Gender Biases in Impressions from Faces: Empirical Studies and Computational Models

Contributors:
  1. Alexander Todorov

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Description: Trustworthiness and dominance impressions summarize trait judgments from faces. Judgments on these key traits are negatively correlated to each other in impressions of female faces, implying less differentiated impressions of female faces. Here we test whether this is true across many trait judgments and whether less differentiated impressions of female faces originate in different facial information used for male and female impressions or different evaluation of the same information. Using multidimensional rating datasets and data-driven modeling, we show that (1) impressions of women are less differentiated and more valence-laden than impressions of men, and find that (2) these impressions are based on similar visual information across face genders. Female face impressions were more highly intercorrelated and were better explained by valence (Study 1). These intercorrelations were higher when raters more strongly endorsed gender stereotypes. Despite the gender difference, male and female impression models – derived from separate trustworthiness and dominance ratings of male and female faces – were similar to each other (Study 2). Further, both male and female models could manipulate impressions of faces of both genders (Study 3). The results highlight the high-level, evaluative effect of face gender in impression formation – women are judged negatively to the extent their looks do not conform to expectations, not because people use different facial information across genders, but because people evaluate the information differently across genders.

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