Integrative processing in artificial and biological vision predicts the perceived beauty of natural images

Abstract

AbstractPrevious research indicates that the beauty of natural images is already determined during perceptual analysis. However, it is still largely unclear which perceptual computations give rise to the perception of beauty. Theories of processing fluency suggest that the ease of processing for an image determines its perceived beauty. Here, we tested whether perceived beauty is related to the amount of spatial integration across an image, a perceptual computation that reduces processing demands by aggregating image elements into more efficient representations of the whole. We hypothesized that higher degrees of integration reduce processing demands in the visual system and thereby predispose the perception of beauty. We quantified integrative processing in an artificial deep neural network model of vision: We compared activations between parts of the image and the whole image, where the degree of integration was determined by the amount of deviation between activations for the whole image and its constituent parts. This quantification of integration predicted the beauty ratings for natural images across four studies, which featured different stimuli and task demands. In a complementary fMRI study, we show that integrative processing in human visual cortex predicts perceived beauty in a similar way as in artificial neural networks. Together, our results establish integration as a computational principle that facilitates perceptual analysis and thereby mediates the perception of beauty.