Multijoint error compensation mediates unstable object control
Multijoint error compensation mediates unstable object control
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DOI：
10.1152/jn.00691.2011
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发表时间：
2012-08-01
影响因子：
2.5
通讯作者：
Balasubramaniam, Ramesh
中科院分区：
文献类型：
--
作者：
Cluff, Tyler;Manos, Aspasia;Balasubramaniam, Ramesh
Cluff T，Manos A，Lee TD，Balasubramaniam R.多关节误差补偿调解不稳定的对象控制。J Neurophysiol 108：1167-1175，2012.首次发表于2012年5月23日; doi：10.1152/jn.00691.2011.-熟练的对象控制的一个关键特征是纠正性能错误的能力。这个过程对于不稳定的对象来说并不简单（例如，例如，在一个实施例中，倒立摆或“棒”平衡），因为物体的力学对小的控制误差敏感，这可能导致快速的性能变化。在这项研究中，我们的特点是联合招聘和协调过程中不稳定的目标控制任务。我们的目标是确定技能的获得是否涉及单个关节或分布式误差补偿的招聘变化。为了解决这个问题，我们在四个实验会话中监测杆平衡性能。我们证实，受试者通过在训练课程中表现出稳定性和平衡试验长度的增加来学习任务。我们证明了运动学习导致了多关节误差补偿策略的发展，这样在训练后，受试者优先约束关节角度方差，从而危及任务性能。失稳关节角度方差的选择性约束是运动学习的一个重要指标。最后，我们进行了一个组合的非受控流形排列分析，以确保方差结构不会被单个关节角度方差的差异所混淆。我们发现，多关节误差补偿的依赖性增加，而个别关节的变化（主要是在腕关节）减少系统的训练。我们提出了一个学习机制，是基于对感官状态的准确估计。
Cluff T, Manos A, Lee TD, Balasubramaniam R. Multijoint error compensation mediates unstable object control. J Neurophysiol 108: 1167-1175, 2012. First published May 23, 2012; doi:10.1152/jn.00691.2011.-A key feature of skilled object control is the ability to correct performance errors. This process is not straightforward for unstable objects (e. g., inverted pendulum or "stick" balancing) because the mechanics of the object are sensitive to small control errors, which can lead to rapid performance changes. In this study, we have characterized joint recruitment and coordination processes in an unstable object control task. Our objective was to determine whether skill acquisition involves changes in the recruitment of individual joints or distributed error compensation. To address this problem, we monitored stick-balancing performance across four experimental sessions. We confirmed that subjects learned the task by showing an increase in the stability and length of balancing trials across training sessions. We demonstrated that motor learning led to the development of a multijoint error compensation strategy such that after training, subjects preferentially constrained joint angle variance that jeopardized task performance. The selective constraint of destabilizing joint angle variance was an important metric of motor learning. Finally, we performed a combined uncontrolled manifold-permutation analysis to ensure the variance structure was not confounded by differences in the variance of individual joint angles. We showed that reliance on multijoint error compensation increased, whereas individual joint variation (primarily at the wrist joint) decreased systematically with training. We propose a learning mechanism that is based on the accurate estimation of sensory states.