Department of Neurology, University Hospital, LMU Munich, Fraunhoferstr. 20, 82152, Planegg, Martinsried, Germany.
School of Kinesiology and Health Science, Centre for Vision Research, York University, 4700 Keele Street, Toronto, ON, M3J 1P3, Canada.
Biol Cybern. 2021 Feb;115(1):59-86. doi: 10.1007/s00422-021-00858-w. Epub 2021 Feb 11.
Trial-to-trial variability during visuomotor adaptation is usually explained as the result of two different sources, planning noise and execution noise. The estimation of the underlying variance parameters from observations involving varying feedback conditions cannot be achieved by standard techniques (Kalman filter) because they do not account for recursive noise propagation in a closed-loop system. We therefore developed a method to compute the exact likelihood of the output of a time-discrete and linear adaptation system as has been used to model visuomotor adaptation (Smith et al. in PLoS Biol 4(6):e179, 2006), observed under closed-loop and error-clamp conditions. We identified the variance parameters by maximizing this likelihood and compared the model prediction of the time course of variance and autocovariance with empiric data. The observed increase in variability during the early training phase could not be explained by planning noise and execution noise with constant variances. Extending the model by signal-dependent components of either execution noise or planning noise showed that the observed temporal changes of the trial-to-trial variability can be modeled by signal-dependent planning noise rather than signal-dependent execution noise. Comparing the variance time course between different training schedules showed that the signal-dependent increase of planning variance was specific for the fast adapting mechanism, whereas the assumption of constant planning variance was sufficient for the slow adapting mechanisms.
在视觉运动适应过程中的trial-to-trial 可变性通常被解释为两个不同来源的结果，即计划噪声和执行噪声。由于它们没有考虑到闭环系统中的递归噪声传播，因此无法通过标准技术（卡尔曼滤波器）从涉及变化的反馈条件的观测中估计潜在的方差参数。因此，我们开发了一种方法来计算作为模型视觉运动适应的时间离散和线性适应系统的输出的精确似然（Smith 等人在 PLoS Biol 4(6):e179, 2006），观察到在闭环和误差钳制条件下。我们通过最大化这个似然来确定方差参数，并将模型对方差和自协方差的时间过程的预测与经验数据进行比较。在早期训练阶段观察到的可变性增加不能用恒定方差的计划噪声和执行噪声来解释。通过扩展执行噪声或计划噪声的信号相关分量的模型表明，观察到的trial-to-trial 可变性的时间变化可以用信号相关的计划噪声而不是信号相关的执行噪声来建模。比较不同训练计划之间的方差时间过程表明，规划方差的信号相关性增加是快速适应机制的特异性，而慢适应机制的假设是恒定的规划方差是足够的。
