Summary: Using a combination of machine learning and neuroimaging data, the researchers revealed a neural basis for aesthetic appreciation.
Source: CalTech
It has been said that there is no account for taste. But what if taste can actually be accounted for, and the things that do the accounting are the neural networks inside your brain?
In a new article published in Nature Communicationa team of Caltech researchers show how they revealed the neural basis of aesthetic preferences in humans using a combination of machine learning and brain scanning equipment.
The work took place in the lab of John O’Doherty, Fletcher Jones Professor of Decisional Neuroscience at Caltech, and builds on research published by that lab in 2021. In this previous research, scientists trained a computer to predict the taste of the volunteers for art by feeding it gives information on the paintings that the volunteers liked and those that they did not like. With sufficient training, the computer has become adept at correctly guessing whether a person would like a Monet or a Rothko, for example.
This act of liking or disliking a work of art seems so innate and happens so instantly and seamlessly in our brains that few of us have probably taken the time to consider why or how it happens. , but aesthetic preferences have been the subject of philosophical discussion for hundreds of years.
“When you see an image, you immediately decide whether you like it or not, but if you think about it, it’s really complicated because the input is very complex,” says lead author Kiyohito Iigaya, formerly of Caltech and now with Columbia University. .
“It’s actually a very open-ended question, and we don’t really know how the brain manages to do it. So we wondered if we could figure it out using a computer modeling method.
This method involved volunteers evaluating paintings (up to a thousand) over the course of four days while their brains were scanned with a functional magnetic resonance imaging (fMRI) machine.
These brain scans and the volunteers’ ratings of the paintings were fed into a machine learning algorithm, along with the output of a neural network trained to examine the paintings for qualities such as contrast, hue, dynamics and concreteness (whether the painting is abstract or realistic).
Data collected by the team showed that areas of the visual cortex, the part of the brain that processes visual input, are responsible for analyzing these qualities. An area in the front of the brain known as the medial prefrontal cortex (mPFC) is responsible for assigning them subjective value.
Basically, the brain breaks down a work of art into its essential qualities and then decides whether those qualities are pleasurable or not. It’s more or less the same way the brain decides whether or not it likes food, according to another study from the O’Doherty lab. This study found that the brain analyzes a food based on its protein, fat, carbohydrate and vitamin content and then determines whether these qualities are enjoyable.
“What they discovered is that the brain integrates these different nutritional characteristics to produce the overall taste of food,” says Iigaya. “It’s actually an inspiration for our work.”
In their paper, the researchers say their findings suggest that this “value building” system may be widespread throughout the brain and may explain many types of preferences.
“I think it’s amazing that this very simple computational model can explain large variations in our preferences,” says Iigaya.
About this art and neuroscience research news
Author: Press office
Source: CalTech
Contact: Press Office – CalTech
Picture: Image is in public domain
See also
Original research: Free access.
“Neural mechanisms underlying the hierarchical construction of perceived aesthetic value” by Kiyohito Iigaya et al. Nature Communication
Abstract
Neural mechanisms underlying the hierarchical construction of perceived aesthetic value
Little is known about how the brain calculates the perceived aesthetic value of complex stimuli such as visual art.
Here, we used computational methods in combination with functional neuroimaging to prove that the aesthetic value of a visual stimulus is hierarchically computed via weighted integration on low- and high-level stimulus features contained in the cortex. early and late visual, extending into parietal and lateral prefrontal cortex.
Feature representations in the parietal and lateral prefrontal cortex can in turn be used to produce overall aesthetic value in the medial prefrontal cortex.
Such brain-wide calculations are not only consistent with a feature-based mechanism for constructing values, but also resemble calculations performed by a deep convolutional neural network.
Our results thus shed light on the existence of a general neurocomputational mechanism for rapidly and flexibly producing value judgments across a range of novel stimuli and complex situations.