Human Systems Laboratory (HSL), Department of Biomedical Engineering and Sciences, School of Mechanical & Manufacturing Engineering, National University of Sciences & Technology, Islamabad 44000, Pakistan.
Comput Math Methods Med. 2013;2013:109497. doi: 10.1155/2013/109497. Epub 2013 May 7.
Human nervous system tries to minimize the effect of any external perturbing force by bringing modifications in the internal model. These modifications affect the subsequent motor commands generated by the nervous system. Adaptive compensation along with the appropriate modifications of internal model helps in reducing human movement errors. In the current study, we studied how motor imagery influences trial-to-trial learning in a robot-based adaptation task. Two groups of subjects performed reaching movements with or without motor imagery in a velocity-dependent force field. The results show that reaching movements performed with motor imagery have relatively a more focused generalization pattern and a higher learning rate in training direction.
人类神经系统通过对内模进行修改来尽力减小任何外部干扰力的影响。这些修改会影响神经系统随后生成的运动指令。自适应补偿以及对内模的适当修改有助于减少人类运动误差。在本研究中，我们研究了运动想象如何影响基于机器人的适应任务中的逐次学习。两组受试者在速度相关力场中进行了有或没有运动想象的伸手运动。结果表明，进行运动想象的伸手运动具有相对更集中的泛化模式和更高的训练方向学习率。
