Stochastic optimal feedforward-feedback control determines timing and variability of arm movements with or without vision.
Stochastic optimal feedforward-feedback control determines timing and variability of arm movements with or without vision.
复制标题
随机最优前馈-反馈控制决定了有或没有视觉的手臂运动的时间和可变性。
DOI：
10.1371/journal.pcbi.1009047
复制
发表时间：
2021-06
影响因子：
4.3
通讯作者：
Burdet E
中科院分区：
文献类型：
--
作者：
Berret B;Conessa A;Schweighofer N;Burdet E
有或没有视觉的人类运动表现出的时间（即速度和持续时间）和变化特性，这是没有很好地捕捉到现有的计算模型。在这里，我们引入了一个随机最优前馈反馈控制（SFFC）模型，可以预测标称的时间和审判的试验变异性的自定步调的手臂达到运动进行或没有在线视觉反馈的手。在SFFC中，运动时间的结果，由于恒定的和信号依赖的运动噪声的努力和方差的内在因素的最小化，和运动的可变性取决于视觉反馈的整合。达到手臂运动数据被用来检查运动的时间和变化的影响，并测试模型的在线视觉。该模型表明，中枢神经系统预测感觉运动噪声的影响，以生成最佳前馈运动命令，并基于可用的肢体状态估计触发对任务相关错误的最佳反馈校正。随机最优反馈控制，这已被广泛用于模拟人类运动控制在过去的二十年中，提出了计算一个最优的电机命令在线的基础上估计的当前系统状态，使用感觉反馈。然而，这种建模方法低估了运动计划在运动开始之前产生适当的前馈运动命令的作用，这在当前肢体状态估计具有很大不确定性的条件下被强调，例如当缺乏视觉反馈时。在这里，我们提出了一个模型相结合的随机前馈和反馈控制来解决这个问题。新的随机前馈反馈（SFFC）模型考虑努力和方差最小化，以及电机和感官噪声的影响，规划和执行手臂动作。通过将随机最优控制的前馈和反馈方面以一种优雅的方式结合起来，SFFC可以预测在有或没有视觉反馈的情况下进行的运动的时间和变化，而以前的模型会在某个方面失败，或者必须使用临时修复。
Human movements with or without vision exhibit timing (i.e. speed and duration) and variability characteristics which are not well captured by existing computational models. Here, we introduce a stochastic optimal feedforward-feedback control (SFFC) model that can predict the nominal timing and trial-by-trial variability of self-paced arm reaching movements carried out with or without online visual feedback of the hand. In SFFC, movement timing results from the minimization of the intrinsic factors of effort and variance due to constant and signal-dependent motor noise, and movement variability depends on the integration of visual feedback. Reaching arm movements data are used to examine the effect of online vision on movement timing and variability, and test the model. This modelling suggests that the central nervous system predicts the effects of sensorimotor noise to generate an optimal feedforward motor command, and triggers optimal feedback corrections to task-related errors based on the available limb state estimate. Stochastic optimal feedback control, which has been extensively used to model human motor control in the last two decades, proposes to compute an optimal motor command online based on an estimation of the current system state using sensory feedback. However, this modelling approach underestimates the role of motor plans to generate appropriate feedforward motor command before the movement starts, which is emphasized in conditions with large uncertainty about current limb state estimates such as when visual feedback is lacking. Here we propose a model combining stochastic feedforward and feedback control to address this issue. The new stochastic feedforward-feedback (SFFC) model considers effort and variance minimization as well as the effects of motor and sensory noise both on planning and execution of arm movements. By combining the feedforward and feedback aspects of stochastically optimal control in an elegant way, SFFC can predict the timing and variability of movements carried out with or without visual feedback, while previous models would fail in one or another aspect, or have to use ad hoc fixes.
登录
查看更多内容
影响因子：
4.6
作者：
Berret B;Castanier C;Bastide S;Deroche T
通讯作者：
Deroche T
影响因子：
4.3
作者：
Berret, Bastien;Jean, Frederic
通讯作者：
Jean, Frederic
影响因子：
64.8
作者：
Harris, CM;Wolpert, DM
通讯作者：
Wolpert, DM
影响因子：
2.5
作者：
Berret, Bastien;Chiovetto, Enrico;Pozzo, Thierry
通讯作者：
Pozzo, Thierry
影响因子：
5.3
作者：
Baraduc, Pierre;Thobois, Stephane;Desmurget, Michel
通讯作者：
Desmurget, Michel