Abstract
Humans are capable of achieving complex tasks with redundant degrees of freedom. Much attention has been paid to task relevant variance modulation as an indication of online feedback control strategies to cope with motor variability. Meanwhile, it has been discussed that the brain learns internal models of environments to realize feedforward control with nominal trajectories. Here we examined trajectory variance in both spatial and temporal domains to elucidate the relative contribution of these control schemas. We asked subjects to learn reaching movements with multiple via-points, and found that hand trajectories converged to stereotyped trajectories with the reduction of task relevant variance modulation as learning proceeded. Furthermore, variance reduction was not always associated with task constraints but was highly correlated with the velocity profile. A model assuming noise both on the nominal trajectory and motor command was able to reproduce the observed variance modulation, supporting an expression of nominal trajectories in the brain. The learning-related decrease in task-relevant modulation revealed a reduction in the influence of optimal feedback around the task constraints. After practice, the major part of computation seems to be taken over by the feedforward controller around the nominal trajectory with feedback added only when it becomes necessary.
Highlights
Has been regarded as evidence of the absence of a plan[1,8,9]
The present study focused on variance modulation during human reaching movements in both spatial and temporal domains
We demonstrated (1) learning-related reduction of variance modulation and (2) velocity-dependent variance modulation. These results demonstrate convergence towards nominal trajectory after learning and suggest an expression of nominal trajectories in the brain
Summary
Has been regarded as evidence of the absence of a plan[1,8,9]. Feedforward control, on the other hand, solves the problem sequentially by dividing a complicated problem into several simple problems (divide and conquer). This approach normally requires an intermediate representation between the task and motor commands, e.g., a desired trajectory, that are not directly specified by the task constraint. Such a strategy assumes hierarchical implementation in the brain. Complicated problems are partly solved at the planning level before the start of movement[10,11,12,13,14] This type of approach stems from control theory in robotics, and physiological and imaging data suggest hierarchical information processing and modular characteristics of the brain[15,16,17]. The experimental paradigm suggests that trajectory optimization should take into account the dynamics of the nonlinear musculoskeletal system, which makes the problem of online optimization complex
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