Speech production involves some of the most precise and finely timed patterns of human movement. Here, in the context of jaw movement in speech, we show that spatial precision in speech production is systematically associated with the regulation of impedance and in particular, with jaw stiffness—a measure of resistance to displacement. We estimated stiffness and also variability during movement using a robotic device to apply brief force pulses to the jaw. Estimates of stiffness were obtained using the perturbed position and force trajectory and an estimate of what the trajectory would be in the absence of load. We estimated this “reference trajectory” using a new technique based on Fourier analysis. A moving-average (MA) procedure was used to estimate stiffness by modeling restoring force as the moving average of previous jaw displacements. The stiffness matrix was obtained from the steady state of the MA model. We applied this technique to data from 31 subjects whose jaw movements were perturbed during speech utterances and kinematically matched nonspeech movements. We observed systematic differences in stiffness over the course of jaw-lowering and jaw-raising movements that were correlated with measures of kinematic variability. Jaw stiffness was high and variability was low early and late in the movement when the jaw was elevated. Stiffness was low and variability was high in the middle of movement when the jaw was lowered. Similar patterns were observed for speech and nonspeech conditions. The systematic relationship between stiffness and variability points to the idea that stiffness regulation is integral to the control of orofacial movement variability.

Previous studies have demonstrated anisotropic patterns of hand impedance under static conditions and during movement. Here we show that the pattern of kinematic error observed in studies of dynamics learning is associated with this anisotropic impedance pattern. We also show that the magnitude of kinematic error associated with this anisotropy dictates the amount of motor learning and, consequently, the extent to which dynamics learning generalizes. Subjects were trained to reach to visual targets while holding a robotic device that applied forces during movement. On infrequent trials, the load was removed and the resulting kinematic error was measured. We found a strong correlation between the pattern of kinematic error and the anisotropic pattern of hand stiffness. In a second experiment subjects were trained under force-field conditions to move in two directions: one in which the dynamic perturbation was in the direction of maximum arm impedance and the associated kinematic error was low and another in which the perturbation was in the direction of low impedance where kinematic error was high. Generalization of learning was assessed in a reference direction that lay intermediate to the two training directions. We found that transfer of learning was greater when training occurred in the direction associated with the larger kinematic error. This suggests that the anisotropic patterns of impedance and kinematic error determine the magnitude of dynamics learning and the extent to which it generalizes.

Humans routinely make movements to targets that have different accuracy requirements in different directions. Examples extend from everyday occurrences such as grasping the handle of a coffee cup to the more refined instance of a surgeon positioning a scalpel. The attainment of accuracy in situations such as these might be related to the nervous system's capacity to regulate the limb's resistance to displacement, or impedance. To test this idea, subjects made movements from random starting locations to targets that had shape-dependent accuracy requirements. We used a robotic device to assess both limb impedance and patterns of movement variability just as the subject reached the target. We show that impedance increases in directions where required accuracy is high. Independent of target shape, patterns of limb stiffness are seen to predict spatial patterns of movement variability. The nervous system is thus seen to modulate limb impedance in entirely predictable environments to aid in the attainment of reaching accuracy.