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.
Highlights
The reduction of kinematic errors during reaching movements in the presence of novel dynamics occurs through at least two complementary mechanisms[1,2]
Participants can use a less-specific strategy of stiffening up the limb through muscle co-contraction[1,7,8,9] to reduce the kinematic errors that result from perturbing dynamics[10,11,12]
Several studies have shown that both these mechanisms contribute to the early compensation for novel dynamics[13,14,15,16,17] and that muscle co-contraction decreases as the internal model is learned[1,18,19,20]
Summary
The reduction of kinematic errors during reaching movements in the presence of novel dynamics occurs through at least two complementary mechanisms[1,2]. Participants can use a less-specific strategy of stiffening up the limb through muscle co-contraction (i.e., impedance control)[1,7,8,9] to reduce the kinematic errors that result from perturbing dynamics[10,11,12]. Several studies have shown that both these mechanisms contribute to the early compensation for novel dynamics[13,14,15,16,17] and that muscle co-contraction decreases as the internal model is learned[1,18,19,20]. We ask whether the increase in muscle co-contraction during exposure to novel dynamics affects the learning of the internal model. Even though kinematic errors are smaller during muscle co-contraction, the motor system may still adapt to the true internal model performance error. Or concurrently, the opposing effects of concentrated learning in state space (hypothesis one) and smaller kinematic errors (hypothesis two) could cancel out such that muscle co-contraction has no effect on learning
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