Revealing Hidden Gender Biases in Competence Impressions from Faces

Contributors:
  1. Elinor A. Buck
  2. Alexander Todorov

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Description: Competence impressions from faces affect important decisions, such as hiring and voting. Here, using data-driven computational models, we identify 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 (Experiments 1b); moreover, those 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 reveal gender biases in how we form important impressions of others.

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  • Revealing Hidden Gender Biases in Competence Impressions from Faces

    Oh, Buck & Todorov
    Competence impressions from faces affect important decisions, such as hiring and voting. Here, using data-driven computational models, we identify the...

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