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复杂连续运动任务中的任务整合与预期

Task integration and anticipation in complex, continuous motor tasks.

作者信息

Beißel Patrick, Künzell Stefan

机构信息

Institute of Sports Sciences, University of Augsburg, Augsburg, Germany.

出版信息

Front Psychol. 2025 Jun 9;16:1557618. doi: 10.3389/fpsyg.2025.1557618. eCollection 2025.

Abstract

Multitasking and sequential motor learning research has advanced greatly in recent years, yet commonly accepted insights are largely based on simple, distinct tasks which cannot accurately reflect the variety of more complex and continuous tasks we encounter in everyday life. This study therefore aims to reassess the influence of task integration on motor sequence learning in complex, continuous tasks through the use of a virtual reality environment and an adapted SRT dual task suited for continuous movements. In our experiment, participants performed a complex, bimanual motor sequence task with varying degrees of suitability for task integration. We could successfully show that task integration has beneficial effects on complex task acquisition if covariations between tasks are consistent and detrimental effects if covariations are too inconsitent or missing. Minor inconsistencies within a repeated sequence can however be mitigated. These results highlight the distinct influence of task integration on complex, continuous motor learning, yet emphasize the need for further research beyond distinct, simple tasks.

摘要

近年来,多任务处理和序列运动学习研究取得了长足进展,但普遍认可的见解很大程度上基于简单、明确的任务,这些任务无法准确反映我们在日常生活中遇到的更复杂、连续任务的多样性。因此,本研究旨在通过使用虚拟现实环境和适合连续运动的适应性SRT双重任务,重新评估任务整合对复杂连续任务中运动序列学习的影响。在我们的实验中,参与者执行了一个复杂的双手运动序列任务,其对任务整合的适合程度各不相同。我们能够成功表明,如果任务之间的协变一致,任务整合对复杂任务的习得有有益影响;如果协变过于不一致或缺失,则有不利影响。然而,重复序列中的微小不一致可以得到缓解。这些结果突出了任务整合对复杂连续运动学习的独特影响,但强调了超越明确、简单任务进行进一步研究的必要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f212/12184655/a0af8ade9aac/fpsyg-16-1557618-g001.jpg

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