Aesthetic preference for art can be predicted from a mixture of low- and high-level visual features
Schedule
Friday, November 12, 2021
22:00 UTC
John O'Doherty
Prof
California Institute of Technology
Host: Sydney Systems Neuroscience and Complexity SNAC
Recording
Open recording
Event Information
Recording
Available
Host
Sydney Systems Neuroscience and Complexity SNAC
Duration
60 minutes
Abstract
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.
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