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The meta-learning toolkit needs stronger constraints

Published online by Cambridge University Press:  23 September 2024

Erin Grant Open the ORCID record for Erin Grant [Opens in a new window]
Erin Grant*
Affiliation:
UCL Gatsby Unit, Sainsbury Wellcome Centre, University College London, London, UK [email protected] https://eringrant.github.io/
*
*Corresponding author.
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Abstract

The implementation of meta-learning targeted by Binz et al. inherits benefits and drawbacks from its nature as a connectionist model. Drawing from historical debates around bottom-up and top-down approaches to modeling in cognitive science, we should continue to bridge levels of analysis by constraining meta-learning and meta-learned models with complementary evidence from across the cognitive and computational sciences.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 47 , 2024 , e152
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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