Movement speed is biased by prior experience.

Movement speed is biased by prior experience.
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DOI:
10.1152/jn.00522.2013
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发表时间:
2014-01
影响因子:
2.5
通讯作者:
Diedrichsen J
Diedrichsen J
中科院分区:
医学3区
文献类型:
--
作者:
Hammerbeck U;Yousif N;Greenwood R;Rothwell JC;Diedrichsen J

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运动系统如何为任何给定的运动选择速度?许多当前的模型假设一个过程,在快速行动的成本和实现目标的回报之间找到最佳平衡。在这里,我们表明,这样的模型还需要考虑到优先的运动速度的先验表示,这可以通过长时间的练习来改变。在一项时间限制的伸手任务中,人类参与者在300、500、700或900毫秒内做25厘米的伸手动作。然后对他们进行为期3天的训练,让他们以最慢(900毫秒)或最快(300毫秒)的速度执行动作。当在第四天重新测试时,在所有四种时间限制下执行的动作都偏向于训练动作的速度。此外,训练后的运动速度在试验间的差异显著减少。这些发现表明了一种使用依赖机制会对速度的选择产生偏见。对于快速训练组来说,速度变异性的减少也与运动幅度误差的减少有关,这几乎完全推广到一个新的运动方向。相反,垂直误差的变化是特定于训练方向的。总之,我们的研究结果表明,在一系列任务约束下,存在一个相对稳定但可修改的首选移动速度先验,它影响着移动速度的选择。
How does the motor system choose the speed for any given movement? Many current models assume a process that finds the optimal balance between the costs of moving fast and the rewards of achieving the goal. Here, we show that such models also need to take into account a prior representation of preferred movement speed, which can be changed by prolonged practice. In a time-constrained reaching task, human participants made 25-cm reaching movements within 300, 500, 700, or 900 ms. They were then trained for 3 days to execute the movement at either the slowest (900-ms) or fastest (300-ms) speed. When retested on the 4th day, movements executed under all four time constraints were biased toward the speed of the trained movement. In addition, trial-to-trial variation in speed of the trained movement was significantly reduced. These findings are indicative of a use-dependent mechanism that biases the selection of speed. Reduced speed variability was also associated with reduced errors in movement amplitude for the fast training group, which generalized nearly fully to a new movement direction. In contrast, changes in perpendicular error were specific to the trained direction. In sum, our results suggest the existence of a relatively stable but modifiable prior of preferred movement speed that influences the choice of movement speed under a range of task constraints.