For human-like models, train on human-like tasks.


Journal

The Behavioral and brain sciences
ISSN: 1469-1825
Titre abrégé: Behav Brain Sci
Pays: England
ID NLM: 7808666

Informations de publication

Date de publication:
06 Dec 2023
Historique:
medline: 7 12 2023
pubmed: 6 12 2023
entrez: 6 12 2023
Statut: epublish

Résumé

Bowers et al. express skepticism about deep neural networks (DNNs) as models of human vision due to DNNs' failures to account for results from psychological research. We argue that to fairly assess DNNs, we must first train them on more human-like tasks which we hypothesize will induce more human-like behaviors and representations.

Identifiants

pubmed: 38054325
doi: 10.1017/S0140525X23001516
pii: S0140525X23001516
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e394

Auteurs

Katherine Hermann (K)

Google DeepMind, Mountain View, CA, USA hermannk@google.com.

Aran Nayebi (A)

McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, USA aran.nayebi@gmail.com https://anayebi.github.io/.

Sjoerd van Steenkiste (S)

Google Research, Mountain View, CA, USA sjoerdvansteenkiste@gmail.com https://www.sjoerdvansteenkiste.com/.

Matt Jones (M)

Google Research, Mountain View, CA, USA sjoerdvansteenkiste@gmail.com https://www.sjoerdvansteenkiste.com/.
Department of Psychology and Neuroscience, University of Colorado, Boulder, CO, USAmcj@colorado.eduhttp://matt.colorado.edu.

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Classifications MeSH