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Aesthetic preference for art can be predicted from a mixture of low- and high-level visual features
It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Here, we developed and tested a computational framework to investigate how aesthetic values are formed. We show that it is possible to explain human preferences for a visual art piece based on a mixture of low- and high-level features of the image. Subjective value ratings could be predicted not only within but also across individuals, using a regression model with a common set of interpretable features. We also show that the features predicting aesthetic preference can emerge hierarchically within a deep convolutional neural network trained only for object recognition. Our findings suggest that human preferences for art can be explained at least in part as a systematic integration over the underlying visual features of an image.
CrossTalk: Conversations at the Intersection of Science and Art
Anjan Chatterjee is a Professor of Neurology, Psychology, and Architecture and the founding Director of the Penn Center for Neuroaesthetics. His research explores the field of neuroaesthetics: how our brain experiences and responds to art. Lucas Kelly is a renowned visual artist, with work featured across several solo and group exhibitions, most notably in the survey of abstract painting “The Painted World” at PS1 Museum of Modern Art. As the inaugural Artist in Residence for the Penn Center for Neuroaesthetics, Lucas has collaborated with Anjan on a forthcoming exhibition, considering the emotions involved in aesthetic engagement informed by research. This event will feature a moderated conversation between Anjan and Lucas, discussing topics at the intersection of neuroscience and experience of visual art.
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