From towering, color-blocked Rothkos, to the soft brushstroke of Monet's landscapes, one's taste in art seems like a deeply personal choice. What moves you is a purely human reaction. A recent study published in the journal Nature Human Behavior has shown it's possible to accurately predict art preferences, using a deep-learning neural network that did not include any previous art training. And while you might think of your own personal art style as boundary-defying and genre-bending, the study found that most participants' art preferences can be grouped into just three categories. If you aren't sure which group you fall into, this new A.I. just might, Kiyohito Iigaya, a postdoctoral scholar at California Institute of Technology and first author on the study, tells Inverse.

As a lay user creates an art piece using an interactive generative art tool, what, if anything, do the choices they make tell us about them and their preferences? These preferences could be in the specific generative art form (e.g., color palettes, density of the piece, thickness or curvatures of any lines in the piece); predicting them could lead to a smarter interactive tool. Or they could be preferences in other walks of life (e.g., music, fashion, food, interior design, paintings) or attributes of the person (e.g., personality type, gender, artistic inclinations); predicting them could lead to improved personalized recommendations for products or experiences. To study this research question, we collect preferences from 311 subjects, both in a specific generative art form and in other walks of life. We analyze the preferences and train machine learning models to predict a subset of preferences from the remaining. We find that preferences in the generative art form we studied cannot predict preferences in other walks of life better than chance (and vice versa). However, preferences within the generative art form are reliably predictive of each other.