A new Caltech study, appearing in the journal Nature Human Behaviour, shows that a simple computer program can accurately predict which style of paintings a person will like. Using 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. The findings not only demonstrated that computers can make these predictions but also led to a new understanding about how people judge art.
The lead author of the paper was Kiyohito Iigaya, a postdoctoral scholar who works in the laboratory of Caltech professor of psychology John O’Doherty, an affiliated member of the Tianqiao and Chrissy Chen Institute for Neuroscience at Caltech.
A new Caltech study, appearing in the journal Nature Human Behaviour, shows that a simple computer program can accurately predict which style of paintings a person will like. Using 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. The findings not only demonstrated that computers can make these predictions but also led to a new understanding about how people judge art.
The lead author of the paper was Kiyohito Iigaya, a postdoctoral scholar who works in the laboratory of Caltech professor of psychology John O’Doherty, an affiliated member of the Tianqiao and Chrissy Chen Institute for Neuroscience at Caltech.
