Explaining Savings for Visuomotor Adaptation: Linear Time-Invariant State-Space Models Are Not Sufficient
Explaining Savings for Visuomotor Adaptation: Linear Time-Invariant State-Space Models Are Not Sufficient
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DOI：
10.1152/jn.90529.2008
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发表时间：
2008-11-01
影响因子：
2.5
通讯作者：
Krakauer, John W.
中科院分区：
文献类型：
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作者：
Zarahn, Eric;Weston, Gregory D.;Krakauer, John W.
扎拉恩 E，韦斯顿 GD，梁 J，马佐尼 P，克拉考尔 JW。解释视觉运动适应的节省：线性时不变状态空间模型是不够的。 《神经生理学杂志》100：2537-2548，2008 年。首次发表于 2008 年 7 月 2 日； doi：10.1152/jn.90529.2008。运动系统对感觉运动扰动的适应是一种与工具使用和应对不断变化的身体相关的学习类型。运动适应记忆可以采取节省的形式：与初始适应相比，再适应的表观速率常数增加。如果受试者在初始适应开始时和再适应开始时经历的感觉错误相同，则节省的评估会被简化。这可以通过引入 1) 足够少量的反扰动试验（反扰动范式 [CP]）或 2）在初始适应和再适应之间引入足够多的零扰动试验（冲洗范式 [WO]）来实现。最近证明，双速率线性时不变状态空间模型 (SSMLTI,2) 理论上可以节省 CP。然而，我们从叠加推断该模型无法解释 WO 的节省。使用相同的任务（平面到达）和扰动类型（视觉运动旋转），我们发现 CP 和 WO 范例的节省相当。尽管 SSMLTI,2 解释了 CP 的某种程度的节省，但它对于 WO 却完全失败了。我们得出的结论是，对于视觉运动旋转而言，总体节省不仅仅是 LTI 动态的结果。相反，视觉运动旋转的节省涉及元学习，我们证明可以将其建模为适应实验阶段中系统参数的变化。
Zarahn E, Weston GD, Liang J, Mazzoni P, Krakauer JW. Explaining savings for visuomotor adaptation: linear time-invariant state-space models are not sufficient. J Neurophysiol 100: 2537-2548, 2008. First published July 2, 2008; doi:10.1152/jn.90529.2008. Adaptation of the motor system to sensorimotor perturbations is a type of learning relevant for tool use and coping with an ever-changing body. Memory for motor adaptation can take the form of savings: an increase in the apparent rate constant of readaptation compared with that of initial adaptation. The assessment of savings is simplified if the sensory errors a subject experiences at the beginning of initial adaptation and the beginning of readaptation are the same. This can be accomplished by introducing either 1) a sufficiently small number of counterperturbation trials (counterperturbation paradigm [ CP]) or 2) a sufficiently large number of zero-perturbation trials (washout paradigm [ WO]) between initial adaptation and readaptation. A two-rate, linear time-invariant state-space model (SSMLTI,2) was recently shown to theoretically produce savings for CP. However, we reasoned from superposition that this model would be unable to explain savings for WO. Using the same task (planar reaching) and type of perturbation (visuomotor rotation), we found comparable savings for both CP and WO paradigms. Although SSMLTI,2 explained some degree of savings for CP it failed completely for WO. We conclude that for visuomotor rotation, savings in general is not simply a consequence of LTI dynamics. Instead savings for visuomotor rotation involves meta-learning, which we show can be modeled as changes in system parameters across the phases of an adaptation experiment.