Predictive Strategies for the Control of Complex Motor Skills: Recent Insights into Individual and Joint Actions.

ArXiv Pub Date : 2024-12-05
Marta Russo, Antonella Maselli, Dagmar Sternad, Giovanni Pezzulo
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Abstract

Humans can perform exquisite sensorimotor skills, both individually and in teams, from athletes performing rhythmic gymnastics to everyday tasks like carrying a cup of coffee. The "predictive brain" framework suggests that mastering these tasks relies on predictive mechanisms, raising the question of how we deploy such predictions for real-time control and coordination. This review highlights two lines of research: one showing that during the control of complex objects people make the interaction with 'tools' predictable; the second one examines dyadic coordination showing that people make their behavior predictable for their partners. These studies demonstrate that to achieve sophisticated motor skills, we play "prediction tricks": we select subspaces of predictable solutions and make sensorimotor interactions more predictable and legible by and for others. This synthesis underscores the critical role of predictability in optimizing control strategies across various contexts and establishes a link between predictive processing and closed-loop control theories of behavior.

复杂运动技能控制的预测策略:对个人和联合行动的最新见解。
无论是个人还是团队,人类都可以表现出精致的感觉运动技能,从表演艺术体操的运动员到端一杯咖啡这样的日常任务。“预测大脑”框架表明,掌握这些任务依赖于预测机制,这就提出了我们如何将这种预测用于实时控制和协调的问题。这篇综述强调了两项研究:一项研究表明,在控制复杂物体的过程中,人们与“工具”的互动是可预测的;第二个研究的是二元协调,表明人们的行为对他们的伴侣是可预测的。这些研究表明,为了获得复杂的运动技能,我们玩“预测技巧”:我们选择可预测解决方案的子空间,使感觉运动相互作用更可预测、更清晰。这种综合强调了可预测性在各种情况下优化控制策略中的关键作用,并在预测处理和行为闭环控制理论之间建立了联系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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