Spontaneous content of impressions of naturalistic face photographs

Abstract

Across two studies (N = 4,526), we characterize a taxonomy of spontaneous face impressions by applying artificial intelligence text analyses to thousands of free-response descriptions of computer-generated faces. The taxonomy codes almost 100% of the impressions into Appearance (including Beauty), Sociability, Morality, Ability, Assertiveness, Emotion, Social Group, socioeconomic Status, Uniqueness, Family, Health, Occupation, Geographic origin, and political-religious Beliefs content. Results suggest that dimensions from low-dimensional models (e.g., Communion, Agency facets) are highly prevalent, but that alternative dimensions such as Uniqueness and Health are also prevalent. Most dimensions show high (positive) directions, and their correlational structure supports the clustering of low-dimensional models as separate from the expanded taxonomy dimensions. Finally, the taxonomy improves predictions of general evaluations of faces (how positive/negative the face is evaluated overall) and decision making in hypothetical scenarios (e.g., how much to prioritize a target for health care access or antidiscrimination protections).