It has been said that there is no accounting for taste. But what if taste can be accounted for, and what if the things doing the accounting are the neural networks inside your brain? Caltech researchers show how they have revealed the neural basis for aesthetic preferences in humans using a combination of machine learning and brain-scanning equipment. Scientists trained a computer to predict volunteers’ taste in art by feeding it data about which paintings the volunteers liked and which they disliked. With enough training, the computer became adept at correctly guessing if a person would like a Monet or a Rothko, for example. That act of liking or disliking a piece of art seems so innate and occurs so instantly and seamlessly in our brains that few of us have probably taken the time to wonder why or how it happens, but aesthetic preferences have been the subject of philosophical discussions for hundreds of years.
That method involved having volunteers rate paintings (as many as a thousand) over the course of four days while their brains were scanned with a functional magnetic resonance imaging (fMRI) machine. An area in the front of the brain known as the medial prefrontal cortex (mPFC) is responsible for assigning a subjective value to them. Those brain scans and the volunteers’ ratings of the paintings were fed into a machine-learning algorithm, along with the output of a neural net trained to examine the paintings for qualities like contrast, hue, dynamics, and concreteness (whether the painting is abstract or realistic). The data the team collected showed that areas within the visual cortex, the part of the brain that processes visual input, are responsible for analyzing those qualities. An area in the front of the brain known as the medial prefrontal cortex (mPFC) is responsible for assigning a subjective value to them.
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