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Observation of an expert model induces a coarticulated drawing movement pattern in a single session

机译:观察专家模型在单个会话中引起一个套装绘图运动模式

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We tested how observation of a skilled pattern of planar movements can assist in the learning of a new motor skill, which otherwise requires rigorous long-term practice to achieve fast and smooth performance. Sixty participants performed a sequence of planar hand movements on pre-test, acquisition, post-test and 24h post-training blocks, under 1 of 4 conditions: an observation group (OG), a slowed observation group (SOG), a random motion control group (RMCG) and a double physical training control group (DPTCG). The OG and SOG observed a co-aligned expert model's right hand performing the study task intermittently throughout acquisition, RMCG observed random dots movement instead of a model. Participants in the DPTCG received extra physical practice trials instead of the visually observed trials. Kinematic analysis revealed that only in conditions with observation of an expert model there was an instant robust improvement in motor planning of the task. This step-wise improvement was not only persistent in post-training retests but was also apparently implicit and subject to further incremental improvements in movement strategy over the period of 24 hours. We suggest that observation of hand movements of an expert model coaligned with self-produced movements during training can significantly condense the time-course of ecologically relevant drawing / writing skill mastery. Current findings may contribute to optimization of motor training and rehabilitation in virtual or simulated environments.
机译:我们测试了如何观察熟练的平面运动模式可以帮助学习新的运动技能,否则需要严格的长期练习来实现快速和平滑的性能。 60名参与者在预测试,采集,测试后和24小时训练障碍中进行了一系列平面手动运动,在4条条件下,观察组(OG),慢速观察组(SOG),随机运动对照组(RMCG)和双重物理训练控制组(DPTCG)。 OG和SOG观察了一个共同对齐的专家模型在整个采集中间歇地执行研究任务,RMCG观察到随机点移动而不是模型。 DPTCG的参与者收到额外的物理实践试验而不是视觉观察的试验。运动学分析表明,只有在观察专家模型的条件下,任务的电机规划都有一个即时的强大改善。这种迈出的改善不仅持续存在于培训后重新测试,而且显然也隐含,并在24小时内进一步增加运动策略的进一步增量改进。我们建议观察在培训期间与自我产生的运动进行自我产生的运动的专家模型的手动运动可以显着地凝结生态相关的绘画/写作技能掌握的时间过程。目前的调查结果可能有助于优化虚拟或模拟环境中的电机训练和康复。

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