Computational Neuroscience Lab, ATR, Soraku-gun, Kyoto, Japan.
J Neurophysiol. 2010 Jul;104(1):382-90. doi: 10.1152/jn.01058.2009. Epub 2010 May 19.
Many real life tasks that require impedance control to minimize motion error are characterized by multiple solutions where the task can be performed either by co-contracting muscle groups, which requires a large effort, or, conversely, by relaxing muscles. However, human motor optimization studies have focused on tasks that are always satisfied by increasing impedance and that are characterized by a single error-effort optimum. To investigate motor optimization in the presence of multiple solutions and hence optima, we introduce a novel paradigm that enables us to let subjects repetitively (but inconspicuously) use different solutions and observe how exploration of multiple solutions affect their motor behavior. The results show that the behavior is largely influenced by motor memory with subjects tending to involuntarily repeat a recent suboptimal task-satisfying solution even after sufficient experience of the optimal solution. This suggests that the CNS does not optimize co-activation tasks globally but determines the motor behavior in a tradeoff of motor memory, error, and effort minimization.
许多需要阻抗控制以最小化运动误差的实际任务具有多种解决方案，这些解决方案可以通过共同收缩肌肉群来完成，这需要很大的努力，或者相反，通过放松肌肉来完成。然而，人类运动优化研究主要集中在那些总是通过增加阻抗来满足的任务上，这些任务的特点是只有一个误差-努力最优解。为了研究存在多种解决方案和因此存在多个最优解的运动优化，我们引入了一种新的范例，使我们能够让受试者重复（但不引人注目地）使用不同的解决方案，并观察探索多种解决方案如何影响他们的运动行为。结果表明，行为在很大程度上受到运动记忆的影响，受试者往往会不自觉地重复最近的次优任务满足解决方案，即使他们已经有了足够的最优解决方案的经验。这表明中枢神经系统不是全局优化协同激活任务，而是在运动记忆、误差和努力最小化的权衡中确定运动行为。
