July 2013
Volume 13, Issue 9
Vision Sciences Society Annual Meeting Abstract  |   July 2013
Gender differences in the visual strategies underlying facial expression categorization
Author Affiliations
  • Caroline Blais
    Département de Psychoéducation et de Psychologie, Université du Québec en Outaouais
  • Daniel Fiset
    Département de Psychoéducation et de Psychologie, Université du Québec en Outaouais
  • Frédéric Gosselin
    Département de Psychologie, Université de Montréal
Journal of Vision July 2013, Vol.13, 598. doi:https://doi.org/10.1167/13.9.598
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      Caroline Blais, Daniel Fiset, Frédéric Gosselin; Gender differences in the visual strategies underlying facial expression categorization. Journal of Vision 2013;13(9):598. https://doi.org/10.1167/13.9.598.

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      © ARVO (1962-2015); The Authors (2016-present)

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Decoding facial expressions of emotions is a crucial ability for successful social interactions. Gender differences have been found in the neural responses to emotional faces (McClure et al., 2004), suggesting that men and women process facial expressions differently. The present study used the Bubbles technique (Gosselin & Schyns, 2001) to verify whether the visual strategies used in facial expression categorization (six basic emotions as well as neutral and pain expressions) differ across gender. Sparse versions of emotional faces were created by sampling facial information at random spatial locations and at five non-overlapping spatial frequency bands. The average accuracy was maintained at 56% (halfway between chance and perfect performance) by adjusting the number of bubbles on a trial-by-trial basis using QUEST (Watson & Pelli, 1983). Thus, the number of bubbles reflected the participants' relative aptitude for this task. Forty-one participants (14 men) each categorized 4000 sparsed stimuli. On average, women performed better than men (t(39) = 3.08, p<0.05). Classification images showing which information in the stimuli correlated with participants’ accuracy were constructed separately for each gender by performing a multiple linear regression on the bubbles' locations and accuracy. A pixel test was applied to the classification image to determine statistical significance (Zcrit = 3.36, p<0.05; corrected for multiple comparisons). Women used both eyes and mouth areas more efficiently than men. In order to verify if the visual strategy is modulated by gender when the ability to perform the task is factored out, we selected 12 men and women that were matched on the average number of bubbles (i.e., the 12 men (vs. women) with the highest (vs. lowest) performance), and we repeated the analysis described above. When performance was controlled for, women used the mouth area more than men, again suggesting that gender influences the visual strategy used for categorizing facial expressions.

Meeting abstract presented at VSS 2013


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