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      Decomposition of a complex motor skill with precise error feedback and intensive training breaks expertise ceiling

      research-article
      1 , , 1 , 2 , 1 , 2
      Communications Biology
      Nature Publishing Group UK
      Motor cortex, Neurophysiology

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          Abstract

          Complex motor skills involve intricate sequences of movements that require precise temporal coordination across multiple body parts, posing challenges to mastery based on perceived error or reward. One approach that has been widely used is to decompose such skills into simpler, constituent movement elements during the learning process, thereby aligning the task complexity with the learners’ capacity for accurate execution. Despite common belief and prevalent adoption, the effectiveness of this method remains elusive. Here we addressed this issue by decomposing a sequence of precisely timed coordination of movements across multiple fingers into individual constituent elements separately during piano practice. The results demonstrated that the decomposition training enhanced the accuracy of the original motor skill, a benefit not achieved through mere repetition of movements alone, specifically when skilled pianists received explicit visual feedback on timing error in the order of milliseconds during training. During the training, the patterns of multi-finger movements changed significantly, suggesting exploration of movements to refine the skill. By contrast, neither unskilled pianists who underwent the same training nor skilled pianists who performed the decomposition training without receiving visual feedback on the error showed improved skill through training. These findings offer novel evidences suggesting that decomposing a complex motor skill, coupled with receiving feedback on subtle movement error during training, further enhances motor expertise of skilled individuals by facilitating exploratory refinement of movements.

          Abstract

          Skill decomposition training, coupled with augmented error feedback, effectively mitigates the constraints of over-learnt motor skills of expert pianists.

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          Most cited references40

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          Temporal structure of motor variability is dynamically regulated and predicts motor learning ability.

          Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward-based learning to shape specific arm movement trajectories and the other relying on error-based learning to adapt movements in novel physical environments. We proceeded to show that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results provide experimental support for the importance of action exploration, a key idea from reinforcement learning theory, showing that motor variability facilitates motor learning in humans and that our nervous systems actively regulate it to improve learning.
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            Internal models for motor control and trajectory planning.

            A number of internal model concepts are now widespread in neuroscience and cognitive science. These concepts are supported by behavioral, neurophysiological, and imaging data; furthermore, these models have had their structures and functions revealed by such data. In particular, a specific theory on inverse dynamics model learning is directly supported by unit recordings from cerebellar Purkinje cells. Multiple paired forward inverse models describing how diverse objects and environments can be controlled and learned separately have recently been proposed. The 'minimum variance model' is another major recent advance in the computational theory of motor control. This model integrates two furiously disputed approaches on trajectory planning, strongly suggesting that both kinematic and dynamic internal models are utilized in movement planning and control.
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              Augmented visual, auditory, haptic, and multimodal feedback in motor learning: a review.

              It is generally accepted that augmented feedback, provided by a human expert or a technical display, effectively enhances motor learning. However, discussion of the way to most effectively provide augmented feedback has been controversial. Related studies have focused primarily on simple or artificial tasks enhanced by visual feedback. Recently, technical advances have made it possible also to investigate more complex, realistic motor tasks and to implement not only visual, but also auditory, haptic, or multimodal augmented feedback. The aim of this review is to address the potential of augmented unimodal and multimodal feedback in the framework of motor learning theories. The review addresses the reasons for the different impacts of feedback strategies within or between the visual, auditory, and haptic modalities and the challenges that need to be overcome to provide appropriate feedback in these modalities, either in isolation or in combination. Accordingly, the design criteria for successful visual, auditory, haptic, and multimodal feedback are elaborated.
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                Author and article information

                Contributors
                yudai.k36@gmail.com
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                24 January 2025
                24 January 2025
                2025
                : 8
                : 118
                Affiliations
                [1 ]Sony Computer Science Laboratories Inc. (Sony CSL), ( https://ror.org/02nc46417) Tokyo, Japan
                [2 ]NeuroPiano Institute, Kyoto, Japan
                Author information
                http://orcid.org/0000-0002-6877-0568
                http://orcid.org/0000-0003-1387-6645
                Article
                7562
                10.1038/s42003-025-07562-6
                11761348
                39856243
                8aaa916e-ee82-4634-8bc3-aa8eac94f3b6
                © The Author(s) 2025

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 22 March 2024
                : 15 January 2025
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100003382, MEXT | JST | Core Research for Evolutional Science and Technology (CREST);
                Award ID: JPMJCR20D4
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100002241, MEXT | Japan Science and Technology Agency (JST);
                Award ID: JPMJMS2012
                Award Recipient :
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                © Springer Nature Limited 2025

                motor cortex,neurophysiology
                motor cortex, neurophysiology

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