Emken Jeremy L, Reinkensmeyer David J
Biomedical Engineering Department, University of California-Irvine, Irvine, CA 92697, USA.
IEEE Trans Neural Syst Rehabil Eng. 2005 Mar;13(1):33-9. doi: 10.1109/TNSRE.2004.843173.
When adapting to novel dynamic environments the nervous system learns to anticipate the imposed forces by forming an internal model of the environmental dynamics in a process driven by movement error reduction. Here, we tested the hypothesis that motor learning could be accelerated by transiently amplifying the environmental dynamics. A novel dynamic environment was created during treadmill stepping by applying a perpendicular viscous force field to the leg through a robotic device. The environmental dynamics were amplified by an amount determined by a computational learning model fit on a per-subject basis. On average, subjects significantly reduced the time required to predict the applied force field by approximately 26% when the field was transiently amplified. However, this reduction was not as great as that predicted by the model, likely due to nonstationarities in the learning parameters. We conclude that motor learning of a novel dynamic environment can be accelerated by exploiting the error-based learning mechanism of internal model formation, but that nonlinearities in adaptive response may limit the feasible acceleration. These results support an approach to movement training devices that amplify rather than reduce movement errors, and provide a computational framework for both implementing the approach and understanding its limitations.
在适应新的动态环境时，神经系统会通过在由运动误差减少驱动的过程中形成环境动力学的内部模型，来学习预测施加的力。在此，我们测试了一个假设，即通过短暂放大环境动力学可以加速运动学习。在跑步机行走过程中，通过一个机器人装置对腿部施加垂直粘性力场，从而创建了一个新的动态环境。环境动力学的放大程度由基于个体拟合的计算学习模型确定。平均而言，当力场被短暂放大时，受试者显著减少了预测施加力场所需的时间，减少幅度约为26%。然而，这种减少并不像模型预测的那么大，可能是由于学习参数的非平稳性。我们得出结论，通过利用基于误差的内部模型形成学习机制，可以加速对新动态环境的运动学习，但适应性反应中的非线性可能会限制可行的加速。这些结果支持一种运动训练装置的方法，即放大而不是减少运动误差，并为实施该方法和理解其局限性提供了一个计算框架。
