Humans readily form social impressions, such as attractiveness and trustworthiness, from a stranger's facial features. Understanding the provenance of these impressions has clear scientific importance and societal implications. Motivated by the efficient coding hypothesis of brain representation, as well as Claude Shannon's theoretical result that maximally efficient representational systems assign shorter codes to statistically more typical data (quantified as log likelihood), we suggest that social "liking" of faces increases with statistical typicality. Combining human behavioral data and computational modeling, we show that perceived attractiveness, trustworthiness, dominance, and valence of a face image linearly increase with its statistical typicality (log likelihood). We also show that statistical typicality can at least partially explain the role of symmetry in attractiveness perception. Additionally, by assuming that the brain focuses on a task-relevant subset of facial features and assessing log likelihood of a face using those features, our model can explain the "ugliness-in-averageness" effect found in social psychology, whereby otherwise attractive, intercategory faces diminish in attractiveness during a categorization task.

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.