Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain

View ORCID ProfileKiyohito Iigaya, Sanghyun Yi, Iman A. Wahle, Koranis Tanwisuth, John P. O’Doherty
doi: https://doi.org/10.1101/2020.02.09.940353
Kiyohito Iigaya
1Division of Humanities and Social Sciences, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Kiyohito Iigaya
  • For correspondence: kiigaya@caltech.edu
Sanghyun Yi
1Division of Humanities and Social Sciences, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Iman A. Wahle
1Division of Humanities and Social Sciences, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Koranis Tanwisuth
1Division of Humanities and Social Sciences, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
John P. O’Doherty
1Division of Humanities and Social Sciences, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesthetic value. We show that it is possible to explain human preferences for a piece of art based on an analysis of features present in the image. This was achieved by analyzing the visual properties of drawings and photographs by multiple means, ranging from image statistics extracted by computer vision tools, subjective human ratings about attributes, to a deep convolutional neural network. Crucially, it is possible to predict subjective value ratings not only within but also across individuals, speaking to the possibility that much of the variance in human visual preference is shared across individuals. Neuroimaging data revealed that preference computations occur in the brain by means of a graded hierarchical representation of lower and higher level features in the visual system. These features are in turn integrated to compute an overall subjective preference in the parietal and prefrontal cortex. Our findings suggest that rather than being idiosyncratic, human preferences for art can be explained at least in part as a product of a systematic neural integration over underlying visual features of an image. This work not only advances our understanding of the brain-wide computations underlying value construction but also brings new mechanistic insights to the study of visual aesthetics and art appreciation.

Footnotes

  • ↵† jdoherty{at}caltech.edu

  • 1 We thank Avi Vaidya and Lesley Fellows for this suggestion.

  • 2 https://github.com/hiroyuki-kasai/SparseGDLibrary

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted February 10, 2020.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain
Kiyohito Iigaya, Sanghyun Yi, Iman A. Wahle, Koranis Tanwisuth, John P. O’Doherty
bioRxiv 2020.02.09.940353; doi: https://doi.org/10.1101/2020.02.09.940353
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain
Kiyohito Iigaya, Sanghyun Yi, Iman A. Wahle, Koranis Tanwisuth, John P. O’Doherty
bioRxiv 2020.02.09.940353; doi: https://doi.org/10.1101/2020.02.09.940353

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (3495)
  • Biochemistry (7341)
  • Bioengineering (5314)
  • Bioinformatics (20245)
  • Biophysics (9998)
  • Cancer Biology (7730)
  • Cell Biology (11289)
  • Clinical Trials (138)
  • Developmental Biology (6429)
  • Ecology (9937)
  • Epidemiology (2065)
  • Evolutionary Biology (13311)
  • Genetics (9357)
  • Genomics (12575)
  • Immunology (7695)
  • Microbiology (18997)
  • Molecular Biology (7432)
  • Neuroscience (40965)
  • Paleontology (300)
  • Pathology (1227)
  • Pharmacology and Toxicology (2133)
  • Physiology (3154)
  • Plant Biology (6855)
  • Scientific Communication and Education (1272)
  • Synthetic Biology (1894)
  • Systems Biology (5308)
  • Zoology (1087)