建模方法对镜像神经元活动以及运动技能习得和保持的影响

Ramin Ashraf, Behrouz Abdoli, Reza Khosrowabadi, Alireza Farsi, Jaime A Pineda
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引用次数: 0

摘要

简介:镜像神经元被认为是观察学习的潜在神经机制:镜像神经元被认为是观察学习的潜在神经机制。本研究旨在探讨自我建模、熟练模型和学习模型对μ节奏抑制和高尔夫推杆习得与保持的影响:研究对象为45名男性志愿者学生(年龄为(19.4±0.37)岁),分为自我建模、熟练模型和学习模型三个实验组,分前测、习得和保持三个阶段进行了6次体能和观察训练。在前测阶段,在初步熟悉技能后,参与者进行 10 个高尔夫推杆动作,同时记录得分。然后,在观察 10 个高尔夫推杆动作的过程中,记录 C3、C4 和 Cz 区域的脑电波,这些脑电波是由与之相关的群体模型发出的。习得期包括六次高尔夫推杆训练,每次训练由六组 10 次试验组成。在每个训练片段之前,受试者通过视频的形式观察了 10 次与本组相关的高尔夫推杆动作。此外,还通过记录 10 次高尔夫推杆动作的得分,以及在观察相关模型的技能时记录脑电波,进行了习得和延迟保持测试:混合方差分析(ANOVA)显示,与熟练模型组和学习模型组相比,自我建模组在前测中的mu节奏抑制更强,但在习得和保持测试中,三组的这种抑制没有显著差异。在任务变量方面,在前测阶段没有显著差异的三组在学习所需的技能方面从前测到习得测试都取得了相当大的进步,而且这种进步在保持测试之前一直比较稳定。此外,无论是在习得阶段还是在保持阶段,自我建模组都比其他两组表现得更好,但这两组之间没有观察到显著差异:这些结果表明,模型与观察者的相似性是建模干预的一个关键因素,会影响μ节律抑制的速度。
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The Effect of Modeling Methods on Mirror Neuron Activity and a Motor Skill Acquisition and Retention.

Introduction: Mirror neurons have been suggested as a potential neural mechanism of observational learning. This study aims to investigate the effect of self-modeling, skilled model, and learning model on mu rhythm suppression and golf putting acquisition and retention.

Methods: The study was conducted on 45 male volunteer students (aged 19.4±0.37 years) in three experimental groups, self-modeling, skilled, and learning models with six sessions of physical and observational training in three periods of pre-test, acquisition, and retention. In the pre-test, after the initial familiarity with the skill, participants performed 10 golf putting actions while scores were recorded. Then, electrical brain waves in C3, C4, and Cz regions were recorded during the observation of 10 golf putting actions by their group-related models. The acquisition period consisted of golf putting training during six sessions, each consisting of six blocks of 10 trials. Before each training block, participants observed golf putting related to their group 10 times in the form of a video. Acquisition and delayed retention tests were also performed by recording scores of 10 golf putting actions, as well as recording electrical brain waves while observing the skill performed by the related model.

Results: Mixed analysis of variance (ANOVA) showed that the mu rhythm suppression in the pre-test was more in the self-modeling group compared to the skilled model and learning model groups, but this suppression was not significantly different in all three groups in the acquisition and retention tests. In putting task variables, all three groups that had no significant difference in the pre-test period made considerable progress in learning the desired skill from the pre-test to the acquisition test, and this progress was somewhat stable until the retention test. Also, both in the acquisition and retention periods, the self-modeling group showed better performance than the other two groups; however, no significant difference was observed between these groups.

Conclusion: These results suggest that the model-observer similarity is a crucial factor in modeling interventions and can affect the rate of mu rhythm suppression.

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