Zarahn Eric, Weston Gregory D, Liang Johnny, Mazzoni Pietro, Krakauer John W
Motor Performance Laboratory, The Neurological Institute, New York, NY 10032, USA.
J Neurophysiol. 2008 Nov;100(5):2537-48. doi: 10.1152/jn.90529.2008. Epub 2008 Jul 2.
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 (SSM(LTI,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 SSM(LTI,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 metalearning, which we show can be modeled as changes in system parameters across the phases of an adaptation experiment.
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。运动适应的记忆可以表现为节省：与初始适应相比，重新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重新适应开始时经历的感觉误差相同，则节省的评估会简化。这可以通过以下两种方法实现：1）在初始适应和重新适应之间引入足够少的反扰动试验（反扰动范式[CP]）或2）足够多的零扰动试验（洗脱范式[WO]）。最近有研究表明，一种双速率、线性时不变状态空间模型（SSM(LTI,2)）在理论上会为CP产生节省。然而，我们基于叠加原理推断，该模型无法解释WO的节省情况。使用相同的任务（平面伸手）和扰动类型（视觉运动旋转），我们发现CP和WO范式的节省情况相当。虽然SSM(LTI,2)解释了CP的一定程度的节省，但对WO完全不适用。我们得出结论，对于视觉运动旋转，一般来说节省不仅仅是线性时不变动力学的结果。相反，视觉运动旋转的节省涉及元学习，我们表明这可以建模为适应实验各阶段系统参数的变化。
