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.
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The data that support the findings of this study are available from the corresponding author upon reasonable request.
The code that support the findings of this study are available from the corresponding author upon reasonable request.
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The authors thank P. Dayan, S. Shimojo, O. Perona, L. Fellows, A. Vaidya, J. Cockburn and L. Cross for discussions and suggestions. The authors also thank S. Iigaya and E. Iigaya for drawing colour field paintings presented in this manuscript. This work was supported by NIDA grant R01DA040011 and the Caltech Conte Center for Social Decision Making (P50MH094258) to J.P.O., the Japan Society for Promotion of Science, the Swartz Foundation and the Suntory Foundation to K.I., and the William H. and Helen Lang SURF Fellowship to I.A.W.. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
The authors declare no competing interests.
Peer review information Nature Human Behaviour thanks Gabriel Kreiman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Iigaya, K., Yi, S., Wahle, I.A. et al. Aesthetic preference for art can be predicted from a mixture of low- and high-level visual features. Nat Hum Behav 5, 743–755 (2021). https://doi.org/10.1038/s41562-021-01124-6
Scientific Reports (2022)
Nature Human Behaviour (2021)