Revealing Hidden Gender Biases in Competence Impressions of Faces

Dong Won Oh, Elinor A. Buck, Alexander Todorov

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Competence impressions from faces affect important decisions, such as hiring and voting. Here, using data-driven computational models, we identified the components of the competence stereotype. Faces manipulated by a competence model varied in attractiveness (Experiment 1a). However, faces could be manipulated on perceived competence controlling for attractiveness (Experiment 1b); moreover, faces perceived as more competent but not attractive were also perceived as more confident and masculine, suggesting a bias to perceive male faces as more competent than female faces (Experiment 2). Correspondingly, faces manipulated to appear competent but not attractive were more likely to be classified as male (Experiment 3). When masculinity cues that induced competence impressions were applied to real-life images, these cues were more effective on male faces (Experiment 4). These findings suggest that the main components of competence impressions are attractiveness, confidence, and masculinity, and they reveal gender biases in how we form important impressions of other people.

Original languageEnglish (US)
Pages (from-to)65-79
Number of pages15
JournalPsychological Science
Volume30
Issue number1
DOIs
StatePublished - Jan 1 2019

All Science Journal Classification (ASJC) codes

  • Psychology(all)

Keywords

  • face perception
  • facial features
  • gender
  • open data
  • open materials
  • stereotypes

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