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首页> 外文期刊>Journal of Neurophysiology >Estimating properties of the fast and slow adaptive processes during sensorimotor adaptation
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Estimating properties of the fast and slow adaptive processes during sensorimotor adaptation

机译：估算Sensomotor适应期间快速和慢速自适应过程的特性

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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.
机译：预测误差的经验招募了多个电机学习过程，有些人从错误中学习强烈，但保持弱的保留和一些从错误中学习的一些人，但表现出强烈的保留。这些过程通常不可观察，而是从它们对行为的集体影响推断出来。是否有一种强大的方式来揭示隐藏的进程？一种标准方法是考虑一个状态空间模型，其中隐藏状态在错误的经验之后改变，然后通过最小化测量和模型预测之间的平方误差来将模型拟合到测量的数据。我们发现，这种最小二乘算法（LMSE）通常会产生关于隐藏状态的不切实际的预测，可能是因为它忽略了基于误差的学习的随机性质。我们发现，适应期间的行为数据更好地解释了基于误差的学习和运动生产是随机过程的系统。要揭示隐藏的学习状态，我们开发了一个广义期望最大化（EM）算法。在仿真中，我们发现，尽管LMSE在Mygly上跟踪了测量数据，但在揭露了学习状态的时间课程和属性中，EM在更准确的情况下更准确。在旨在测量介入对感觉电流学习的效果的功率分析中，EM显着降低了有效假设检测所需的受试者的数量。总之，我们开发了一种用于分析传感器实验中数据的新方法。新算法改进了揭示源于误差的多个进程的能力。

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