In many situations, the human movement system has more degrees of freedom than needed to achieve a given movement task. Martin et al. (Neural Comput 21(5):1371-1414, 2009) accounted for signatures of such redundancy like self-motion and motor equivalence in a process model in which a neural oscillator generated timed end-effector virtual trajectories that a neural dynamics transformed into joint virtual trajectories while decoupling task-relevant and task-irrelevant combinations of joint angles. Neural control of muscle activation and the biomechanical dynamics of the arm were taken into account. The model did not address the main signature of redundancy, however, the UCM structure of variance: Many experimental studies have shown that across repetitions, variance of joint configuration trajectories is structured. Combinations of joint angles that affect task variables (lying in the uncontrolled manifold, UCM) are much more variable than combinations of joint angles that do not. This finding has been robust across movement systems, age, and tasks and is often preserved in clinical populations as well. Here, we provide an account for the UCM structure of variance by adding four types of noise sources to the model of Martin et al. (Neural Comput 21(5):1371-1414, 2009). Comparing the model to human pointing movements and systematically examining the role of each noise source and mechanism, we identify three causes of the UCM effect, all of which, we argue, contribute: (1) the decoupling of motor commands across the task-relevant and task-irrelevant subspaces together with "neural" noise at the level of these motor commands; (2) "muscle noise" combined with imperfect control of the limb; (3) back-coupling of sensed joint configurations into the motor commands which then yield to the sensed joint configuration within the UCM.

中文翻译：

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指向运动中关节空间变化的不受控制的流形结构的过程说明。

在许多情况下，人类运动系统具有的自由度超过了实现给定运动任务所需的自由度。马丁等。（Neural Comput 21（5）：1371-1414，2009）解释了过程模型中这种冗余的特征，例如自运动和运动等效，其中神经振荡器生成定时的末端执行器虚拟轨迹，神经动力学将其转换为关节虚拟轨迹，同时将与任务相关和与任务无关的关节角度组合解耦。考虑了肌肉激活的神经控制和手臂的生物力学动力学。该模型未解决冗余的主要特征，但是，UCM的方差结构：许多实验研究表明，在重复过程中，关节配置轨迹的方差是结构化的。影响任务变量的关节角度组合（位于不受控制的歧管中，UCM）比不影响关节角度的组合具有更大的可变性。这一发现在运动系统，年龄和任务之间都非常可靠，并且通常也保留在临床人群中。在这里，我们通过在Martin等人的模型中添加四种类型的噪声源来说明UCM的方差结构。（Neural Comput 21（5）：1371-1414，2009）。将模型与人类指向运动进行比较，并系统地检查每个噪声源和机制的作用，我们确定了UCM效应的三个原因，我们认为，所有这些原因都有助于：（1）与任务相关的运动命令的去耦和与任务无关的子空间以及这些电动机命令级别的“神经”噪声；（2）“