Second, using a data-driven statistical model for face representation, we build and validate models for representing face trustworthiness and face dominance. First, using a principal components analysis of trait judgments of emotionally neutral faces, we identify two orthogonal dimensions, valence and dominance, that are sufficient to describe face evaluation and show that these dimensions can be approximated by judgments of trustworthiness and dominance. Based on behavioral studies and computer modeling, we develop a 2D model of face evaluation. People automatically evaluate faces on multiple trait dimensions, and these evaluations predict important social outcomes, ranging from electoral success to sentencing decisions.
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February 2023
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