Emerged human-like facial expression representation in a deep convolutional neural network.

ABSTRACT: Recent studies found that the deep convolutional neural networks (DCNNs) trained to recognize facial identities spontaneously learned features that support facial expression recognition, and vice versa. Here, we showed that the self-emerged expression-selective units in a VGG-Face trained for facial identification were tuned to distinct basic expressions and, importantly, exhibited hallmarks of human expression recognition (i.e., facial expression confusion and categorical perception). We then investigated whether the emergence of expression-selective units is attributed to either face-specific experience or domain-general processing by conducting the same analysis on a VGG-16 trained for object classification and an untrained VGG-Face without any visual experience, both having the identic

SUBMITTER: Zhou L 

PROVIDER: S-EPMC8942361 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature