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Stable, flexible, common, and distinct behaviors support rule-based and information-integration category learning

The ability to organize variable sensory signals into discrete categories is a fundamental process in human cognition thought to underlie many real-world learning problems. Decades of research suggests that two learning systems may support category learning and that categories with different distrib...

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Autores principales: Roark, Casey L., Chandrasekaran, Bharath
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10183008/
https://www.ncbi.nlm.nih.gov/pubmed/37179364
http://dx.doi.org/10.1038/s41539-023-00163-0
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author Roark, Casey L.
Chandrasekaran, Bharath
author_facet Roark, Casey L.
Chandrasekaran, Bharath
author_sort Roark, Casey L.
collection PubMed
description The ability to organize variable sensory signals into discrete categories is a fundamental process in human cognition thought to underlie many real-world learning problems. Decades of research suggests that two learning systems may support category learning and that categories with different distributional structures (rule-based, information-integration) optimally rely on different learning systems. However, it remains unclear how the same individual learns these different categories and whether the behaviors that support learning success are common or distinct across different categories. In two experiments, we investigate learning and develop a taxonomy of learning behaviors to investigate which behaviors are stable or flexible as the same individual learns rule-based and information-integration categories and which behaviors are common or distinct to learning success for these different types of categories. We found that some learning behaviors are stable in an individual across category learning tasks (learning success, strategy consistency), while others are flexibly task-modulated (learning speed, strategy, stability). Further, success in rule-based and information-integration category learning was supported by both common (faster learning speeds, higher working memory ability) and distinct factors (learning strategies, strategy consistency). Overall, these results demonstrate that even with highly similar categories and identical training tasks, individuals dynamically adjust some behaviors to fit the task and success in learning different kinds of categories is supported by both common and distinct factors. These results illustrate a need for theoretical perspectives of category learning to include nuances of behavior at the level of an individual learner.
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spelling pubmed-101830082023-05-15 Stable, flexible, common, and distinct behaviors support rule-based and information-integration category learning Roark, Casey L. Chandrasekaran, Bharath NPJ Sci Learn Article The ability to organize variable sensory signals into discrete categories is a fundamental process in human cognition thought to underlie many real-world learning problems. Decades of research suggests that two learning systems may support category learning and that categories with different distributional structures (rule-based, information-integration) optimally rely on different learning systems. However, it remains unclear how the same individual learns these different categories and whether the behaviors that support learning success are common or distinct across different categories. In two experiments, we investigate learning and develop a taxonomy of learning behaviors to investigate which behaviors are stable or flexible as the same individual learns rule-based and information-integration categories and which behaviors are common or distinct to learning success for these different types of categories. We found that some learning behaviors are stable in an individual across category learning tasks (learning success, strategy consistency), while others are flexibly task-modulated (learning speed, strategy, stability). Further, success in rule-based and information-integration category learning was supported by both common (faster learning speeds, higher working memory ability) and distinct factors (learning strategies, strategy consistency). Overall, these results demonstrate that even with highly similar categories and identical training tasks, individuals dynamically adjust some behaviors to fit the task and success in learning different kinds of categories is supported by both common and distinct factors. These results illustrate a need for theoretical perspectives of category learning to include nuances of behavior at the level of an individual learner. Nature Publishing Group UK 2023-05-13 /pmc/articles/PMC10183008/ /pubmed/37179364 http://dx.doi.org/10.1038/s41539-023-00163-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Roark, Casey L.
Chandrasekaran, Bharath
Stable, flexible, common, and distinct behaviors support rule-based and information-integration category learning
title Stable, flexible, common, and distinct behaviors support rule-based and information-integration category learning
title_full Stable, flexible, common, and distinct behaviors support rule-based and information-integration category learning
title_fullStr Stable, flexible, common, and distinct behaviors support rule-based and information-integration category learning
title_full_unstemmed Stable, flexible, common, and distinct behaviors support rule-based and information-integration category learning
title_short Stable, flexible, common, and distinct behaviors support rule-based and information-integration category learning
title_sort stable, flexible, common, and distinct behaviors support rule-based and information-integration category learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10183008/
https://www.ncbi.nlm.nih.gov/pubmed/37179364
http://dx.doi.org/10.1038/s41539-023-00163-0
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