Vision-to-value transformations in artificial neural networks and human brain

Abstract

Humans and now computers can derive subjective valuations from sensory events although such transformation process is essentially unknown. In this study, we elucidated unknown neural mechanisms by comparing convolutional neural networks (CNNs) to their corresponding representations in humans. Specifically, we optimized CNNs to predict aesthetic valuations of paintings and examined the relationship between the CNN representations and brain activity via multivoxel pattern analysis. Primary visual cortex and higher association cortex activities were similar to computations in shallow CNN layers and deeper layers, respectively. The vision-to-value transformation is hence proved to be a hierarchical process which is consistent with the principal gradient that connects unimodal to transmodal brain regions (i.e. default mode network). The activity of the frontal and parietal cortices was approximated by goal-driven CNN. Consequently, representations of the hidden layers of CNNs can be understood and visualized by their correspondence with brain activity-facilitating parallels between artificial intelligence and neuroscience.