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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.

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Fig. 1: Testing the LFS model that constructs aesthetic value of visual stimuli.
Fig. 2: The LFS model successfully predicts the subjective value of paintings.
Fig. 3: Cluster analysis in feature space suggests distinct groups of individuals who vary in their preference computations across our online sample.
Fig. 4: The LFS model also predicts subjective liking ratings for various kinds of photographs.
Fig. 5: A DCNN can predict subjective values (that is, liking ratings) of art stimuli, and the features that we introduced to our LFS model spontaneously emerge in the hidden layers of the network.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

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.

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Authors and Affiliations



K.I. and J.P.O. conceived and designed the project. K.I., S.Y., I.A.W. and K.T. performed experiments, and K.I., S.Y., I.A.W., K.T. and J.P.O. analysed and discussed the results. K.I., S.Y., I.A.W. and J.P.O. wrote the manuscript.

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Correspondence to Kiyohito Iigaya.

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The authors declare no competing interests.

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Peer review informationNature 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).

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