ATR Computational Neuroscience Laboratories, Kyoto, Japan.
J Neurophysiol. 2013 Jul;110(1):1-11. doi: 10.1152/jn.00794.2011. Epub 2013 Apr 3.
We investigate adaptation under a reaching task with an acceleration-based force field perturbation designed to alter the nominal straight hand trajectory in a potentially benign manner: pushing the hand off course in one direction before subsequently restoring towards the target. In this particular task, an explicit strategy to reduce motor effort requires a distinct deviation from the nominal rectilinear hand trajectory. Rather, our results display a clear directional preference during learning, as subjects adapted perturbed curved trajectories towards their initial baselines. We model this behavior using the framework of stochastic optimal control theory and an objective function that trades off the discordant requirements of 1) target accuracy, 2) motor effort, and 3) kinematic invariance. Our work addresses the underlying objective of a reaching movement, and we suggest that robustness, particularly against internal model uncertainly, is as essential to the reaching task as terminal accuracy and energy efficiency.
我们研究了在基于加速度的力场干扰下进行的伸展任务中的适应情况，该干扰旨在以一种潜在良性的方式改变标称的直线手部轨迹：先将手推向一个方向，然后再向目标方向恢复。在这个特定的任务中，一种明确的降低运动努力的策略需要明显偏离标称的直线手部轨迹。然而，我们的结果显示，在学习过程中存在明显的方向偏好，因为受试者将受干扰的弯曲轨迹适应为其初始基线。我们使用随机最优控制理论的框架和一个目标函数来模拟这种行为，该目标函数权衡了 1）目标准确性、2）运动努力和 3）运动不变性的不协调要求。我们的工作解决了伸展运动的基本目标，我们认为稳健性，特别是对内部模型不确定性的稳健性，对于伸展任务来说与终端准确性和能量效率一样重要。
