Tee Keng Peng, Franklin David W, Kawato Mitsuo, Milner Theodore E, Burdet Etienne
Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore.
Biol Cybern. 2010 Jan;102(1):31-44. doi: 10.1007/s00422-009-0348-z. Epub 2009 Nov 21.
This article examines the validity of a model to explain how humans learn to perform movements in environments with novel dynamics, including unstable dynamics typical of tool use. In this model, a simple rule specifies how the activation of each muscle is adapted from one movement to the next. Simulations of multijoint arm movements with a neuromuscular plant that incorporates neural delays, reflexes, and signal-dependent noise, demonstrate that the controller is able to compensate for changing internal or environment dynamics and noise properties. The computational model adapts by learning both the appropriate forces and required limb impedance to compensate precisely for forces and instabilities in arbitrary directions with patterns similar to those observed in motor learning experiments. It learns to regulate reciprocal activation and co-activation in a redundant muscle system during repeated movements without requiring any explicit transformation from hand to muscle space. Independent error-driven change in the activation of each muscle results in a coordinated control of the redundant muscle system and in a behavior that reduces instability, systematic error, and energy.
本文探讨了一个模型的有效性，该模型用于解释人类如何在具有新型动力学的环境中学习执行动作，包括工具使用中典型的不稳定动力学。在这个模型中，一个简单的规则规定了每块肌肉的激活是如何从一个动作适应到下一个动作的。对包含神经延迟、反射和信号相关噪声的神经肌肉装置进行多关节手臂运动模拟，结果表明该控制器能够补偿不断变化的内部或环境动力学以及噪声特性。该计算模型通过学习适当的力和所需的肢体阻抗来进行自适应，从而能够以与运动学习实验中观察到的模式相似的方式，精确补偿任意方向上的力和不稳定性。它学会在重复运动过程中调节冗余肌肉系统中的交互激活和共同激活，而无需从手部空间到肌肉空间的任何明确转换。每块肌肉激活的独立误差驱动变化会导致对冗余肌肉系统的协调控制，并产生一种减少不稳定性、系统误差和能量的行为。
