Linking motor working memory to explicit and implicit motor learning

Explicit and implicit components of motor learning have been widely studied, but the extent to which movement information encoded and maintained in working memory (Motor Working Memory; MWM) contributes to motor learning remains unclear. Building on recent work pointing to separate effector-independent (abstract) and effector-specific (limb-bound) information formats in MWM, we conducted a correlational study in which human participants completed both a MWM task and a visuomotor rotation task. We observed that: <i>1</i>) the fidelity of effector-independent MWM was selectively correlated with the degree of explicit visuomotor learning, and <i>2</i>) the fidelity of inferred effector-specific MWM was selectively correlated with the degree of implicit visuomotor learning. Together, these results point to a possible relationship in which different formats of motor information stored in WM may contribute to distinct components of long-term motor learning, shedding light on a novel cognitive-motor interaction.<b>NEW & NOTEWORTHY</b> Working memory is important for motor learning, yet its role beyond visuospatial domains remains unclear. Here, we examine whether and how non-visual Motor Working Memory (MWM) is related to long-term motor learning. Specifically, we identified selective correlations between effector-independent MWM and explicit motor learning processes, and between effector-specific MWM and implicit motor learning processes. These findings extend prior research relating visuospatial working memory to motor learning and highlight distinct MWM mechanisms supporting different learning processes.

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Publication Details

Journal
Journal of Neurophysiology
Published
2025-11-06
DOI
https://doi.org/10.1152/jn.00105.2025
Citations
1
Primary Topic
Motor Control and Adaptation
Type
article
Field-Weighted Citation Impact
1.43
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article

Linking motor working memory to explicit and implicit motor learning

Hanna Hillman, Samuel D. McDougle, Taylor McClure
1 citations
Journal of Neurophysiology
Motor Control and Adaptation
1.43
article

Linking motor working memory to explicit and implicit motor learning

Hanna Hillman, Samuel D. McDougle, Taylor McClure
article en
1 citations

Abstract

Explicit and implicit components of motor learning have been widely studied, but the extent to which movement information encoded and maintained in working memory (Motor Working Memory; MWM) contributes to motor learning remains unclear. Building on recent work pointing to separate effector-independent (abstract) and effector-specific (limb-bound) information formats in MWM, we conducted a correlational study in which human participants completed both a MWM task and a visuomotor rotation task. We observed that: <i>1</i>) the fidelity of effector-independent MWM was selectively correlated with the degree of explicit visuomotor learning, and <i>2</i>) the fidelity of inferred effector-specific MWM was selectively correlated with the degree of implicit visuomotor learning. Together, these results point to a possible relationship in which different formats of motor information stored in WM may contribute to distinct components of long-term motor learning, shedding light on a novel cognitive-motor interaction.<b>NEW & NOTEWORTHY</b> Working memory is important for motor learning, yet its role beyond visuospatial domains remains unclear. Here, we examine whether and how non-visual Motor Working Memory (MWM) is related to long-term motor learning. Specifically, we identified selective correlations between effector-independent MWM and explicit motor learning processes, and between effector-specific MWM and implicit motor learning processes. These findings extend prior research relating visuospatial working memory to motor learning and highlight distinct MWM mechanisms supporting different learning processes.

Journal of NeurophysiologyVol. 134(6)
Yale University (US)
Openalex Percentile: Top 14%
Motor Control and Adaptation
1.43
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Linking motor working memory to explicit and implicit motor learning — Hanna Hillman, Samuel D. McDougle, et al. · Journal of Neurophysiology (2025) | TGRS Research Map | TGRS