Franklin David W, Osu Rieko, Burdet Etienne, Kawato Mitsuo, Milner Theodore E
ATR Computational Neuroscience Laboratories, Kyoto 619-0288, Japan.
J Neurophysiol. 2003 Nov;90(5):3270-82. doi: 10.1152/jn.01112.2002.
This study compared adaptation in novel force fields where trajectories were initially either stable or unstable to elucidate the processes of learning novel skills and adapting to new environments. Subjects learned to move in a null force field (NF), which was unexpectedly changed either to a velocity-dependent force field (VF), which resulted in perturbed but stable hand trajectories, or a position-dependent divergent force field (DF), which resulted in unstable trajectories. With practice, subjects learned to compensate for the perturbations produced by both force fields. Adaptation was characterized by an initial increase in the activation of all muscles followed by a gradual reduction. The time course of the increase in activation was correlated with a reduction in hand-path error for the DF but not for the VF. Adaptation to the VF could have been achieved solely by formation of an inverse dynamics model and adaptation to the DF solely by impedance control. However, indices of learning, such as hand-path error, joint torque, and electromyographic activation and deactivation suggest that the CNS combined these processes during adaptation to both force fields. Our results suggest that during the early phase of learning there is an increase in endpoint stiffness that serves to reduce hand-path error and provides additional stability, regardless of whether the dynamics are stable or unstable. We suggest that the motor control system utilizes an inverse dynamics model to learn the mean dynamics and an impedance controller to assist in the formation of the inverse dynamics model and to generate needed stability.
本研究比较了在轨迹最初稳定或不稳定的新型力场中的适应性，以阐明学习新技能和适应新环境的过程。受试者学习在零力场（NF）中移动，该零力场意外地变为速度依赖力场（VF），这导致手部轨迹受到干扰但稳定，或者变为位置依赖发散力场（DF），这导致轨迹不稳定。通过练习，受试者学会了补偿两种力场产生的干扰。适应性的特征是所有肌肉的激活最初增加，随后逐渐减少。激活增加的时间进程与DF的手部路径误差减少相关，但与VF无关。对VF的适应可能仅通过形成逆动力学模型来实现，而对DF的适应仅通过阻抗控制来实现。然而，学习指标，如手部路径误差、关节扭矩以及肌电图激活和去激活表明，中枢神经系统在适应两种力场的过程中结合了这些过程。我们的结果表明，在学习的早期阶段，端点刚度会增加，这有助于减少手部路径误差并提供额外的稳定性，无论动力学是稳定还是不稳定。我们认为，运动控制系统利用逆动力学模型来学习平均动力学，并利用阻抗控制器来协助逆动力学模型的形成并产生所需的稳定性。
