scene perception [10,11], description [10] and catego-
rization [12], showing that Westerners focus analytically on salient
objects and use categorical rules when organizing their environ-
ment. By contrast, people from China, Korea and Japan - all East
Asian cultures - focus more holistically on relationships and
similarities among objects when organizing the environment. All
these studies point to a similar pattern of results, revealing that
orthogonal mechanisms influence visual perception and categori-
zation across cultures.
Until recently it was unknown whether these cultural perceptual
differences arise at the encoding, retrieval or more elaborate stages
of information processing. Tracking eye movements is an
appropriate 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 a
scene, 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 are
heterogeneous visual inputs with complex statistical properties
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[14]. However, human faces are homogenous objects, roughly
symmetrical, that share similar salient shapes arranged in fixed
locations across exemplars (e.g. two eyes above a central nose and
mouth). It is unclear whether these perceptual cultural differences
in scene processing would generalize to the biologically relevant
class of human faces, since faces are arguably the most important
and salient visual stimulus a human ever encounters. The ability to
identify conspecifics from the face is of primary interest for human
social behavior and is routinely and effortlessly achieved in every
culture. For this reason, perhaps, literature on face processing, and
in particular studies of eye movements, has so far largely ignored
the role of culture and generalized findings of Western Caucasian
observers to the entire human population.
Human beings have developed through social experience a
natural 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 more
than 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 of
fourteen 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 the
familiar faces was changed between learning and recognition to
prevent trivial image matching strategies to memorize face
identities. Also, to prevent anticipatory strategies and to ensure
that the location of the first fixation on the faces is self-determined
by the observer, faces were pseudorandomly presented in one of
four 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
movements of the same observers in the less demanding task of
face categorization by race [18], which relies on using information
common 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 consistent-
ly fixated the eye region, and partially the mouth, whereas East
Asian observers fixated more on the central region of the face.
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, g
2
= 0.330) (Figure 1).
Post hoc two-tailed paired t-tests revealed that the advantage in
recognizing same-race faces was present in both groups of
observers (Western Caucasians (p,.01); East Asians (p,.04)).
Neither the main effect of Race of the face (F(1, 26) = 1.93, p= .17,
g
2
= .069) nor the Culture of the observer (F(1, 26) = .59, p= .44,
g
2
= .022) reached significance.
As for correct response times (Table 1), neither the main effect
of Race of the face (F(1, 26) = 2.96, p= .10, g
2
= .102) nor Culture of the
observer (F(1, 26) = 1.04, p= .31, g
2
= .038) reached significance.
The interaction between these factors also failed to reach
significance (F(1, 26) = 2.36, p= .13, g
2
= .083).
Categorization by race. Observers of both cultures showed
ceiling 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 the
main effect of Race of the face (F(1, 26) = .78, p= .38g
2
= .029), nor
Culture of the observer (F(1, 26) = 1.11, p= .30, g
2
= .041) reached
significance. The interaction between these factors also failed to
reach significance, (F(1, 26) = .021, p= .88. g
2
= .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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Eye movements
On average, observers performed about 14 fixations per trial
during learning and 5 fixations during recognition (Table 1). Note
that 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] (Z
crit
.|4.25|, p,.05 – see Methods and
Figure 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
design ANOVAs with Face regions as within-subject factor and
Culture 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 two-
tailed t-tests carried out on the percentage of fixations in these
regions for each task separately (Fig. 3). Both cultural biases for
these facial features were reliable and robust, as highlighted by the
large magnitude of Cohen’s d effect size values.
Notably, observers from a given culture fixated the same face
regions regardless of the race of the face. Western Caucasian
observers prominently fixated the eye region during learning and
recognition. In contrast, East Asians consistently fixated the
central region of the face. The less demanding race categorization
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 Classification
WC obs EA obs WC obs EA obs WC obs EA obs
WC faces EA faces WC faces EA faces WC faces EA faces WC faces EA faces WC faces EA faces WC faces EA faces
RT — — — — 1567 (122) 1723 (134) 1478 (112) 1486 (100) 751 (50) 764 (52) 692 (33) 701 (27)
Fixations 14.2 (0.6) 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
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 (Z
crit
.|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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task elicited eye movement patterns similar to the more taxing face
recognition, 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 of
smaller number of fixations for Western Caucasian faces
(M= 4.56) compared to East Asian faces (M= 4.89) during face
recognition (F(1, 26) = 4.63, p= .04, g
2
= .151).
The time course of fixation maps during learning, recognition
and categorization by race was tracked by computing fixation
maps on 20 ms time slots separately for each group of observers
and applying a one-tailed Pixel test (Z
crit
.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 the
eyes (and partially the mouth) by Western Caucasian observers
and the nose, the central region of the face, by East Asian
observers across time and tasks (see also the supporting
QuickTime
TM
movies for these tasks: Video S1: learning, Video
S2: recognition and Video S3: categorization).
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 from
different cultures are reported at the bottom of the bars (* =p,.05; ** = p,.01). Right yaxis: Cohen’s d (1988) effect size values. Note, that the absence
of 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
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Discussion
We report a striking cultural contrast: 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 to extract information from faces. This finding
was consistent when fixation scanpaths were compared across
three different face processing tasks: learning, recognition and
categorization 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
Figure 4. Time course of significant fixations on facial features (eyes, nose and mouth) during face learning, recognition and
categorization by race, for WC (red square - top rows) and EA (green circle - bottom rows) observers (Z
crit
.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
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rude in East Asian cultures [21] and this social norm might have
determined gaze avoidance in East Asian observers. To some
extent our findings are consistent with observations that
Westerners tend to engage analytic perceptual strategies for
processing the visual environment, whereas East Asians use holistic
perceptual strategies [8,9,11–13]. Westerners might allocate their
attention to single facial features (i.e. eyes and the mouth) with a
bias 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 a
tuning 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 cell
density and visual resolution decrease steeply towards the
peripheral visual field, the center of the face is likely to be the
most 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 mech-
anisms 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 compu-
tational 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 face
processing show the opposite pattern. East Asian observers focused
on 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 wide
saccadic eye movements. In our experiment, a single face was
presented 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 the
precise nature of the visual strategies used across these tasks
remains to be clarified, both findings show a marked cultural
diversity in eye movements.
As expected, observers of both races were more accurate at
recognizing 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 the
perceptual strategies they adopted. Therefore, the question of
whether information used for face recognition is modulated by the
race 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 the
faces and the task at hand. Observers do not change perceptual
strategies as a function of the race of the face, highlighting the
robustness of the perceptual mechanisms engaged during face
processing. In addition, observers from different cultures reached a
comparable behavioral performance by using different scanpaths,
which, at first sight, could appear puzzling. However, eye
movements 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 shape
human eye movements for processing faces, which contradicts the
view 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 and
23.2 years respectively) participated in this study. All East Asian
participants 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 the
participants gave written informed consent and the protocol was
approved 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 and
females. The images were 3826390 pixels in size, subtending 14u
degrees of visual angle vertically and 10udegrees 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-shaven
and 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 a
10246768 pixel white background and displayed on a 210Iiyama
HM204DTA monitor with a refresh rate of 120 Hz. Presentation
of 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 although
viewing 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 Manual
for 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 the
screen to perform an automatic drift correction.
Procedure
Observers were informed that they would be presented with a
series 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 was
familiar 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 comple-
tion of the face recognition tasks, observers were instructed to
perform 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 dominant
hand. 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 a
face presented pseudorandomly in one of four quadrants of the
computer screen. Faces were presented for 5 seconds duration in
the learning phase and until the observer responded in the
recognition and race categorization phase. Each face was
subsequently followed by the central fixation dot which preceded
the next face stimulus.
Data analyses
Only correct trials were analyzed. Fixation distribution maps
were extracted individually for Western Caucasian and East Asian
observers and face race, for the learning, recognition and
categorization tasks separately. The fixation maps were computed
by summing, across all (correct) trials, the fixation location
coordinates (x,y) across time. Since more than one pixel is
processed 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 (see
supporting 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 for
both groups and used the mean and the standard deviation for
Western Caucasian and East Asian faces to separately normalize the
data (supporting Figure S1b). Finally, to clearly reveal the difference
of 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, we
used a robust statistical approach correcting for multiple compar-
isons in the fixation map space, by applying a two-tailed Pixel test [20]
on the differential fixation maps with the following threshold
(Z
crit
.|4.25|; p,.05 – areas delimited with white borders in
supporting Figure S1c). Thereafter, to further highlight the
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 the
percentage of fixations landing on those regions. To control for
differences 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 of
interest, by pooling the standard deviations of both groups in the
conditions 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 (xcoordinate, ycoordinate and time) by dividing each trial
into 20 ms bins, and calculated for each of the bins the number of
times 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 the
temporal domain. Finally, we transformed the 3D maps into Z-
scores, by using the mean and standard deviation across all the
dimensions and applied a one-tailed Pixel test to isolate the areas
that elicited significant fixations (Z
crit
.4.64; p,.05). The statistical
threshold provided by the Pixel test corrects for multiple
comparisons across time and space while taking the spatial and
temporal 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 the
significant fixation effects over time on the 3-D maps (see also the
supporting QuickTime
TM
movies). We highlighted the presence of
significant fixations in these regions by weighting the results as a
function 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).
Supporting Information
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 and
categorization by race tasks. Please refer to the main text for
details.
Found at: doi:10.1371/journal.pone.0003022.s001 (15.14 MB
DOC)
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
The authors wish to thank Fre´de´ric Gosselin for his helpful insights on the
analyses and David Kelly for insightful comments on a previous version of
this paper.
Author Contributions
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 the
experiment and trained the authors to the eye movement technique: CS.
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Culture Shapes Eye Movements
PLoS ONE | www.plosone.org 8 August 2008 | Volume 3 | Issue 8 | e3022