Clarifying status of DNNs as models of human vision

Bowers, Jeffrey S.; Malhotra, Gaurav; Dujmovic, Marin; Montero, Milton L.; Tsvetkov, Christian; Biscione, Valerio; Puebla, Guillermo; Adolfi, Federico; Hummel, John E.; Heaton, Rachel F.; Evans, Benjamin D.; Mitchell, Jeffrey; Blything, Ryan

Abstract

On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that deep neural networks (DNNs) will play an important role in modelling human vision going forward. But there are also disagreements about what models are for, how DNN-human correspondences should be evaluated, the value of alternative modelling approaches, and impact of marketing hype in the literature. In our view, these latter issues are contributing to many unjustified claims regarding DNN-human correspondences in vision and other domains of cognition. We explore all these issues in this response.

Más información

Título según WOS: ID WOS:001114546900001 Not found in local WOS DB
Título de la Revista: BEHAVIORAL AND BRAIN SCIENCES
Volumen: 46
Editorial: CAMBRIDGE UNIV PRESS
Fecha de publicación: 2023
DOI:

10.1017/S0140525X23002777

Notas: ISI