Bimanual proprioception: are two hands better than one?

Information about the position of an object that is held in both hands, such as a golf club or a tennis racquet, is transmitted to the human central nervous system from peripheral sensors in both left and right arms. How does the brain combine these two sources of information? Using a robot to move participant's passive limbs, we performed psychophysical estimates of proprioceptive function for each limb independently and again when subjects grasped the robot handle with both arms. We compared empirical estimates of bimanual proprioception to several models from the sensory integration literature: some that propose a combination of signals from the left and right arms (such as a Bayesian maximum-likelihood estimate), and some that propose using unimanual signals alone. Our results are consistent with the hypothesis that the nervous system both has knowledge of and uses the limb with the best proprioceptive acuity for bimanual proprioception. Surprisingly, a Bayesian model that postulates optimal combination of sensory signals could not predict empirically observed bimanual acuity. These findings suggest that while the central nervous system seems to have information about the relative sensory acuity of each limb, it uses this information in a rather rudimentary fashion, essentially ignoring information from the less reliable limb.

Spatially selective enhancement of proprioceptive acuity following motor learning.

It is well recognized that the brain uses sensory information to accurately produce motor commands. Indeed, most research into the relationship between sensory and motor systems has focused on how sensory information modulates motor function. In contrast, recent studies have begun to investigate the reverse: how sensory and perceptual systems are tuned based on motor function, and specifically motor learning. In the present study we investigated changes to human proprioceptive acuity following recent motor learning. Sensitivity to small displacements of the hand was measured before and after 10 min of motor learning, during which subjects grasped the handle of a robotic arm and guided a cursor to a series of visual targets randomly located within a small workspace region. We used a novel method of assessing proprioceptive acuity that avoids active movement, interhemispheric transfer, and intermodality coordinate transformations. We found that proprioceptive acuity improved following motor learning, but only in the region of the arm's workspace explored during learning. No proprioceptive improvement was observed when motor learning was performed in a different location or when subjects passively experienced limb trajectories matched to those of subjects who actively performed motor learning. Our findings support the idea that sensory changes occur in parallel with changes to motor commands during motor learning.

Mapping proprioception across a 2D horizontal workspace.

Relatively few studies have been reported that document how proprioception varies across the workspace of the human arm. Here we examined proprioceptive function across a horizontal planar workspace, using a new method that avoids active movement and interactions with other sensory modalities. We systematically mapped both proprioceptive acuity (sensitivity to hand position change) and bias (perceived location of the hand), across a horizontal-plane 2D workspace. Proprioception of both the left and right arms was tested at nine workspace locations and in 2 orthogonal directions (left-right and forwards-backwards). Subjects made repeated judgments about the position of their hand with respect to a remembered proprioceptive reference position, while grasping the handle of a robotic linkage that passively moved their hand to each judgement location. To rule out the possibility that the memory component of the proprioceptive testing procedure may have influenced our results, we repeated the procedure in a second experiment using a persistent visual reference position. Both methods resulted in qualitatively similar findings. Proprioception is not uniform across the workspace. Acuity was greater for limb configurations in which the hand was closer to the body, and was greater in a forward-backward direction than in a left-right direction. A robust difference in proprioceptive bias was observed across both experiments. At all workspace locations, the left hand was perceived to be to the left of its actual position, and the right hand was perceived to be to the right of its actual position. Finally, bias was smaller for hand positions closer to the body. The results of this study provide a systematic map of proprioceptive acuity and bias across the workspace of the limb that may be used to augment computational models of sensory-motor control, and to inform clinical assessment of sensory function in patients with sensory-motor deficits.

Effect of trial order and error magnitude on motor learning by observing.

Watching an actor make reaching movements in a perturbing force field provides the observer with information about how to compensate for that force field. Here we asked two questions about the nature of information provided to the observer. Is it important that the observer learn the difference between errant (curved) movements and goal (straight) movements by watching the actor progress in a relatively orderly fashion from highly curved to straight movements over a series of trials? Or is it sufficient that the observer sees only reaching errors in the force field (FF)? In the first experiment, we found that observers performed better if they observed reaches in a FF that was congruent, rather than incongruent, with the FF used in a later test. Observation-trial order had no effect on performance, suggesting that observers understood the goal in advance and perhaps learned about the force-field by observing movement curvature. Next we asked whether observers learn optimally by observing the actor's mistakes (high-error trials), if they learn by watching the actor perform with expertise in the FF (low-error trials), or if they need to see contrast between errant and goal behavior (a mixture of both high- and low-error trials). We found that observers who watched high-error trials were most affected by observation but that significant learning also occurred if observers watched only some high-error trials. This result suggests that observers learn to adapt their reaching to an unpredictable FF best when they see the actor making mistakes.

Limb stiffness is modulated with spatial accuracy requirements during movement in the absence of destabilizing forces.

The motor system can use a number of mechanisms to increase movement accuracy and compensate for perturbing external forces, interaction torques, and neuromuscular noise. Empirical studies have shown that stiffness modulation is one adaptive mechanism used to control arm movements in the presence of destabilizing external force loads. Other work has shown that arm muscle activity is increased at movement end for reaching movements to small visual targets and that changes in stiffness at movement end are oriented to match changes in visual accuracy requirements such as target shape. In this study, we assess whether limb stiffness is modulated to match spatial accuracy requirements during movement, conveyed using visual stimuli, in the absence of external force loads. Limb stiffness was estimated in the middle of reaching movements to visual targets located at the end of a narrow (8 mm) or wide (8 cm) visual track. When greater movement accuracy was required, we observed modest but reliable increases in limb stiffness in a direction perpendicular to the track. These findings support the notion that the motor system uses stiffness control to augment movement accuracy during movement and does so in the absence of external unstable force loads, in response to changing accuracy requirements conveyed using visual cues.

Repetitive transcranial magnetic stimulation to the primary motor cortex interferes with motor learning by observing.

Neural representations of novel motor skills can be acquired through visual observation. We used repetitive transcranial magnetic stimulation (rTMS) to test the idea that this "motor learning by observing" is based on engagement of neural processes for learning in the primary motor cortex (M1). Human subjects who observed another person learning to reach in a novel force environment imposed by a robot arm performed better when later tested in the same environment than subjects who observed movements in a different environment. rTMS applied to M1 after observation reduced the beneficial effect of observing congruent forces, and eliminated the detrimental effect of observing incongruent forces. Stimulation of a control site in the frontal cortex had no effect on reaching. Our findings represent the first direct evidence that neural representations of motor skills in M1, a cortical region whose role has been firmly established for active motor learning, also underlie motor learning by observing.

The influence of visual perturbations on the neural control of limb stiffness.

To adapt to novel unstable environments, the motor system modulates limb stiffness to produce selective increases in arm stability. The motor system receives information about the environment via somatosensory and proprioceptive signals related to the perturbing forces and visual signals indicating deviations from an expected hand trajectory. Here we investigated whether subjects modulate limb stiffness during adaptation to a purely visual perturbation. In a first experiment, measurements of limb stiffness were taken during adaptation to an elastic force field (EF). Observed changes in stiffness were consistent with previous reports: subjects increased limb stiffness and did so only in the direction of the environmental instability. In a second experiment, stiffness changes were measured during adaptation to a visual perturbing environment that magnified hand-path deviations in the lateral direction. In contrast to the first experiment, subjects trained in this visual task showed no accompanying change in stiffness, despite reliable improvements in movement accuracy. These findings suggest that this sort of visual information alone may not be sufficient to engage neural systems for stiffness control, which may depend on sensory signals more directly related to perturbing forces, such as those arising from proprioception and somatosensation.

Motor force field learning influences visual processing of target motion.

There are reciprocal connections between visual and motor areas of the cerebral cortex. Although recent studies have provided intriguing new insights, in comparison with volume of research on the visual control of movement, relatively little is known about how movement influences vision. The motor system is perfectly suited to learn about environmental forces. Does environmental force information, learned by the motor system, influence visual processing? Here, we show that learning to compensate for a force applied to the hand influenced how participants predicted target motion for interception. Ss trained in one of three constant force fields by making reaching movements while holding a robotic manipulandum. The robot applied forces in a null [null force field (NFF)], leftward [leftward force field (LFF)], or [rightward force field (RFF)] direction. Training was followed immediately with an interception task. The target accelerated from left to right and Ss's task was to stab it. When viewing time was optimal for prediction, the RFF group initiated their responses earlier and hit more targets, and the LFF group initiated their responses later and hit fewer targets, than the NFF group. In follow-up experiments, we show that motor learning is necessary, and we rule out the possibility that explicit force direction information drives how Ss altered their predictions of visual motion. Environmental force information, acquired by motor learning, influenced how the motion of nearby visual targets was predicted.