Rxivist logo

Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 73,582 bioRxiv papers from 320,155 authors.

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

By Kiyohito Iigaya, Sanghyun Yi, Iman A Wahle, Koranis Tanwisuth, John P. O’Doherty

Posted 10 Feb 2020
bioRxiv DOI: 10.1101/2020.02.09.940353

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.

Download data

  • Downloaded 554 times
  • Download rankings, all-time:
    • Site-wide: 21,031 out of 73,584
    • In neuroscience: 3,555 out of 13,227
  • Year to date:
    • Site-wide: 313 out of 73,584
  • Since beginning of last month:
    • Site-wide: 313 out of 73,584

Altmetric data

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News