Bähner, F. orcid.org/0000-0002-6747-0045, Popov, T., Boehme, N. et al. (7 more authors) (2025) Abstract rule learning promotes cognitive flexibility in complex environments across species. Nature Communications, 16 (1). 5396. ISSN 2041-1723
Abstract
Rapid learning in complex and changing environments is a hallmark of intelligent behavior. Humans achieve this in part through abstract concepts applicable to multiple, related situations. It is unclear, however, whether the computational mechanisms underlying rapid learning are unique to humans or also exist in other species. We combined behavioral, computational and electrophysiological analyses of a multidimensional rule-learning paradigm in male rats and in humans. We report that both species infer task rules by sequentially testing different hypotheses, rather than learning the correct action for all possible cue combinations. Neural substrates of hypothetical rules were detected in prefrontal network activity of both species. This species-conserved mechanism reduces task dimensionality and explains key experimental observations: sudden behavioral transitions and facilitated learning after prior experience. Our findings help to narrow the explanatory gap between human macroscopic and rodent microcircuit levels and provide a foundation for the translational investigation of impaired cognitive flexibility.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Keywords: | Cognitive control; Learning algorithms; Neurophysiology |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 30 Jun 2025 13:57 |
Last Modified: | 30 Jun 2025 13:57 |
Status: | Published |
Publisher: | Springer Science and Business Media LLC |
Refereed: | Yes |
Identification Number: | 10.1038/s41467-025-60943-7 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:228541 |