A new study showing that computers can predict what paintings people will like offers insight into how our brains make aesthetic judgments.

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.

«I used to think the evaluation of art was personal and subjective, so I was surprised by this result,» says lead author Kiyohito Iigaya, a postdoctoral scholar who works in the laboratory of Caltech professor of psychology John O’Doherty.

The findings not only demonstrated that computers can make these predictions but also led to a new understanding about how people judge art.

«The main point is that we are gaining an insight into the mechanism that people use to make aesthetic judgments,» says O’Doherty. «That is, that people appear to use elementary image features and combine over them. That’s a first step to understanding how the process works.»

In the study, the team programmed the computer to break a painting’s visual attributes down into what they called low-level features — traits like contrast, saturation, and hue — as well as high-level features, which require human judgment and include traits such as whether the painting is dynamic or still.

Story Source: Materials provided by California Institute of Technology. Original written by Whitney Clavin. Note: Content may be edited for style and length.