From likely to likable: The role of statistical typicality in human social assessment of faces.

Abstract:

: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.

authors

Ryali CK,Goffin S,Winkielman P,Yu AJ

doi

10.1073/pnas.1912343117

subject

Has Abstract

pub_date

2020-11-24 00:00:00

pages

29371-29380

issue

47

eissn

0027-8424

issn

1091-6490

pii

1912343117

journal_volume

117

pub_type

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