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Is human compositionality meta-learned?

Published online by Cambridge University Press:  23 September 2024

Jacob Russin ,
Sam Whitman McGrath ,
Ellie Pavlick  and
Michael J. Frank Open the ORCID record for Michael J. Frank [Opens in a new window]
Jacob Russin
Affiliation:
Department of Computer Science, Brown University, Providence, RI, USA jake_russin@brown.edu ellie_pavlick@brown.edu https://jlrussin.github.io/ https://cs.brown.edu/people/epavlick/ Department of Cognitive and Psychological Sciences, Brown University, Providence, RI, USA
Sam Whitman McGrath
Affiliation:
Department of Philosophy, Brown University, Providence, RI, USA sam_mcgrath1@brown.edu https://scholar.google.com/citations?user=B3b7kAYAAAAJ&hl=en
Ellie Pavlick
Affiliation:
Department of Computer Science, Brown University, Providence, RI, USA jake_russin@brown.edu ellie_pavlick@brown.edu https://jlrussin.github.io/ https://cs.brown.edu/people/epavlick/
Michael J. Frank*
Affiliation:
Department of Cognitive and Psychological Sciences, Carney Institute for Brain Science, Brown University, Providence, RI, USA michael_frank@brown.edu http://ski.clps.brown.edu/
*
*Corresponding author.
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Abstract

Recent studies suggest that meta-learning may provide an original solution to an enduring puzzle about whether neural networks can explain compositionality – in particular, by raising the prospect that compositionality can be understood as an emergent property of an inner-loop learning algorithm. We elaborate on this hypothesis and consider its empirical predictions regarding the neural mechanisms and development of human compositionality.

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Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 47 , 2024 , e162
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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