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运动控制系统是否使用多种模型和上下文切换来应对多变的环境?

Does the motor control system use multiple models and context switching to cope with a variable environment?

作者信息

Karniel A, Mussa-Ivaldi F A

机构信息

Department of Physiology, Northwestern University Medical School, and Sensory Motor Performance Program, Rehabilitation Institute of Chicago, IL 60611, USA.

出版信息

Exp Brain Res. 2002 Apr;143(4):520-4. doi: 10.1007/s00221-002-1054-4. Epub 2002 Mar 1.

DOI:10.1007/s00221-002-1054-4
PMID:11914799
Abstract

Studies of arm movements have shown that subjects learn to compensate predictable mechanical perturbations by developing a representation of the relation between the state of motion of the arm and the perturbing forces. Here, we tested the hypothesis that subjects construct internal representations of two different force fields and switch between them when presented with an alternating sequence of these fields. Our results do not support this hypothesis. Subjects performed reaching movements in four sessions over 4 days. On the 1st day the robotic manipulandum perturbed the movement by perpendicular force that alternated its direction after each movement. Subjects were unable to construct the two underlying models and switch between them. On the 2nd day only one field was applied and well learned. On the 3rd day only the other field was applied and well learned. Then the experiment of the 1st day was repeated on the 4th day. Even after this extensive training subjects showed no signs of improved performance with alternating fields. This result combined with previous studies suggests that the central nervous system has a strong tendency to employ a single internal model when dealing with a sequence of perturbations.

摘要

对手臂运动的研究表明,受试者通过建立手臂运动状态与干扰力之间关系的表征,学会补偿可预测的机械干扰。在此,我们测试了这样一个假设,即受试者构建两种不同力场的内部表征,并在呈现这些力场的交替序列时在它们之间进行切换。我们的结果不支持这一假设。受试者在4天内分4次进行伸手动作。第一天,机器人操作器通过垂直力干扰运动,每次运动后垂直力方向交替。受试者无法构建两个潜在模型并在它们之间切换。第二天只应用一个力场并得到很好的学习。第三天只应用另一个力场并得到很好的学习。然后在第四天重复第一天的实验。即使经过这种广泛训练,受试者在交替力场下也没有表现出性能改善的迹象。这一结果与先前的研究相结合表明,中枢神经系统在处理一系列干扰时,有强烈倾向采用单一内部模型。

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