Takiyama Ken
Brain Science Institute, Tamagawa University Tokyo, Japan.
Front Comput Neurosci. 2015 Feb 4;9:4. doi: 10.3389/fncom.2015.00004. eCollection 2015.
Recent theoretical models suggest that motor learning includes at least two processes: error minimization and memory decay. While learning a novel movement, a motor memory of the movement is gradually formed to minimize the movement error between the desired and actual movements in each training trial, but the memory is slightly forgotten in each trial. The learning effects of error minimization trained with a certain movement are partially available in other non-trained movements, and this transfer of the learning effect can be reproduced by certain theoretical frameworks. Although most theoretical frameworks have assumed that a motor memory trained with a certain movement decays at the same speed during performing the trained movement as non-trained movements, a recent study reported that the motor memory decays faster during performing the trained movement than non-trained movements, i.e., the decay rate of motor memory is movement or context dependent. Although motor learning has been successfully modeled based on an optimization framework, e.g., movement error minimization, the type of optimization that can lead to context-dependent memory decay is unclear. Thus, context-dependent memory decay raises the question of what is optimized in motor learning. To reproduce context-dependent memory decay, I extend a motor primitive framework. Specifically, I introduce motor effort optimization into the framework because some previous studies have reported the existence of effort optimization in motor learning processes and no conventional motor primitive model has yet considered the optimization. Here, I analytically and numerically revealed that context-dependent decay is a result of motor effort optimization. My analyses suggest that context-dependent decay is not merely memory decay but is evidence of motor effort optimization in motor learning.
最近的理论模型表明，运动学习至少包括两个过程：误差最小化和记忆衰退。在学习一种新的运动时，会逐渐形成该运动的运动记忆，以在每次训练试验中最小化期望运动与实际运动之间的运动误差，但在每次试验中记忆会稍有遗忘。用某种运动训练的误差最小化学习效果在其他未训练的运动中部分可用，并且这种学习效果的迁移可以通过某些理论框架来再现。尽管大多数理论框架都假定，用某种运动训练的运动记忆在执行训练运动期间与未训练运动期间以相同速度衰退，但最近一项研究报告称，在执行训练运动期间运动记忆的衰退比未训练运动期间更快，即运动记忆的衰退率取决于运动或情境。尽管基于优化框架（例如运动误差最小化）已经成功地对运动学习进行了建模，但尚不清楚哪种优化类型会导致与情境相关的记忆衰退。因此，与情境相关的记忆衰退提出了运动学习中优化了什么的问题。为了再现与情境相关的记忆衰退，我扩展了一个运动基元框架。具体来说，我将运动努力优化引入该框架，因为之前一些研究报告了运动学习过程中存在努力优化，并且尚无传统的运动基元模型考虑过这种优化。在此，我通过分析和数值方法揭示，与情境相关的衰退是运动努力优化的结果。我的分析表明，与情境相关的衰退不仅仅是记忆衰退，而是运动学习中运动努力优化的证据。
