Journal of Experimental Psychology: General
Internal Representations Reveal Cultural Diversity in
Expectations of Facial Expressions of Emotion
Rachael E. Jack, Roberto Caldara, and Philippe G. Schyns
Online First Publication, April 25, 2011. doi: 10.1037/a0023463

CITATION
Jack, R. E., Caldara, R., & Schyns, P. G. (2011, April 25). Internal Representations Reveal
Cultural Diversity in Expectations of Facial Expressions of Emotion. Journal of Experimental
Psychology: General. Advance online publication. doi: 10.1037/a0023463

Journal of Experimental Psychology: General
2011, Vol. ●●, No. ●, 000 – 000© 2011 American Psychological Association
0096-3445/11/$12.00 DOI: 10.1037/a0023463

Internal Representations Reveal Cultural Diversity in Expectations ofFacial Expressions of Emotion
Rachael E. Jack, Roberto Caldara, and Philippe G. Schyns
University of Glasgow
Facial expressions have long been considered the “universal language of emotion.” Yet consistentcultural differences in the recognition of facial expressions contradict such notions (e.g., R. E. Jack, C.
Blais, C. Scheepers, P. G. Schyns, & R. Caldara, 2009). Rather, culture—as an intricate system of social
concepts and beliefs— could generate different expectations (i.e., internal representations) of facial
expression signals. To investigate, they used a powerful psychophysical technique (reverse correlation)
to estimate the observer-specific internal representations of the 6 basic facial expressions of emotion (i.e.,
happy, surprise, fear, disgust, anger, and sad) in two culturally distinct groups (i.e., Western Caucasian[WC] and East Asian [EA]). Using complementary statistical image analyses, cultural specificity was
directly revealed in these representations. Specifically, whereas WC internal representations predominantly featured the eyebrows and mouth, EA internal representations showed a preference for expressive
information in the eye region. Closer inspection of the EA observer preference revealed a surprisingfeature: changes of gaze direction, shown primarily among the EA group. For the first time, it is revealed
directly that culture can finely shape the internal representations of common facial expressions ofemotion, challenging notions of a biologically hardwired “universal language of emotion.”
Keywords: culture, facial expressions of emotion, perception, expectations, internal representations
Supplemental materials: http://dx.doi.org/10.1037/a0023463.supp

continues as to whether facial expressions of emotion are biologically hardwired and universal, or malleable to the influences ofculture. Here we address this debate.
How could culture exert such an influence on the production andperception of basic emotion signals? Each culture embraces a
specific conceptual framework of beliefs, values, and knowledge,
which shapes thought and action. Culture-specific ideologies could
exert powerful top-down influences on the perception of the visualenvironment by imposing particular cognitive styles. For example,
individualistic (e.g., Western) cultures could generate tendencies
to adopt local feature-processing strategies, whereas collectivist(e.g., East Asian) cultures may promote the use of global processing strategies, as suggested by relative size judgments (Davidoff,
Fonteneau, & Goldstein, 2008), categorical reasoning styles(Norenzayan, Smith, Kim, Nisbett, 2002), change blindness sensitivities (Masuda & Nisbett, 2006), and eye movements (Blais,Jack, Scheepers, Fiset, & Caldara, 2008; Caldara, Zhou, & Miellet,
2010; Kelly, Miellet, & Caldara, 2010). By using distinct cognitive
processing strategies, observers likely acquire culture-specific perceptual experiences of the visual environment, including facial
expression signals.
Similarly, ideological concepts underlying societal functioning(e.g., Triandis, 1989) highlight important cultural differences,which likely influence the production of facial expressions. For
example, individualistic versus collectivist cultures may adopt
different display rules that govern when, how, and to whom
emotions are expressed (e.g., Matsumoto, Seung Hee, & Fontaine,2008), thus diminishing, enhancing, or altering facial expression
signals. As a result, cultural differences in the expectations ofexpressive signals could give rise to the reported cultural confu-

One of the most fundamental aspects of human social interaction is the communication of emotions, achieved primarily by theexchange of a set of social signals: facial expressions. With biological and evolutionary origins (e.g., Darwin, 1872; Susskind etal., 2008), facial expressions have long been considered a hardwired human behavior, supporting notions of a “universal language of emotion” (e.g., Izard, 1994; Matsumoto & Willingham,2009). However, cultural differences in cognition and behavior(see Nisbett & Masuda, 2003, for a review) showing consistentdifferences in the recognition of facial expression (e.g., Biehl et al.,
1997; Ekman et al., 1987; Ekman, Sorenson, & Friesen, 1969;
Jack, Blais, Scheepers, Schyns, & Caldara, 2009; Matsumoto,
1992; Matsumoto & Ekman, 1989; Moriguchi et al., 2005) andsupporting views of cultural accents in facial expression signals(see Elfenbein, Beaupre, Levesque, & Hess, 2007; Marsh, Elfenbein, & Ambady, 2003) challenge the universality hypothesis.
With reconciliation of these opposing views still pending, debate

Rachael E. Jack, Roberto Caldara, and Philippe G. Schyns, Institute of
Neuroscience and Psychology, College of Medical, Veterinary and Life
Sciences, University of Glasgow, Glasgow, Scotland.
We would like to thank both the Economic and Social Research Council
and the Medical Research Council (ESRC/MRC-060-25-0010 to Philippe G.
Schyns; PTA-031-2006-00192 to Rachael E. Jack) for supporting this research.
Correspondence concerning this article should be addressed to Rachael
E. Jack, Institute of Neuroscience and Psychology, College of Medical,
Veterinary and Life Sciences, University of Glasgow, 58 Hillhead Street,
Scotland, United Kingdom, G12 8QB. E-mail: rachael@psy.gla.ac.uk
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JACK, CALDARA, AND SCHYNS

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sions (e.g., Biehl et al., 1997; Ekman et al., 1987; Ekman et al.,
1969; Jack et al., 2009; Matsumoto, 1992; Matsumoto & Ekman,
1989; Moriguchi et al., 2005) when expectations are not met.
With such broad impacts upon social behavior, cognitive processing strategies and visual experience, observers from different
cultures likely acquire markedly different internal representationsof the environment. It is important to note that internal representations provide predictive information about the world based on
previous experience, thus shaping expectations and guiding behavior. For example, even when viewing identical face stimuli, Western Caucasian (WC) and East Asian (EA) observers use culturespecific fixation patterns during face identification (Blais et al.,2008) and facial expression recognition (Jack et al., 2009) tasks.
Although culture-specific eye movements reflect cultural specificity
in internal representations or cognitive processing styles, they cannot
alone directly reveal the information observers expect to see or actually use during facial expression recognition. Yet with no objective
method available to accurately access the mind of observers, obtaining direct and detailed evidence of culture-specific internal representations of facial expressions has remained challenging.
Here we address this issue using a powerful reverse correlation(RC) technique to access the “mind’s eye” of two culturally
distinct observer groups (i.e., WC and EA). For each individual
observer, we estimated and analyzed their internal representationof each of the six basic facial expressions of emotion: happy,
surprise, fear, disgust, anger and sad. As a result, we captured
culture-specific information reflecting both past experience andfuture expectations of facial expression signals. Our data show that
WC and EA observers expect emotion to be expressed using
distinct facial signals, supporting views of cultural specificity inemotion communication.

MethodObservers
Two cultural groups of observers participated: WC and EA. The
WC group consisted of 15 observers (14 European, one North
American, M age ⫽ 27.3 years, SD ⫽ 6.4; 8 men, 7 women). The
EA group consisted of 15 observers (15 Chinese, M age ⫽ 23.5
years, SD ⫽ 2.1; 5 men, 10 women). All EA observers had beenborn in East Asia, having arrived in a Western country (United
Kingdom) for the first time, with an average residence of 1.8
months (SD ⫽ 2.1) and a minimum International English Language Testing System score of 6.0 (Competent User) at the time oftesting. All observers had normal or corrected-to-normal vision,
gave written informed consent, and confirmed minimal prior experience with other cultures as assessed by questionnaire (seeonline supplemental materials). We paid all observers £6 per hourfor participating. The University of Glasgow Department of Psychology ethical committee approved the experimental protocol.

Stimuli
Each experimental stimulus consisted of the same backgroundstimulus (a gray-scale, race-, gender-, and emotion-neutral face)superimposed with a different pattern of uniform white noise (seeFigure 1, Design for an illustration). To compute the race-,
gender-, and emotion-neutral background face, we averaged acrosseight identities (half female, half EA; Matsumoto & Ekman, 1988)previously cross-culturally verified as neutral (Biehl et al., 1997;
Ekman et al., 1987; Ekman et al., 1969; Jack et al., 2009; Matsumoto, 1992; Matsumoto & Ekman, 1989; Moriguchi et al., 2005).
Prior to averaging, we aligned each neutral face on the eye andmouth positions using Psychomorph software (Tiddeman, Burt, &Perrett, 2001), cropping the final image around the face using
Adobe Photoshop CS.

Design and Procedure
On each trial of the experiment, we added white noise to the race-,
gender-, and emotion-neutral expression face (see online supplemental materials for additional details). The effect of adding white noiseis that it perceptively changes the appearance of the neutral face byaltering the face features. For example, consider a trial where adding
white noise results in white pixels being added to the white eye regionof the neutral face: The eye region now appears more white. Consequently, observers may interpret the stimulus as expressive, because
the whites of the eyes correspond to the observer’s internal representation of the facial expression “surprise” (see Figure 1, Design). Thus,
when the observer categorizes the stimulus as surprise, we capture theinformation the observer wants to see added to the neutral face tocreate a facial expression of surprise—in this case, the observer wants
to see white pixels added to the eye region to create the characteristic
whites of the eyes.
We instructed observers to perform a seven alternative forced
choice facial expression categorization task according to the six
basic facial expressions of emotion (i.e., happy, surprise, fear,
disgust, anger and sad), plus a “don’t know” response. Observers
each categorized 12,000 such trials, producing a set of white noise
templates associated with each subjective categorical judgment,
which contains the information the observer wants to see added to
the neutral face to create a specific facial expression (see Figure 1,
Analysis for color-coded examples: red for sad, green for anger).
Observers viewed stimuli (380 ⫻ 280 pixels) on a midgray background displayed on a 19-in. (48.26-cm) flat panel monitor. A chinrest ensured a constant viewing distance of 85 cm, with images
subtending 4.9° ⫻ 6.8° of visual angle. Each stimulus remained
visible until the observer responded using a keyboard.
Prior to the experiment, we established familiarity with each of theemotion categories by asking observers to provide correct synonyms
and descriptions of each. All observers remained naı̈ve to the neutral
nature of the underlying background face throughout testing.

Computation: Estimating Internal Representations ofFacial Expressions
For each observer, to estimate the internal representation of eachfacial expression, we first averaged the set of white noise templates
in the relevant category (e.g., “sad” outlined in red) before smoothing with a Gaussian kernel (␴ ⫽ 3 pixels; see Figure 1, Analysis
for an illustration). This averaged white noise represents thechanges in gray-level pixel values that, when added to the neutral
face, lead the observer to perceive an emotions. To visualize theinternal representations, we then added the averaged white noise to
the neutral face (see Figure 1, Analysis for examples). As a result,each internal representation shows the features the observer expects to constitute a particular facial expression. For example, as

CULTURAL INTERNAL REPRESENTATIONS OF FACIAL EXPRESSIONS

Figure 1. Illustration of the reverse correlation (RC) technique used to estimate observer-specific internal representations. Design: Stimulus generation and task. On each trial, we added to a neutral face a white noise. Naı̈veobservers categorized each stimulus according to the six basic facial expressions of emotion (i.e., happy, surprise, fear,
disgust, anger, and sad), plus a “don’t know” response. Analysis: Reconstruction of cultural internal representations.
For each observer and facial expression separately, we averaged the set of noise templates associated with theobserver’s categorization responses (e.g., sad color-coded in red and anger color-coded in green) to reconstruct theirinternal representation. Averaged noise templates are outlined in black and illustrated for two expressions: sad forWestern Caucasian (WC) observer CLM and East Asian (EA) observer FF and anger for WC observer AG and EA
observer SW. Results: Cultural internal representations. Each row represents the cultural internal representations of
the six basic facial expressions of emotion (i.e., happy, surprise, fear, disgust, anger, and sad) estimated using RC.
Each internal representation is selected from a different observer (i.e., labeled in capital letters for WC observers andEA observers) and is presented to illustrate the effectiveness of the RC technique.

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JACK, CALDARA, AND SCHYNS

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shown in Figure 1, Analysis, the internal representation of sad for
WC observer CLM contains a down-turned mouth, whereas for EA
observer FF, the internal representation clearly contains changes inthe eye region. To isolate the significant pixels for further analysis(not shown in Figure 1), we applied a statistical threshold ( p ⬍.05; Chauvin, Worsley, Schyns, Arguin, & Gosselin, 2005) afterZ-scoring the averaged noise template. The strength of this RC
technique is that it flexibly reconstructs internal representations thatare free from potential limitations imposed by prescribed stimuli (seeGosselin & Schyns, 2003; Kontsevich & Tyler, 2004, for examples of
the application of the current RC technique; see Gosselin & Schyns,
2004; Murray & Gold, 2004, for a comparison with other RC techniques that isolate information required for categorization).

Computation: Statistics Over Facial Features
Classification image techniques are powerful because they estimate internal representations of information, for each individual
observer. Here we wish to extract an estimate of how culture
modifies internal representations across individual observers. First,
we established five face regions of interest (i.e., left eyebrow, righteyebrow, left eye, right eye, and mouth) because, at the level ofindividual observers, most statistically significant pixels were represented in these regions (78.4% and 70.8% of significant pixels
for WC and EA observers, respectively). For each observer andexpression independently, we established a threshold number ofsignificant pixels (⳵ ⱖ 10) for each region of interest. We determined a hit for this region if the considered internal representation
reached the threshold. Finally, for each expression independently,
we counted the number of observers in each cultural group who
had a hit, in each of the separate five face regions. Thus, for eachface region, we examined how culture shapes the representation of
expressive information over the face.

Results
To analyze cultural differences, we compared the number of WC
and EA observers representing features in each of the face regions ofinterest—specifically, left eyebrow, right eyebrow, left eye, right eye,and mouth. Chi-square tests of association revealed consistent culturalpreferences across all six facial expressions ( p ⬍ .05). Figure 2
illustrates the results for each facial expression using color-coded
regions to indicate cultural preferences (red indicates a strong WC
preference; blue indicates a strong EA preference, with a maximum of
15 observers in one group reconstructing a face feature vs. zero
observers in the other group).
As shown in Figure 2 (top row), WC internal representations
featured the eyebrows and the mouth significantly more than in EA
representations. In contrast, EA representations (see Figure 2,
bottom row) primarily featured the eyes in comparison with WC
observers. These differences contradict the universality hypothesis, which predicts reconstruction of similar features across cultural groups and instead supports theories of cultural differences infacial expression signals.
Further inspection of the data revealed another intriguing cultural difference: changes of gaze direction, shown primarily in the
EA group. Although not pervasive across the group, changes ofgaze direction nevertheless emerged as a strong feature among EA
observers, as suggested by literature (e.g., Knapp & Hall, 2005),
therefore validating our methods to reveal cultural differences.
Figure 3 shows all such internal representations showing changes
of gaze direction.

Validation of Internal Representations
The RC technique used here estimates observer-specific internal
representations by adding random features to a neutral face via theaddition of white noise. Given that the white noise added to theneutral face is unstructured (i.e., each pixel value is random andindependent) and contains no features per se (e.g., smilingmouth), the current RC technique could lack the specificity tocapture accurately the features of internal representations, resulting in low validity. Here, we randomly subsampled theoriginal WC and EA observers to validate their own internal
representations.

Figure 2. Cultural differences in expressive features. In each row, color-coded areas show cultural preferencesin the reconstruction of features for each facial expression of emotion. Magnitude of bias (i.e., absolutedifference in the number of observers) is represented by color-coded bars in each row. For example, for surprisethe eyes appeared in the internal representations of eight more East Asian [EA] observers than Western
Caucasian [WC] observers.

CULTURAL INTERNAL REPRESENTATIONS OF FACIAL EXPRESSIONS

5

Figure 3. Internal representations showing changes of gaze direction. Each face demonstrates the use ofchanges of gaze to communicate emotion. Each example is selected from a different observer (initials labeledin capital letters) from the Western Caucasian [WC] and East Asian [EA] groups. Whereas nine EA internal
representations showed changes of gaze, only two WC internal representations displayed changes of gaze. Notethat for the EA group, changes of gaze direction feature in a wider range of emotions (i.e., surprise, fear, disgust,anger, and sad) compared with the WC internal representations (i.e., sad only).

MethodResults

Observers. Ten observers from each original cultural group
participated (WC ⫽ 10 European, M age ⫽ 26.1 years, SD ⫽ 5.1,
4 men, 6 women; EA ⫽ 10 Chinese, M age ⫽ 23.8, SD ⫽ 2.2, 2men, 8 women). All observers gave written informed consent,
received £6 per hour for participating, and remained naı̈ve to the(observer-specific) nature of the stimuli throughout testing. The
University of Glasgow Department of Psychology ethical committee approved the experimental protocol.
Stimuli. For each observer, we generated a single set of six
stimuli, where each stimulus depicted the observer’s own internal
representation of happy, surprise, fear, disgust, anger, and sad. To
create each stimulus, we used the reconstruction methods described previously (see the Computation: Estimating Internal Representations of Facial Expressions section and Figure 1, Analysis
for an illustration).
Design and procedure. We presented stimuli simultaneously
in a single randomly ordered row on a computer monitor. Stimuli
remained visible as long as observers wished. Observers categorized each stimulus according to the six basic facial expressions ofemotion (i.e., happy, surprise, fear, disgust, anger, and sad) by
writing down the expression name corresponding to each stimulus.

Analysis of recognition performance showed high accuracy,with observers categorizing 94.2% of all reconstructed internal
representations correctly (WC ⫽ 100%, EA ⫽ 88.33%). Table 1
shows the percentage of internal representations accurately categorized for each emotion category and cultural group of observers.
Note that observers correctly categorized 88.9% of the internal
representations displaying changes of gaze (see Figure 3 for examples).

Validation of Internal Representations Showing
Changes of Gaze
To verify objectively the number of internal representations
showing changes of gaze, we conducted a validation task using a
different set of WC and EA observers.

MethodObservers. Twenty-two observers participated: 11 WC (11European; 5 women, 6 men; M age ⫽ 24.09 years, SD ⫽ 5.61) and
11 EA (11 Chinese, 5 women, 6 men; M age ⫽ 24.09 years, SD ⫽

JACK, CALDARA, AND SCHYNS

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Table 1
Percentage Accuracy for Western Caucasian (WC) and East Asian (EA) Observers Categorizing
Their Own Internal Representations of the Six Basic Facial Expressions of Emotion
Group

Happy

Surprise

Fear

Disgust

Anger

Sad

WC
EA

100
100

100
100

100
80

100
80

100
80

100
90

3.08). All EA observers had been born in East Asia, having arrived
in a Western country (United Kingdom) for the first time, with anaverage residence of 4.5 months (SD ⫽ 1.03) duration and aminimum International English Language Testing System score of
6.0 at the time of testing. All observers had normal or correctedto-normal vision, gave written informed consent, and were paid £6
per hour for participating. The University of Glasgow Departmentof Psychology ethical committee approved the experimental protocol.
Stimuli. To create the stimuli, we reconstructed all internal
representations (i.e., 2 Culture of Observers ⫻ 6 Facial Expressions of Emotion ⫻ 15 Observers) using the same methods described previously (see the Computation: Estimating Internal Representations of Facial Expressions section and Figure 1, Analysis
for an illustration).
Design and procedure. Observers performed a five alternative forced choice categorization task by categorizing each stimulus according to five gaze directions (i.e., up, down, left, right,
and center). Observers viewed stimuli (8.5cm ⫻ 6.75cm) on a
midgray background displayed on a 19-in. (48.26-cm) flat panel
monitor. A chin rest maintained a constant viewing distance of 77
cm, with images subtended 6.31° ⫻ 5.05° of visual angle. We
repeated each stimulus five times and presented stimuli in random
order. Stimuli remained visible until observers responded using akeyboard.

Results
To identify the internal representations showing changes ofgaze, we compared the total number of responses for each gaze
direction (i.e., up, down, left, right, or center), collapsed across allobservers, for each stimulus independently. Chi-square tests ofassociation revealed a marked cultural contrast: Nine EA internal
representations showed significant changes of gaze, comparedwith only two WC internal representations ( p ⬍ .0001). Figure 3
shows each of the internal representations showing changes ofgaze—note that for the EA internal representations, changes ofgaze feature in a wider range of emotions (i.e., surprise, fear,
disgust, anger, and sad), compared with the WC internal representations (i.e., sad). For each internal representation showing achange of gaze, WC and EA observers responded equally within
the significant gaze category, thus validating all changes of gazeacross both cultural groups.
Together, these results demonstrate both the validity of our RC
technique to accurately capture internal representations and theecological validity of the culture-specific facial expression signals
reported here, including surprising changes of gaze.

Discussion and Conclusion
Using templates of white noise, we randomly added features toa race-, gender-, and emotion-neutral face and instructed observers

from two cultural groups to categorize each image by facial
expression. We then reconstructed the internal representations ofeach facial expression of emotion in each individual observer fromboth cultural groups. Using statistical image-processing analyses
to examine the properties of each internal representation, we
revealed clear cultural differences in the facial expression signals
expected by each cultural group. Specifically, whereas WC internal representations distributed expressive features across the face(e.g., the eyebrows and mouth), EA internal representations
showed a consistent preference for the eyes. Further inspection of
the EA eye region preference revealed a surprising feature among
the EA group: changes of gaze direction.
To interpret our results, it is important to consider what an internal
representation represents. Created from observer-specific experiences,internal representations provide predictive information about the environment, guiding thought and action. Thus, by reconstructing theinternal representations of facial expressions in individual observers,
we captured information that reflects both past experiences and future
expectations of emotion communication.
Cultural specificity in the internal representations of facial expressions shown here may reflect differences in the experience offacial expression signals across cultures. As predicted by culturespecific display rules and accents (Elfenbein et al., 2007; Marsh etal., 2003), modulations in the production of facial expressions
could give rise to the differences in the reported internal representations, demonstrating cultural diversity in emotion signals. Contrary to notions of a universal language of emotion, ecologicallyvalid representations of facial expressions would include expressive features characteristic of the culture. For example, we showthat observers within the EA group expect changes of gaze to be acomponent of facial expressions, which does not currently feature
in widely used facial expression stimuli (e.g., Matsumoto &Ekman, 1988). Furthermore, although modulations in gaze play asocial role in revealing sources of attention (see Kingstone, 2009;
Klein, Shepherd, & Platt, 2009, for reviews), we show that gaze
direction plays a wider role in social interaction by reflecting
internal emotional states.
Correspondence between the location of expected expressive
features and culture-specific fixations patterns (Jack et al., 2009)highlights the role of top-down factors on biological visual systems used to select information for categorization, which could
give rise to differential perceptual experiences of facial expressions across cultures. Yet eye movements can show where (on theface) fixations are directed but cannot alone reveal the information
actually used or expected for emotion categorization. We significantly expand on previous eye-movement studies (e.g., Jack et al.,2009) by reconstructing the specific information the visual systemseeks to extract for accurate categorization of facial expressions.
Together, eye-movement data and techniques that isolate the in-CULTURAL INTERNAL REPRESENTATIONS OF FACIAL EXPRESSIONS

formation used for categorization (i.e., RC) can more accurately
identify the information used for social interaction.
Finally, culture-specific expectations of facial expression signals likely contribute to consistent cultural differences in therecognition of universal facial expressions (e.g., Biehl et al., 1997;
Ekman et al., 1987; Ekman et al., 1969; Jack et al., 2009; Matsumoto, 1992; Matsumoto & Ekman, 1989; Moriguchi et al., 2005),
as information conflicting with internal representations would generate confusion. Moreover, culture-specific fixation patterns usedduring facial expression recognition (Jack et al., 2009; see also
Yuki, Maddux, & Masuda, 2007) would further hinder the processby subsampling culturally incongruent facial expression signals.
In sum, our data directly show, for the first time, differences inthe expectations of facial expression signals across diverse cultures, challenging notions of a universal language of emotion andrevealing a source of potential confusion during cross-culturalcommunication.

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Received December 30, 2010
Revision received February 16, 2011
Accepted February 17, 2011 䡲