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Where do the hypotheses come from? Data-driven learning in science and the brain

Published online by Cambridge University Press:  06 December 2023

Barton L. Anderson Open the ORCID record for Barton L. Anderson [Opens in a new window] ,
Katherine R. Storrs  and
Roland W. Fleming
Barton L. Anderson
Affiliation:
School of Psychology, University of Sydney, Sydney, Australia [email protected]
Katherine R. Storrs
Affiliation:
Department of Psychology, University of Auckland, Auckland, New Zealand [email protected]
Roland W. Fleming
Affiliation:
Department of Psychology, Justus Liebig University of Giessen, Giessen, Germany [email protected] Center for Mind, Brain and Behavior, Universities of Marburg and Giessen, Giessen, Germany
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Abstract

Everyone agrees that testing hypotheses is important, but Bowers et al. provide scant details about where hypotheses about perception and brain function should come from. We suggest that the answer lies in considering how information about the outside world could be acquired – that is, learned – over the course of evolution and development. Deep neural networks (DNNs) provide one tool to address this question.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e386
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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References

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