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New study offers insight into how people make aesthetic judgments by California Institute of Technology.
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| Impressionist painting by Worthington Whittredge. Photo: Smithsonian American Art Museum, L.E. Katzenbach Fund |
Or do you prefer
the bold colors and abstract shapes of a Rothko? Individual art tastes
have a certain mystique to them, but now a new Caltech study shows that a
simple computer program can accurately predict which paintings a person
will like.
The new study, appearing in the journal Nature Human
Behaviour, utilized Amazon's crowdsourcing platform Mechanical Turk to
enlist more than 1,500 volunteers to rate paintings in the genres of
impressionism, cubism, abstract, and color field. The volunteers'
answers were fed into a computer program and then, after this training
period, the computer could predict the volunteers' art preferences much
better than would happen by chance...
In this case, the deep-learning approach did not include any of the
selected low- or high-level visual features used in the first part of
the study, so the computer had to "decide" what features to analyze on
its own.
"In deep-neural-network models, we do not actually know
exactly how the network is solving a particular task because the models
learn by themselves much like real brains do," explains Iigaya. "It can
be very mysterious, but when we looked inside the neural network, we
were able to tell that it was constructing the same feature categories
we selected ourselves." These results hint at the possibility that
features used for determining aesthetic preference might emerge
naturally in a brain-like architecture.
Additional resources
Reference: Iigaya K, Yi S, Wahle IA, Tanwisuth K, O’Doherty JP.
Aesthetic preference for art can be predicted from a mixture of low- and
high-level visual features. Nat Hum Behav. 2021;5(6):743-755. doi: 10.1038/s41562-021-01124-6
Source: Technology Networks
