Social perception inferences of computer-generated faces: an Asian Indian and United States cultural comparison.

Results from research with computer-generated faces have demonstrated that participants are able to make different trait inferences to different generated faces. However, only a few studies using computer-generated faces with cross-cultural samples have been done. This study compared the facial trait inference results from India and the United States, using three validated neutral expression computer-generated faces from the University of Chicago Perception and Judgment Lab database as facial stimuli. The three faces varied in perceived threat. Participants were asked about the attractiveness, pleasing-ness (to look at), honesty, and potential threat in each of the three faces. Results indicated that participants from both cultural samples made the same inferences to the three faces; participants rated the attractiveness, pleasing-ness, and honesty highest in the low threat face and lowest in the high threat face. Indian participants perceive the high threat face to be less threatening than the United States participants. Participants were also asked about the emotional expression on each of the faces, even though the faces were presumably neutral. United States participants were significantly more likely to indicate that the faces in all three threat conditions were emotionally neutral, compared to Indian participants, reflecting a cultural In-group bias, in which members of a culture are more accurately able to identify expressions on faces from their own culture.