Andrew A. G. Mattar

Motor learning by observing

Learning complex motor behaviors like riding a bicycle or swinging a golf club is based on acquiring neural representations of the mechanical requirements of movement (e.g., coordinating muscle forces to control the club). Here we provide evidence that mechanisms matching observation and action facilitate motor learning. Subjects who observed a video depicting another person learning to reach in a novel mechanical environment (imposed by a robot arm) performed better when later tested in the same environment than subjects who observed similar movements but no learning; moreover, subjects who observed learning of a different environment performed worse. We show that this effect is not based on conscious strategies but instead depends on the implicit engagement of neural systems for movement planning and control.

Somatosensory Plasticity and Motor Learning

Motor learning is dependent upon plasticity in motor areas of the brain, but does it occur in isolation, or does it also result in changes to sensory systems? We examined changes to somatosensory function that occur in conjunction with motor learning. We found that even after periods of training as brief as 10 min, sensed limb position was altered and the perceptual change persisted for 24 h. The perceptual change was reflected in subsequent movements; limb movements following learning deviated from the prelearning trajectory by an amount that was not different in magnitude and in the same direction as the perceptual shift. Crucially, the perceptual change was dependent upon motor learning. When the limb was displaced passively such that subjects experienced similar kinematics but without learning, no sensory change was observed. The findings indicate that motor learning affects not only motor areas of the brain but changes sensory function as well.

Hand-eye coordination for rapid pointing movements. Arm movement direction and distance are specified prior to saccade onset.

Visually guided arm movements such as reaching or pointing are accompanied by saccadic eye movements that typically begin prior to motion of the arm. In the past, some degree of coupling between the oculomotor and limb motor systems has been demonstrated by assessing the relative onset times of eye and arm movement, and by the demonstration of a gap effect for arm movement reaction times. However, measures of limb movement onset time based on kinematics are affected by factors such as the relatively high inertia of the limb and neuromechanical delays. The goal of the present study was thus to assess the relative timing of rapid eye and arm movements made to visual targets by examining electromyographic (EMG) activity of limb muscles in conjunction with eye and arm position measures. The observation of a positive correlation between eye and limb EMG onset latencies, and the presence of a gap effect for limb EMG onset times (a reduction in reaction time when a temporal gap is introduced between the disappearance of a central fixation point and the appearance of a new target) both support the idea that eye and arm movement initiation are linked. However, limb EMG onset in most cases precedes saccade onset, and the magnitude of EMG activity prior to eye movement is correlated with both the direction and amplitude of the upcoming arm movement. This suggests that, for the rapid movements studied here, arm movement direction and distance are specified prior to the onset of saccades.

Generalization of dynamics learning across changes in movement amplitude.

Studies on generalization show the nature of how learning is encoded in the brain. Previous studies have shown rather limited generalization of dynamics learning across changes in movement direction, a finding that is consistent with the idea that learning is primarily local. In contrast, studies show a broader pattern of generalization across changes in movement amplitude, suggesting a more general form of learning. To understand this difference, we performed an experiment in which subjects held a robotic manipulandum and made movements to targets along the body midline. Subjects were trained in a velocity-dependent force field while moving to a 15 cm target. After training, subjects were tested for generalization using movements to a 30 cm target. We used force channels in conjunction with movements to the 30 cm target to assess the extent of generalization. Force channels restricted lateral movements and allowed us to measure force production during generalization. We compared actual lateral forces to the forces expected if dynamics learning generalized fully. We found that, during the test for generalization, subjects produced reliably less force than expected. Force production was appropriate for the portion of the transfer movement in which velocities corresponded to those experienced with the 15 cm target. Subjects failed to produce the expected forces when velocities exceeded those experienced in the training task. This suggests that dynamics learning generalizes little beyond the range of ones experience. Consistent with this result, subjects who trained on the 30 cm target showed full generalization to the 15 cm target. We performed two additional experiments that show that interleaved trials to the 30 cm target during training on the 15 cm target can resolve the difference between the current results and those reported previously.

Motor learning and its sensory effects: time course of perceptual change and its presence with gradual introduction of load.

A complex interplay has been demonstrated between motor and sensory systems. We showed recently that motor learning leads to changes in the sensed position of the limb (Ostry DJ, Darainy M, Mattar AA, Wong J, Gribble PL. J Neurosci 30: 5384-5393, 2010). Here, we document further the links between motor learning and changes in somatosensory perception. To study motor learning, we used a force field paradigm in which subjects learn to compensate for forces applied to the hand by a robotic device. We used a task in which subjects judge lateral displacements of the hand to study somatosensory perception. In a first experiment, we divided the motor learning task into incremental phases and tracked sensory perception throughout. We found that changes in perception occurred at a slower rate than changes in motor performance. A second experiment tested whether awareness of the motor learning process is necessary for perceptual change. In this experiment, subjects were exposed to a force field that grew gradually in strength. We found that the shift in sensory perception occurred even when awareness of motor learning was reduced. These experiments argue for a link between motor learning and changes in somatosensory perception, and they are consistent with the idea that motor learning drives sensory change.

Effects of Human Arm Impedance on Dynamics Learning and Generalization

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.

Sensory change following motor learning.

Here we describe two studies linking perceptual change with motor learning. In the first, we document persistent changes in somatosensory perception that occur following force field learning. Subjects learned to control a robotic device that applied forces to the hand during arm movements. This led to a change in the sensed position of the limb that lasted at least 24 h. Control experiments revealed that the sensory change depended on motor learning. In the second study, we describe changes in the perception of speech sounds that occur following speech motor learning. Subjects adapted control of speech movements to compensate for loads applied to the jaw by a robot. Perception of speech sounds was measured before and after motor learning. Adapted subjects showed a consistent shift in perception. In contrast, no consistent shift was seen in control subjects and subjects that did not adapt to the load. These studies suggest that motor learning changes both sensory and motor function.