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Increasing muscle co-contraction speeds up internal model acquisition during dynamic motor learning

机译:在动态运动学习过程中肌肉共收缩的增加加快了内部模型的获取

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摘要

During reaching movements in the presence of novel dynamics, participants initially co-contract their muscles to reduce kinematic errors and improve task performance. As learning proceeds, muscle co-contraction decreases as an accurate internal model develops. The initial co-contraction could affect the learning of the internal model in several ways. By ensuring the limb remains close to the target state, co-contraction could speed up learning. Conversely, by reducing kinematic errors, a key training signal, it could slow down learning. Alternatively, given that the effects of muscle co-contraction on kinematic errors are predictable and could be discounted when assessing the internal model error, it could have no effect on learning. Using a sequence of force pulses, we pretrained two groups to either co-contract (stiff group) or relax (relaxed group) their arm muscles in the presence of dynamic perturbations. A third group (control group) was not pretrained. All groups performed reaching movements in a velocity-dependent curl field. We measured adaptation using channel trials and found greater adaptation in the stiff group during early learning. We also found a positive correlation between muscle co-contraction, as measured by surface electromyography, and adaptation. These results show that muscle co-contraction accelerates the rate of dynamic motor learning.
机译:在存在新颖动力的情况下达到运动的过程中,参与者最初会共同收缩他们的肌肉,以减少运动学错误并提高任务绩效。随着学习的进行,随着精确内部模型的发展,肌肉的共收缩能力降低。初始共收缩可能以多种方式影响内部模型的学习。通过确保肢体保持接近目标状态,共收缩可以加快学习速度。相反,通过减少运动学错误(一种关键的训练信号),它可以减慢学习速度。另外,假设肌肉共收缩对运动学误差的影响是可预测的,并且在评估内部模型误差时可以忽略,因此对学习没有影响。使用一系列的力脉冲,我们在动态扰动的情况下对两组人进行了预训练,使其共同收缩(僵硬组)或放松(松弛组)手臂肌肉。第三组(对照组)未接受预训练。所有组均在依赖于速度的卷曲场中进行运动。我们使用渠道试验测量了适应性,发现在早期学习中,僵硬组的适应性更大。我们还发现通过表面肌电图测量的肌肉共收缩与适应之间存在正相关。这些结果表明,肌肉共收缩可加快动态运动学习的速度。

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