Reduction in Learning Rates Associated with Anterograde Interference Results from Interactions between Different Timescales in Motor Adaptation
Reduction in Learning Rates Associated with Anterograde Interference Results from Interactions between Different Timescales in Motor Adaptation
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DOI：
10.1371/journal.pcbi.1000893
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发表时间：
2010-08-01
影响因子：
4.3
通讯作者：
Smith, Maurice A.
中科院分区：
文献类型：
--
作者：
Sing, Gary C.;Smith, Maurice A.
过去的经验会影响未来的行动。这些经验不仅可以驱动运动输出的适应性变化，而且还可以调节这些适应性变化发生的速率。在这里，我们研究了运动适应中的顺行干扰--先前学习的运动任务（任务A）降低随后学习不同（通常是相反的）运动任务（任务B）的速度的能力。我们研究了形成的运动系统的能力顺行干扰的自适应控制的人达到手臂运动，通过确定干扰量后，不同的时间暴露于任务A（13，41，112，230，和369试验）。我们发现在任务B的学习中观察到的顺行干扰量随着任务A的持续时间而增加。然而，这种增加并没有无限期地持续下去;相反，在任务A的15-40次试验后，干扰达到渐近线。有趣的是，我们发现，最近提出的运动适应的多速率模型，由两个不同的，但相互作用的自适应过程，预测我们观察到的干扰模式的几个关键特征。具体来说，这个计算模型（没有任何自由参数）预测的初始增长和平稳的顺行干扰，我们描述，以及我们观察到的实验（R-2 = 0.91）的干扰的渐近量。了解运动适应中顺行干扰的机制可能有助于开发改善的训练和康复模式，以减轻不必要的干扰。
Prior experiences can influence future actions. These experiences can not only drive adaptive changes in motor output, but they can also modulate the rate at which these adaptive changes occur. Here we studied anterograde interference in motor adaptation - the ability of a previously learned motor task (Task A) to reduce the rate of subsequently learning a different (and usually opposite) motor task (Task B). We examined the formation of the motor system's capacity for anterograde interference in the adaptive control of human reaching-arm movements by determining the amount of interference after varying durations of exposure to Task A (13, 41, 112, 230, and 369 trials). We found that the amount of anterograde interference observed in the learning of Task B increased with the duration of Task A. However, this increase did not continue indefinitely; instead, the interference reached asymptote after 15-40 trials of Task A. Interestingly, we found that a recently proposed multi-rate model of motor adaptation, composed of two distinct but interacting adaptive processes, predicts several key features of the interference patterns we observed. Specifically, this computational model (without any free parameters) predicts the initial growth and leveling off of anterograde interference that we describe, as well as the asymptotic amount of interference that we observe experimentally (R-2 = 0.91). Understanding the mechanisms underlying anterograde interference in motor adaptation may enable the development of improved training and rehabilitation paradigms that mitigate unwanted interference.