The origin of variability in goal-directed movements is not well understood. Variability can originate from several neural processes such as target localization, movement planning, and movement execution. Here we examine variability resulting from noise in movement execution. In several experiments, subjects moved their unseen hand to visual targets, under conditions which were designed to minimize the variability expected from localization and planning processes. We tested short movements in 32 directions in a center-out reaching task. The variability in the movement endpoints and in the initial movement direction varied systematically with the movement direction, with some directions having up to twice the variability of others. In a second experiment we tested four movements in the same direction but with different extents. Here, the longer movements were systematically curved, and the endpoint ellipses were not aligned with the straight line between starting and end position, but they were roughly aligned with the last part of the trajectory. We show that the variability observed in these experiments cannot be explained by planning noise but is well explained by noise in movement execution. A combination of both signal-dependent and signal-independent noise in the amplitude of the motor commands and temporal noise in their duration can explain the observed variability. Our results suggest that, in general, execution noise accounts for at least a large proportion of movement variability.

/pdf/the-role-of-execution-noise-in-movement-variability-43zn28sadw.pdf

The Role of Execution Noise in Movement Variability

Unifying principles of movement have emerged from the computational study of motor control. We review several of these principles and show how they apply to processes such as motor planning, control, estimation, prediction and learning. Our goal is to demonstrate how specific models emerging from the computational approach provide a theoretical framework for movement neuroscience.

/pdf/computational-motor-control-1rnsitnwx5.pdf

Computational motor control

The nervous system controls the behavior of complex kinematically redundant biomechanical systems. How it computes appropriate commands to generate movements is unknown. Here we propose a model based on the assumption that the nervous system: 1) processes static (e.g., gravitational) and dynamic (e.g., inertial) forces separately; 2) calculates appropriate dynamic controls to master the dynamic forces and progress toward the goal according to principles of optimal feedback control; 3) uses the size of the dynamic commands (effort) as an optimality criterion; and 4) can specify movement duration from a given level of effort. The model was used to control kinematic chains with 2, 4, and 7 degrees of freedom [planar shoulder/elbow, three-dimensional (3D) shoulder/elbow, 3D shoulder/elbow/wrist] actuated by pairs of antagonist muscles. The muscles were modeled as second-order nonlinear filters and received the dynamics commands as inputs. Simulations showed that the model can quantitatively reproduce characteristic features of pointing and grasping movements in 3D space, i.e., trajectory, velocity profile, and final posture. Furthermore, it accounted for amplitude/duration scaling and kinematic invariance for distance and load. These results suggest that motor control could be explained in terms of a limited set of computational principles.

/pdf/computational-motor-control-redundancy-and-invariance-dfr6s8u7fj.pdf

Computational Motor Control: Redundancy and Invariance

During bouncing gaits (running, hopping, trotting), passive compliant structures (e.g. tendons, ligaments) store and release part of the stride energy. Here, active muscles must provide the required force to withstand the developing tendon strain and to compensate for the inevitable energy losses. This requires an appropriate control of muscle activation. In this study, for hopping, the potential involvement of afferent information from muscle receptors (muscle spindles, Golgi tendon organs) is investigated using a two-segment leg model with one extensor muscle. It is found that: (i) positive feedbacks of muscle-fibre length and muscle force can result in periodic bouncing; (ii) positive force feedback (F+) stabilizes bouncing patterns within a large range of stride energies (maximum hopping height of 16.3 cm, almost twofold higher than the length feedback); and (iii) when employing this reflex scheme, for moderate hopping heights (up to 8.8 cm), an overall elastic leg behaviour is predicted (hopping frequency of 1.4-3 Hz, leg stiffness of 9-27 kN m(-1)). Furthermore, F+ could stabilize running. It is suggested that, during the stance phase of bouncing tasks, the reflex-generated motor control based on feedbacks might be an efficient and reliable alternative to central motor commands.

/pdf/positive-force-feedback-in-bouncing-gaits-1b8w8m31j7.pdf

Positive force feedback in bouncing gaits

During bouncing gaits (running, hopping, trotting), passive compliant structures (e.g. tendons, ligaments) store and release part of the stride energy. Here, active muscles must provide the required force to withstand the developing tendon strain and to compensate for the inevitable energy losses. This requires an appropriate control of muscle activation. In this study, for hopping, the potential involvement of afferent information from muscle receptors (muscle spindles, Golgi tendon organs) is investigated using a two–segment leg model with one extensor muscle. It is found that: (i) positive feedbacks of muscle–fibre length and muscle force can result in periodic bouncing; (ii) positive force feedback (F+) stabilizes bouncing patterns within a large range of stride energies (maximum hopping height of 16.3 cm, almost twofold higher than the length feedback); and (iii) when employing this reflex scheme, for moderate hopping heights (up to 8.8 cm), an overall elastic leg behaviour is predicted (hopping frequency of 1.4–3 Hz, leg stiffness of 9−27 kN m−1). Furthermore, F+ could stabilize running. It is suggested that, during the stance phase of bouncing tasks, the reflex–generated motor control based on feedbacks might be an efficient and reliable alternative to central motor commands.

Positive force feedback in bouncing gates

The Central Nervous System is unique in its capacity to control a wide variety of tasks, ranging from standing, walking, and jumping to fine motor tasks, such as grasping and manipulating. Typically, the actions of a controller require knowledge about the system to be controlled. It is likely that the CNS takes advantage of, or at least takes into account, nonlinear dynamic features of the musculoskeletal system resulting from multiple degree-of-freedom joints, ligaments, muscles, but also kinematic and actuator redundancy.

Leonard A. Rozendaal

Papers

Musculoskeletal Systems with Intrinsic and Proprioceptive Feedback