Computational nature of human adaptive control during learning of reaching movements in force fields
Computational nature of human adaptive control during learning of reaching movements in force fields
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DOI：
10.1007/s004220050543
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发表时间：
1999-07-01
影响因子：
1.9
通讯作者：
Shadmehr, R
中科院分区：
文献类型：
--
作者：
Bhushan, N;Shadmehr, R
学习在力场中进行伸手运动被用作探索生物自适应控制器的系统架构的范例。我们比较了作用于人类手臂神经肌肉系统模型的多个候选控制系统的性能，并询问候选系统的动态与 16 名受试者的运动特征相比如何。我们发现，通过利用自适应逆模型的脊髓上系统进行控制会产生与我们的受试者中观察到的动力学相似的动力学，但缺乏基本特征。这些特征指向不同的架构，其中下行命令受到自适应前向模型的影响。然而，我们发现仅通过正向模型进行控制也会导致与人臂行为不匹配的动力学。我们考虑了第三种控制架构，其中将正向模型与逆向模型结合使用，发现所产生的动态与实验数据中观察到的动态非常相似。这种控制架构的基本特性是，它预测了新颖力场中手部轨迹近乎不连续的复杂模式。在我们的受试者中观察到几乎相同的模式，这表明下降运动命令的生成可能是通过包括自适应正向和逆向模型的控制系统架构来实现的。我们发现，当受试者学会进行伸展运动时，可以独立估计正向和反向模型的适应率，并且可以准确地解释受试者在不同运动之间的表现变化。结果表明，正向模型的适应在受试者的运动学习中起着主导作用。经过一段时间的巩固后，内部模型的适应率明显高于记忆巩固前观察到的适应率。这表明运动记忆的巩固与释放某些计算资源以供后续学习同时发生。
Learning to make reaching movements in force fields was used as a paradigm to explore the system architecture of the biological adaptive controller. We compared the performance of a number of candidate control systems that acted on a model of the neuromuscular system of the human arm and asked how well the dynamics of the candidate system compared with the movement characteristics of 16 subjects. We found that control via a supra-spinal system that utilized an adaptive inverse model resulted in dynamics that were similar to that observed in our subjects, but lacked essential characteristics. These characteristics pointed to a different architecture where descending commands were influenced by an adaptive forward model. However, we found that control via a forward model alone also resulted in dynamics that did not match the behavior of the human arm. We considered a third control architecture where a forward model was used in conjunction with an inverse model and found that the resulting dynamics were remarkably similar to that observed in the experimental data. The essential property of this control architecture was that it predicted a complex pattern of near-discontinuities in hand trajectory in the novel force field. A nearly identical pattern was observed in our subjects, suggesting that generation of descending motor commands was likely through a control system architecture that included both adaptive forward and inverse models. We found that as subjects learned to make reaching movements, adaptation rates for the forward and inverse models could be independently estimated and the resulting changes in performance of subjects from movement to movement could be accurately accounted for. Results suggested that the adaptation of the forward model played a dominant role in the motor learning of subjects. After a period of consolidation, the rates of adaptation in the internal models were significantly larger than those observed before the memory had consolidated. This suggested that consolidation of motor memory coincided with freeing of certain computational resources for subsequent learning.