Beauty-in-averageness and its contextual modulations: A Bayesian statistical account

Abstract

Understanding how humans perceive the likability of high-dimensional "objects" such as faces is an important problem in both cognitive science and AI/ML. Existing models of human preferences generally assume these preferences to be fixed. However, human assessment of facial attractiveness have been found to be highly context-dependent. Specifically, the classical Beauty-in-Averageness (BiA) effect, whereby a face blended from two original faces is judged to be more attractive than the originals, is significantly diminished or reversed when the original faces are recognizable, or when the morph is mixed-race/mixed gender and the attractiveness judgment is preceded by a race/gender categorization. This effect, dubbed Uglinessin-Averageness (UiA), has previously been attributed to a disfluency account, which is both qualitative and clumsy in explaining BiA. We hypothesize, instead, that these contextual influences on face processing result from the dependence of attractiveness perception on an element of statistical typicality, and from an attentional mechanism that restricts face representation to a task-relevant subset of features, thus redefining typicality within that subspace. Furthermore, we propose a principled explanation of why statistically atypical objects are less likable: they incur greater encoding or processing cost associated with a greater prediction error, when the brain uses predictive coding to compare the actual stimulus properties with those expected from its associated categorical prototype. We use simulations to show our model provides a parsimonious, statistically grounded, and quantitative account of contextual dependence of attractiveness. We also validate our model using experimental data from a gender categorization task. Finally, we make model predictions for a proposed experiment that can disambiguate the previous disfluency account and our statistical typicality theory.Preprint. Work in progress.

The architecture of prototype preferences: Typicality, fluency, and valence.

“…The present pattern of findings cannot be explained by models proposing a general dislike for “deviants” (Symons, 1979), or a general preference for certainty (Hsu & Preuschoff, 2015), familiarity (Titchener, 1915) or statistical typicality (Ryali & Yu, 2018). They also challenge any strong claims from the hypothesis about hedonic marking of fluency (Winkielman et al, 2003).…”

“…Related arguments emphasize that prototypes are statistically typical and thus more efficiently coded (Dotsch, Hassin, & Todorov, 2016; Vogel, Carr, Davis, & Winkielman, 2018). Thus, prototypes could be preferred because they are simpler and less energy-demanding (Ryali & Yu, 2018).…”

Comparing activation typicality and sparsity in a deep CNN to predict facial beauty

“…Previous research suggests that the categorization of faces according to gender and ethnicity can impact perceived attractiveness (Potter & Corneille, 2008;Ryali & Yu, 2018). We therefore investigate if a more specialized reference Probability Density Function (PDF) -built using a reference dataset tailored more speci cally to a particular gender, ethnic group, or a combination of the two -could enhance the ability of statistical typicality to explain variation in attractiveness.…”