Aesthetic preference for art can be predicted from a mixture of low- and high-level visual features

Kiyohito Iigaya (), Sanghyun Yi, Iman A. Wahle, Koranis Tanwisuth and John P. O’Doherty
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Kiyohito Iigaya: Division of Humanities and Social Sciences, California Institute of Technology
Sanghyun Yi: Division of Humanities and Social Sciences, California Institute of Technology
Iman A. Wahle: Division of Humanities and Social Sciences, California Institute of Technology
Koranis Tanwisuth: Division of Humanities and Social Sciences, California Institute of Technology
John P. O’Doherty: Division of Humanities and Social Sciences, California Institute of Technology

Nature Human Behaviour, 2021, vol. 5, issue 6, 743-755

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41562-021-01124-6

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