Modeling naturalistic face processing in humans with deep convolutional neural networks.

ABSTRACT: Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The ways in which the internal face representations in DCNNs relate to human cognitive representations and brain activity are not well understood. Nearly all previous studies focused on static face image processing with rapid display times and ignored the processing of naturalistic, dynamic information. To address this gap, we developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces). We used this naturalistic dataset to compare representational geometries estimated from DCNNs, behavioral responses, and brain responses. We found that DCNN representational geometries were consistent

SUBMITTER: Jiahui G 

PROVIDER: S-EPMC10614847 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature