Huang Vincent S, Shadmehr Reza
Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.
J Neurophysiol. 2007 Jun;97(6):3976-85. doi: 10.1152/jn.01281.2006. Epub 2007 Apr 11.
When a movement results in error, the nervous system amends the motor commands that generate the subsequent movement. Here we show that this adaptation depends not just on error, but also on passage of time between the two movements. We observed that subjects learned a reaching task faster, i.e., with fewer trials, when the intertrial time intervals (ITIs) were lengthened. We hypothesized two computational mechanisms that could have accounted for this. First, learning could have been driven by a Bayesian process where the learner assumed that errors are the result of perturbations that have multiple timescales. In theory, longer ITIs can produce faster learning because passage of time might increase uncertainty, which in turn increases sensitivity to error. Second, error in a trial may result in a trace that decays with time. If the learner continued to sample from the trace during the ITI, then adaptation would increase with increased ITIs. The two models made separate predictions: The Bayesian model predicted that when movements are separated by random ITIs, the learner would learn most from a trial that followed a long time interval. In contrast, the trace model predicted that the learner would learn most from a trial that preceded a long time interval. We performed two experiments to test for these predictions and in both experiments found evidence for the trace model. We suggest that motor error produces an error memory trace that decays with a time constant of about 4 s, continuously promoting adaptation until the next movement.
当一个动作产生误差时，神经系统会修正产生后续动作的运动指令。我们在此表明，这种适应性不仅取决于误差，还取决于两个动作之间的时间间隔。我们观察到，当试验间隔时间（ITI）延长时，受试者学习一项伸手任务的速度更快，即试验次数更少。我们假设了两种可能解释这一现象的计算机制。首先，学习可能由贝叶斯过程驱动，学习者假设误差是具有多个时间尺度的扰动的结果。理论上，较长的ITI可以产生更快的学习，因为时间的流逝可能会增加不确定性，进而增加对误差的敏感度。其次，一次试验中的误差可能会产生随时间衰减的痕迹。如果学习者在ITI期间继续从该痕迹中采样，那么适应性会随着ITI的增加而增强。这两种模型做出了不同的预测：贝叶斯模型预测，当动作被随机的ITI隔开时，学习者会从长时间间隔后的试验中学到最多。相反，痕迹模型预测，学习者会从长时间间隔前的试验中学到最多。我们进行了两项实验来检验这些预测，并且在两项实验中都找到了支持痕迹模型的证据。我们认为运动误差会产生一个误差记忆痕迹，其以约4秒的时间常数衰减，持续促进适应性，直到下一个动作。
