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Toward biologically plausible artificial vision

Published online by Cambridge University Press:  28 September 2023

Mason Westfall Open the ORCID record for Mason Westfall [Opens in a new window]
Mason Westfall*
Affiliation:
Department of Philosophy, Philosophy–Neuroscience–Psychology Program, Washington University in St. Louis, St. Louis, MO, USA w.mason@wustl.edu http://www.masonwestfall.com
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Abstract

Quilty-Dunn et al. argue that deep convolutional neural networks (DCNNs) optimized for image classification exemplify structural disanalogies to human vision. A different kind of artificial vision – found in reinforcement-learning agents navigating artificial three-dimensional environments – can be expected to be more human-like. Recent work suggests that language-like representations substantially improves these agents’ performance, lending some indirect support to the language-of-thought hypothesis (LoTH).

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Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e290
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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