Albert Scott T, Shadmehr Reza
Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine , Baltimore, Maryland.
J Neurophysiol. 2018 Apr 1;119(4):1367-1393. doi: 10.1152/jn.00197.2017. Epub 2017 Nov 29.
Experience of a prediction error recruits multiple motor learning processes, some that learn strongly from error but have weak retention and some that learn weakly from error but exhibit strong retention. These processes are not generally observable but are inferred from their collective influence on behavior. Is there a robust way to uncover the hidden processes? A standard approach is to consider a state space model where the hidden states change following experience of error and then fit the model to the measured data by minimizing the squared error between measurement and model prediction. We found that this least-squares algorithm (LMSE) often yielded unrealistic predictions about the hidden states, possibly because of its neglect of the stochastic nature of error-based learning. We found that behavioral data during adaptation was better explained by a system in which both error-based learning and movement production were stochastic processes. To uncover the hidden states of learning, we developed a generalized expectation maximization (EM) algorithm. In simulation, we found that although LMSE tracked the measured data marginally better than EM, EM was far more accurate in unmasking the time courses and properties of the hidden states of learning. In a power analysis designed to measure the effect of an intervention on sensorimotor learning, EM significantly reduced the number of subjects that were required for effective hypothesis testing. In summary, we developed a new approach for analysis of data in sensorimotor experiments. The new algorithm improved the ability to uncover the multiple processes that contribute to learning from error. NEW & NOTEWORTHY Motor learning is supported by multiple adaptive processes, each with distinct error sensitivity and forgetting rates. We developed a generalized expectation maximization algorithm that uncovers these hidden processes in the context of modern sensorimotor learning experiments that include error-clamp trials and set breaks. The resulting toolbox may improve the ability to identify the properties of these hidden processes and reduce the number of subjects needed to test the effectiveness of interventions on sensorimotor learning.
预测误差的经历会引发多种运动学习过程，其中一些过程能从误差中强烈学习但保持力较弱，而另一些过程从误差中学习较弱但保持力很强。这些过程通常无法直接观察到，而是从它们对行为的综合影响中推断出来。有没有一种可靠的方法来揭示这些隐藏的过程呢？一种标准方法是考虑一个状态空间模型，其中隐藏状态会随着误差经历而变化，然后通过最小化测量值与模型预测之间的平方误差，将模型拟合到测量数据上。我们发现这种最小二乘算法（LMSE）常常对隐藏状态产生不切实际的预测，可能是因为它忽略了基于误差学习的随机性。我们发现，适应过程中的行为数据可以用一个系统更好地解释，在这个系统中，基于误差的学习和动作产生都是随机过程。为了揭示学习的隐藏状态，我们开发了一种广义期望最大化（EM）算法。在模拟中，我们发现尽管LMSE在追踪测量数据方面比EM略好，但在揭示学习隐藏状态的时间进程和特性方面，EM要准确得多。在一项旨在测量干预对感觉运动学习影响的功效分析中，EM显著减少了有效假设检验所需的受试者数量。总之，我们开发了一种用于感觉运动实验数据分析的新方法。这种新算法提高了揭示从误差中学习所涉及的多个过程的能力。新内容与值得注意之处 运动学习由多种适应性过程支持，每个过程具有不同的误差敏感性和遗忘率。我们开发了一种广义期望最大化算法，该算法能在现代感觉运动学习实验（包括误差钳制试验和设置休息）的背景下揭示这些隐藏过程。由此产生的工具箱可能会提高识别这些隐藏过程特性的能力，并减少测试干预对感觉运动学习有效性所需的受试者数量。
