Aesthetic preference for art can be predicted from a mixture of low- and high-level visual features
John O'Doherty · California Institute of Technology
Fri, Nov 12, 2021 · 22:00 UTC
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 p