August 2012
Volume 12, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2012
Low sensitivities but surprisingly high efficiencies for face-gender discrimination from interattribute distances
Author Affiliations
  • Nicolas Dupuis-Roy
    Université de Montréal, Département de psychologie, CERNEC
  • Kim Dufresne
    Université de Montréal, Département de psychologie, CERNEC
  • Alexandre Couet-Garand
    Université de Montréal, Département de psychologie, CERNEC
  • Daniel Fiset
    Université du Québec en Outaouais, Département de psychoéducation et de psychologie
  • Frédéric Gosselin
    Université de Montréal, Département de psychologie, CERNEC
Journal of Vision August 2012, Vol.12, 495. doi:10.1167/12.9.495
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Nicolas Dupuis-Roy, Kim Dufresne, Alexandre Couet-Garand, Daniel Fiset, Frédéric Gosselin; Low sensitivities but surprisingly high efficiencies for face-gender discrimination from interattribute distances. Journal of Vision 2012;12(9):495. doi: 10.1167/12.9.495.

      Download citation file:


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

      ×
  • Supplements
Abstract

According to an influential view, relational cues such as the distances between the main internal features of a face (i.e. mouth, eyes, eyebrows and nose) play a predominant role in face processing (Maurer et al., 2002; but see Taschereau-Dumouchel et al., 2010). Studies on face-gender perception are no exception (see Campbell et al.,1999). Nevertheless, the use of real-world interattribute distances for face-gender discrimination in humans has never been examined. This was the aim of the present study. In Exp. 1 we tested whether observers can discriminate the gender of faces based solely on real-world interattribute distances. Participants had to discriminate the gender of two androgynous faces of the same identity that were presented simultaneously on the screen: one had real-world interattribute distances of a woman and, the other, of a man. Despite relatively low sensitivities (average d’= 0.40 +/- 0.31, ranging from 0.82 to 0.05), 9 out of 11 observers performed significantly above chance (p<0.05). Surprisingly, statistical efficiencies were relatively high (M=13.91%, SD=12.85%, ranging from 37.35% to 0%). This is because real-world interattribute distances contain little face-gender information: A linear classifier trained on the interattribute distances of 250 faces (125 men) and tested on 250 novel faces (125 men) obtained a d’=1.34. Would real-world interattribute distances still contribute to gender discrimination when more informative cues such as attribute shapes and skin properties are available? In Exp. 2, we tested this by manipulating realistically the interattribute distances of 500 faces to make them more or less congruent with the gender of the face. Results showed that indeed congruency had a significant positive effect on the sensitivity (F(2.6, 44.4)=16.75, p<0.0001).

Meeting abstract presented at VSS 2012

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×