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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 barton.anderson@sydney.edu.au
Katherine R. Storrs
Affiliation:
Department of Psychology, University of Auckland, Auckland, New Zealand katherine.storrs@gmail.com
Roland W. Fleming
Affiliation:
Department of Psychology, Justus Liebig University of Giessen, Giessen, Germany roland.w.fleming@psychol.uni-giessen.de 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.

Information

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

Anderson, B. L., & Marlow, P. J. (2023). Perceiving the shape and material properties of 3D surfaces. Trends in Cognitive Sciences, 27(1), 98–110. doi:10.1016/j.tics.2022.10.005CrossRefGoogle ScholarPubMed
Golan, T., Raju, P. C., & Kriegeskorte, N. (2020). Controversial stimuli: Pitting neural networks against each other as models of human cognition. Proceedings of the National Academy of Sciences of the United States of America, 117(47), 29330–29337.CrossRefGoogle ScholarPubMed
Ho, Y. X., Landy, M. S., & Maloney, L. T. (2008). Conjoint measurement of gloss and surface texture. Psychological Science, 19, 196–204.CrossRefGoogle ScholarPubMed
Marlow, P., & Anderson, B. (2021). The cospecification of the shape and material properties of light permeable materials. Proceedings of the National Academy of Sciences of the United States of America, 118(14), e2024798118.CrossRefGoogle ScholarPubMed
Marlow, P., Kim, J., & Anderson, B. (2012). The perception and misperception of specular surface reflectance. Current Biology, 22(20), 1–5.CrossRefGoogle ScholarPubMed
Marlow, P., Mooney, S., & Anderson, B. (2019). Photogeometric cues to perceived surface shading. Current Biology, 29(2), 306–311.CrossRefGoogle ScholarPubMed
Storrs, K. R., Anderson, B. L., & Fleming, R. W. (2021b). Unsupervised learning predicts human perception and misperception of gloss. Nature Human Behavior, 5, 1402–1417. https://doi.org/10.1038/s41562-021-01097-6CrossRefGoogle ScholarPubMed
Storrs, K. R., Kietzmann, T. C., Walther, A., Mehrer, J., & Kriegeskorte, N. (2021a). Diverse deep neural networks all predict human inferior temporal cortex well, after training and fitting. Journal of Cognitive Neuroscience, 33(10), 2044–2064.Google ScholarPubMed
Wang, Z., & Simoncelli, E. P. (2008). Maximum differentiation (MAD) competition: A methodology for comparing computational models of perceptual quantities. Journal of Vision, 8(12), 8.CrossRefGoogle ScholarPubMed
Yamins, D., & DiCarlo, J. (2016). Using goal-driven deep learning models to understand sensory cortex. Nature Neuroscience, 19, 356–365. https://doi.org/10.1038/nn.4244CrossRefGoogle ScholarPubMed