Task space exploration improves adaptation after incompatible virtual surgeries

Abstract

Humans have a remarkable capacity to learn new motor skills, a process that requires novel muscle activity patterns. Muscle synergies may simplify the generation of muscle patterns through the selection of a small number of synergy combinations. Learning new motor skills may then be achieved by acquiring novel muscle synergies. In a previous study, we used myoelectric control to construct virtual surgeries that altered the mapping from muscle activity to cursor movements. After compatible virtual surgeries, which could be compensated by recombining subject-specific muscle synergies, participants adapted quickly. In contrast, after incompatible virtual surgeries, which could not be compensated by recombining existing synergies, participants explored new muscle patterns, but failed to adapt. Here, we tested whether task space exploration can promote learning of novel muscle synergies, required to overcome an incompatible surgery. Participants performed the same reaching task as in our previous study, but with more time to complete each trial, thus allowing for exploration. We found an improvement in trial success after incompatible virtual surgeries. Remarkably, improvements in movement direction accuracy after incompatible surgeries occurred faster for corrective movements than for the initial movement, suggesting that learning of new synergies is more effective when used for feedback control. Moreover, reaction time was significantly higher after incompatible than after compatible virtual surgeries, suggesting an increased use of an explicit adaptive strategy to overcome incompatible surgeries. Taken together, these results indicate that exploration is important for skill learning and suggest that human participants, with sufficient time can learn new muscle synergies.

Independent synaptic inputs to motor neurons driving antagonist muscles

“…This study suggests the existence of muscle synergies specifically recruited to modulate impedance and encoding co-contraction in their structures. The existence of muscle synergies has been supported by the low-dimensionality observed in the muscle patterns during several tasks (Borzelli et al, 2013;D'Avella et al, 2006;Dominici et al, 2011;Overduin et al, 2012;Torres-Oviedo & Ting, 2010) and by slower learning after EMG-to-force remapping that require new synergies (Borzelli et al, 2013;D'Avella et al, 2006;Dominici et al, 2011;Giszter et al, 2007;Hart & Giszter, 2010;Overduin et al, 2012;Torres-Oviedo & Ting, 2010), by slower learning after EMG-to-force remapping that require new synergies (Berger et al, 2013(Berger et al, , 2022Borzelli et al, 2022), but how a synergistic command drives pools of MNs of different muscles to modulate their firing rates is still unclear. Muscles driven by the same synergy show higher coherence (Danna-Dos-Santos et al, 2014;De Marchis et al, 2015) and pools of MNs of a single (Hug et al, 2021) or two synergistic (Del Vecchio et al, 2022) muscles are driven by different synaptic inputs.…”