Do we use a priori knowledge of gravity when making elbow rotations?

In this study, we aim to investigate whether motor commands, emanating from movement planning, are customized to movement orientation relative to gravity from the first trial on. Participants made fast point-to-point elbow flexions and extensions in the transverse plane. We compared movements that had been practiced in reclined orientation either against or with gravity with the same movement relative to the body axis made in the upright orientation (neutral compared to gravity). For each movement type, five rotations from reclined to upright orientation were made. For each rotation, we analyzed the first trial in upright orientation and the directly preceding trial in reclined orientation. Additionally, we analyzed the last five trials of a 30-trial block in upright position and compared these trials with the first trials in upright orientation. Although participants moved fast, gravitational torques were substantial. The change in body orientation affected movement planning: we found a decrease in peak angular velocity and a decrease in amplitude for the first trials made in the upright orientation, regardless of whether the previous movements in reclined orientation were made against or with gravity. We found that these decreases disappeared after participants familiarized themselves with moving in upright position in a 30-trial block. These results indicate that participants used a general strategy, corresponding to the strategy observed in situations with unreliable or limited information on external conditions. From this, we conclude that during movement planning, a priori knowledge of gravity was not used to specifically customize motor commands for the neutral gravity condition

Optimality and modularity in human movement: from optimal control to muscle synergies

International audienceIn this chapter, we review recent work related to the optimal and modular control hypotheses for human movement. Optimal control theory is often thought to imply that the brain continuously computes global optima for each motor task it faces. Modular control theory typically assumes that the brain explicitly stores genuine synergies in specific neural circuits whose combined recruitment yields task-effective motor inputs to muscles. Put this way, these two influential motor control theories are pushed to extreme positions. A more nuanced view, framed within Marr’s tri-level taxonomy of a computational theory of movement neuroscience, is discussed here. We argue that optimal control is best viewed as helping to understand “why” certain movements are preferred over others but does not say much about how the brain would practically trigger optimal strategies. We also argue that dimensionality reduction found in muscle activities may be a by-product of optimality and cannot be attributed to neurally hardwired synergies stricto sensu, in particular when the synergies are extracted from simple factorization algorithms applied to electromyographic data; their putative nature is indeed strongly dictated by the methodology itself. Hence, more modeling work is required to critically test the modularity hypothesis and assess its potential neural origins. We propose that an adequate mathematical formulation of hierarchical motor control could help to bridge the gap between optimality and modularity, thereby accounting for the most appealing aspects of the human motor controller that robotic controllers would like to mimic: rapidity, efficiency, and robustness