Computational insights into human perceptual expertise for familiar and unfamiliar face recognition.

ABSTRACT: Humans are generally thought to be experts at face recognition, and yet identity perception for unfamiliar faces is surprisingly poor compared to that for familiar faces. Prior theoretical work has argued that unfamiliar face identity perception suffers because the majority of identity-invariant visual variability is idiosyncratic to each identity, and thus, each face identity must be learned essentially from scratch. Using a high-performing deep convolutional neural network, we evaluate this claim by examining the effects of visual experience in untrained, object-expert and face-expert networks. We found that only face training led to substantial generalization in an identity verification task of novel unfamiliar identities. Moreover, generalization increased with the number of previously

SUBMITTER: Blauch NM 

PROVIDER: S-EPMC9944378 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature