RT Journal Article
SR Electronic
T1 Vision-to-value transformations in artificial network and human brains
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.03.18.435929
DO 10.1101/2021.03.18.435929
A1 Trung Quang Pham
A1 Takaaki Yoshimoto
A1 Haruki Niwa
A1 Haruka K Takahashi
A1 Ryutaro Uchiyama
A1 Teppei Matsui
A1 Adam K Anderson
A1 Norihiro Sadato
A1 Junichi Chikazoe
YR 2021
UL http://biorxiv.org/content/early/2021/03/20/2021.03.18.435929.abstract
AB Humans and now computers can derive subjective valuations from sensory events although such transformation process is largely a black box. In this study, we elucidate unknown neural mechanisms by comparing representations of humans and convolutional neural networks (CNNs). We optimized CNNs to predict aesthetic valuations of paintings and examined the relationship between the CNN representations and brain activity by using multivoxel pattern analysis. The activity in the primary visual cortex was similar to computations in shallow CNN layers, while that in the higher association cortex was similar to computations in deeper layers, being consistent with the principal gradient that connects unimodal to transmodal brain regions. As a result, representations of the hidden layers of CNNs can be understood and visualized by the correspondence with brain activity. These relations can provide parallels between artificial intelligence and neuroscience.Competing Interest StatementThe authors have declared no competing interest.