For the purpose of motor coordination, the nervous system faces a complex control problem, involving redundant degrees of freedom, nonlinear dynamics of limbs and actuators, noise, and perturbations. Models and architectures have been proposed to describe motor coordination in terms of computational processes, and identify possible simplifying strategies that would alleviate the workload of the nervous system. Here, we review several strategies ranging from biomechanical to function levels. We conclude that none of the proposed strategies actually tackle the overall problem of motor coordination. Then we present a principled approach that provides an overarching account to motor control.

Models and architectures for motor control: Simple or complex?

We analyzed age-related changes in motor response in a visuomotor compensatory tracking task. Subjects used a manipulandum to attempt to keep a displayed cursor at the center of a screen despite random perturbations to its location. Cross-correlation analysis of the perturbation and the subject response showed no age-related increase in latency until the onset of response to the perturbation, but substantial slowing of the response itself. Results are consistent with age-related deterioration in the ratio of signal to noise in visuomotor response. The task is such that it is tractable to use Bayesian and quadratic optimality assumptions to construct a model for behavior. This model assumes that behavior resembles an optimal controller subject to noise, and parametrizes response in terms of latency, willingness to expend effort, noise intensity, and noise bandwidth. The model is consistent with the data for all young (n = 12, age 20-30) and most elderly (n = 12, age 65-92) subjects. The model reproduces the latency result from the cross-correlation method. When presented with increased noise, the computational model reproduces the experimentally observed age-related slowing and the observed lack of increased latency. The model provides a precise way to quantitatively formulate the long-standing hypothesis that age-related slowing is an adaptation to increased noise. Language: en

/pdf/slower-visuomotor-corrections-with-unchanged-latency-are-2r60i7dusn.pdf

Slower Visuomotor Corrections with Unchanged Latency are Consistent with Optimal Adaptation to Increased Endogenous Noise in the Elderly

This paper describes the methods used by the 2005 Cornell RoboCup team to generate and execute trajectories for its holonomic omnidirectional robots. The vehicles are described and equations of motion are presented. The estimation, motion planning, and control methods are described in the order in which they are executed by the system components. Alternative methods are briefly described. Experimental results are included.

/pdf/bayesian-and-quadratic-methods-in-system-identification-and-uxqjdd54nl.pdf

Bayesian And Quadratic Methods In System Identification And Iterative Learning Control

Computational and neural principles for human motor control

We showed that age-dependent changes in the vestibuloocular reflex are explained by a Bayesian optimal adaptation in the brain that occurs in response to age-dependent sensory anatomical changes. T...

Bayesian optimal adaptation explains age-related human sensorimotor changes.

We present a method of characterizing visuomotor response by inferring subject-specific physiologically meaningful parameters within the framework of optimal control theory. The characterization of visuomotor response is of interest in the assessment of impairment and rehabilitation, the analysis of man--machine systems, and sensorimotor research. We model the visuomotor response as a linear quadratic Gaussian (LQG) controller, a Bayesian optimal state estimator in series with a linear quadratic regulator. Subjects used a modified computer mouse to attempt to keep a displayed cursor at a fixed desired location despite a Gaussian random disturbance and simple cursor dynamics. Nearly all subjects' behavior was consistent with the hypothesized optimality. Experimental data were used to fit an LQG model whose assumptions are simple and consistent with other sensorimotor work. The parametrization is parsimonious and yields quantities of clear physiological meaning: noise intensity, level of exertion, delay, and noise bandwidth. Significant variations in response were observed, consistent with signal-dependent noise and changes in exerted effort. This is a novel example of the role of optimal control theory in explaining variance in human visuomotor response. We also present technical improvements on the use of LQG in human operator modeling.

/pdf/visuomotor-optimality-and-its-utility-in-parametrization-of-1qs6nyb9dy.pdf

Visuomotor Optimality and its Utility in Parametrization of Response

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Visuomotor Optimality and its Utility in Parametrization of Response