Cited by Lee Sonogan
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: https://doi.org/10.1038/s41562-021-01124-6
https://www.nature.com/articles/s41562-021-01124-6#citeas (Plenty more sections, figures and references in ths article)