Increasing muscle co-contraction speeds up internal model acquisition during dynamic motor learning

Abstract

During reaching movements in the presence of novel dynamics, participants initially co-contract their muscles to reduce kinematic errors and improve task performance. As learning proceeds, muscle co-contraction decreases as an accurate internal model develops. The initial co-contraction could affect the learning of the internal model in several ways. By ensuring the limb remains close to the target state, co-contraction could speed up learning. Conversely, by reducing kinematic errors, a key training signal, it could slow down learning. Alternatively, given that the effects of muscle co-contraction on kinematic errors are predictable and could be discounted when assessing the internal model error, it could have no effect on learning. Using a sequence of force pulses, we pretrained two groups to either co-contract (stiff group) or relax (relaxed group) their arm muscles in the presence of dynamic perturbations. A third group (control group) was not pretrained. All groups performed reaching movements in a velocity-dependent curl field. We measured adaptation using channel trials and found greater adaptation in the stiff group during early learning. We also found a positive correlation between muscle co-contraction, as measured by surface electromyography, and adaptation. These results show that muscle co-contraction accelerates the rate of dynamic motor learning.

Adaptation of reach action to a novel force-field is not predicted by acuity of dynamic proprioception in either older or younger adults

“…Unlike Huang and Ahmed (2014), we did not observe age differences in adaptation. Accordingly, it could be that our older adults reduced their initially high co-contraction over time, as observed in younger adults (Heald et al 2018;Huang et al 2012;Huang and Ahmed 2014), thus minimising maladaptive consequences on their overall performance. Ability to modulate co-contraction and subsequent limb stiffness may, therefore, be a factor determining adaptation in advanced age.…”

Online corrective responses following target jump in altered gravitoinertial force field point to nested feedforward and feedback control

“…Increasing muscle activity when learning new arm dynamics is known to reduce movement errors and to accelerate the adaptation process (Heald et al 2018). This raises the possibility that the greater activity observed here in the pectoralis, biceps brachii and posterior deltoid muscles during movement corrections may have improved the efficiency of feedback control.…”

Adaptation of reach action to a novel force-field is not predicted by acuity of dynamic proprioception in either older or younger adults

“…Unlike Huang & Ahmed (2014), we did not observe age differences in adaptation. Accordingly, it could be that our older adults reduced their initially high co-contraction over time, as observed in younger adults (Heald et al, 2018;Huang et al, 2012;Huang & Ahmed, 2014), thus minimizing maladaptive consequences on their overall performance. Ability to modulate co-contraction and subsequent limb stiffness may therefore be a factor determining adaptation in advanced age.…”