Parallel model-based and model-free reinforcement learning for card sorting performance

Abstract The Wisconsin Card Sorting Test (WCST) is considered a gold standard for the assessment of cognitive flexibility. On the WCST, repeating a sorting category following negative feedback is typically treated as indicating reduced cognitive flexibility. Therefore such responses are referred to...

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Autores principales:	Alexander Steinke, Florian Lange, Bruno Kopp
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d202d99dd7e14776a906d2ed6ffe2d31
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spelling	oai:doaj.org-article:d202d99dd7e14776a906d2ed6ffe2d312021-12-02T18:48:22ZParallel model-based and model-free reinforcement learning for card sorting performance10.1038/s41598-020-72407-72045-2322https://doaj.org/article/d202d99dd7e14776a906d2ed6ffe2d312020-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-72407-7https://doaj.org/toc/2045-2322Abstract The Wisconsin Card Sorting Test (WCST) is considered a gold standard for the assessment of cognitive flexibility. On the WCST, repeating a sorting category following negative feedback is typically treated as indicating reduced cognitive flexibility. Therefore such responses are referred to as ‘perseveration’ errors. Recent research suggests that the propensity for perseveration errors is modulated by response demands: They occur less frequently when their commitment repeats the previously executed response. Here, we propose parallel reinforcement-learning models of card sorting performance, which assume that card sorting performance can be conceptualized as resulting from model-free reinforcement learning at the level of responses that occurs in parallel with model-based reinforcement learning at the categorical level. We compared parallel reinforcement-learning models with purely model-based reinforcement learning, and with the state-of-the-art attentional-updating model. We analyzed data from 375 participants who completed a computerized WCST. Parallel reinforcement-learning models showed best predictive accuracies for the majority of participants. Only parallel reinforcement-learning models accounted for the modulation of perseveration propensity by response demands. In conclusion, parallel reinforcement-learning models provide a new theoretical perspective on card sorting and it offers a suitable framework for discerning individual differences in latent processes that subserve behavioral flexibility.Alexander SteinkeFlorian LangeBruno KoppNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-18 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alexander Steinke Florian Lange Bruno Kopp Parallel model-based and model-free reinforcement learning for card sorting performance
description	Abstract The Wisconsin Card Sorting Test (WCST) is considered a gold standard for the assessment of cognitive flexibility. On the WCST, repeating a sorting category following negative feedback is typically treated as indicating reduced cognitive flexibility. Therefore such responses are referred to as ‘perseveration’ errors. Recent research suggests that the propensity for perseveration errors is modulated by response demands: They occur less frequently when their commitment repeats the previously executed response. Here, we propose parallel reinforcement-learning models of card sorting performance, which assume that card sorting performance can be conceptualized as resulting from model-free reinforcement learning at the level of responses that occurs in parallel with model-based reinforcement learning at the categorical level. We compared parallel reinforcement-learning models with purely model-based reinforcement learning, and with the state-of-the-art attentional-updating model. We analyzed data from 375 participants who completed a computerized WCST. Parallel reinforcement-learning models showed best predictive accuracies for the majority of participants. Only parallel reinforcement-learning models accounted for the modulation of perseveration propensity by response demands. In conclusion, parallel reinforcement-learning models provide a new theoretical perspective on card sorting and it offers a suitable framework for discerning individual differences in latent processes that subserve behavioral flexibility.
format	article
author	Alexander Steinke Florian Lange Bruno Kopp
author_facet	Alexander Steinke Florian Lange Bruno Kopp
author_sort	Alexander Steinke
title	Parallel model-based and model-free reinforcement learning for card sorting performance
title_short	Parallel model-based and model-free reinforcement learning for card sorting performance
title_full	Parallel model-based and model-free reinforcement learning for card sorting performance
title_fullStr	Parallel model-based and model-free reinforcement learning for card sorting performance
title_full_unstemmed	Parallel model-based and model-free reinforcement learning for card sorting performance
title_sort	parallel model-based and model-free reinforcement learning for card sorting performance
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/d202d99dd7e14776a906d2ed6ffe2d31
work_keys_str_mv	AT alexandersteinke parallelmodelbasedandmodelfreereinforcementlearningforcardsortingperformance AT florianlange parallelmodelbasedandmodelfreereinforcementlearningforcardsortingperformance AT brunokopp parallelmodelbasedandmodelfreereinforcementlearningforcardsortingperformance
_version_	1718377652000129024

Parallel model-based and model-free reinforcement learning for card sorting performance

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