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

Abstract

Processing fluency, which describes the subjective sensation of ease with which information is processed by the sensory systems and the brain, has become one of the most popular explanations of aesthetic appreciation and beauty. Two metrics have recently been proposed to model fluency: the sparsity of neuronal activation, characterizing the extent to which neurons in the brain are unequally activated by a stimulus, and the statistical typicality of activations, describing how well the encoding of a stimulus matches a reference representation of stimuli of the category to which it belongs. Using Convolutional Neural Networks (CNNs) as a model for the human visual system, this study compares the ability of these metrics to explain variation in facial attractiveness. Our findings show that the sparsity of neuronal activations is a more robust predictor of facial beauty than statistical typicality. Refining the reference representation to a single ethnicity or gender does not increase the explanatory power of statistical typicality. However, statistical typicality and sparsity predict facial beauty based on different layers of the CNNs, suggesting that they describe different neural mechanisms underlying fluency.