Current Biology 19, 1543–1548, September 29, 2009 ª2009 Elsevier Ltd All rights reserved

DOI 10.1016/j.cub.2009.07.051

ReportCultural Confusions Show that
Facial Expressions Are Not Universal
Rachael E. Jack,1,2,* Caroline Blais,3 Christoph Scheepers,1Philippe G. Schyns,1,2 and Roberto Caldara1,2,*
1Department of Psychology
2Centre for Cognitive Neuroimaging (CCNi)
University of Glasgow, Glasgow G12 8QB, Scotland, UK
3Départment de Psychologie, Université de Montréal,Montreal, PQ H3C 3J7, Canada

Summary
Central to all human interaction is the mutual understandingof emotions, achieved primarily by a set of biologicallyrooted social signals evolved for this purpose—facial
expressions of emotion. Although facial expressions arewidely considered to be the universal language of emotion[1–3], some negative facial expressions consistently elicit
lower recognition levels among Eastern compared toWestern groups (see [4] for a meta-analysis and [5, 6] forreview). Here, focusing on the decoding of facial expression
signals, we merge behavioral and computational analyses
with novel spatiotemporal analyses of eye movements,
showing that Eastern observers use a culture-specific decoding strategy that is inadequate to reliably distinguish
universal facial expressions of ‘‘fear’’ and ‘‘disgust.’’ Rather
than distributing their fixations evenly across the face as
Westerners do, Eastern observers persistently fixate the
eye region. Using a model information sampler, we demonstrate that by persistently fixating the eyes, Easternobservers sample ambiguous information, thus causing
significant confusion. Our results question the universalityof human facial expressions of emotion, highlighting theirtrue complexity, with critical consequences for crosscultural communication and globalization.
Results
To examine the decoding of facial expressions acrosscultures, we recorded the eye movements of 13 Western
Caucasian (WC) and 13 East Asian (EA) observers while they
performed a seven-alternative forced-choice (7AFC) facial
expression categorization task (i.e., ‘‘happy,’’ ‘‘surprise,’’‘‘fear,’’ ‘‘disgust,’’ ‘‘anger,’’ and ‘‘sadness’’ plus ‘‘neutral’’)with same-race (SR) and other-race (OR) Facial Action Coding
System (FACS)-coded faces [7, 8]. We chose FACS-coded
faces both for the purpose of presenting well-controlled
stimuli and for their assumed universality in transmitting facial
expression signals, although we fully acknowledge their realworld limitations. We then related, for each expression, categorization performance with corresponding eye movement
strategies (see Experimental Procedures).
Task Performance
A three-way (two cultures of observers, two races of face,seven facial expressions) mixed analysis of variance (ANOVA)*Correspondence: rachael@psy.gla.ac.uk (R.E.J.), roberto@psy.gla.ac.uk(R.C.)

on mean categorization accuracy (see Table S1 available
online) showed a significant culture of observer 3 facial
expression interaction [F(6,144) = 5.608, p < 0.001]. Post hoc
Bonferroni comparisons showed that EA observers madesignificantly more errors when categorizing ‘‘disgust’’ (p <0.05) and ‘‘fear’’ (p < 0.001) than WC observers did. In contrast,
WC observers categorized all facial expressions with comparably high accuracy. Within-group homogeneity was confirmed for both groups: A one-way repeated-measures
ANOVA across individual observers revealed no significant
differences within the WC group. Only two significant differences were found within the EA group [F(12,156) = 7.33, p <0.001)], with post hoc Bonferroni comparisons showing bothto be related to one unusually accurate observer (p < 0.001).
Although consistent with previous observations [5, 9, 10,11], a critical question remains: Why do EA observers systematically miscategorize certain facial expressions (i.e., ‘‘fear’’and ‘‘disgust’’)? Further inspection of EA categorization errors
revealed that ‘‘fear’’ and ‘‘disgust’’ were consistently confusedwith ‘‘surprise’’ and ‘‘anger,’’ respectively (see Figure S1 forcategorization confusions).
Eye Movements
Eye movements provide an index of overt attention and cantherefore reveal the information strategically selected to categorize expressions. Thus, we examined the location, frequency,
and temporal order of fixations in both observer groups.
To the ANOVA design of task performance, we added thefourth factor of fixations to face regions (i.e., ‘‘left eye,’’ ‘‘righteye,’’ ‘‘bridge of nose,’’ ‘‘center of face,’’ and ‘‘mouth’’; seeExperimental Procedures). We revealed a significant culture ofobserver 3 face region interaction [F(4,96) = 3.65, p < 0.01)].
Post hoc comparisons showed that EA observers made significantly more ‘‘left eye’’ (p < 0.01) and ‘‘right eye’’ (p < 0.001) fixations compared to ‘‘mouth.’’ Figure 1A summarizes this interaction, with the corresponding fixation maps collapsed across allseven expressions (see Figure S2 for the complete analysis,
showing EA bias fixations toward the eyes across all seven
expressions). In contrast, WC observers fixated all face regions
equally. Figure 1B presents the fixation maps for the expressions eliciting significant errors (i.e., ‘‘fear’’ and ‘‘disgust’’) andthe expressions with which they were systematically confused(i.e., ‘‘surprise’’ and ‘‘anger,’’ respectively).
To further characterize biases in information sampling strategies, we analyzed the order in which the face regions werevisited by using minimum description length (MDL; see Experimental Procedures). MDL is a statistical method that extracts
regular patterns from data set sequences [12, 13]. Here, asequence consisted of the succession of fixations on faceregions (e.g., ‘‘left eye’’ / ‘‘right eye’’ / ‘‘left eye’’). To illustrate the MDL results, we focused on the same four facial
expression categories as above (i.e., ‘‘surprise,’’ ‘‘fear,’’‘‘disgust,’’ and ‘‘anger’’; see Figure S3 for MDL applied to all
conditions of the design). In Figure 2, color-coded circles corresponding to face regions represent the fixation sequences.
For example, the fixation sequence ‘‘left eye’’ / ‘‘right eye’’ /‘‘left eye’’ (indicated by the black arrow) is represented bya succession of circles colored blue, green, and blue.

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Figure 1. Fixation Distributions(A) Fixation distributions for each observer group collapsed across race of face and seven expression categories (see Figure S2 for fixation distributions foreach condition separately). Color-coded distributions represent the density of fixations across face regions, with red showing the most densely fixatedregions. Note that for East Asian (EA) observers, fixations are biased toward the upper part of the face as compared to Western Caucasian (WC) observers,
where fixations are more evenly distributed across the face, including the mouth.
(B) Fixation distributions for ‘‘surprise,’’ ‘‘fear,’’ ‘‘disgust,’’ and ‘‘anger.’’ Color-coded distributions presented on grayscale sample stimuli show the relative
distributions of fixations across face regions. Color coding is as follows: blue, ‘‘left eye’’; green, ‘‘right eye’’; yellow, ‘‘bridge of nose’’; orange, ‘‘center of
face’’; red, ‘‘mouth.’’ Higher color saturation indicates higher fixation density, shown relative to all conditions. Note that the red ‘‘mouth’’ fixations for EA
observers are less intense as compared to WC observers across conditions. Color-coded bars to the left of each face represent the mean categorization
accuracy for that condition, with red indicating a significant difference in categorization errors between groups (p < 0.05). Error bars indicate standard errorof the mean.

MDL results revealed a clear contrast: EA observers madesignificantly more systematic fixation sequences than WC
observers (as shown by a chi-square test of association[c2(1) = 366.79, p < 0.001]). Note that the high number ofcolor-coded successions of circles for EA observers in Figure 2
is valid across all experimental conditions (see Figure S3) andfor a majority of individual observers (see Figure S4).
A significant majority of these fixation sequences involved
exclusively ‘‘left eye’’ and ‘‘right eye’’ [c2(1) = 395.38, p < 0.001],
with significantly more use for negative expressions (i.e.,‘‘fear,’’ ‘‘disgust,’’ and ‘‘anger’’) compared to other expressions[c2(1) = 15.97, p < 0.001]. Furthermore, EA observers usedsimilar fixation sequences for the expressions that they consistently confused (i.e., ‘‘surprise’’ and ‘‘fear’’; ‘‘disgust’’ and‘‘anger’’). Therefore, by persistently biasing fixations toward
the eyes, EA observers could have extracted eye informationthat was too similar to discriminate certain expressions.
Model Observer
To objectively determine whether sampling the eyes while neglecting more diagnostic face regions (e.g., the mouth regionfor ‘‘fear’’ and ‘‘disgust’’ [e.g., 14]) could elicit behavioral
confusions, we built a model observer that sampled information to categorize expressions (see Experimental Procedures).
Figure 3 illustrates the model observer with ‘‘fear’’ and‘‘surprise,’’ ‘‘anger,’’ and ‘‘disgust’’ as above (see Figure S5
for a complete illustration of the computations).
Consider the facial information sampled by the modelobserver in Figure 3A. When sampling the eye region of‘‘fear,’’ the information is most similar to (i.e., confusablewith) that of ‘‘surprise’’ and less so for other expressions(see red box). Thus, sampling from the eye region produces
a pattern of confusions (i.e., Pearson correlation values), represented by the dashed red line in Figure 3B. In contrast,
sampling from the mouth (see green box) produces a different
pattern of confusions (dashed green line in Figure 3B),
whereby ‘‘fear’’ and ‘‘surprise’’ are distinguishable. Our model
randomly sampled information from the face, compared (i.e.,
fitted) each confusion pattern to the behavioral confusion
pattern of EA observers (solid black line in Figure 3B), andrank ordered each sample according to its fit to EA observers’
behavioral confusions.
Figure 4 illustrates the results with ‘‘surprise,’’ ‘‘fear,’’‘‘anger,’’ and ‘‘disgust.’’ Color-coded contour plots represent
the rank order of all samples from ‘‘best’’ (red) to ‘‘worst’’ (blue)
R2 values. The model observer most closely replicated EA
observers’ confusions when sampling the eye (delimited withorange contours) and eyebrow (delimited with red contours)
regions. Note the higher density of EA observer fixations(based on error trials, shown by the relative distributions inCultural Decoding of Facial Expressions
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Figure 2. Fixation Sequences for ‘‘Surprise,’’ ‘‘Fear,’’ ‘‘Anger,’’ and ‘‘Disgust’’
Successions of color-coded circles represent the fixation sequences extracted via minimum description length analysis, with each circle representing a faceregion. Face regions are color-coded as in Figure 1B. For example, the succession of blue / green / blue circles (indicated by the black arrow) corresponds to the fixation sequence ‘‘left eye’’ / ‘‘right eye’’ / ‘‘left eye.’’ Single color-coded circles correspond to fixations that do not appear as part ofa sequence. Black and white bars to the right of the fixation sequences represent how frequently the fixation sequence appeared in the data set, with black
indicating correct trials and white indicating incorrect trials. Different levels of gray in each condition represent the order of the fixation sequences (seeExperimental Procedures). Note the higher number of fixations sequences for EA observers compared to WC observers across expressions (see also
Figure S3).

Figure 4, scale on right) within face regions ranked as ‘‘best fit’’(see Figure S6 for analysis conducted across all expressions).
This demonstrates that EA behavioral confusions are

symptomatic of a strategy that samples ambiguous information (i.e., the eyes and eyebrows) and neglects diagnostic
features (i.e., the mouth).
Figure 3. The Model Observer: Illustration of the Procedure
to Compute Estimated Patterns of Confusion(A) Information samples. To compute estimated patterns ofconfusion, we used the model to sample face informationfrom the stimulus expression (e.g., ‘‘fear’’) and from thesame location on the other expressions (e.g., ‘‘surprise,’’‘‘anger,’’ and ‘‘disgust’’). The face images illustrate an
example of the information sampled.
(B) Pattern of confusions. The model then Pearson correlated the stimulus expression sample with each of the otherexpression samples. These correlations (plotted in dashedcolor-coded lines beneath each corresponding face) represented the confusions of the model and were fitted (using
ordinary least squares) against the behavioral confusionsof the EA observers (plotted in black). The behavioral confusions of the EA observers were calculated by categorizing
each incorrect trial by response for each expression (e.g.,for ‘‘fear’’ trials, the numbers of incorrect responses werecomputed for ‘‘surprise,’’ ‘‘anger,’’ and ‘‘disgust’’). We
repeated the sampling and correlation process for 10,000
individual samples selected randomly across the face andfinally sorted each information sample according to its fit
to the behavioral confusions of the EA observers (‘‘best’’ to‘‘worst’’ fits are shown in Figure 4). We followed the sameprocedure for each expression (see Figure S5 for a full illustration).

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Figure 4. Model Observer and EA Observer Fixation Maps
Contour plots: color-coded lines represent the rank order of information samples according to fit, with red showing the ‘‘best’’ fit (scale on left). For example,
by sampling the eyebrow (delimited in red) and eye region (delimited in orange) in ‘‘fear,’’ the model produced a pattern of confusions most similar to that of
the EA observers. In contrast, the lower part of the face (delimited in blue and green) produced a pattern of confusions most dissimilar to that of the EA
observers.
Fixation patterns: for each expression, fixations leading to behavioral confusions are shown by relative distributions presented on grayscale sample stimuli.
Red areas indicate higher fixation density for each expression (scale on right). Note the higher density of EA observer fixations within face regions ranked as‘‘best fit.’’ This demonstrates that the behavioral confusions of the EA observers are symptomatic of an information sampling strategy that selects ambiguous information (i.e., the eyes and eyebrows) while neglecting more diagnostic features (i.e., the mouth).

Discussion
Here, we report marked differences between EA and WC
observers in the decoding of universal facial expressions. EAobservers exhibited a significant deficit in categorizing ‘‘fear’’and ‘‘disgust’’ compared to WC observers. Also, WC
observers distributed their fixations evenly across the face,whereas EA observers systematically biased theirs toward
the eye region. A model observer revealed that EA observers
sample information that is highly similar between certain
expressions (i.e., ‘‘fear’’ and ‘‘surprise’’; ‘‘disgust’’ and‘‘anger’’). Despite the apparent lack of diagnostic information(see [14]), EA observers persisted in repetitively sampling theeye regions of ‘‘fear,’’ ‘‘disgust,’’ and ‘‘anger.’’Do EA observers make categorization errors simply because
they select ambiguous information? Behavioral results showedthat in response to ambiguity, EA observers tended to bias theircategorization responses toward less socially threatening
emotions (e.g., ‘‘surprise’’). Instead, facial expression categorization is likely to be conjunctively influenced by predeterminedsocial motivations and cultural concepts (see [15–17]) as wellas information sampling biases. However, our eye movement
data refute ‘‘decoding rules’’ hypotheses of substituting an
initial correct categorization with a more socially acceptableemotion [10, 11, 18] or attenuated intensity [9, 11].
Cultural differences in fixation patterns may reflect cultural
specificity in the transmission of facial expression signals.
EA observers systematically neglect critical aspects of
FACS-codes faces (e.g., action units 20, 26, and 27 [14]),demonstrating that FACS-coded facial expression signals do
not accurately represent the diagnostic features of EA facial
expressions. Rather, EA fixation patterns predict the eyeregion as primarily diagnostic for facial expression categorization with minimal involvement of the mouth (see also [19]), as
reflected by the EA emoticons (O.O) for surprise and (^.^) for
happy [20].
Finally, our data question gaze avoidance in face processing
for EA observers (see [21]). Instead, their fixation patterns
probably reflect strategies developed to achieve diagnostic
recognition [15], with modulations in fixation patterns across
categorization tasks indicative of specific information selection (e.g., for identification [21]; see also [22–27]).

In sum, our data demonstrate genuine perceptual differences
between WC and EA observers and show that FACS-codedfacial expressions are not universal signals of human emotion.
From here on, examining how the different facets of cultural
ideologies and concepts (see [28] for an overview and also
[16, 17]) have diversified these basic social skills will elevateknowledge of human emotion processing from a reductionist
to a more authentic representation. Otherwise, when it comesto communicating emotions across cultures, Easterners andWesterners will continue to find themselves lost in translation.
Experimental Procedures
Observers
Thirteen Western Caucasian (13 European, 7 female and 6 male, mean age
24 years 5 months) and 13 East Asian (12 Chinese and 1 Japanese, 8 female
and 5 male, mean age 23 years 2 months) observers participated. All EA
observers were East Asia nationals, with an average UK residence of one
week and a minimum International English Language Testing System scoreof 6.0 at the time of testing. All participants had minimal prior experience ofother-race (OR) faces (assessed by questionnaire; see Supplemental Experimental Procedures), had normal or corrected vision, gave written informed
consent, and were paid £6 per hour for participating. The University of Glasgow Department of Psychology ethical committee approved the experimental protocol.
Materials
Stimuli [7] consisted of 56 images displaying six FACS-coded facial expressions (‘‘happy,’’ ‘‘surprise,’’ ‘‘fear,’’ ‘‘disgust,’’ ‘‘anger,’’ and ‘‘sadness’’) plus‘‘neutral.’’ Gender and race of face were equally distributed for each expression. Same-race (SR) faces for Chinese observers were Japanese faces [29,30]. We cropped the images with Adobe Photoshop CS and aligned the eye
and mouth positions with Psychomorph software [31]. Images (280 3 380
pixels) were viewed on a 1024 3 768 pixel white background on a 21-inch
Iiyama HM204DTA monitor (120 Hz refresh rate) at a distance of 60 cm (anatural distance for social interaction [32], representing faces as theaverage size of a real face [33]) and subtended 10 3 14 of visual angle.
Stimulus presentation was controlled by SR Research Experiment Builder
software, version 1.4202. Eye movements were recorded at a sampling
rate of 500 Hz (pupil-only mode) with an EyeLink II head-mounted eye
tracker (SR Research), which has an average gaze position error of <0.5 ,a resolution of 1 arc minute, and a linear output over the monitor’s range.
Procedure
Participants performed a 7AFC facial expression categorization task with
SR and OR faces. Prior to testing, we established participants’ familiarity
with the categorical labels, determined ocular dominance via the MilesCultural Decoding of Facial Expressions
1547

Acknowledgments

test [34], and performed a nine-point fixation procedure (implemented in the
EyeLink application programming interface) to establish optimal calibration(drift correction < 1 of visual angle).We tracked only the dominant eye,although viewing was binocular. A chin rest minimized head movements
and maintained viewing distance. Images were presented pseudorandomly
in one of four quadrants of the screen and remained until the participant
responded. Manual responses were accompanied by a verbal response(to eliminate eye movements toward response keys), recorded by the experimenter.

P.G.S. and R.C. were supported by the Economic and Social ResearchCouncil and Medical Research Council (ESRC/MRC-060-25-0010). R.E.J.
was supported by ESRC PhD studentship PTA-031-2006-00192, and C.B.
was supported by a PhD studentship provided by Fonds Québécois de laRecherche sur la Nature et les Technologies.

Fixation Maps
In Figure 1, Figure 4, Figure S2, and Figure S6, fixation maps were computedfor each condition by plotting all fixation (x, y) coordinates across time into
a 380 3 280 matrix. To represent the foveated region (2 visual angle), wesmoothed each fixation with a Gaussian kernel (a = 10 pixels). Fixation
maps in Figure 1 and Figure S2 are based on correct trials, whereas thosein Figure 4 and Figure S6 are based on error trials.

References

Face Regions
We established face regions to provide a common frame of reference todescribe the location of fixations and conduct MDL analysis. First, weapplied the pixel test (p < 0.05; [35]) to fixation maps in each condition toreveal the significantly fixated regions, calculating a centroid for each significant region. We pooled all centroids across conditions and performeda k-means clustering [36] to calculate a single centroid for each nonoverlapping significantly fixated region. Five centroids corresponded to ‘‘left eye,’’‘‘right eye,’’ ‘‘bridge of nose,’’ ‘‘center of face,’’ and ‘‘mouth.’’Minimum Description Length
MDL is a statistical method that extracts regular patterns from data set
sequences [12, 13]. Here, a pattern consisted of the succession of fixations
on face regions (e.g., ‘‘left eye’’ / ‘‘right eye’’ / ‘‘left eye’’). We calculated
fixation patterns by categorizing fixations by face region based on theirminimum distance to a face region centroid. We collapsed fixations occurring consecutively within the same face region into a single fixation. We conducted MDL on each condition separately (correct and incorrect trials
included) from zero (single fixation sequences) to third order (four fixation
sequences) inclusive. To eliminate fixation sequences occurring by chance,
we used the Monte Carlo simulation method to pseudorandomly sample theface regions (biased to replicate the distribution of observer fixations acrossface regions). We conducted 10,000 simulations per condition, computing
a frequency distribution for each fixation pattern. We then calculated theprobability of each fixation pattern appearing in the observer data set,
including those occurring significantly frequently (a = 0.05) in the results.
Model Observer
We used a model observer to build estimated patterns of confusions basedon samples of face information. Each sample of information was obtained
via a method that approximates the information extracted by the visualsystem during a fixation. First, we decomposed each image (averaged
across all identities) into a four-level Laplacian pyramid [37]. We then filteredeach level by applying a Gaussian window (a = 10 pixels) to the same relative
image location before recombining the levels to produce the desired image(see Figure S5A). To estimate patterns of confusions (i.e., similarity) basedon the sampled information, the model observer Pearson correlated thestimulus expression (e.g., ‘‘fear’’ in Figure S5A) with each of the otherexpressive faces (e.g., ‘‘surprise,’’ ‘‘anger,’’ ‘‘disgust,’’ ‘‘sadness,’’ and‘‘neutral’’ in Figure S5A). The correlation values (plotted in dashed colorcoded lines in Figure S5B) representing the model observer confusions
were fitted (using ordinary least squares) against the behavioral confusionsof the EA observers (plotted in black in Figure S5B; see Figure S1 for confusion matrices). We repeated the sampling process for 10,000 individual
samples randomly located across the face. Finally, we sorted each information sample according to its fit to the behavioral confusions of the EA
observers (see contour plots of Figure S6 for rank order of ‘‘best’’ to ‘‘worst’’fits). We followed the same procedure for each expression (except ‘‘happy,’’
as it was seldom confused with any other expression).
Supplemental Data
Supplemental Data include Supplemental Experimental Procedures, one
table, and six figures and can be found with this article online at http://
www.cell.com/current-biology/supplemental/S0960-9822(09)01477-8.

Received: May 12, 2009
Revised: July 12, 2009
Accepted: July 13, 2009
Published online: August 13, 2009

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