Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses

Tal Golan; JohnMark Taylor; Heiko Schütt; Benjamin Peters; Rowan P. Sommers; Katja Seeliger; Adrien Doerig; Paul Linton; Talia Konkle; Marcel van Gerven; Konrad Kording; Blake Richards; Tim C. Kietzmann; Grace W. Lindsay; Nikolaus Kriegeskorte

doi:10.1017/S0140525X23001553

Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses

Published online by Cambridge University Press: 06 December 2023

Talia Konkle and

Tal Golan: Affiliation:
Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel [email protected] brainsandmachines.org
JohnMark Taylor: Affiliation:
Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA [email protected] [email protected] [email protected] [email protected] johnmarktaylor.com hebartlab.com https://linton.vision/
Heiko Schütt: Affiliation:
Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA [email protected] [email protected] [email protected] [email protected] johnmarktaylor.com hebartlab.com https://linton.vision/ Center for Neural Science, New York University, New York, NY, USA
Benjamin Peters: Affiliation:
School of Psychology & Neuroscience, University of Glasgow, Glasgow, UK [email protected]
Rowan P. Sommers: Affiliation:
Department of Neurobiology of Language, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands [email protected]
Katja Seeliger: Affiliation:
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany [email protected]
Adrien Doerig: Affiliation:
Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany [email protected] [email protected] kietzmannlab.org kietzmannlab.org
Paul Linton: Affiliation:
Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA [email protected] [email protected] [email protected] [email protected] johnmarktaylor.com hebartlab.com https://linton.vision/ Presidential Scholars in Society and Neuroscience, Center for Science and Society, Columbia University, New York, NY, USA Italian Academy for Advanced Studies in America, Columbia University, New York, NY, USA
Talia Konkle: Affiliation:
Department of Psychology and Center for Brain Sciences, Harvard University, Cambridge, MA, USA [email protected] https://konklab.fas.harvard.edu/
Marcel van Gerven: Affiliation:
Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands artcogsys.com
Konrad Kording: Affiliation:
Departments of Bioengineering and Neuroscience, University of Pennsylvania, Philadelphia, PA, USA [email protected] kordinglab.com Learning in Machines and Brains Program, CIFAR, Toronto, ON, Canada [email protected] linclab.org
Blake Richards: Affiliation:
Learning in Machines and Brains Program, CIFAR, Toronto, ON, Canada [email protected] linclab.org Mila, Montreal, QC, Canada School of Computer Science, McGill University, Montreal, QC, Canada Department of Neurology & Neurosurgery, McGill University, Montreal, QC, Canada Montreal Neurological Institute, Montreal, QC, Canada
Tim C. Kietzmann: Affiliation:
Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany [email protected] [email protected] kietzmannlab.org kietzmannlab.org
Grace W. Lindsay: Affiliation:
Department of Psychology and Center for Data Science, New York University, New York, NY, USA [email protected] lindsay-lab.github.io
Nikolaus Kriegeskorte: Affiliation:
Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA [email protected] [email protected] [email protected] [email protected] johnmarktaylor.com hebartlab.com https://linton.vision/ Departments of Psychology, Neuroscience, and Electrical Engineering, Columbia University, New York, NY, USA

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Abstract

An ideal vision model accounts for behavior and neurophysiology in both naturalistic conditions and designed lab experiments. Unlike psychological theories, artificial neural networks (ANNs) actually perform visual tasks and generate testable predictions for arbitrary inputs. These advantages enable ANNs to engage the entire spectrum of the evidence. Failures of particular models drive progress in a vibrant ANN research program of human vision.

Type: Open Peer Commentary
Information: Behavioral and Brain Sciences , Volume 46 , 2023 , e392

DOI: https://doi.org/10.1017/S0140525X23001553 [Opens in a new window]
Copyright: Copyright © The Author(s), 2023. Published by Cambridge University Press

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