Learning a reach trajectory based on binary reward feedback

Abstract Binary reward feedback on movement success is sufficient for learning some simple sensorimotor mappings in a reaching task, but not for some other tasks in which multiple kinematic factors contribute to performance. The critical condition for learning in more complex tasks remains unclear....

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Autores principales:	Katinka van der Kooij, Nina M. van Mastrigt, Emily M. Crowe, Jeroen B. J. Smeets
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e17f23876a6e43f5bb08ca42b22748b5
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spelling	oai:doaj.org-article:e17f23876a6e43f5bb08ca42b22748b52021-12-02T10:48:32ZLearning a reach trajectory based on binary reward feedback10.1038/s41598-020-80155-x2045-2322https://doaj.org/article/e17f23876a6e43f5bb08ca42b22748b52021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80155-xhttps://doaj.org/toc/2045-2322Abstract Binary reward feedback on movement success is sufficient for learning some simple sensorimotor mappings in a reaching task, but not for some other tasks in which multiple kinematic factors contribute to performance. The critical condition for learning in more complex tasks remains unclear. Here, we investigate whether reward-based motor learning is possible in a multi-dimensional trajectory matching task and whether simplifying the task by providing feedback on one factor at a time (‘factorized feedback’) can improve learning. In two experiments, participants performed a trajectory matching task in which learning was measured as a reduction in the error. In Experiment 1, participants matched a straight trajectory slanted in depth. We factorized the task by providing feedback on the slant error, the length error, or on their composite. In Experiment 2, participants matched a curved trajectory, also slanted in depth. In this experiment, we factorized the feedback by providing feedback on the slant error, the curvature error, or on the integral difference between the matched and target trajectory. In Experiment 1, there was anecdotal evidence that participants learnt the multidimensional task. Factorization did not improve learning. In Experiment 2, there was anecdotal evidence the multidimensional task could not be learnt. We conclude that, within a complexity range, multiple kinematic factors can be learnt in parallel.Katinka van der KooijNina M. van MastrigtEmily M. CroweJeroen B. J. SmeetsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Katinka van der Kooij Nina M. van Mastrigt Emily M. Crowe Jeroen B. J. Smeets Learning a reach trajectory based on binary reward feedback
description	Abstract Binary reward feedback on movement success is sufficient for learning some simple sensorimotor mappings in a reaching task, but not for some other tasks in which multiple kinematic factors contribute to performance. The critical condition for learning in more complex tasks remains unclear. Here, we investigate whether reward-based motor learning is possible in a multi-dimensional trajectory matching task and whether simplifying the task by providing feedback on one factor at a time (‘factorized feedback’) can improve learning. In two experiments, participants performed a trajectory matching task in which learning was measured as a reduction in the error. In Experiment 1, participants matched a straight trajectory slanted in depth. We factorized the task by providing feedback on the slant error, the length error, or on their composite. In Experiment 2, participants matched a curved trajectory, also slanted in depth. In this experiment, we factorized the feedback by providing feedback on the slant error, the curvature error, or on the integral difference between the matched and target trajectory. In Experiment 1, there was anecdotal evidence that participants learnt the multidimensional task. Factorization did not improve learning. In Experiment 2, there was anecdotal evidence the multidimensional task could not be learnt. We conclude that, within a complexity range, multiple kinematic factors can be learnt in parallel.
format	article
author	Katinka van der Kooij Nina M. van Mastrigt Emily M. Crowe Jeroen B. J. Smeets
author_facet	Katinka van der Kooij Nina M. van Mastrigt Emily M. Crowe Jeroen B. J. Smeets
author_sort	Katinka van der Kooij
title	Learning a reach trajectory based on binary reward feedback
title_short	Learning a reach trajectory based on binary reward feedback
title_full	Learning a reach trajectory based on binary reward feedback
title_fullStr	Learning a reach trajectory based on binary reward feedback
title_full_unstemmed	Learning a reach trajectory based on binary reward feedback
title_sort	learning a reach trajectory based on binary reward feedback
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e17f23876a6e43f5bb08ca42b22748b5
work_keys_str_mv	AT katinkavanderkooij learningareachtrajectorybasedonbinaryrewardfeedback AT ninamvanmastrigt learningareachtrajectorybasedonbinaryrewardfeedback AT emilymcrowe learningareachtrajectorybasedonbinaryrewardfeedback AT jeroenbjsmeets learningareachtrajectorybasedonbinaryrewardfeedback
_version_	1718396620989530112

Learning a reach trajectory based on binary reward feedback

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