Revealing Hidden Gender Biases in Competence Impressions of Faces.

DongWon Oh, Elinor A Buck, Alexander Todorov
Author Information
  1. DongWon Oh: Department of Psychology, Princeton University. ORCID
  2. Elinor A Buck: Department of Psychology, Princeton University.
  3. Alexander Todorov: Department of Psychology, Princeton University.

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.

Keywords

MeSH Term

Adult
Beauty
Facial Recognition
Female
Femininity
Humans
Male
Masculinity
Middle Aged
Sexism
Social Perception
Stereotyping
Young Adult