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Why psychologists should embrace rather than abandon DNNs

Published online by Cambridge University Press:  06 December 2023

Galit Yovel Open the ORCID record for Galit Yovel [Opens in a new window]  and
Naphtali Abudarham
Galit Yovel
Affiliation:
School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel gality@tauex.tau.ac.il; https://people.socsci.tau.ac.il/mu/galityovel/ naphtool@gmail.com Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
Naphtali Abudarham
Affiliation:
School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel gality@tauex.tau.ac.il; https://people.socsci.tau.ac.il/mu/galityovel/ naphtool@gmail.com
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Abstract

Deep neural networks (DNNs) are powerful computational models, which generate complex, high-level representations that were missing in previous models of human cognition. By studying these high-level representations, psychologists can now gain new insights into the nature and origin of human high-level vision, which was not possible with traditional handcrafted models. Abandoning DNNs would be a huge oversight for psychological sciences.

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Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e414
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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