Different mechanisms involved in adaptation to stable and unstable dynamics
Different mechanisms involved in adaptation to stable and unstable dynamics
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DOI：
10.1152/jn.00073.2003
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发表时间：
2003-11-01
影响因子：
2.5
通讯作者：
Kawato, M
中科院分区：
文献类型：
--
作者：
Osu, R;Burdet, E;Kawato, M
最近，我们证明了人类可以通过控制端点阻抗的大小，形状和方向来学习在不稳定的环境中进行准确的运动。虽然以前对人类运动学习的研究表明，大脑获得了新环境的逆动力学模型，但尚不清楚这种控制机制是否在不稳定的环境中起作用。我们比较了在“速度依赖力场”（VF）中多关节手臂运动的学习，该力场以稳定的方式与手臂相互作用，并在“发散力场”（DF）中学习，其中相互作用是不稳定的。两个领域的误差演化特征有显著差异。在学习的早期阶段，DF中的轨迹误差的方向向左和向右交替;也就是说，符号误差从运动到运动是不一致的，并且不能指导逆动力学模型的学习。这与VF中的轨迹误差形成鲜明对比，VF最初以与快速反馈误差学习一致的方式偏置和衰减。在DF和VF学习前后记录的EMG也与适应稳定和不稳定动力学的不同学习和控制机制一致，即逆动力学模型形成和阻抗控制。我们还研究了适应旋转DF检查逆动力学模型的形成和阻抗控制之间的相互作用。我们的研究结果表明，逆动力学模型可以与阻抗控制器并行工作，以补偿不稳定环境中的一致扰动力。
Recently, we demonstrated that humans can learn to make accurate movements in an unstable environment by controlling magnitude, shape, and orientation of the endpoint impedance. Although previous studies of human motor learning suggest that the brain acquires an inverse dynamics model of the novel environment, it is not known whether this control mechanism is operative in unstable environments. We compared learning of multijoint arm movements in a "velocity-dependent force field" (VF), which interacted with the arm in a stable manner, and learning in a "divergent force field" (DF), where the interaction was unstable. The characteristics of error evolution were markedly different in the 2 fields. The direction of trajectory error in the DF alternated to the left and right during the early stage of learning; that is, signed error was inconsistent from movement to movement and could not have guided learning of an inverse dynamics model. This contrasted sharply with trajectory error in the VF, which was initially biased and decayed in a manner that was consistent with rapid feedback error learning. EMG recorded before and after learning in the DF and VF are also consistent with different learning and control mechanisms for adapting to stable and unstable dynamics, that is, inverse dynamics model formation and impedance control. We also investigated adaptation to a rotated DF to examine the interplay between inverse dynamics model formation and impedance control. Our results suggest that an inverse dynamics model can function in parallel with an impedance controller to compensate for consistent perturbing force in unstable environments.