July 2013
Volume 13, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   July 2013
Dynamic mental models of culture-specific emotions
Author Affiliations
  • Wei Sun
    Institute of Neuroscience and Psychology (INP), University of Glasgow, United Kingdom, G12 8QB\nCentre for Cognitive Neuroimaging (CCNi), University of Glasgow, United Kingdom, G12 8QB
  • Oliver G.B.Garrod
    Institute of Neuroscience and Psychology (INP), University of Glasgow, United Kingdom, G12 8QB\nCentre for Cognitive Neuroimaging (CCNi), University of Glasgow, United Kingdom, G12 8QB
  • Philippe G.Schyns
    Institute of Neuroscience and Psychology (INP), University of Glasgow, United Kingdom, G12 8QB\nCentre for Cognitive Neuroimaging (CCNi), University of Glasgow, United Kingdom, G12 8QB
  • Rachael E.Jack
    Institute of Neuroscience and Psychology (INP), University of Glasgow, United Kingdom, G12 8QB\nCentre for Cognitive Neuroimaging (CCNi), University of Glasgow, United Kingdom, G12 8QB
Journal of Vision July 2013, Vol.13, 593. doi:10.1167/13.9.593
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      Wei Sun, Oliver G.B.Garrod, Philippe G.Schyns, Rachael E.Jack; Dynamic mental models of culture-specific emotions. Journal of Vision 2013;13(9):593. doi: 10.1167/13.9.593.

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Abstract
 

According to the Universality Hypothesis, facial expressions of emotion comprise a universal set of six basic signals common to all humans (i.e., happy, surprise, fear, disgust, anger, sad; Ekman et al., 1969). In contrast, Jack et al. (2012) demonstrate that although Western Caucasians (WC) represent the six basic emotions with the same dynamic facial movements, East Asians (EA) do not. This raises the questions of (1) what are the basic emotions in the EA culture? and (2) what are the corresponding culture-specific facial signals of emotion transmission?

 

To address the first question, we clustered emotion words in WC (English, 50 observers) and EA (Chinese, 50 observers) culture. Each participant rated on a bipolar scale the pairwise similarity of selected emotion words in their own language (see Methods). We applied clustering analyses to these data. The English clusters comprised 8 emotion categories including the six basic ones, plus pride and shame (e.g., Tracey & Robins, 2004). In contrast, the Chinese clusters showed a more complex structure of 10 basic emotions (see Figure S1).

 

Using the resulting basic emotion categories as response labels, we applied 4-dimensional reverse correlation (Yu et al. 2012) to reconstruct culture-specific face signals that transmit each emotion. As in Jack et al. (2012), on each experimental trial the observer viewed a random facial animation generated by computer graphics platform. Observers interpreted the facial animation as expressive when the facial movements corresponded with their mental representation of that emotion (Figure S2). Reverse correlation analyses produced, for each observer, a dynamic model per basic emotion.

 

Our analyses revealed culture-specific facial expression signals, refuting the University Hypothesis. For the first time, we also derive the cultural face signals that articulate emotion communication in the EA culture (see Figure S3 for examples).

 

Meeting abstract presented at VSS 2013

 
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