Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain

Abstract

It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesthetic value. We show that it is possible to explain human preferences for a piece of art based on an analysis of features present in the image. This was achieved by analyzing the visual properties of drawings and photographs by multiple means, ranging from image statistics extracted by computer vision tools, subjective human ratings about attributes, to a deep convolutional neural network. Crucially, it is possible to predict subjective value ratings not only within but also across individuals, speaking to the possibility that much of the variance in human visual preference is shared across individuals. Neuroimaging data revealed that preference computations occur in the brain by means of a graded hierarchical representation of lower and higher level features in the visual system. These features are in turn integrated to compute an overall subjective preference in the parietal and prefrontal cortex. Our findings suggest that rather than being idiosyncratic, human preferences for art can be explained at least in part as a product of a systematic neural integration over underlying visual features of an image. This work not only advances our understanding of the brain-wide computations underlying value construction but also brings new mechanistic insights to the study of visual aesthetics and art appreciation. Abstract art Color fields Impressionism Cubism Example stimuli M-turk (short task): 1359 participants On-site (long task): 7 participants Predictive accuracy (r) 0 0.2 0.4 0.6 M t u r k

A Theoretical Framework for How We Learn Aesthetic Values

“…While we agree, we suggest that linearity is a suitable starting point. Recent work comparing a linear rule versus a deep neural network to predict subjective aesthetic value found that both fared comparably (Iigaya et al, 2020). We argue that this also applies for our theoretical framework for aesthetic learning.…”

“…Instead, we chose to limit the complexity of our theoretical framework at this first iteration to serve as a basic building block on which to incorporate the aforementioned factors. However, even a model based on low-level features can still be highly informative on aesthetic preferences of individuals, as recently demonstrated by Iigaya et al (2020). Additionally, a reinforcement learning circuit is easily amenable to additional factors, for example Leong et al incorporate attention directly into the reinforcement-learning circuitry computing subjective value, as we did with motivation (Leong et al, 2017).…”

art.pics Database: An Open Access Database for Art Stimuli for Experimental Research

“…Visual and psychological processes related to art perception and processing have been proposed previously (Ramachandran and Hirstein, 1999;Leder et al, 2004), and neuroscientific studies assessed and localized brain activity in relation to esthetic value (Cela-Conde et al, 2004;Kawabata and Zeki, 2004;Vartanian and Skov, 2014;Lebreton et al, 2015). While esthetic value is considerably subjective, a recent study shows that (visual) esthetic value can be predicted by brain activity based on the integration and different weighing of (visual) features of the presented art image (Iigaya et al, 2020), including low-level (hue, saturation, lightness, color, brightness, blurring effects, edge detecting) (Li and Chen, 2009) and high-level features (color temperature, depth, abstract, emotion, complexity) (Chatterjee et al, 2010). Thus, presumably, primary and secondary rewards are not "randomly" processed in the brain but have -at least to a certain extent -a common ground in human brain computations of stimulus features, which have most likely evolved to serve adaptive behaviors in different environments (Skov and Nadal, 2018;Skov and Skov, 2019).…”

“…Others showed that fat and carbohydrate content elicit a supra-additive response for food valuation in the ventral striatum independent of liking (DiFeliceantonio et al, 2018), further highlighting that the brain's reward evaluation for food involve nutrient sensors in the gut (De Araujo et al, 2020). Considering art evaluation, a recent preprint suggests that feature integration of artistic stimuli might be ordered in an hierarchical way from visual processing up to the integration from low-and high-level image features in the brain, in particular in higher-order areas such as parietal and prefrontal cortex (Iigaya et al, 2020). While it might seem counterintuitive to want art similar to wanting food, it has been argued that art objects, such as prints of art paintings or photographs, are often object of desire, not only of art collectors (Berridge and Kringelbach, 2008).…”

“…However, knowledge about (secondary) reward-related neurobiology is still fragmentary, especially with regard to art and related value representations. The encoding of art viewing and experiencing seems to be multi-fold and research on its neural correlates has only begun to discover specific brain signaling (Chatterjee and Vartanian, 2016;Iigaya et al, 2020). Viewing artworks, i.e., paintings, elicits for example activation of the default mode network (DMN) and in subcortical areas like the striatum in relation to the ratings of the painting (Vessel et al, 2012(Vessel et al, , 2019.…”