Perception, 2010, volume 39, pages 1491 ^ 1503

doi:10.1068/p6750

Inverting faces does not abolish cultural diversity in eye
movements
Helen Rodger, David J Kelly, Caroline Blais½, Roberto Caldara#ô

Centre for Cognitive Neuroimaging (CCNi), Institute of Neuroscience and Psychology, Universityof Glasgow, Glasgow G12 8QB, Scotland, UK; e-mail: roberto.caldara@unifr.ch; ½ Dëpartement
de Psychologie, Universitë de Montrëal, Montrëal, Canada; # also Department of Psychology,
University of Fribourg, 1700 Fribourg, Switzerland
Received 15 June 2010, in revised form 28 September 2010

Abstract. Face processing is widely understood to be a basic, universal visual function effortlessly
achieved by people from all cultures and races. The remarkable recognition performance forfaces is markedly and specifically affected by picture-plane inversion: the so-called face-inversion
effect (FIE), a finding often used as evidence for face-specific mechanisms. However, it has
recently been shown that culture shapes the way people deploy eye movements to extract information from faces. Interestingly, the comparable lack of experience with inverted faces acrosscultures offers a unique opportunity to establish the extent to which such cultural perceptual
biases in eye movements are robust, but also to assess whether face-specific mechanisms areuniversally tuned. Here we monitored the eye movements of Western Caucasian (WC) and East
Asian (EA) observers while they learned and recognised WC and EA inverted faces. Both groupsof observers showed a comparable impairment in recognising inverted faces of both races.
WC observers deployed a scattered inverted triangular scanpath with a bias towards the mouth,
whereas EA observers uniformly extended the focus of their fixations from the centre towards
the eyes. Overall, our data show that cultural perceptual differences in eye movements persist
during the FIE, questioning the universality of face-processing mechanisms.

1 Introduction
The accurate perception of faces is a critical cognitive function and is fundamental
to the interpretation of the complex social interactions we experience. The ability toprocess and recognise faces is a basic visual skill exercised by healthy humans from theearly stages of development, which increases in accuracy as the visual system maturesand experience in social perception widens (Pascalis and Kelly 2009). As face processing represents a basic biological skill that is routinely performed by people from all
cultures and races, it has typically been assumed that the visual system achieves thisperceptual function invariantly. Perceptual strategies elicited during face scanning demonstrate which visual information is critical for performing common face-processing tasks.
For example, early seminal studies of eye movements during face recognition revealed
that visual information is extracted from faces by a series of saccadic eye movementswith predominant foveal fixations to the eye and mouth features (Yarbus 1967). Subsequent eye-movement studies have consistently replicated this triangular sequence offixations to the eyes and mouth during face encoding and recognition (eg Groneret al 1984; Henderson et al 2005; Walker-Smith et al 1977). However, despite the social
significance of face perception, this commonly reported face-scanning strategy wasobserved in studies conducted solely with adults from Western cultures. Consequently,
investigation of cultural variance in eye movements was overlooked.
To address this gap, a recent eye-movement study by Blais et al (2008) was conductedwith both Western Caucasian (WC) and East Asian (EA) adults to establish (i) whether
people from different cultures use the same perceptual strategies to process faces,and (ii) whether the extraction of visual information changes according to the race ofô Author to whom all correspondence should be addressed.

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H Rodger, D J Kelly, C Blais, R Caldara

the face observed during face-learning, recognition, and categorisation-by-race tasks. As
expected, WC adults reproduced the established triangular fixation pattern during learning,
recognition, and categorisation. Surprisingly, EA adults directed fixations to the central
area of the face, around the nose, and less to the eyes area (figure 1). These culturally
divergent scan patterns were consistent across all tasks (learning, recognition, and categorisation), regardless of the race (Caucasian or Asian) of the face observed. Blais et al(2008) posited that culture significantly influences the way observers look at faces during
face recognition and expression categorisation (Jack et al 2009), but further studies arenecessary to identify the origins of such cultural diversity in visual processing.
Robust cross-cultural empirical differences in face processing have been reported
in the framework of the other-race effect (OREöMalpass and Kravitz 1969; see review
by Meissner and Brigham 2001), a phenomenon in which memory for own-race faces
is greater than for faces from another, less familiar race (Caldara and Abdi 2006;
Vizioli et al 2010a). To date, studies have established that the ORE and the popularbelief that other-race faces all look alike are not accounted for by anthropometricvariations in other-race faces (Caldara and Abdi 2006; Caldara et al 2010; Goldstein
1979a, 1979b), but by a genuine lack of expertise with them. The ORE has been shownto interact with a similarly robust face-recognition performance constraint, the faceinversion effect (FIEöYin 1969; for a review, see Rossion and Gauthier 2002), in which
recognition of inverted faces is disproportionately impaired compared to recognitionof other mono-oriented homogeneous object categories. The FIE is thus considered bymany as strong evidence for face-specific processing, as the impairment suggests there
is a qualitative difference in how faces are processed compared to other non-face visualobjects: holistic processing (of the spatial relationships between features) is engaged
during the processing of upright faces, whereas inexperience with inverted faces
engages a qualitatively distinct strategy, featural encoding, or, at least, impaired holistic
processing (eg Rossion and Gauthier 2002). Furthermore, own-race faces are thought
to be processed more holistically than other-race faces (Michel et al 2006a, 2006b;
Tanaka et al 2004); therefore, according to the FIE qualitative encoding switch hypothesis, holistic processing of own-race faces should produce a greater inversion effectthan of other-race faces that enlist featural encoding. Several studies of the ORE ^ FIE
interaction have revealed a stronger inversion effect for own-race compared to otherrace faces (McKone et al 2007; Rhodes et al 1989; Vizioli et al 2010b). This interaction
is assumed to be related to experience, as familiarity with own-race faces produces astronger inversion effect than of less familiar other-race faces (Sangrigoli and de Schonen
2004).
Only in a few empirical studies has the relationship between eye movements and theFIE been examined. Williams and Henderson (2007) examined scan patterns during
encoding and recognition of inverted faces to identify whether eye movements havea role in producing the FIE. They hypothesised that face inversion could impede
scanning of critical facial features as inversion disorients the regular topographyof these features. Measuring the dispersion of eye-movement fixations over 7 facial
regions that were uniquely defined for each stimulus, Williams and Henderson (2007)
found that fixation patterns were similar for both upright and inverted faces acrossface regions, concluding that eye-movement patterns are not causally related to theinversion effect. The only significant effect of orientation was for the mean proportionof trials, in which the mouth region was viewed on a greater proportion of trials forinverted faces. Barton et al (2006) similarly found that both the number of fixations
and duration spent viewing the mouth increased for inverted faces in comparison to
upright faces. In contrast to Williams and Henderson (2007), Barton et al (2006) foundorientation had an effect on fixation patterns as the scanning sequence for inverted faces
became more random and fixations were redistributed to the mouth and lower face.

Inverting faces does not abolish cultural diversity in eye movements

Learning

Recognition

1493

Categorisation

8
6
4
2
0
ÿ2

EA faces

ÿ4

Z -scores=fixation bias

WC faces

WC observers

ÿ6
ÿ8
EA observers

Figure 1. Blais et al (2008): 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 categorisation by race. Areas
showing a significant fixation bias are delimited by white borders (Zcrit 4 j4:25j; p 5 0:05); values near 0 indicate similar magnitude in fixation between observers from different cultures.

This discrepancy in the effect of orientation on scanpaths could result from differences
in both the definition of facial regions and the analysis of fixations used in each study.
Williams and Henderson (2007) analysed 7 rectangular facial regions that wereuniquely defined for each stimulus. By contrast, Barton et al (2006) analysed 8 facial
regions, previously defined by Groner et al (1984), which were also calculated for eachstimulus used. Delineating face regions of interest in this way imposes a dichotomic
analysis of eye fixations, as a fixation 1 pixel outside the border of the region of interest
will be excluded from analysis for that region despite the fact that a foveal fixation,around 2 deg visual angle, processes more information than is contained in a single
pixel for faces and scenes (Miellet et al 2010). The definition of facial regions of interest
can therefore bias fixation analyses and compromise the generalisation of findings
across studies. Instead of defining facial regions, Blais et al (2008) smoothed fixations
by applying a spatial filter (Gaussian kernel a  10 pixels) to represent the foveatedarea (2 deg visual angle), enabling densely fixated areas to be computed and renderedin fixation distribution maps (further details are described in section 2).
Here we took advantage of this unbiased method of eye-movement data analysis(i MapöCaldara and Miellet, submitted) to isolate the fixation patterns deployed during
the FIE across cultures. Apart from the limited number of eye-movement studiesof the FIE, no cross-cultural comparisons of scan patterns for inverted faces have beenreported. The comparable lack of experience across cultures with inverted faces offersa unique opportunity to establish the extent to which cultural perceptual strategies
are robust. In this study we aim to extend the paradigm used by Blais et al (2008) toestablish whether cultural variance in information extraction strategies persists during
processing of inverted faces by monitoring the eye movements of WC and EA observers.
Finally, to investigate whether the FIE modulates eye-movement strategies compared tonatural upright viewing conditions, we compared the present data with those collected
for upright face recognition (Blais et al 2008).

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H Rodger, D J Kelly, C Blais, R Caldara

2 Method
2.1 Participants
Fourteen Western Caucasian (eight female) and fourteen East Asian (eight female)adults participated (mean ages 23 and 24 years, respectively). WC participants wererecruited from the Psychology Department undergraduate participant pool at the
University of Glasgow, and were all born in the UK. EA participants were recruited
through advertisements placed in the university library. All EA participants were bornin China, had not previously resided in a Western country, and had been in the UK
for an average of 5 weeks. All participants had normal or corrected vision and were
paid »6 per hour for their participation. All participants gave written informed consent
and the Faculty Ethical Committee approved the experimental protocol.
2.2 Stimulus and apparatus
The stimuli consisted of 56 grey-scale images. 28 Caucasian faces (14 female) wereobtained from The Karolinska Directed Emotional Faces database (Lundqvist et al 1998),
and 28 Asian faces (14 female) from the Asian Face Image Database (Bang et al 2001).
The faces conveyed either a neutral, happy, or disgusted expression. The images were
cropped so that only the head was visible, and did not include clothing or distinctive
features (eg facial hair, jewelry). Images were spatially normalised by aligning theeye and mouth positions, and image luminance was also normalised. The images were3906382 pixels in size, subtending 15.6 deg horizontally and 15.3 deg vertically. Participants
viewed the faces at a distance of 70 cm so the experimental setup was as representative
as possible of interacting with an adult human face at a natural distance (Hall 1966).
Chin-and-forehead rests were used to maintain an equidistant viewing position and tohelp minimise head movements. The images were displayed on an 8006600 pixel grey
background with a Dell P1130 19 inch CRT monitor with a refresh rate of 170 Hz. The
images were displayed in random locations on the screen to prevent anticipatory eye
movements. Stimulus presentation was controlled by software written in MATLAB with
the Psychophysics (PTB-3) and EyeLink Toolbox extensions (Brainard 1997; Cornelissen
et al 2002).
2.3 Eye movements
Eye movements were recorded at a sampling rate of 1000 Hz with an SR Research
Desktop-Mount EyeLink 2K binocular eye-tracking system. The EyeLink 2K has anaverage gaze position error of 5 0:5 deg horizontally, 5 1:5 deg vertically, a resolutionof 1 min of arc, and a linear output over the range of the monitor used. Although viewing was binocular, only the participant's dominant eye was recorded. Eye fixations
were calibrated manually prior to each recording session by a nine-point fixation calibration and validation procedure (as implemented in the EyeLink API software; see
the EyeLink Manual for details) to ensure that the eye tracker could discriminate thepupil/corneal reflection accurately in all gaze directions. Participants were instructed
to fixate a dot in the centre of the screen at the beginning of each trial that served as adrift correction of the gaze estimate. If the drift correction was greater than 1 deg thenthe calibration and validation procedure was repeated until an optimal gaze estimate
was achieved.
2.4 Design
Participants completed two blocks of learning and recognition per race condition. The
race condition was counterbalanced across observers. The emotional expressions (neutral,
happy, or disgust) of the faces were similarly counterbalanced across the race conditions.
Each block comprised 14 inverted faces (7 female) in the learning phase, followed bya recognition phase of 28 inverted faces (14 old, 14 new).

Inverting faces does not abolish cultural diversity in eye movements

1495

2.5 Procedure
Participants were informed that the experiment comprised two blocks of face learningand recognition, each containing different face stimuli. In the learning session, participants were instructed to study the faces carefully, as they would subsequently be testedon their memory for the faces in the recognition phase. Participants were informedthat the emotional expression of a face in the learning phase would be different in therecognition phase. The expression of faces was changed between learning and recognition
to prevent trivial image matching strategies in memorising face identities. Participants
were seated and asked to make minimal head movements during the task. Eye fixations were calibrated manually at the beginning of each block, and the experimenter
initiated a trial when the participant fixated a dot in the centre of the screen thatserved as a drift correction. Participants began with a training session of 4 novel images(1 male and 1 female of each race) to become familiar with examples of the stimuli.
Each image was presented for 5 s in the learning phase, and until the participant
made a keyboard press in the recognition phase. At the beginning of the recognition
phase, participants were requested to gauge as quickly and accurately as possible if theface appeared in the learning phase by pressing the `a' or `l' key to indicate a yes/no
response. The experimenter initiated the recognition phase when the participant's fingers
were placed on the correct keys.
2.6 Data analyses
Only correct trials were analysed. Fixation distribution maps were computed individuallyfor EA and WC observers for each race condition, and face learning and recognition
phases separately, with the use of MATLAB. More than 1 pixel is processed during
a fixation, so each fixation was smoothed with a Gaussian kernel (a  10 pixels) torepresent the foveated area (2 deg visual angle). Fixation distribution maps were computed by summing all fixation locations (x, y coordinates) across time for all correct
trials. Blinks and fixations outside of the stimulus area were excluded from the fixation maps. Fixation maps were then calculated for each cultural group by summingthe individual maps of observers belonging to each culture.
Group fixation maps were Z - scored with the assumption that eye-movement
distributions of WC and EA observers are identical for both races of faces, forming
the null hypothesis. The fixation distributions of each culture were collated, and themean and standard deviation were obtained for each race condition (WC and EA
faces) and used to normalise the data. To establish any difference in fixation patterns
across cultural groups, the EA group fixation map was subtracted from the WC group
fixation map, and the resulting distribution was Z -scored. Significance was establishedby correcting for multiple comparisons in the fixation map space with a one-tailed
Pixel test (Chauvin et al 2005; Zcrit 4 j4:64j; p 5 0:05) for the group fixation maps, anda two-tailed Pixel test (Zcrit 4 j4:25j; p 5 0:05) for the differential fixation maps. Finally,for each condition we extracted the average Z-score value for each observer individually, for each area showing significance in the differential fixation maps. Cohen's deffect sizes (Cohen 1988) were calculated from two-way mixed design ANOVAs on the
average Z -scores with the region of the face and the culture of the observer as factors
carried out for the learning and recognition conditions separately.
In order to clarify the results of the fixation strategies during inverted-face viewing,
we compared these findings with previously collected data on upright-face recognition(Blais et al 2008) that used the same methodological framework (with the exemptionof stimulus orientation). Since both studies used identical stimulus material and a fullycomparable experimental design, this comparison is valid and appropriate.
For a detailed discussion of the novel approach used to analyse eye-movement
data (i Map) and the MATLAB code, please refer to Caldara and Miellet (submitted).

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H Rodger, D J Kelly, C Blais, R Caldara

3 Results
3.1 Behavioural
3.1.1 Face recognition accuracy. Figure 2 illustrates the d 0 accuracy scores by culture
for recognition. WC observers showed a d 0 accuracy of 0.74 (SE   0:18) for Western
Caucasian faces and 0.78 (SE   0:11) for East Asian faces. EA observers had d 0
accuracy scores of 0.57 (SE   0:23) and 0.39 (SE   0:17) for Western Caucasianand East Asian faces, respectively. A 2 (race of face)62 (culture of the observer) mixedmodel ANOVA revealed no significant main effects for the race of face (F1, 26  0:13,
p  0:71) or culture of the observer (F1, 26  2:4, p  0:12) on recognition performance.
The interaction between the race of face and culture of the observer also failed to reachstatistical significance (F1, 26  0:34, p  0:56). Recognition performance was comparable
across cultures and races of faces observed.
1.1
WC faces
0.9

EA faces

d0

0.7
0.5
0.3
0.1
WC observers

EA observers

Figure 2. d 0 Accuracy scores of theold/new face-recognition paradigm,
for Western Caucasian (WC) andEast Asian (EA) observers. Error
bars report 1 SEM.

3.1.2 Reaction times. Figure 3 illustrates the mean response times for each culturalgroup. WC observers had a mean recognition response time of 1941 (SE  129) ms
and 2145 (SE  128) ms to WC and EA faces, respectively. EA observers had amean recognition response time of 2083 (SE  178) ms and 1956 (SE  146) ms
to WC and EA faces, respectively. A 262 mixed-model ANOVA revealed no significant main effects for the race of face (F1, 26  0:56, p  0:45) or culture of the observer(F1, 26  0:01, p  0:9) on mean reaction time. The interaction between race of face andculture of the observer was significant (F1, 26  10:61, p  0:001). A posteriori twotailed paired t-tests revealed that Western Caucasian observers responded significantly
2400
2300
WC faces

2200

EA faces

RT=ms

2100
2000
1900
1800

Figure 3. Mean response times tothe old/new face-recognition paradigm, for Western Caucasian (WC)and East Asian (EA) observers. Error
bars report 1 SEM.

1700
1600
1500

WC observers

EA observers

Inverting faces does not abolish cultural diversity in eye movements

1497

faster to own-race faces than EA faces (t13  ÿ3:35, p  0:005), but there was no
significant difference in recognition response time to the race of face for East Asianobservers (t13  ÿ1:56, p  0:142).
3.2 Eye movements
3.2.1 Number of fixations. Table 1 shows that, on average, observers made 14 fixations
per trial during face learning and 6 fixations during recognition. A two-way mixedmodel ANOVA revealed no main effects for race of face (F1, 26  0:04, p  0:84), or cultureof the observer (F1, 26  0:04, p  0:84), on number of fixations during face learning.
There was no significant interaction between race of face and culture of the observer(F1, 26  2:10, p  0:84). During face recognition there was no main effect of culture
on number of fixations (F1, 26  3:04, p  0:09). There was a main effect of race of faceWC participants
Learning

EA participants
WC observers

8

6

2

0
Recognitionÿ2

Z -scores=fixation bias

4

ÿ4

ÿ6

ÿ8
EA observers

Figure 4. Fixation maps of Western Caucasian (WC) and East Asian (EA) observers forinverted-face learning and recognition (dark blue images). Differential fixation maps (green
images) were computed by subtracting WC (red) Z -scored group fixation maps from EA (blue)
maps for each condition. Areas showing a significant fixation bias are delimited by white borders(Zcrit 4 j4:25j; p 5 0:05); values near 0 indicate similar magnitude in fixation between observers
from different cultures.

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H Rodger, D J Kelly, C Blais, R Caldara

on number of fixations (F1, 26  4:19, p  0:05) and a significant interaction between
the race of face and culture of the observer (F1, 38  14:91, p  0:001). A posteriori
two-tailed paired t-tests revealed that WC observers made significantly fewer fixations
to own-race faces than EA faces (t13  ÿ5:13, p  0:001), whereas EA observers did not(t13  1:10, p  0:287).
3.2.2 Fixation frequency. Table 2 shows the mean number of fixations during facelearning and recognition for each group of observers. The average fixation frequency
during face learning was similar across cultures. The main effect of culture did notreach statistical significance (F1, 26  0:074, p  0:787). The main effect of race offace was non-significant (F1, 26  0:074, p  0:787). The interaction term also failed toreach significance (F1, 26  0:251, p  0:618). On average, WC observers made slightly
more fixations during face recognition than EA observers to both WC faces (3.14
versus 2.77), and EA faces (3.17 versus 2.77). The main effect of culture was significant (F1, 26  9:66, p  0:003) and a posteriori two-tailed paired t-tests demonstrated
that WC observers made significantly more fixations during recognition (t13  3:166,p  0:002). However, neither cultural group differed significantly in frequency of fixations to same-race or other-race faces (F1, 26  0:025, p  0:874). The interaction effectwas non-significant (F1, 26  0:013, p  0:909).
3.2.3 Fixation distribution maps. Figure 4 shows significant differences (Zcrit 4 j4:64j;
p 5 0:05) in fixation locations for both cultures during inverted-face learning andrecognition, and significant differences (Zcrit 4 j4:25j; p 5 0:05) in fixation strategies
across both groups of observers. WC observers deployed a triangular pattern of fixations to both eyes and the mouth. They also showed a greater tendency to fixate themouth. By contrast, EA observers fixated different facial regions from inverted faces
than WC observers, by most densely fixating the inner right eye and top of the nose
across conditions. During recognition, EA observers also made significant fixations to
the mouth.
In the differential group fixation maps, WC observers made significantly more
fixations to the left eye and mouth than the EA observers in all conditions except
learning of EA faces, where only the mouth was significantly fixated. Alternatively,
EA observers made significantly more fixations to the inner-right eye and part of theTable 1. Mean number of fixations for Western Caucasian (WC) and East Asian (EA) observers
during WC and EA face-learning and recognition by race. Numbers in parentheses report 1 SEM.
The presentation time was fixed during learning (5 s).
Observers

WC
EA

Learning

Recognition

EA faces

WC faces

EA faces

WC faces

13.7 (0.5)
13.7 (0.5)

14.0 (0.4)
13.5 (0.5)

6.7 (0.4)
5.4 (0.5)

6.0 (0.4)
6.8 (0.5)

Table 2. Mean fixation frequencies during face learning and recognition for Western Caucasian(WC) and East Asian (EA) observers. Numbers in parentheses report 1 SEM. The presentation
time was fixed during learning (5 s).
Observers

WC
EA

Learning

Recognition

EA faces

WC faces

EA faces

WC faces

2.743 (0.11)
2.77 (0.11)

2.8 (0.10)
2.72 (0.11)

3.17 (0.11)
2.77 (0.14)

3.14 (0.11)
2.77 (0.14)

Inverting faces does not abolish cultural diversity in eye movements

1499

Table 3. Cohen's d effect sizes by culture for significantly fixated facial features.
EffectLearning

Observers

WC
EA

Recognition

WC
EA

Facial feature

Race of face

eyes

nose

mouth

rest

0.31
0.35
0.29
0.37

0.11
0.23
0.20
0.30

0.54
0.39
0.47
0.30

0.04
0.03
0.04
0.04

WC
EA
WC
EA

0.26
0.20
0.37
0.30

0.31
0.46
0.31
0.38

0.40
0.29
0.29
0.22

0.03
0.05
0.03
0.11

WC
EA
WC
EA

nose than WC observers across all conditions except recognition of EA observers
where this strategy is again present but fails to reach significance. Table 3 shows the
Cohen's d effect size values for significantly fixated facial features.
Finally, figure 5 shows the differences between inverted and upright (Blais et al's
2008 data) face learning and recognition strategies for both groups of cultures. This
analysis did not reveal significant differences in the scanning strategies for upright andinverted faces for either culture, since no facial region elicited significant fixationbiases for either of the conditions (Zcrit 4 j4:25j; p 5 0:05).
4 Discussion
We report a cultural contrast in relative fixation biases for inverted faces, which isconsistent with the cultural variance in information extraction strategies for uprightfaces (Blais et al 2008). Importantly, inverted-face recognition performance was comparable across cultures. In addition, both cultural groups maintained differential fixation
patterns for upright and inverted faces, regardless of the race of the face observed. During
processing of inverted faces WC observers, in comparison to EA observers, continued toconsistently deploy preferential fixations to the eye and the mouth regions, as previously
reported for upright faces (Blais et al 2008). In line with previous findings on WC
observers with inverted unfamiliar faces (Williams and Henderson 2007), there wereno significant differences between the fixation strategies used for inverted and uprightfaces. It is worth noting that the novel analysis of eye movements applied in this study
revealed a trend in both cultural groups of greater fixations to the mouth for unfamiliar
inverted faces, a fixation bias previously reported in the literature during the recognitionof famous inverted faces (Barton et al 2006). However, this fixation bias towards themouth region was not robust when directly compared to upright eye-movement strategies
for either cultural group (Blais et al 2008). This finding is also in line with recent
neuroimaging studies showing the existence of a tuning towards the upper visual fieldin the face-sensitive areas (Caldara et al 2006; Caldara and Seghier 2009). Although
the fixation strategy of EA observers appears to be disrupted by inversion with theestablished central fixation bias observed for upright faces (Blais et al 2008; Caldara
et al 2010; Kelly et al 2010) expanding towards the inner right eye, this apparent
disruption failed to reach statistical significance when compared directly to the uprightstrategy of EA observers. Therefore, similarly to the WC group, the fixation strategy
of EA observers for upright faces is maintained for inverted faces.
Previous studies have revealed an interaction between the FIE and the ORE, demonstrating a stronger inversion effect for own-race compared to other-race faces (eg McKone
et al 2007; Rhodes et al 1989). Although recognition accuracy for both cultural groupsof observers followed this trend in our study, the effect was not statistically reliable.

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H Rodger, D J Kelly, C Blais, R Caldara

EA participants
Learning

WC participants

Upright
2
1.5

0.5
0
ÿ0.5
Recognition

ÿ1
ÿ1.5

Z -scores=fixation bias

1

ÿ2
ÿ2.5
Inverted

Figure 5. Differential fixation maps between upright [data from the Blais et al (2008) study]
and inverted (present study) face learning and recognition, computed by subtracting Z -scored
Western Caucasian (WC) group fixation maps from East Asian (EA) maps for each condition.

Potentially, the strength of this effect could have been impeded by the change of images
used between face learning and recognition, which showed different facial expressions forthe same facial identity. To the best of our knowledge, previous studies of FIE and ORE
have used the same image during both face learning and recognition, so further studies
are necessary to directly address the question whether the use of strong constraints infacial identity encoding affects face recognition performance during inversion.
It would be tempting to account for the different eye-movement strategies deployed
by observers from different cultures by the anthropometric variations inherent to faces
from different races (for instance, more information in the nose region in EA faces).
However, we have previously demonstrated that the stimulus set of EA and WC faces usedhere does not contain any significant differences in the pixel space across faces fromdifferent races (Caldara et al 2010). This observation is consistent with previous studies
of a similar issue (Caldara and Abdi 2006; Goldstein 1979a, 1979b). Therefore, there
is no obvious diagnostic information inherent in faces from a particular race that could
explain the observed fixation strategies. In addition, a recent study examined information use during face processing with a gaze-contingent paradigm (Caldara et al 2010).
Facial information available to viewers was restricted to 2, 5, and 8 deg `Spotlight'
apertures that light up the blackened stimulus display as a function of the current gaze
position. Critically, in the 2 and 5 deg conditions the Spotlight was large enough toreveal individual facial features, but both eyes and the mouth were not visible simultaneously when the nose was fixated. By contrast in the 8 deg condition, informationfrom both eyes and the nose was simultaneously available when fixating the nose. The
results revealed that when constrained by the smaller apertures, the EA and WC fixation strategies were comparable as both cultural groups fixated the eyes and partiallythe mouth. However, when both eye and mouth information was available from the
8 deg Spotlight, cultural strategies diverged: WC observers maintained their existing
strategy by fixating the eyes and mouth, whereas EA observers solely fixated thenose, their preferred strategy as established by the original free-viewing condition (Blais
et al 2008). Determining that information from the eyes remains necessary for EA
recognition strategies, despite the central fixation bias, is consistent with classification

Inverting faces does not abolish cultural diversity in eye movements

1501

image techniques (eg `Bubbles' öGosselin and Schyns 2001; Caldara et al 2005) thatreveal the eye region is critical for accurate face identification. Since both cultures
require the same diagnostic information to achieve face recognition, it becomes natural
to ask why observers from different cultures employ diverse eye-movement strategies toadapt to the very same face-recognition constraints.
Blais et al (2008) suggested that the systematic differences in perceptual processing
across cultures, identified by recent studies (eg perceptual categorisation öNorenzayan
et al 2002; perceptual judgment ö Kitayama et al 2003; and scene perception ö
Miyamoto et al 2006), might expand and generalise to face processing. Blais et al (2008)
therefore aligned cultural diversity in face scanning with a recent cultural paradigm that
proposes that culture influences perception by producing qualitative differences in the way
people process information from the visual environment (Nisbett and Miyatomo 2005).
The holistic (1) versus analytic theory of culture describes EA perceptual strategies as
holistic as visual attention is largely directed toward the context and relationships ofenvironmental stimuli in their entirety. By contrast, WC observers tend to use more
analytical perceptual strategies, attending to focal information within their field ofvision. In this way, Blais et al (2008) suggest that by allocating attention to isolated
facial features (eg the eyes and mouth) WC adults demonstrate an analytical perceptual
strategy during upright-face processing. Conversely, by substantially fixating the centre
of upright faces, the perceptual strategy of EA adults maximises the amount of information that can be integrated from this location, and is therefore suggestive of holistic(global) processing. It is worth noting that fixation biases to the central location of faces
cannot straightforwardly be related to the holistic processes suggested to be recruitedduring face processing. Observers from both cultures might construct identical representations to process faces, by using distinct fixation scanpaths (for a detailed discussion
on this theoretical point see Kelly et al 2010). The present eye-movement data suggestthat the holistic central fixation bias of the EA upright strategy is still the most effective
strategy under the constraint of an unfamiliar inverted orientation. Similarly, the WC
upright strategy is also maintained for inverted faces (eg Sekuler et al 2004), whichindicates that the information sampled from the eyes and mouth during scanning issufficient for identification regardless of orientation.
Critically, the present data show that cultural differences in the eye-movement
strategies deployed by human observers are present even in a marker of face specificity
such as the FIE. These cultural perpetual biases are robust and point towards theexistence of cultural-specific mechanisms to extract and process information from faces.
Acknowledgments. DK was supported by The Economic and Social Research Council (RES-00022-3338); RC was supported by The Economic and Social Research Council and Medical ResearchCouncil (ESRC/RES-060-25-0010).
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