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

Author

Listed:
  • 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
    Department of Psychology, University of California, Berkeley)

  • John P. O’Doherty

    (Division of Humanities and Social Sciences, California Institute of Technology)

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.

Suggested Citation

  • Kiyohito Iigaya & Sanghyun Yi & Iman A. Wahle & Koranis Tanwisuth & John P. O’Doherty, 2021. "Aesthetic preference for art can be predicted from a mixture of low- and high-level visual features," Nature Human Behaviour, Nature, vol. 5(6), pages 743-755, June.
    • Handle: RePEc:nat:nathum:v:5:y:2021:i:6:d:10.1038_s41562-021-01124-6
    DOI: 10.1038/s41562-021-01124-6
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    Cited by:

    1. Kiyohito Iigaya & Sanghyun Yi & Iman A. Wahle & Sandy Tanwisuth & Logan Cross & John P. O’Doherty, 2023. "Neural mechanisms underlying the hierarchical construction of perceived aesthetic value," Nature Communications, Nature, vol. 14(1), pages 1-19, December.

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