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

Iigaya, Kiyohito and Yi, Sanghyun and Wahle, Iman A. and Tanwisuth, Koranis and O'Doherty, John P. (2021) Aesthetic preference for art can be predicted from a mixture of low- and high-level visual features. Nature Human Behaviour, 5 (6). pp. 743-755. ISSN 2397-3374. https://resolver.caltech.edu/CaltechAUTHORS:20210524-113359099

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1038/s41562-021-01124-6DOIArticle
https://rdcu.be/cldDxPublisherFree ReadCube access
ORCID:
AuthorORCID
Iigaya, Kiyohito0000-0002-4748-8432
Yi, Sanghyun0000-0003-1274-6523
Tanwisuth, Koranis0000-0003-3563-6781
O'Doherty, John P.0000-0003-0016-3531
Additional Information:© 2021 Springer Nature Limited. Received 25 February 2020. Accepted 21 April 2021. Published 20 May 2021. 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. 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. 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.
Group:Tianqiao and Chrissy Chen Institute for Neuroscience
Funders:
Funding AgencyGrant Number
NIHR01DA040011
NIHP50MH094258
Japan Society for the Promotion of Science (JSPS)UNSPECIFIED
Swartz FoundationUNSPECIFIED
Suntory FoundationUNSPECIFIED
Caltech Summer Undergraduate Research Fellowship (SURF)UNSPECIFIED
Subject Keywords:Cognitive neuroscience; Computational neuroscience; Cultural and media studies; Psychology
Issue or Number:6
Record Number:CaltechAUTHORS:20210524-113359099
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210524-113359099
Official Citation: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
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109241
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:24 May 2021 19:34
Last Modified:21 Jun 2021 22:13

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