Strategy Development and Feedback Processing During Complex Category Learning

In this study, 38 young adults participated in a probabilistic A/B prototype category learning task under observational and feedback-based conditions. The study compared learning success (testing accuracy) and strategy use (multi-cue vs. single feature vs. random pattern) between training conditions...

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Autores principales:	Victoria Tilton-Bolowsky, Sofia Vallila-Rohter, Yael Arbel
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	strategy development category learning feedback processing feedback related negativity probabilistic learning Psychology BF1-990
Acceso en línea:	https://doaj.org/article/dfbb58dd4fce45428841ff8ec66d1b77
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spelling	oai:doaj.org-article:dfbb58dd4fce45428841ff8ec66d1b772021-11-10T15:34:28ZStrategy Development and Feedback Processing During Complex Category Learning1664-107810.3389/fpsyg.2021.672330https://doaj.org/article/dfbb58dd4fce45428841ff8ec66d1b772021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fpsyg.2021.672330/fullhttps://doaj.org/toc/1664-1078In this study, 38 young adults participated in a probabilistic A/B prototype category learning task under observational and feedback-based conditions. The study compared learning success (testing accuracy) and strategy use (multi-cue vs. single feature vs. random pattern) between training conditions. The feedback-related negativity (FRN) and P3a event related potentials were measured to explore the relationships between feedback processing and strategy use under a probabilistic paradigm. A greater number of participants were found to utilize an optimal, multi-cue strategy following feedback-based training than observational training, adding to the body of research suggesting that feedback can influence learning approach. There was a significant interaction between training phase and strategy on FRN amplitude. Specifically, participants who used a strategy in which category membership was determined by a single feature (single feature strategy) exhibited a significant decrease in FRN amplitude from early training to late training, perhaps due to reduced utilization of feedback or reduced prediction error. There were no significant main or interaction effects between valence, training phase, or strategy on P3a amplitude. Findings are consistent with prior research suggesting that learners vary in their approach to learning and that training method influences learning. Findings also suggest that measures of feedback processing during probabilistic category learning may reflect changes in feedback utilization and may further illuminate differences among individual learners.Victoria Tilton-BolowskySofia Vallila-RohterYael ArbelFrontiers Media S.A.articlestrategy developmentcategory learningfeedback processingfeedback related negativityprobabilistic learningPsychologyBF1-990ENFrontiers in Psychology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	strategy development category learning feedback processing feedback related negativity probabilistic learning Psychology BF1-990
spellingShingle	strategy development category learning feedback processing feedback related negativity probabilistic learning Psychology BF1-990 Victoria Tilton-Bolowsky Sofia Vallila-Rohter Yael Arbel Strategy Development and Feedback Processing During Complex Category Learning
description	In this study, 38 young adults participated in a probabilistic A/B prototype category learning task under observational and feedback-based conditions. The study compared learning success (testing accuracy) and strategy use (multi-cue vs. single feature vs. random pattern) between training conditions. The feedback-related negativity (FRN) and P3a event related potentials were measured to explore the relationships between feedback processing and strategy use under a probabilistic paradigm. A greater number of participants were found to utilize an optimal, multi-cue strategy following feedback-based training than observational training, adding to the body of research suggesting that feedback can influence learning approach. There was a significant interaction between training phase and strategy on FRN amplitude. Specifically, participants who used a strategy in which category membership was determined by a single feature (single feature strategy) exhibited a significant decrease in FRN amplitude from early training to late training, perhaps due to reduced utilization of feedback or reduced prediction error. There were no significant main or interaction effects between valence, training phase, or strategy on P3a amplitude. Findings are consistent with prior research suggesting that learners vary in their approach to learning and that training method influences learning. Findings also suggest that measures of feedback processing during probabilistic category learning may reflect changes in feedback utilization and may further illuminate differences among individual learners.
format	article
author	Victoria Tilton-Bolowsky Sofia Vallila-Rohter Yael Arbel
author_facet	Victoria Tilton-Bolowsky Sofia Vallila-Rohter Yael Arbel
author_sort	Victoria Tilton-Bolowsky
title	Strategy Development and Feedback Processing During Complex Category Learning
title_short	Strategy Development and Feedback Processing During Complex Category Learning
title_full	Strategy Development and Feedback Processing During Complex Category Learning
title_fullStr	Strategy Development and Feedback Processing During Complex Category Learning
title_full_unstemmed	Strategy Development and Feedback Processing During Complex Category Learning
title_sort	strategy development and feedback processing during complex category learning
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/dfbb58dd4fce45428841ff8ec66d1b77
work_keys_str_mv	AT victoriatiltonbolowsky strategydevelopmentandfeedbackprocessingduringcomplexcategorylearning AT sofiavallilarohter strategydevelopmentandfeedbackprocessingduringcomplexcategorylearning AT yaelarbel strategydevelopmentandfeedbackprocessingduringcomplexcategorylearning
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Strategy Development and Feedback Processing During Complex Category Learning

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