Implicit reward-based motor learning.

Implicit learning Reinforcement learning Reward Reward-based motor learning Use-dependent learning Visuomotor rotation

Journal

Experimental brain research
ISSN: 1432-1106
Titre abrégé: Exp Brain Res
Pays: Germany
ID NLM: 0043312

Informations de publication

Date de publication:
Sep 2023
Historique:
received: 05 05 2023
accepted: 02 08 2023
medline: 1 9 2023
pubmed: 15 8 2023
entrez: 14 8 2023
Statut: ppublish

Résumé

Binary feedback, providing information solely about task success or failure, can be sufficient to drive motor learning. While binary feedback can induce explicit adjustments in movement strategy, it remains unclear if this type of feedback also induces implicit learning. We examined this question in a center-out reaching task by gradually moving an invisible reward zone away from a visual target to a final rotation of 7.5° or 25° in a between-group design. Participants received binary feedback, indicating if the movement intersected the reward zone. By the end of the training, both groups modified their reach angle by about 95% of the rotation. We quantified implicit learning by measuring performance in a subsequent no-feedback aftereffect phase, in which participants were told to forgo any adopted movement strategies and reach directly to the visual target. The results showed a small, but robust (2-3°) aftereffect in both groups, highlighting that binary feedback elicits implicit learning. Notably, for both groups, reaches to two flanking generalization targets were biased in the same direction as the aftereffect. This pattern is at odds with the hypothesis that implicit learning is a form of use-dependent learning. Rather, the results suggest that binary feedback can be sufficient to recalibrate a sensorimotor map.

Identifiants

pubmed: 37580611
doi: 10.1007/s00221-023-06683-w
pii: 10.1007/s00221-023-06683-w
pmc: PMC10471724
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

2287-2298

Subventions

Organisme : NINDS NIH HHS
ID : R01 NS105839
Pays : United States
Organisme : NINDS NIH HHS
ID : R35 NS116883
Pays : United States

Commentaires et corrections

Type : UpdateOf

Informations de copyright

© 2023. The Author(s).

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Auteurs

Nina M van Mastrigt (NM)

Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. n.m.van.mastrigt@vu.nl.

Jonathan S Tsay (JS)

CognAc Lab, UC Berkeley, Berkeley, CA, USA.

Tianhe Wang (T)

CognAc Lab, UC Berkeley, Berkeley, CA, USA.

Guy Avraham (G)

CognAc Lab, UC Berkeley, Berkeley, CA, USA.

Sabrina J Abram (SJ)

CognAc Lab, UC Berkeley, Berkeley, CA, USA.

Katinka van der Kooij (K)

Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Jeroen B J Smeets (JBJ)

Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Richard B Ivry (RB)

CognAc Lab, UC Berkeley, Berkeley, CA, USA.

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