Al Borno, M. | Search bioRxiv

bioRxiv2019

High-fidelity Musculoskeletal Modeling Reveals a Motor Planning Contribution to the Speed-Accuracy Tradeoff

The speed-accuracy tradeoff is a fundamental aspect of goal-directed motor behavior, empirically formalized by Fitts law, which relates movement duration to movement distance and target width. Here, we introduce a computational model of three-dimensional upper extremity movements that reproduces well-known features of reaching movements and is more biomechanically realistic than previous models. Critically, these features arise without the need of signal-dependent noise. We analyzed motor cortical neural activity from monkeys reaching to targets of different sizes. We found that the contribution of preparatory neural states to movement duration variability was greater for smaller targets than larger targets, and that movements to smaller targets exhibited less variability in preparatory neural states, but greater movement duration variability. Taken together, these results suggest that Fitts law emerges from greater task demands constraining the optimization landscape in a fashion that reduces the number of "good" control solutions (i.e., faster reaches). Thus, the speed-accuracy tradeoff could be a consequence of motor planning variability and optimal control theory, and not exclusively signal-dependent noise, as is currently held. Significance StatementA long-standing challenge in motor neuroscience is to understand the relationship between movement speed and accuracy, known as the speed-accuracy tradeoff. We introduce a computational model of reaching movements based on optimal control theory using a realistic model of musculoskeletal dynamics. The model synthesizes three-dimensional point-to-point reaching movements that reproduce kinematics features reported in motor control studies. Such high-fidelity modeling reveals that the speed-accuracy tradeoff as described by Fitts law emerges even without the presence of motor noise, which is commonly believed to underlie the speed-accuracy tradeoff. This suggests an alternative theory based on suboptimal control solutions. The crux of this theory is that some features of human movement are attributable to planning variability rather than execution noise.

neuroscience↗

bioRxiv2019

The Effects of Motor Modularity on Performance, Generalizability and Learning in Upper-Extremity Reaching: a Computational Analysis

It has been hypothesized that the central nervous system simplifies the production of movement by limiting motor commands to a small set of modules known as muscle synergies. Recently, investigators have questioned whether a low-dimensional controller can produce the rich and flexible behaviors seen in everyday movements. To study this issue, we implemented muscle synergies in a biomechanically realistic model of the human upper extremity and performed computational experiments to determine whether synergies introduced task performance deficits, facilitated the learning of movements, and generalized to different movements. We derived sets of synergies from the muscle excitations our dynamic optimizations computed for a nominal task (reaching in a plane). Then we compared the performance and learning rates of a controller that activated all muscles independently to controllers that activated the synergies derived from the nominal reaching task. We found that a controller based on synergies had errors within 1 cm of a full-dimensional controller and achieved faster learning rates (as estimated from computational time to converge). The synergy-based controllers could also accomplish new tasks-such as reaching to targets on a higher or lower plane, and starting from alternate initial poses-with average errors similar to a full-dimensional controller.

physiology↗