Separate representations of dynamics in rhythmic and discrete movements: evidence from motor learning.

Rhythmic and discrete arm movements occur ubiquitously in everyday life, and there is a debate as to whether these two classes of movements arise from the same or different underlying neural mechanisms. Here we examine interference in a motor-learning paradigm to test whether rhythmic and discrete movements employ at least partially separate neural representations. Subjects were required to make circular movements of their right hand while they were exposed to a velocity-dependent force field that perturbed the circularity of the movement path. The direction of the force-field perturbation reversed at the end of each block of 20 revolutions. When subjects made only rhythmic or only discrete circular movements, interference was observed when switching between the two opposing force fields. However, when subjects alternated between blocks of rhythmic and discrete movements, such that each was uniquely associated with one of the perturbation directions, interference was significantly reduced. Only in this case did subjects learn to corepresent the two opposing perturbations, suggesting that different neural resources were employed for the two movement types. Our results provide further evidence that rhythmic and discrete movements employ at least partially separate control mechanisms in the motor system.

Context-dependent partitioning of motor learning in bimanual movements.

Human subjects easily adapt to single dynamic or visuomotor perturbations. In contrast, when two opposing dynamic or visuomotor perturbations are presented sequentially, interference is often observed. We examined the effect of bimanual movement context on interference between opposing perturbations using pairs of contexts, in which the relative direction of movement between the two arms was different across the pair. When each perturbation direction was associated with a different bimanual context, such as movement of the arms in the same direction versus movement in the opposite direction, interference was dramatically reduced. This occurred over a short period of training and was seen for both dynamic and visuomotor perturbations, suggesting a partitioning of motor learning for the different bimanual contexts. Further support for this was found in a series of transfer experiments. Having learned a single dynamic or visuomotor perturbation in one bimanual context, subjects showed incomplete transfer of this learning when the context changed, even though the perturbation remained the same. In addition, we examined a bimanual context in which one arm was moved passively and show that the reduction in interference requires active movement. The sensory consequences of movement are thus insufficient to allow opposing perturbations to be co-represented. Our results suggest different bimanual movement contexts engage at least partially separate representations of dynamics and kinematics in the motor system.

Statistics of natural movements are reflected in motor errors.

Humans use their arms to engage in a wide variety of motor tasks during everyday life. However, little is known about the statistics of these natural arm movements. Studies of the sensory system have shown that the statistics of sensory inputs are key to determining sensory processing. We hypothesized that the statistics of natural everyday movements may, in a similar way, influence motor performance as measured in laboratory-based tasks. We developed a portable motion-tracking system that could be worn by subjects as they went about their daily routine outside of a laboratory setting. We found that the well-documented symmetry bias is reflected in the relative incidence of movements made during everyday tasks. Specifically, symmetric and antisymmetric movements are predominant at low frequencies, whereas only symmetric movements are predominant at high frequencies. Moreover, the statistics of natural movements, that is, their relative incidence, correlated with subjects' performance on a laboratory-based phase-tracking task. These results provide a link between natural movement statistics and motor performance and confirm that the symmetry bias documented in laboratory studies is a natural feature of human movement.