Changes in the structure of motor variability during practicing a bimanual pointing task were investigated using the framework of the uncontrolled manifold (UCM) hypothesis. The subjects performed fast and accurate planar movements with both arms, one moving the pointer and the other moving the target. The UCM hypothesis predicts that joint kinematic variability will be structured to selectively stabilize important task variables. This prediction was tested with respect to selective stabilization of the trajectory of the endpoint of each arm (unimanual control hypotheses) and with respect to selective stabilization of the timecourse of the vectorial distance between the target and the pointer tip (bimanual control hypothesis). Components of joint position variance not affecting and affecting a mean value of a selected variable were computed at each 10% of normalized movement time. The ratio of these two components ( R(V)) served as a quantitative index of selective stabilization. Both unimanual control hypotheses and the bimanual control hypothesis were supported both prior to and after practice. However, the R(V) values for the bimanual control hypothesis were significantly higher than for either of the unimanual control hypothesis, suggesting that the bimanual synergy was not simply a simultaneous execution of two unimanual synergies. After practice, an improvement in both movement speed and accuracy was accompanied by counterintuitive changes in the structure of kinematic variability. Components of joint position variance affecting and not affecting a mean value of a selected variable decreased, but there was a significantly larger drop in the latter when applied on each of the three selected task variables corresponding to the three control hypotheses. We conclude that the UCM hypothesis allows quantitative assessment of the degree of stabilization of selected performance variables and provides information on changes in the structure of a multijoint synergy that may not be reflected in its overall performance.

The structure of joint angle variability and its changes with practice were investigated using the uncontrolled manifold (UCM) computational approach. Subjects performed fast and accurate bimanual pointing movements in 3D space, trying to match the tip of a pointer, held in the right hand, with the tip of one of three different targets, held in the left hand during a pre-test, several practice sessions and a post-test. The prediction of the UCM approach about the structuring of joint angle variance for selective stabilization of important task variables was tested with respect to selective stabilization of time series of the vectorial distance between the pointer and aimed target tips (bimanual control hypothesis) and with respect to selective stabilization of the endpoint trajectory of each arm (unimanual control hypothesis). The components of the total joint angle variance not affecting (V(COMP)) and affecting (V(UN)) the value of a selected task variable were computed for each 10% of the normalized movement time. The ratio of these two components R(V)=V(COMP)/V(UN) served as a quantitative index of selective stabilization. Both the bimanual and unimanual control hypotheses were supported, however the R(V) values for the bimanual hypothesis were significantly higher than those for the unimanual hypothesis applied to the left and right arm both prior to and after practice. This suggests that the CNS stabilizes the relative trajectory of one endpoint with respect to the other more than it stabilizes the trajectories of each of the endpoints in the external space. Practice-associated improvement in both movement speed and accuracy was accompanied by counter-intuitive lack of changes in R(V). Both V(COMP) and V(UN) variance components decreased such that their ratio remained constant prior to and after practice. We conclude that the UCM approach offers a unique and under-explored opportunity to track changes in the organization of multi-effector systems with practice and allows quantitative assessment of the degree of stabilization of selected performance variables.

It is well documented that neurological deficits after stroke can disrupt motor control processes that affect the smoothness of reaching movements. The smoothness of hand trajectories during multi-joint reaching depends on shoulder and elbow joint angular velocities and their successive derivatives as well as on the instantaneous arm configuration and its rate of change. Right-handed survivors of unilateral hemiparetic stroke and neurologically-intact control participants held the handle of a twojoint robot and made horizontal planar reaching movements. We decomposed endpoint jerk into components related to shoulder and elbow joint angular velocity, acceleration, and jerk. We observed an abnormal decomposition pattern in the most severely impaired stroke survivors consistent with deficits of inter-joint coordination. We then used numerical simulations of reaching movements to test whether the specific pattern of inter-joint coordination deficits observed experimentally could be explained by either a general increase in motor noise related to weakness or by an impaired ability to compensate for multi-joint interaction torque. Simulation results suggest that observed deficits in movement smoothness after stroke more likely reflect an impaired ability to compensate for multijoint interaction torques rather than the mere presence of elevated motor noise.