Proceedings of the Annual Meeting of the Cognitive Science Society

Humans readily form social impressions of faces at a glance,whether assessing trustworthiness, attractiveness, or domi-nance. However, little is understood about how such compu-tations are carried out neurally. Here, we leverage a computa-tional model of human face perception to quantify and charac-terize the extent to which macaque monkey face patch neuronsencode information relevant for social trait perception. Specif-ically, we use a social trait prediction model to estimate thesocial trait ratings for face stimuli viewed by monkeys duringa neural recording experiment. We find that, while the monkeyface patch neurons are linearly tuned to facial features differ-ent from those used by humans to make social judgments, thesubspace spanned by the face patch neurons and the subspacespanned by the facial features supporting human social percep-tion are highly overlapping. This result implies that the infor-mation present in the monkey face patch neurons are largelysufficient, after linear decoding, to support human social per-ception, thus shedding light on the biological origin of humansocial processing of faces.