身体-机器界面的协同练习:对个人和集体运动学习的影响。

IF 3.8
Amy Bellitto, Ferdinando A Mussa-Ivaldi, Camilla Pierella, Maura Casadio
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引用次数: 0

摘要

目标:人体-机器接口(BoMIs)将人体运动转化为控制外部设备(如计算机光标)的命令。这一过程使研究人员能够研究逆向模型的发展和改进,从而产生实现所需运动所需的运动命令。传统上,运动学习主要集中在单独练习上,但最近的研究已经转向探索二元任务,即两个人一起练习。在二元任务中,合作伙伴为共同目标而合作的协同实践在提高绩效和减少压力方面显示出了希望。然而,在协同实践中,每个合作伙伴的贡献对个人和集体学习的影响仍未得到充分探讨。本研究旨在(i)调查协同练习中不同水平的贡献如何影响个人和集体的动作学习,以及(ii)评估这些贡献水平对个人回归独奏练习时表现的影响。方法:40名naïve参与者进行了个人练习、二元协同练习和最后一轮使用BoMI控制光标的个人练习。参与者根据他们在二元练习中光标轨迹的参与程度被划分为高贡献者或低贡献者。我们分析了这些贡献水平是如何影响表现、运动策略和内部模型的。主要结果:在并行式练习中,高贡献者保持了与他们最初的独奏表现相似的运动策略,而低贡献者则表现出明显的偏差。在回到单独练习后,高贡献者保持了更好的任务表现,而低贡献者最初有所下降,但通过额外的练习迅速提高,最终达到高贡献者的表现水平。意义:这种理解对优化二元实践具有实际意义。我们的研究揭示了协同练习对随后个人运动表现的影响,有助于更清楚地了解其优势和局限性,以实现最佳实施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Synergic practice with a body-machine interface: implications for individual and collective motor learning.

Objective. Body-machine interfaces (BoMIs) translate human body movements into commands for controlling external devices, such as computer cursors. This process allows researchers to study the development and refinement of inverse models, which generate motor commands necessary for achieving desired movements. Traditionally, motor learning has focused on solo practice, but recent research has shifted towards exploring dyadic tasks, where two individuals practice together. Within dyadic tasks, synergic practice-where partners collaborate toward a shared goal-has shown promise in enhancing performance and reducing stress. However, the impact of contributions of each partner within synergic practice on individual and collective learning remains underexplored. This study aims to (i) investigate how different levels of contribution during synergic practice affect both individual and collective motor learning, and (ii) assess the impact of these contribution levels on individual performance when returning to solo practice.Approach. Forty naïve participants underwent individual practice, dyadic synergic practice, and a final round of individual practice using a BoMI to control a cursor. Participants were classified as high or low contributors based on their participation in the cursor trajectory during dyadic practice. We analyzed how these contribution levels influenced performance, motor strategies, and internal models during and after the dyadic phase.Main results. During dyadic practice, high contributors maintained motor strategies similar to their initial solo performance, while low contributors showed significant deviations. After returning to solo practice, high contributors retained better task performance, whereas low contributors initially regressed but quickly improved with additional practice, eventually matching high contributors' performance levels.Significance. This understanding holds practical implications for optimizing dyadic practice. Our study sheds light on the influence of synergic practice on subsequent individual motor performance, contributing to a clearer understanding of its advantages and limitations for optimal implementation.

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