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对运动图像的不同教学方法都展示了个性化的运动模式,以便在学习复杂的运动技能时获得更好的性能结果

Riki S Lindsay¹, John Komar², Jia Yi Chow²

¹Institute for Health and Sport, Victoria University, Melbourne, Australia.

|November 29, 2023

概括

运动图像 (MI) 与非线性教学 (NLP) 结合,在举重初学者获得技能方面,与线性教学 (LP) 没有显著差异. 任务约束,而不是具体的指示,影响了运动探索和自我组织以提高性能.

科学领域:

运动学习和技能获取
认知心理学认知心理学
运动科学运动科学

背景情况:

运动影像 (MI) 是一种模拟运动的认知技术,用于增强技能学习.
目前的MI研究缺乏与非线性教学 (NLP) 等当代运动学习方法的整合.
基于生态动态的NLP,将熟练行动视为由环境相互作用和任务约束操纵产生的过程.

研究的目的:

探索非线性教学 (NLP) 方法在技能获取的运动图像 (MI) 中的应用.
为了比较NLP集成MI与传统线性教学 (LP) MI在学习复杂的举重衍生物的有效性.

主要方法:

14名初学者举重运动员通过使用NLP (基于类比的指令,任务约束) 或LP (处方技术指令) 进行了为期4周的干预.
性能准确性,杆轨迹,动力学数据和运动探索被评估为干预前,中间和后.
统计分析比较了NLP和LP组之间的勘探量和技术评估.

主要成果:

在LP和NLP条件之间没有发现运动探索的显著差异.
在各组之间观察到相应的向后移位变化,并且在技术评估中没有显著差异.
无论是NLP和LP组,都主要表现出"低于最佳"的3型杆轨迹,这表明固有的任务约束会影响运动解决方案.

结论:

规定特定技术 (LP) 的MI指令可能不会有效地限制运动探索;固有的任务限制驱动自我组织.
学习者可以自主组织运动,以满足个人和任务的约束,导致不论MI指令类型的性能提高.
将任务约束纳入的以结果为中心的MI脚本可以更好地促进个人探索和技能发展.

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