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Meta-learning goes hand-in-hand with metacognition

Published online by Cambridge University Press:  23 September 2024

Chris Fields Open the ORCID record for Chris Fields [Opens in a new window]  and
James F. Glazebrook Open the ORCID record for James F. Glazebrook [Opens in a new window]
Chris Fields
Affiliation:
Allen Discovery Center, Tufts University, Medford, MA, USA [email protected] https://chrisfieldsresearch.com
James F. Glazebrook*
Affiliation:
Department of Mathematics and Computer Science, Eastern Illinois University, Charleston, IL, USA [email protected] Adjunct Faculty (Mathematics), University of Illinois at Urbana-Champaign, Urbana, IL, USA https://faculy.math.illinois.edu/glazebro/
*
*Corresponding author.
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Abstract

Binz et al. propose a general framework for meta-learning and contrast it with built-by-hand Bayesian models. We comment on some architectural assumptions of the approach, its relation to the active inference framework, its potential applicability to living systems in general, and the advantages of the latter in addressing the explanation problem.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 47 , 2024 , e151
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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