Abstract
What makes us like a particular scene or object and dislike another? A variety of visual properties, the observers’ experience, familiarity, processing fluency, and self-relevance have been suggested to underlie aesthetic liking. Here we investigate whether the brain’s goal to reduce energy costs (Olshausen and Field 1997; Friston, 2010) explains the construction of aesthetic appreciation. We propose a simple, straightforward approach to explaining neural responses to visual stimuli with different levels of aesthetic preference: the total metabolic cost of firing of neurons within relevant regions of interest. We test this hypothesis in an in-silico model of the visual system (VGG19) as well as human observers and find strong evidence in both. Specifically, we compare the metabolic cost incurred by 4914 images of objects and scenes from the BOLD5000 dataset for a VGG19 network pretrained for object and scene categorization with randomly initialized versions of VGG19. We find a strong inverse relationship between aesthetic preferences for the images and their metabolic cost, but only in the network trained for categorization. We then test the same hypothesis in the human visual system by comparing aesthetic liking of visual stimuli to the metabolic activity measured with functional magnetic resonance imaging. Crucially, we find strong evidence for the hypothesized inverse relationship between metabolic expense and aesthetic liking in both early visual brain regions (V1 and V4) and high-level regions (FFA, OPA, PPA). These findings represent the first direct evidence for a physiological basis of visual aesthetics at the level of energy consumption by the visual system. Aesthetic pleasure may function as an adaptive homeostatic signal to help conserve energy resources for survival. Our metabolic account for aesthetic liking unifies empirical evidence for visual discomfort with theories of processing fluency, image complexity, expertise, and prototypicality for aesthetic liking in a simple, physiologically plausible framework.