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AESTHETICS

Beauty is in the eye of the machine

Nature Human Behaviour volume 5, pages 675–676 (2021)Cite this article

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Ansel Adams said, “There are no rules for good photographs, there are only good photographs.” Is it possible to predict our fickle and subjective appraisal of ‘aesthetically pleasing’ visual art? Iigaya et al. used an artificial intelligence approach to show how human aesthetic preference can be partially explained as an integration of hierarchical constituent image features.

Appraisal of aesthetic value by humans is fickle and subjective. However, empirical investigations have long shown that there exists some level of universality in art appreciation across cultures and history1. Such correlations lead to the question of whether there are any rules governing our preferences for visual arts. Previous studies have hinted at the use of feature integration frameworks to predict aesthetic value preferences2. However, previous work has focused on the effect of either one specific visual feature or a pool of complex features, without a clear delineation of how these features weigh towards generating value judgements. The high visual complexity of each piece of art and the large heterogeneity among different pieces of art make the selection of relevant features for value judgements challenging.

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Fig. 1: “Edmond de Belamy, from La Famille de Belamy”.

References

  1. Kant, I. & Bernard, J. H. Kant’s Critique of Judgement. 2d ed. (Macmillan, 1931).

  2. Kong, S., Shen, X., Lin, Z., Mech, R. & Fowlkes, C. Photo aesthetics ranking network with attributes and content adaptation. in European Conference on Computer Vision (eds Leibe B., Matas J., Sebe N. & Welling M.) 662–679 (2016).

  3. Iigaya, K., Yi, S., Wahle, I., Tanwisuth, K. & O’Doherty, J. Aesthetic preference for art can be predicted from a mixture of low- and high-level visual features Nat. Hum. Behav. https://doi.org/10.1038/s41562-021-01124-6 (2021).

  4. Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Preprint at arXiv https://arxiv.org/abs/1409.1556 (2015).

  5. Kreiman, G. Biological and Computer Vision (Cambridge Univ. Press, 2021).

  6. Russakovsky, O., Li, L.-J. & Li, F.-F. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2121–2131 (Computer Vision Foundation, 2014).

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Authors and Affiliations

  1. Children’s Hospital, Harvard Medical School, Boston, MA, USA

    Mengmi Zhang & Gabriel Kreiman

  2. Center for Brains, Minds and Machines, Cambridge, MA, USA

    Mengmi Zhang & Gabriel Kreiman

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  1. Mengmi Zhang
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  2. Gabriel Kreiman
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Correspondence to Gabriel Kreiman.

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Zhang, M., Kreiman, G. Beauty is in the eye of the machine. Nat Hum Behav 5, 675–676 (2021). https://doi.org/10.1038/s41562-021-01125-5

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  • DOI: https://doi.org/10.1038/s41562-021-01125-5

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