Computation or Weight Adaptation? Rethinking the Role of Plasticity in Learning.
Overview
- School of Computer Science and Engineering The Hebrew University of Jerusalem
- Department of Cognitive and Brain Sciences The Hebrew University of Jerusalem
- Department of Computer Science and Technology University of Cambridge
- Center for Human Inspired AI University of Cambridge
Abstract
The human brain is an adaptive learning system that can generalize to new tasks and unfamiliar environments. The traditional view is that such adaptive behavior requires a structural change of the learning system (e.g., via neural plasticity). In this work, we use artificial neural networks, specifically large language models (LLMs), to challenge this traditional view and suggest that such adaptive behavior can be also achieved solely through computation if the learning system is sufficiently trained. We focus on statistical learning paradigms. These require identifying underlying regularities in seemingly arbitrary word sequences and are primarily considered to require neural plasticity. LLMs can capture arbitrary structures without requiring weight adaptation, even though these structures carry no semantic content and lie far from the natural language distribution on which the models were trained. Our work provides novel insights into the role of plasticity in learning, demonstrating that sufficiently trained learning systems are highly flexible, adapting to new tasks and environments solely through computation, much more than previously acknowledged. Furthermore, our work opens the door for future research to use deep learning models to conjure hypotheses about the brain.
Reproduced under the paper's license (CC BY-NC), from the paper cited above.
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colab.research.google.com/drive/1llf_gxk9zq4yl6c7f3gnaz7fqsateimf
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Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 7 MeSH terms, 2 funders, 40 references.
Cite
This paper
Lior, G., Shalev, Y., Stanovsky, G., & Goldstein, A. (2026). Computation or Weight Adaptation? Rethinking the Role of Plasticity in Learning. Cognitive science, 50(11), e70271. https://
BibTeX
@article{lior2026computa
author = {Lior, Gili and Shalev, Yuval and Stanovsky, Gabriel and Goldstein, Ariel},
title = {{Computation or Weight Adaptation? Rethinking the Role of Plasticity in Learning}},
journal = {Cognitive science},
year = {2026},
month = nov,
volume = {50},
number = {11},
pages = {e70271},
publisher = {Wiley},
issn = {0364-0213},
doi = {10.1111/
url = {https://
pmid = {42965894},
pmcid = {PMC13723303}
}
RIS
TY - JOUR
AU - Lior, Gili
AU - Shalev, Yuval
AU - Stanovsky, Gabriel
AU - Goldstein, Ariel
TI - Computation or Weight Adaptation? Rethinking the Role of Plasticity in Learning
T2 - Cognitive science
J2 - Cogn Sci
PY - 2026
DA - 2026/
VL - 50
IS - 11
SP - e70271
SN - 0364-0213
PB - Wiley
DO - 10.1111/
UR - https://
LA - en
ER -
CSL-JSON
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