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

Cited by Lee Sonogan | Aesthetic tumblr backgrounds, Vaporwave wallpaper, Aesthetic  desktop wallpaper

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

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.

Publication: Nature Human Behavior (Peer-Reviewed Journal)

Pub Date: 20 May 2021 Doi: (Plenty more sections, figures and references in ths article)

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