Shared internal models for feedforward and feedback control of arm dynamics in non-human primates.

Previous work has shown that humans account for and learn novel properties or the arm's dynamics, and that such learning causes changes in both the predictive (i.e., feedforward) control of reaching and reflex (i.e., feedback) responses to mechanical perturbations. Here we show that similar observations hold in old-world monkeys (Macaca fascicularis). Two monkeys were trained to use an exoskeleton to perform a single-joint elbow reaching and to respond to mechanical perturbations that created pure elbow motion. Both of these tasks engaged robust shoulder muscle activity as required to account for the torques that typically arise at the shoulder when the forearm rotates around the elbow joint (i.e., intersegmental dynamics). We altered these intersegmental arm dynamics by having the monkeys generate the same elbow movements with the shoulder joint either free to rotate, as normal, or fixed by the robotic manipulandum, which eliminates the shoulder torques caused by forearm rotation. After fixing the shoulder joint, we found a systematic reduction in shoulder muscle activity. In addition, after releasing the shoulder joint again, we found evidence of kinematic aftereffects (i.e., reach errors) in the direction predicted if failing to compensate for normal arm dynamics. We also tested whether such learning transfers to feedback responses evoked by mechanical perturbations and found a reduction in shoulder feedback responses, as appropriate for these altered arm intersegmental dynamics. Demonstrating this learning and transfer in non-human primates will allow the investigation of the neural mechanisms involved in feedforward and feedback control of the arm's dynamics.

Learning New Feedforward Motor Commands Based on Feedback Responses.

Learning a new motor task modifies feedforward (i.e., voluntary) motor commands and such learning also changes the sensitivity of feedback responses (i.e., reflexes) to mechanical perturbations [1-9]. For example, after people learn to generate straight reaching movements in the presence of an external force field or learn to reduce shoulder muscle activity when generating pure elbow movements with shoulder fixation, evoked stretch reflex responses to mechanical perturbations reflect the learning expressed during self-initiated reaching. Such a transfer from feedforward motor commands to feedback responses is thought to take place because of shared neural circuits at the level of the spinal cord, brainstem, and cerebral cortex [10-13]. The presence of shared neural resources also predicts the transfer from feedback responses to feedforward motor commands. Little is known about such a transfer presumably because it is relatively hard to elicit learning in reflexes without engaging associated voluntary responses following mechanical perturbations. Here, we demonstrate such transfer by leveraging two approaches to elicit stretch reflexes while minimizing engagement of voluntary motor responses in the learning process: applying very short mechanical perturbations [14-19] and instructing participants to not respond to them [20-26]. Taken together, our work shows that transfer between feedforward and feedback control is bidirectional, furthering the notion that these processes share common neural circuits that underlie motor learning and transfer.

Sensory information from a slipping object elicits a rapid and automatic shoulder response.

Humans have the remarkable ability to hold, grasp, and manipulate objects. Previous work has reported rapid and coordinated reactions in hand and shoulder muscles in response to external perturbations to the arm during object manipulation; however, little is known about how somatosensory feedback of an object slipping in the hand influences responses of the arm. We built a handheld device to stimulate the sensation of slipping at all five fingertips. The device was integrated into an exoskeleton robot that supported it against gravity. The setup allowed us to decouple somatosensory stimulation in the fingers from forces applied to the arm, two variables that are highly interdependent in real-world scenarios. Fourteen participants performed three experiments in which we measured their arm feedback responses during slip stimulation. Slip stimulations were applied horizontally in one of two directions, and participants were instructed to either follow the slip direction or move the arm in the opposite direction. Participants showed shoulder muscle responses within ∼67 ms of slip onset when following the direction of slip but significantly slower responses when instructed to move in the opposite direction. Shoulder responses were modulated by the speed but not the distance of the slip. Finally, when slip stimulation was combined with mechanical perturbations to the arm, we found that sensory information from the fingertips significantly modulated the shoulder feedback responses. Overall, the results demonstrate the existence of a rapid feedback system that stabilizes handheld objects.NEW & NOTEWORTHY We tested whether the sensation of an object slipping from the fingers modulates shoulder feedback responses. We found rapid shoulder feedback responses when participants were instructed to follow the slip direction with the arm. Shoulder responses following mechanical joint perturbations were also potentiated when combined with slipping. These results demonstrate the existence of fast and automatic feedback responses in the arm in reaction to sensory input to the fingertips that maintain grip on handheld objects.

Generalizing movement patterns following shoulder fixation.

Generalizing newly learned movement patterns beyond the training context is challenging for most motor learning situations. Here we tested whether learning of a new physical property of the arm during self-initiated reaching generalizes to new arm configurations. Human participants performed a single-joint elbow reaching task and/or countered mechanical perturbations that created pure elbow motion with the shoulder joint free to rotate or locked by the manipulandum. With the shoulder free, we found activation of shoulder extensor muscles for pure elbow extension trials, appropriate for countering torques that arise at the shoulder due to forearm rotation. After locking the shoulder joint, we found a partial reduction in shoulder muscle activity, appropriate because locking the shoulder joint cancels the torques that arise at the shoulder due to forearm rotation. In our first three experiments, we tested whether and to what extent this partial reduction in shoulder muscle activity generalizes when reaching in different situations: 1) different initial shoulder orientation, 2) different initial elbow orientation, and 3) different reach distance/speed. We found generalization for the different shoulder orientation and reach distance/speed as measured by a reliable reduction in shoulder activity in these situations but no generalization for the different elbow orientation. In our fourth experiment, we found that generalization is also transferred to feedback control by applying mechanical perturbations and observing reflex responses in a distinct shoulder orientation. These results indicate that partial learning of new intersegmental dynamics is not sufficient for modifying a general internal model of arm dynamics.NEW & NOTEWORTHY Here we show that partially learning to reduce shoulder muscle activity following shoulder fixation generalizes to other movement conditions, but it does not generalize globally. These findings suggest that the partial learning of new intersegmental dynamics is not sufficient for modifying a general internal model of the arm's dynamics.

Feedforward and Feedback Control Share an Internal Model of the Arm's Dynamics.

Recent work has shown that, when countering external forces, the nervous system adjusts not only predictive (i.e., feedforward) control of reaching but also reflex (i.e., feedback) responses to mechanical perturbations. Here we show that altering the physical properties of the arm (i.e., intersegmental dynamics) causes the nervous system to adjust feedforward control and that this learning transfers to feedback responses even though the latter were never directly trained. Forty-five human participants (30 females) performed a single-joint elbow reaching task and countered mechanical perturbations that created pure elbow motion. In our first experiment, we altered intersegmental dynamics by asking participants to generate pure elbow movements when the shoulder joint was either free to rotate or locked by the robotic manipulandum. With the shoulder unlocked, we found robust activation of shoulder flexor muscles for pure elbow flexion trials, as required to counter the interaction torques that arise at the shoulder because of forearm rotation. After locking the shoulder joint, which cancels these interaction torques, we found a substantial reduction in shoulder muscle activity over many trials. In our second experiment, we tested whether such learning transfers to feedback control. Mechanical perturbations applied to the arm with the shoulder unlocked revealed that feedback responses also account for intersegmental dynamics. After locking the shoulder joint, we found a substantial reduction in shoulder feedback responses, as appropriate for the altered intersegmental dynamics. Our work suggests that feedforward and feedback control share an internal model of the arm's dynamics.SIGNIFICANCE STATEMENT Here we show that altering the physical properties of the arm causes people to learn new motor commands and that this learning transfers to their reflex responses to unexpected mechanical perturbations, even though the reflex responses were never directly trained. Our results suggest that feedforward motor commands and reflex responses share an internal model of the arm's dynamics.

Long-term retention and reconsolidation of a visuomotor memory.

Visuomotor adaptation is a form of motor learning that enables accurate limb movements in the presence of altered environmental or internal conditions. It requires updating the mapping between visual input and motor output, and can occur when learning a new device/tool or during rehabilitation after neurological injury. In either case, it is desirable to stabilize, or consolidate, this visuomotor memory for long-term usage. However, reactivation of a consolidated memory, whether it is motor-based or not, is thought to render it temporarily fragile again, and thus susceptible to interference or modification. Here, we determined if visuomotor memories demonstrate long-term retention but are fragile once reactivated. We used prism lenses to create a novel visuomotor mapping, which participants learned while having to walk and step to the center of targets. We re-tested this memory after one week and one year. We found that the mapping is retained for at least one year, regardless of whether participants were exposed to an interfering (i.e., opposing) mapping in the first session. We also found that presenting an opposing mapping in a block of trials following reactivation of the memory one year later did not disrupt subsequent performance when we re-tested the original memory. Our results suggest that these visuomotor memories are stored for extended periods of time and have limited fragility. Taken together, our results highlight the robustness of visuomotor memories associated with walking.

Compensating for intersegmental dynamics across the shoulder, elbow, and wrist joints during feedforward and feedback control.

Moving the arm is complicated by mechanical interactions that arise between limb segments. Such intersegmental dynamics cause torques applied at one joint to produce movement at multiple joints, and in turn, the only way to create single joint movement is by applying torques at multiple joints. We investigated whether the nervous system accounts for intersegmental limb dynamics across the shoulder, elbow, and wrist joints during self-initiated planar reaching and when countering external mechanical perturbations. Our first experiment tested whether the timing and amplitude of shoulder muscle activity account for interaction torques produced during single-joint elbow movements from different elbow initial orientations and over a range of movement speeds. We found that shoulder muscle activity reliably preceded movement onset and elbow agonist activity, and was scaled to compensate for the magnitude of interaction torques arising because of forearm rotation. Our second experiment tested whether elbow muscles compensate for interaction torques introduced by single-joint wrist movements. We found that elbow muscle activity preceded movement onset and wrist agonist muscle activity, and thus the nervous system predicted interaction torques arising because of hand rotation. Our third and fourth experiments tested whether shoulder muscles compensate for interaction torques introduced by different hand orientations during self-initiated elbow movements and to counter mechanical perturbations that caused pure elbow motion. We found that the nervous system predicted the amplitude and direction of interaction torques, appropriately scaling the amplitude of shoulder muscle activity during self-initiated elbow movements and rapid feedback control. Taken together, our results demonstrate that the nervous system robustly accounts for intersegmental dynamics and that the process is similar across the proximal to distal musculature of the arm as well as between feedforward (i.e., self-initiated) and feedback (i.e., reflexive) control.NEW & NOTEWORTHY Intersegmental dynamics complicate the mapping between applied joint torques and the resulting joint motions. We provide evidence that the nervous system robustly predicts these intersegmental limb dynamics across the shoulder, elbow, and wrist joints during reaching and when countering external perturbations.

Consolidation of visuomotor adaptation memory with consistent and noisy environments.

Our understanding of how we learn and retain motor behaviors is still limited. For instance, there is conflicting evidence as to whether the memory of a learned visuomotor perturbation consolidates; i.e., the motor memory becomes resistant to interference from learning a competing perturbation over time. Here, we sought to determine the factors that influence consolidation during visually guided walking. Subjects learned a novel mapping relationship, created by prism lenses, between the perceived location of two targets and the motor commands necessary to direct the feet to their positions. Subjects relearned this mapping 1 wk later. Different groups experienced protocols with or without a competing mapping (and with and without washout trials), presented either on the same day as initial learning or before relearning on day 2 We tested identical protocols under constant and noisy mapping structures. In the latter, we varied, on a trial-by-trial basis, the strength of prism lenses around a non-zero mean. We found that a novel visuomotor mapping is retained at least 1 wk after initial learning. We also found reduced foot-placement error with relearning in constant and noisy mapping groups, despite learning a competing mapping beforehand, and with the exception of one protocol, with and without washout trials. Exposure to noisy mappings led to similar performance on relearning compared with the equivalent constant mapping groups for most protocols. Overall, our results support the idea of motor memory consolidation during visually guided walking and suggest that constant and noisy practices are effective for motor learning. NEW & NOTEWORTHY: The adaptation of movement is essential for many daily activities. To interact with targets, this often requires learning the mapping to produce appropriate motor commands based on visual input. Here, we show that a novel visuomotor mapping is retained 1 wk after initial learning in a visually guided walking task. Furthermore, we find that this motor memory consolidates (i.e., becomes more resistant to interference from learning a competing mapping) when learning in constant and noisy mapping environments.

Foot placement relies on state estimation during visually guided walking.

As we walk, we must accurately place our feet to stabilize our motion and to navigate our environment. We must also achieve this accuracy despite imperfect sensory feedback and unexpected disturbances. In this study we tested whether the nervous system uses state estimation to beneficially combine sensory feedback with forward model predictions to compensate for these challenges. Specifically, subjects wore prism lenses during a visually guided walking task, and we used trial-by-trial variation in prism lenses to add uncertainty to visual feedback and induce a reweighting of this input. To expose altered weighting, we added a consistent prism shift that required subjects to adapt their estimate of the visuomotor mapping relationship between a perceived target location and the motor command necessary to step to that position. With added prism noise, subjects responded to the consistent prism shift with smaller initial foot placement error but took longer to adapt, compatible with our mathematical model of the walking task that leverages state estimation to compensate for noise. Much like when we perform voluntary and discrete movements with our arms, it appears our nervous systems uses state estimation during walking to accurately reach our foot to the ground. NEW & NOTEWORTHY: Accurate foot placement is essential for safe walking. We used computational models and human walking experiments to test how our nervous system achieves this accuracy. We find that our control of foot placement beneficially combines sensory feedback with internal forward model predictions to accurately estimate the body's state. Our results match recent computational neuroscience findings for reaching movements, suggesting that state estimation is a general mechanism of human motor control.

From specific training to global shift of manual preference in Kung Fu experts.

Manual preference and intermanual performance asymmetry have been approached from a multidimensional and dynamic perspective. A point of interest from that approach is the role of lateralized motor experiences on handedness. In this study, intermanual performance asymmetry in sport-specific movements and manual preference in daily living tasks were compared between Kung Fu athletes and novices. Analysis of movement time in the performance of interlaterally symmetric and asymmetric movement patterns showed smaller intermanual performance asymmetry in experts. Analysis of manual preference using the Edinburgh Handedness Inventory indicated that experts presented predominantly weak or moderate strength of right hand preference. Novices, conversely, were found to have predominantly strong right hand preference. These results suggest that extensive bimanual training by experts leads to a global shift of manual preference, affecting hand selection in distinct tasks.