Learning to move amid uncertainty
Learning to move amid uncertainty
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DOI：
10.1152/jn.2001.86.2.971
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发表时间：
2001-08-01
影响因子：
2.5
通讯作者：
Mussa-Ivaldi, FA
中科院分区：
文献类型：
--
作者：
Scheidt, RA;Dingwell, JB;Mussa-Ivaldi, FA
我们研究了受试者如何学会在不可预测的干扰下做出动作。12名健康的人体受试者在握住两关节机械手的手柄的同时，在水平面上进行目标定向的伸展运动。机器人产生了粘性力场，使肢体垂直于所需的运动方向。粘性场的幅度(但不是方向)随试验的不同而随机变化。系统识别技术被用来描述受试者如何适应这些随机扰动。受试者的表现主要是使用与直线手部路径的峰值偏差来量化的。受试者调整他们的手臂动作以适应随机的力场幅度序列。这种自适应响应补偿了来自随机扰动序列的近似平均值，并且不依赖于该序列的统计分布。受试者并不是通过直接抵消每次试验中的平均场强本身来适应，而是通过使用有关之前有限试验中的扰动和运动误差的信息来调整后续试验中的运动指令。这一策略允许受试者在保持计算效率的同时获得近乎最优的性能(定义为在最小二乘意义上最小化运动误差)。一个简单的模型使用之前单一试验的运动误差和扰动幅度信息，预测了受试者在随机环境中的高保真表现，并进一步预测了在非随机环境中观察到的关键表现特征。这表明，在运动适应过程中修改的神经结构只需要短期记忆。在过去的几次试验中，关于动作的明确表示没有被用来在任何给定的试验中产生最佳的运动反应。
We studied how subjects learned to make movements against unpredictable perturbations. Twelve healthy human subjects made goal-directed reaching movements in the horizontal plane while holding the handle of a two-joint robotic manipulator. The robot generated viscous force fields that perturbed the limb perpendicular to the desired direction of movement. The amplitude (but not the direction) of the viscous field varied randomly from trial to trial. Systems identification techniques were employed to characterize how subjects adapted to these random perturbations. Subject performance was quantified primarily using the peak deviation from a straight-line hand path. Subjects adapted their arm movements to the sequence of random force-field amplitudes. This adaptive response compensated for the approximate mean from the random sequence of perturbations and did not depend on the statistical distribution of that sequence. Subjects did not adapt by directly counteracting the mean field strength itself on each trial but rather by using information about perturbations and movement errors from a limited number of previous trials to adjust motor commands on subsequent trials. This strategy permitted subjects to achieve near-optimal performance (defined as minimizing movement errors in a least-squares sense) while maintaining computational efficiency. A simple model using information about movement errors and perturbation amplitudes from a single previous trial predicted subject performance in stochastic environments with a high degree of fidelity and further predicted key performance features observed in nonstochastic environments. This suggests that the neural structures modified during motor adaptation require only short-term memory. Explicit representations regarding movements made more than a few trials in the past are not used in generating optimal motor responses on any given trial.