Long-Latency Responses During Reaching Account for the Mechanical Interaction Between the Shoulder and Elbow Joints

Behavioral Motor Performance

The human sensorimotor control system has exceptional abilities to perform skillful actions. We easily switch between strenuous tasks that involve brute force, such as lifting a heavy sewing machine, and delicate movements such as threading a needle in the same machine. Using a structure with different control architectures, the motor system is capable of updating its ability to perform through our daily interaction with the fluctuating environment. However, there are issues that make this a difficult computational problem for the brain to solve. The brain needs to control a nonlinear, nonstationary neuromuscular system, with redundant and occasionally undesired degrees of freedom, in an uncertain environment using a body in which information transmission is subject to delays and noise. To gain insight into the mechanisms of motor control, here we survey movement laws and invariances that shape our everyday motion. We then examine the major solutions to each of these problems in the three parts of the sensorimotor control system, sensing, planning, and acting. We focus on how the sensory system, the control architectures, and the structure and operation of the muscles serve as complementary mechanisms to overcome deviations and disturbances to motor behavior and give rise to skillful motor performance. We conclude with possible future research directions based on suggested links between the operation of the sensorimotor system across the movement stages. © 2024 American Physiological Society. Compr Physiol 14:5179-5224, 2024.

The nervous system tunes sensorimotor gains when reaching in variable mechanical environments

Humans often move in the presence of mechanical disturbances that can vary in direction and amplitude throughout movement. These disturbances can jeopardize the outcomes of our actions, such as when drinking from a glass of water on a turbulent flight or carrying a cup of coffee while walking on a busy sidewalk. Here, we examine control strategies that allow the nervous system to maintain performance when reaching in the presence of mechanical disturbances that vary randomly throughout movement. Healthy participants altered their control strategies to make movements more robust against disturbances. The change in control was associated with faster reaching movements and increased responses to proprioceptive and visual feedback that were tuned to the variability of the disturbances. Our findings highlight that the nervous system exploits a continuum of control strategies to increase its responsiveness to sensory feedback when reaching in the presence of increasingly variable physical disturbances.

Adaptive Feedback Control in Human Reaching Adaptation to Force Fields

Sensorimotor adaptation is a central function of the nervous system, as it allows humans and other animals to flexibly anticipate their interaction with the environment. In the context of human reaching adaptation to force fields, studies have traditionally separated feedforward (FF) and feedback (FB) processes involved in the improvement of behavior. Here, we review computational models of FF adaptation to force fields and discuss them in light of recent evidence highlighting a clear involvement of feedback control. Instead of a model in which FF and FB mechanisms adapt in parallel, we discuss how online adaptation in the feedback control system can explain both trial-by-trial adaptation and improvements in online motor corrections. Importantly, this computational model combines sensorimotor control and short-term adaptation in a single framework, offering novel perspectives for our understanding of human reaching adaptation and control.

Similar stretch reflexes and behavioral patterns are expressed by the dominant and nondominant arms during postural control

Limb dominance is evident in many daily activities, leading to the prominent idea that each hemisphere of the brain specializes in controlling different aspects of movement. Past studies suggest that the dominant arm is primarily controlled via an internal model of limb dynamics that enables the nervous system to produce efficient movements. In contrast, the nondominant arm may be primarily controlled via impedance mechanisms that rely on the strong modulation of sensory feedback from individual joints to control limb posture. We tested whether such differences are evident in behavioral responses and stretch reflexes following sudden displacement of the arm during posture control. Experiment 1 applied specific combinations of elbow-shoulder torque perturbations (the same for all participants). Peak joint displacements, return times, end point accuracy, and the directional tuning and amplitude of stretch reflexes in nearly all muscles were not statistically different between the two arms. Experiment 2 induced specific combinations of joint motion (the same for all participants). Again, peak joint displacements, return times, end point accuracy, and the directional tuning and amplitude of stretch reflexes in nearly all muscles did not differ statistically when countering the imposed loads with each arm. Moderate to strong correlations were found between stretch reflexes and behavioral responses to the perturbations with the two arms across both experiments. Collectively, the results do not support the idea that the dominant arm specializes in exploiting internal models and the nondominant arm in impedance control by increasing reflex gains to counter sudden loads imposed on the arms during posture control.NEW & NOTEWORTHY A prominent hypothesis is that the nervous system controls the dominant arm through predictive internal models and the nondominant arm through impedance mechanisms. We tested whether stretch reflexes of muscles in the two arms also display such specialization during posture control. Nearly all behavioral responses and stretch reflexes did not differ statistically but were strongly correlated between the arms. The results indicate individual signatures of feedback control that are common for the two arms.

Rapid Feedback Responses Parallel the Urgency of Voluntary Reaching Movements

Optimal feedback control is a prominent theory used to interpret human motor behaviour. The theory posits that skilled actions emerge from control policies that link voluntary motor control (feedforward) with flexible feedback corrections (feedback control). It is clear the nervous system can generate flexible motor corrections (reflexes) when performing actions with different goals. We know little, however, about shared features of voluntary actions and feedback control in human movement. Here we reveal a link between the timing demands of voluntary actions and flexible responses to mechanical perturbations. In two experiments, 40 human participants (21 females) made reaching movements with different timing demands. We disturbed the arm with mechanical perturbations at movement onset (Experiment 1) and at locations ranging from movement onset to completion (Experiment 2). We used the resulting muscle responses and limb displacements as a proxy for the control policies that support voluntary reaching movements. We observed an increase in the sensitivity of elbow and shoulder muscle responses and a reduction in limb motion when the task imposed greater urgency to respond to the same perturbations. The results reveal a relationship between voluntary actions and feedback control as the limb was displaced less when moving faster in perturbation trials. Muscle responses scaled with changes in the displacement of the limb in perturbation trials within each timing condition. Across both experiments, human behaviour was captured by simulations based on stochastic optimal feedback control. Taken together, the results highlight flexible control that links sensory processing with features of human reaching movements.

Scaling of Joint Motion and Muscle Activation for 3-Dimensional Control of Reach Extent

This study investigated the scaling of upper arm kinematics, joint motion, and muscle activation for three-dimensional (3D) reaches to targets of increasing distance. Fifteen participants completed 108 total reaches to targets placed 7, 14, and 21 cm across midline. Peak velocity, acceleration, and time to peak velocity scaled to both target and movement distance. Shoulder and elbow excursion scaled to target distance and were highly coordinated. Anterior deltoid activation scaled to both target and movement distance in the early and late phases of reach control. Biceps and triceps activation scaled to movement distance primarily in the late phase. Scaling of these outcome variables provides a model for understanding the control of reach distance in a 3D environment.

Vestibular contributions to online reach execution are processed via mechanisms with knowledge about limb biomechanics

Studies of reach control with the body stationary have shown that proprioceptive and visual feedback signals contributing to rapid corrections during reaching are processed by neural circuits that incorporate knowledge about the physical properties of the limb (an internal model). However, among the most common spatial and mechanical perturbations to the limb are those caused by our body's own motion, suggesting that processing of vestibular signals for online reach control may reflect a similar level of sophistication. We investigated this hypothesis using galvanic vestibular stimulation (GVS) to selectively activate the vestibular sensors, simulating body rotation, as human subjects reached to remembered targets in different directions (forward, leftward, rightward). If vestibular signals contribute to purely kinematic/spatial corrections for body motion, GVS should evoke reach trajectory deviations of similar size in all directions. In contrast, biomechanical modeling predicts that if vestibular processing for online reach control takes into account knowledge of the physical properties of the limb and the forces applied on it by body motion, then GVS should evoke trajectory deviations that are significantly larger during forward and leftward reaches as compared with rightward reaches. When GVS was applied during reaching, the observed deviations were on average consistent with this prediction. In contrast, when GVS was instead applied before reaching, evoked deviations were similar across directions, as predicted for a purely spatial correction mechanism. These results suggest that vestibular signals, like proprioceptive and visual feedback, are processed for online reach control via sophisticated neural mechanisms that incorporate knowledge of limb biomechanics.NEW & NOTEWORTHY Studies examining proprioceptive and visual contributions to rapid corrections for externally applied mechanical and spatial perturbations during reaching have provided evidence for flexible processing of sensory feedback that accounts for musculoskeletal system dynamics. Notably, however, such perturbations commonly arise from our body's own motion. In line with this, we provide compelling evidence that, similar to proprioceptive and visual signals, vestibular signals are processed for online reach control via sophisticated mechanisms that incorporate knowledge of limb biomechanics.

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.

Distinct sensorimotor feedback loops for dynamic and static control of primate precision grip

Volitional limb motor control involves dynamic and static muscle actions. It remains elusive how such distinct actions are controlled through separated or shared neural circuits. Here we explored the potential separation for dynamic and static controls in primate hand actions, by investigating the neuronal coherence between local field potentials (LFPs) of the spinal cord and the forelimb electromyographic activity (EMGs), and LFPs of the motor cortex and the EMGs during the performance of a precision grip in macaque monkeys. We observed the emergence of beta-range coherence with EMGs at spinal cord and motor cortex in the separated phases; spinal coherence during the grip phase and cortical coherence during the hold phase. Further, both of the coherences were influenced by bidirectional interactions with reasonable latencies as beta oscillatory cycles. These results indicate that dedicated feedback circuits comprising spinal and cortical structures underlie dynamic and static controls of dexterous hand actions.

Rapid crossed responses in an intrinsic hand muscle during perturbed bimanual movements

Mechanical perturbations in one upper limb often elicit corrective responses in both the perturbed as well as its contralateral and unperturbed counterpart. These crossed corrective responses have been shown to be sensitive to the bimanual requirements of the perturbation, but crossed responses (CRs) in hand muscles are far less well studied. Here, we investigate corrective CRs in an intrinsic hand muscle, the first dorsal interosseous (1DI), to clockwise and anticlockwise mechanical perturbations to the contralateral index finger while participants performed a bimanual finger abduction task. We found that the CRs in the unperturbed 1DI were sensitive to the direction of the perturbation of the contralateral index finger. However, the size of the CRs was not sensitive to the amplitude of the contralateral perturbation nor its context within the bimanual task. The onset latency of the CRs was too fast to be purely transcortical (<70 ms) in 12/12 participants. This confirms that during isolated bimanual finger movements, sensory feedback from one hand can influence the other, but the pathways mediating the earliest components of this interaction are likely to involve subcortical systems such as the brainstem or spinal cord, which may afford less flexibility to the task demands.NEW & NOTEWORTHY An intrinsic hand muscle shows a crossed response to a perturbation of the contralateral index finger. The crossed response is dependent on the direction of the contralateral perturbation but not on the amplitude or the bimanual requirements of the movement, suggesting a far less flexible control policy than those governing crossed responses in more proximal muscles. The crossed response is too fast to be purely mediated by transcortical pathways, suggesting subcortical contributions.

Decision-making in sensorimotor control

Skilled sensorimotor interactions with the world result from a series of decision-making processes that determine, on the basis of information extracted during the unfolding sequence of events, which movements to make and when and how to make them. Despite this inherent link between decision-making and sensorimotor control, research into each of these two areas has largely evolved in isolation, and it is only fairly recently that researchers have begun investigating how they interact and, together, influence behaviour. Here, we review recent behavioural, neurophysiological and computational research that highlights the role of decision-making processes in the selection, planning and control of goal-directed movements in humans and nonhuman primates.

Stiffness as a control factor for object manipulation

During manipulation, force is exerted with the expectation that an object will move in an intended manner. This prediction is a learned coordination between force and movement. Mechanically, impedance is a way to describe this coordination, and object interaction could be anticipated by setting impedance before the hand moves the object. This strategy would be especially important at the end of a reach, because feedback is ineffective for rapid force changes. Since mechanical impedance is not subject to the time delays of feedback, it can, if set properly, produce the desired motion on impact. We examined this possibility by instructing subjects to move a handle to a specific target position along a track. The handle was locked in place until the subject exerted enough force to cross a threshold; the handle was then released abruptly to move along the track. We hypothesized that this ballistic release task would encourage subjects to modify impedance in anticipation of the upcoming movement and found that one component of impedance, stiffness, varied in a way that matched the behavioral demands of the task. Analysis suggests that this stiffness was set before the handle moved and governed the subsequent motion. We also found separate components of muscle activity that corresponded to stiffness and to changes in force. Our results show that subjects used a robust and efficient strategy to coordinate force and displacement by modulating muscle activity in a way that was behaviorally relevant in the task.NEW & NOTEWORTHY The arm can behave like a spring, and this mechanical behavior can be advantageous in situations requiring rapid changes in force and/or displacement. Selection of a proper "virtual" spring before the occurrence of a rapid transient could facilitate a desired responsive movement. We show that these spring-like arm mechanics, set in anticipation of an instantaneous force change, function as an efficient strategy to control movement when feedback is ineffective.

Shoulder reflexes integrate elbow information at "long-latency" delay throughout a corrective action

Previous studies have demonstrated a progression of function when healthy subjects counter a sudden mechanical load. Short-latency reflexes are linked to local stretch of the particular muscle and its antagonist. Long-latency reflexes integrate stretch information from both local sources and muscles crossing remote joints appropriate for a limb's mechanical interactions. Unresolved is how sensory information is processed throughout the corrective response, since capabilities at some time can be produced by circuits acting at that delay and at briefer delays. One possibility is that local abilities are always expressed at a short-latency delay and integrative abilities are always expressed at a long-latency delay. Alternatively, the neural circuits may be altered over time, leading to a temporal shift in expressing certain abilities; a refractory period could retard integrative responses to a second perturbation, whereas priming could enable integrative responses at short latency. We tested between these three hypotheses in a shoulder muscle by intermixing trials of step torque with either torque pulses ( experiment 1) or double steps of torque ( experiment 2). The second perturbation occurred at 35, 60, and 110 ms after the first perturbation to probe processing throughout the corrective action. The second perturbation reliably evoked short-latency responses in the shoulder muscle linked to only shoulder motion and long-latency responses linked to both shoulder and elbow motion. This pattern is best accounted by the continuous action of controllers with fixed functions. NEW & NOTEWORTHY Sudden displacement of the limb evokes a short-latency reflex, 20-50 ms, based on local muscle stretch and a long-latency reflex based on integrating muscle stretch at different joints. A novel double-perturbation paradigm tested if these abilities are temporally conserved throughout the corrective response or are shifted (retarded or delayed) due to functional changes in the responsible circuits. Multi-joint integration was reliably expressed at a long-latency delay consistent with the continuous operation of circuits with fixed abilities.

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-latency reflexes for inter-effector coordination reflect a continuous state feedback controller

Successful performance in many everyday tasks requires compensating unexpected mechanical disturbance to our limbs and body. The long-latency reflex plays an important role in this process because it is the fastest response to integrate sensory information across several effectors, like when linking the elbow and shoulder or the arm and body. Despite the dozens of studies on inter-effector long-latency reflexes, there has not been a comprehensive treatment of how these reveal the basic control organization that sets constraints on any candidate model of neural feedback control such as optimal feedback control. We considered three contrasting ways that controllers can be organized: multiple independent controllers vs. a multiple-input multiple-output (MIMO) controller, a continuous feedback controller vs. an intermittent feedback controller, and a direct MIMO controller vs. a state feedback controller. Following a primer on control theory and review of the relevant evidence, we conclude that continuous state feedback control best describes the organization of inter-effector coordination by the long-latency reflex.

Rapid feedback responses are flexibly coordinated across arm muscles to support goal-directed reaching

A transcortical pathway helps support goal-directed reaching by processing somatosensory information to produce rapid feedback responses across multiple joints and muscles. Here, we tested whether such feedback responses can account for changes in arm configuration and for arbitrary visuomotor transformations-two manipulations that alter how muscles at the elbow and wrist need to be coordinated to achieve task success. Participants used a planar three degree-of-freedom exoskeleton robot to move a cursor to a target following a mechanical perturbation that flexed the elbow. In our first experiment, the cursor was mapped to the veridical position of the robot handle, but participants grasped the handle with two different hand orientations (thumb pointing upward or thumb pointing downward). We found that large rapid feedback responses were evoked in wrist extensor muscles when wrist extension helped move the cursor to the target (i.e., thumb upward), and in wrist flexor muscles when wrist flexion helped move the cursor to the target (i.e., thumb downward). In our second experiment, participants grasped the robot handle with their thumb pointing upward, but the cursor's movement was either veridical or was mirrored such that flexing the wrist moved the cursor as if the participant extended their wrist, and vice versa. After extensive practice, we found that rapid feedback responses were appropriately tuned to the wrist muscles that supported moving the cursor to the target when the cursor was mapped to the mirrored movement of the wrist, but were not tuned to the appropriate wrist muscles when the cursor was remapped to the wrist's veridical movement. NEW & NOTEWORTHY We show that rapid feedback responses were evoked in different wrist muscles depending on the arm's orientation, and this muscle activity was appropriate to generate the wrist motion that supported a reaching action. Notably, we also show that these rapid feedback responses can be evoked in wrist muscles that are detrimental to a reaching action if a nonveridical mapping between wrist and hand motion is extensively learned.

Perspectives on classical controversies about the motor cortex

Primary motor cortex has been studied for more than a century, yet a consensus on its functional contribution to movement control is still out of reach. In particular, there remains controversy as to the level of control produced by motor cortex ("low-level" movement dynamics vs. "high-level" movement kinematics) and the role of sensory feedback. In this review, we present different perspectives on the two following questions: What does activity in motor cortex reflect? and How do planned motor commands interact with incoming sensory feedback during movement? The four authors each present their independent views on how they think the primary motor cortex (M1) controls movement. At the end, we present a dialogue in which the authors synthesize their views and suggest possibilities for moving the field forward. While there is not yet a consensus on the role of M1 or sensory feedback in the control of upper limb movements, such dialogues are essential to take us closer to one.

Rapid visuomotor feedback gains are tuned to the task dynamics

Here, we test whether rapid visuomotor feedback responses are selectively tuned to the task dynamics. The responses do not exhibit gain scaling, but they do vary with the level and stability of task dynamics. Moreover, these feedback gains are independently tuned to perturbations to the left and right, depending on these dynamics. Our results demonstrate that the sensorimotor control system regulates the feedback gain as part of the adaptation process, tuning them appropriately to the environment.

Adaptation to novel dynamics requires learning a motor memory, or a new pattern of predictive feedforward motor commands. Recently, we demonstrated the upregulation of rapid visuomotor feedback gains early in curl force field learning, which decrease once a predictive motor memory is learned. However, even after learning is complete, these feedback gains are higher than those observed in the null field trials. Interestingly, these upregulated feedback gains in the curl field were not observed in a constant force field. Therefore, we suggest that adaptation also involves selectively tuning the feedback sensitivity of the sensorimotor control system to the environment. Here, we test this hypothesis by measuring the rapid visuomotor feedback gains after subjects adapt to a variety of novel dynamics generated by a robotic manipulandum in three experiments. To probe the feedback gains, we measured the magnitude of the motor response to rapid shifts in the visual location of the hand during reaching. While the feedback gain magnitude remained similar over a larger than a fourfold increase in constant background load, the feedback gains scaled with increasing lateral resistance and increasing instability. The third experiment demonstrated that the feedback gains could also be independently tuned to perturbations to the left and right, depending on the lateral resistance, demonstrating the fractionation of feedback gains to environmental dynamics. Our results show that the sensorimotor control system regulates the gain of the feedback system as part of the adaptation process to novel dynamics, appropriately tuning them to the environment.

NEW & NOTEWORTHY Here, we test whether rapid visuomotor feedback responses are selectively tuned to the task dynamics. The responses do not exhibit gain scaling, but they do vary with the level and stability of task dynamics. Moreover, these feedback gains are independently tuned to perturbations to the left and right, depending on these dynamics. Our results demonstrate that the sensorimotor control system regulates the feedback gain as part of the adaptation process, tuning them appropriately to the environment.

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.

Human-Inspired Eigenmovement Concept Provides Coupling-Free Sensorimotor Control in Humanoid Robot

Control of a multi-body system in both robots and humans may face the problem of destabilizing dynamic coupling effects arising between linked body segments. The state of the art solutions in robotics are full state feedback controllers. For human hip-ankle coordination, a more parsimonious and theoretically stable alternative to the robotics solution has been suggested in terms of the Eigenmovement (EM) control. Eigenmovements are kinematic synergies designed to describe the multi DoF system, and its control, with a set of independent, and hence coupling-free, scalar equations. This paper investigates whether the EM alternative shows “real-world robustness” against noisy and inaccurate sensors, mechanical non-linearities such as dead zones, and human-like feedback time delays when controlling hip-ankle movements of a balancing humanoid robot. The EM concept and the EM controller are introduced, the robot's dynamics are identified using a biomechanical approach, and robot tests are performed in a human posture control laboratory. The tests show that the EM controller provides stable control of the robot with proactive (“voluntary”) movements and reactive balancing of stance during support surface tilts and translations. Although a preliminary robot-human comparison reveals similarities and differences, we conclude (i) the Eigenmovement concept is a valid candidate when different concepts of human sensorimotor control are considered, and (ii) that human-inspired robot experiments may help to decide in future the choice among the candidates and to improve the design of humanoid robots and robotic rehabilitation devices.

Rapid and flexible whole body postural responses are evoked from perturbations to the upper limb during goal-directed reaching

An important aspect of motor control is the ability to perform tasks with the upper limbs while maintaining whole body balance. However, little is known about the coordination of upper limb voluntary and whole body postural control after mechanical disturbances that require both upper limb motor corrections to attain a behavioral goal and lower limb motor responses to maintain whole body balance. The present study identified the temporal organization of muscle responses and center of pressure (COP) changes following mechanical perturbations during reaching. Our results demonstrate that muscle responses in the upper limb are evoked first (∼50 ms), with lower limb muscle activity occurring immediately after, in as little as ∼60 ms after perturbation. Hand motion was immediately altered by the load, while COP changes occurred after ∼100 ms, when lower limb muscle activity was already present. Our secondary findings showed that both muscle activity and COP changes were influenced by behavioral context (by altering target shape, circle vs. rectangle). Voluntary and postural actions initially directed the hand toward the center of both target types, but after the perturbation upper limb and postural responses redirected the hand toward different spatial locations along the rectangle. Muscle activity was increased for both upper and lower limbs when correcting to the circle vs. the rectangle, and these differences emerged as early as the long-latency epoch (∼75-120 ms). Our results demonstrate that postural responses are rapidly and flexibly altered to consider the behavioral goal of the upper limb.NEW & NOTEWORTHY The present work establishes that, when reaching to a target while standing, perturbations applied to the upper limb elicit a rapid response in lower limb muscles. Unlike voluntary movements, postural responses do not occur before corrections of the upper limb. We show the first evidence that corrective postural adjustments are modulated by upper limb behavioral context (target shape). Importantly, this indicates that postural responses take into account upper limb feedback for online control.

Coordinating long-latency stretch responses across the shoulder, elbow, and wrist during goal-directed reaching

The long-latency stretch response (muscle activity 50-100 ms after a mechanical perturbation) can be coordinated across multiple joints to support goal-directed actions. Here we assessed the flexibility of such coordination and whether it serves to counteract intersegmental dynamics and exploit kinematic redundancy. In three experiments, participants made planar reaches to visual targets after elbow perturbations and we assessed the coordination of long-latency stretch responses across shoulder, elbow, and wrist muscles. Importantly, targets were placed such that elbow and wrist (but not shoulder) rotations could help transport the hand to the target-a simple form of kinematic redundancy. In experiment 1 we applied perturbations of different magnitudes to the elbow and found that long-latency stretch responses in shoulder, elbow, and wrist muscles scaled with perturbation magnitude. In experiment 2 we examined the trial-by-trial relationship between long-latency stretch responses at adjacent joints and found that the magnitudes of the responses in shoulder and elbow muscles, as well as elbow and wrist muscles, were positively correlated. In experiment 3 we explicitly instructed participants how to use their wrist to move their hand to the target after the perturbation. We found that long-latency stretch responses in wrist muscles were not sensitive to our instructions, despite the fact that participants incorporated these instructions into their voluntary behavior. Taken together, our results indicate that, during reaching, the coordination of long-latency stretch responses across multiple joints counteracts intersegmental dynamics but may not be able to exploit kinematic redundancy.

Distributed task-specific processing of somatosensory feedback for voluntary motor control

Corrective responses to limb disturbances are surprisingly complex, but the neural basis of these goal-directed responses is poorly understood. Here we show that somatosensory feedback is transmitted to many sensory and motor cortical regions within 25 ms of a mechanical disturbance applied to the monkey’s arm. When limb feedback was salient to an ongoing motor action (task engagement), neurons in parietal area 5 immediately (~25 ms) increased their response to limb disturbances, whereas neurons in other regions did not alter their response until 15 to 40 ms later. In contrast, initiation of a motor action elicited by a limb disturbance (target selection) altered neural responses in primary motor cortex ~65 ms after the limb disturbance, and then in dorsal premotor cortex, with no effect in parietal regions until 150 ms post-perturbation. Our findings highlight broad parietofrontal circuits that provide the neural substrate for goal-directed corrections, an essential aspect of highly skilled motor behaviors.

DOI:http://dx.doi.org/10.7554/eLife.13141.001

Humans and other animals can change a movement they are making in a split second, such as when a basketball player has to move around an unexpected opponent to shoot a ball through the hoop. These on-the-fly corrections rely on information about the movement that comes in from the senses. However, it was unclear how the brain and spinal cord process this sensory information to guide movement.

Omrani et al. have now recorded electrical activity from the brains of monkeys while the animals tried to keep their hand at a target. Each monkey wore a robotic exoskeleton that would occasionally move the monkey’s arm. Even if the monkey was not engaged in a motor task, a small nudge of the limb by the robot caused neural activity to spread rapidly throughout the sensory and motor regions of the cerebral cortex (the outer layer of the brain).

In some trials, when the monkey was actively trying to keep its hand at a target, the robot would again nudge the monkey’s arm slightly. Omrani et al. observed that within 25 milliseconds of this nudge, the activity in an area of the cortex called parietal area 5 responded even more, suggesting that this area was using information from the senses to actively deal with the change in arm position. This change in activity then spread to other parts of the brain.

In another set of trials, the monkey was trained to move to a second target if the robot nudged its arm. In this case, the activity in an area called the primary motor cortex increased even more, likely supporting the monkey’s ability to rapidly move to this second target. Overall, the study by Omrani et al. highlights the complex way that sensory feedback is processed in the cerebral cortex, supporting our ability to perform highly skilled motor actions.

DOI:http://dx.doi.org/10.7554/eLife.13141.002

Haptic Guidance Needs to Be Intuitive Not Just Informative to Improve Human Motor Accuracy

Humans make both random and systematic errors when reproducing learned movements. Intuitive haptic guidance that assists one to make the movements reduces such errors. Our study examined whether any additional haptic information about the location of the target reduces errors in a position reproduction task, or whether the haptic guidance needs to be assistive to do so. Holding a haptic device, subjects made reaches to visible targets without time constraints. They did so in a no-guidance condition, and in guidance conditions in which the direction of the force with respect to the target differed, but the force scaled with the distance to the target in the same way. We examined whether guidance forces directed towards the target would reduce subjects’ errors in reproducing a prior position to the same extent as do forces rotated by 90 degrees or 180 degrees, as it might because the forces provide the same information in all three cases. Without vision of the arm, both the accuracy and precision were significantly better with guidance directed towards the target than in all other conditions. The errors with rotated guidance did not differ from those without guidance. Not surprisingly, the movements tended to be faster when guidance forces directed the reaches to the target. This study shows that haptic guidance significantly improved motor performance when using it was intuitive, while non-intuitively presented information did not lead to any improvements and seemed to be ignored even in our simple paradigm with static targets and no time constraints.

Long-latency reflexes of elbow and shoulder muscles suggest reciprocal excitation of flexors, reciprocal excitation of extensors, and reciprocal inhibition between flexors and extensors

Postural corrections of the upper limb are required in tasks ranging from handling an umbrella in the changing wind to securing a wriggling baby. One complication in this process is the mechanical interaction between the different segments of the arm where torque applied at one joint induces motion at multiple joints. Previous studies have shown the long-latency reflexes of shoulder muscles (50-100 ms after a limb perturbation) account for these mechanical interactions by integrating information about motion of both the shoulder and elbow. It is less clear whether long-latency reflexes of elbow muscles exhibit a similar capability and what is the relation between the responses of shoulder and elbow muscles. The present study utilized joint-based loads tailored to the subjects' arm dynamics to induce well-controlled displacements of their shoulder and elbow. Our results demonstrate that the long-latency reflexes of shoulder and elbow muscles integrate motion from both joints: the shoulder and elbow flexors respond to extension at both joints, whereas the shoulder and elbow extensors respond to flexion at both joints. This general pattern accounts for the inherent flexion-extension coupling of the two joints arising from the arm's intersegmental dynamics and is consistent with spindle-based reciprocal excitation of shoulder and elbow flexors, reciprocal excitation of shoulder and elbow extensors, and across-joint inhibition between the flexors and extensors.

Goal-dependent modulation of the long-latency stretch response at the shoulder, elbow, and wrist

Many studies have demonstrated that muscle activity 50-100 ms after a mechanical perturbation (i.e., the long-latency stretch response) can be modulated in a manner that reflects voluntary motor control. These previous studies typically assessed modulation of the long-latency stretch response from individual muscles rather than how this response is concurrently modulated across multiple muscles. Here we investigated such concurrent modulation by having participants execute goal-directed reaches to visual targets after mechanical perturbations of the shoulder, elbow, or wrist while measuring activity from six muscles that articulate these joints. We found that shoulder, elbow, and wrist muscles displayed goal-dependent modulation of the long-latency stretch response, that the relative magnitude of participants' goal-dependent activity was similar across muscles, that the temporal onset of goal-dependent muscle activity was not reliably different across the three joints, and that shoulder muscles displayed goal-dependent activity appropriate for counteracting intersegmental dynamics. We also observed that the long-latency stretch response of wrist muscles displayed goal-dependent modulation after elbow perturbations and that the long-latency stretch response of elbow muscles displayed goal-dependent modulation after wrist perturbations. This pattern likely arises because motion at either joint could bring the hand to the visual target and suggests that the nervous system rapidly exploits such simple kinematic redundancy when processing sensory feedback to support goal-directed actions.

Neurons in red nucleus and primary motor cortex exhibit similar responses to mechanical perturbations applied to the upper-limb during posture

Primary motor cortex (M1) and red nucleus (RN) are brain regions involved in limb motor control. Both structures are highly interconnected with the cerebellum and project directly to the spinal cord, although the contribution of RN is smaller than M1. It remains uncertain whether RN and M1 serve similar or distinct roles during posture and movement. Many neurons in M1 respond rapidly to mechanical disturbances of the limb, but it remains unclear whether RN neurons also respond to such limb perturbations. We have compared discharges of single neurons in RN (n = 49) and M1 (n = 109) of one monkey during a postural perturbation task. Neural responses to whole-limb perturbations were examined by transiently applying (300 ms) flexor or extensor torques to the shoulder and/or elbow while the monkeys attempted to maintain a static hand posture. Relative to baseline discharges before perturbation onset, perturbations evoked rapid (<100 ms) changes of neural discharges in many RN (28 of 49, 57%) and M1 (43 of 109, 39%) neurons. In addition to exhibiting a greater proportion of perturbation-related neurons, RN neurons also tended to exhibit higher peak discharge frequencies in response to perturbations than M1 neurons. Importantly, neurons in both structures exhibited similar response latencies and tuning properties (preferred torque directions and tuning widths) in joint-torque space. Proximal arm muscles also displayed similar tuning properties in joint-torque space. These results suggest that RN is more sensitive than M1 to mechanical perturbations applied during postural control but both structures may play a similar role in feedback control of posture.

Long-latency reflexes account for limb biomechanics through several supraspinal pathways

Accurate control of body posture is enforced by a multitude of corrective actions operating over a range of time scales. The earliest correction is the short-latency reflex (SLR) which occurs between 20–45 ms following a sudden displacement of the limb and is generated entirely by spinal circuits. In contrast, voluntary reactions are generated by a highly distributed network but at a significantly longer delay after stimulus onset (greater than 100 ms). Between these two epochs is the long-latency reflex (LLR) (around 50–100 ms) which acts more rapidly than voluntary reactions but shares some supraspinal pathways and functional capabilities. In particular, the LLR accounts for the arm’s biomechanical properties rather than only responding to local muscle stretch like the SLR. This paper will review how the LLR accounts for the arm’s biomechanical properties and the supraspinal pathways supporting this ability. Relevant experimental paradigms include clinical studies, non-invasive brain stimulation, neural recordings in monkeys, and human behavioral studies. The sum of this effort indicates that primary motor cortex and reticular formation (RF) contribute to the LLR either by generating or scaling its structured response appropriate for the arm’s biomechanics whereas the cerebellum scales the magnitude of the feedback response. Additional putative pathways are discussed as well as potential research lines.

Context-dependent inhibition of unloaded muscles during the long-latency epoch

A number of studies have highlighted the sophistication of corrective responses in lengthened muscles during the long-latency epoch. However, in various contexts, unloading can occur, which requires corrective actions from a shortened muscle. Here, we investigate the sophistication of inhibitory responses in shortened muscles due to unloading. Our first experiment quantified the inhibitory responses following an unloading torque that displaced the hand either into or away from a peripheral target. We observed larger long-latency inhibitory responses when perturbed into the peripheral target compared with away from the target. In our second experiment, we characterized the degree of inhibition following unloading with respect to different levels of preperturbation muscle activity. We initially observed that the inhibitory activity during the short-latency epoch scaled with increased levels of preperturbation muscle activity. However, this scaling peaked early in the R2 epoch (∼ 50 ms) but then quickly diminished through the rest of the long-latency epoch. Finally, in experiment 3, we investigated whether inhibitory perturbation responses consider intersegmental dynamics of the limb. We quantified unloading responses for either pure shoulder or pure elbow torques that evoked similar motion at the shoulder but different elbow motion. The long-latency inhibitory response in the shoulder, unlike the short-latency, was greater for the shoulder torque compared with the response following an elbow torque, as previously observed for a loading response. Taken together, these results illustrate that the long-latency unloading response is capable of a similar level of complexity as observed when loads are applied to the limb.

Primary motor cortex and fast feedback responses to mechanical perturbations: a primer on what we know now and some suggestions on what we should find out next

Many researchers have drawn a clear distinction between fast feedback responses to mechanical perturbations (e.g., stretch responses) and voluntary control processes. But this simple distinction is difficult to reconcile with growing evidence that long-latency stretch responses share most of the defining capabilities of voluntary control. My general view—and I believe a growing consensus—is that the functional similarities between long-latency stretch responses and voluntary control processes can be readily understood based on their shared neural circuitry, especially a transcortical pathway through primary motor cortex. Here I provide a very brief and selective account of the human and monkey studies linking a transcortical pathway through primary motor cortex to the generation and functional sophistication of the long-latency stretch response. I then lay out some of the notable issues that are ready to be answered.

Rapid online selection between multiple motor plans

Recent theories of voluntary control predict that multiple motor strategies can be precomputed and expressed throughout movement. We examined online decisional processing in humans by asking them to make reaching movements with obstacles located just to the sides of a direct path between start and end targets. On random trials, the limb was perturbed with one of four mechanical loads that varied in direction and amplitude. Notably, we observed two different strategies when we applied a perturbation (left medium-sized) that deviated the participants' hand directly toward an obstacle. In some trials, subjects directed their hand between the obstacles and in other trials to the left of the obstacles. Importantly, changes in the muscle stretch response between these two strategies were observed in <60 ms after perturbation, during the R2 long-latency epoch (~45-75 ms). As predicted, the selected strategy depended on the estimated position of the limb when it was perturbed. In our second experiment, we presented either one or three potential goal targets. Movements initially directed to the closest target could be quickly redirected to other potential targets after a perturbation. Differences in muscle stretch responses for redirected movements were observed ~75 ms after perturbation during the R3 long-latency epoch (~75-105 ms). The results show that decisional processes are rapidly implemented during movement execution. In addition, our data suggest a hierarchical process with corrective responses on "how" to attain a behavioral goal expressed during the R2 epoch and responses on "what" goal to attain during the R3 epoch.

Fast feedback control involves two independent processes utilizing knowledge of limb dynamics

Corrective muscle responses occurring 50-100 ms after a mechanical perturbation are tailored to the mechanical features of the limb and its environment. For example, the evoked response by the shoulder's extensor muscle counters an imposed shoulder torque, rather than local shoulder motion, by integrating motion information from the shoulder and elbow appropriate for their dynamic interaction. Previous studies suggest that arm muscle activity within this epoch, alternately termed the R2/3 response, or long-latency reflex, reflects the summed result of two distinct components: an activity-dependent component which scales with the background muscle activity, and a task-dependent component which scales with the required vigor of the behavioral task. Here we examine how the knowledge of limb dynamics expressed during the shoulder muscle's R2/3 epoch is related to these two functional components. Subjects countered torque steps applied to their shoulder and/or elbow under different conditions of background torque and target size to recruit the activity-dependent and task-dependent component in varying degrees. Experiment 1 involved four torque perturbations, two levels of background torques and two target sizes; this design revealed that both background torque and target size impacted the shoulder's R2/3 activity, indicative of knowledge of limb dynamics. Experiment 2 involved two perturbation torques, five levels of background torque and two target sizes; this design demonstrated that the two factors had an independent impact on the R2/3 activity indicative of knowledge of limb dynamics. We conclude that a sophisticated feature of upper limb feedback control reflects the summation of two processes having a common capability.

Rapid feedback responses correlate with reach adaptation and properties of novel upper limb loads

A hallmark of voluntary motor control is the ability to adjust motor patterns for novel mechanical or visuomotor contexts. Recent work has also highlighted the importance of feedback for voluntary control, leading to the hypothesis that feedback responses should adapt when we learn new motor skills. We tested this prediction with a novel paradigm requiring that human subjects adapt to a viscous elbow load while reaching to three targets. Target 1 required combined shoulder and elbow motion, target 2 required only elbow motion, and target 3 (probe target) required shoulder but no elbow motion. This simple approach controlled muscle activity at the probe target before, during, and after the application of novel elbow loads. Our paradigm allowed us to perturb the elbow during reaching movements to the probe target and identify several key properties of adapted stretch responses. Adapted long-latency responses expressed (de-) adaptation similar to reaching errors observed when we introduced (removed) the elbow load. Moreover, reaching errors during learning correlated with changes in the long-latency response, showing subjects who adapted more to the elbow load displayed greater modulation of their stretch responses. These adapted responses were sensitive to the size and direction of the viscous training load. Our results highlight an important link between the adaptation of feedforward and feedback control and suggest a key part of motor adaptation is to adjust feedback responses to the requirements of novel motor skills.

The temporal evolution of feedback gains rapidly update to task demands

Recent theoretical frameworks such as optimal feedback control suggest that feedback gains should modulate throughout a movement and be tuned to task demands. Here we measured the visuomotor feedback gain throughout the course of movements made to "near" or "far" targets in human subjects. The visuomotor gain showed a systematic modulation over the time course of the reach, with the gain peaking at the middle of the movement and dropping rapidly as the target is approached. This modulation depends primarily on the proportion of the movement remaining, rather than hand position, suggesting that the modulation is sensitive to task demands. Model-predictive control suggests that the gains should be continuously recomputed throughout a movement. To test this, we investigated whether feedback gains update when the task goal is altered during a movement, that is when the target of the reach jumped. We measured the visuomotor gain either simultaneously with the jump or 100 ms after the jump. The visuomotor gain nonspecifically reduced for all target jumps when measured synchronously with the jump. However, the visuomotor gain 100 ms later showed an appropriate modulation for the revised task goal by increasing for jumps that increased the distance to the target and reducing for jumps that decreased the distance. We conclude that visuomotor feedback gain shows a temporal evolution related to task demands and that this evolution can be flexibly recomputed within 100 ms to accommodate online modifications to task goals.

Instruction-dependent modulation of the long-latency stretch reflex is associated with indicators of startle

Long-latency responses elicited by postural perturbation are modulated by how a subject is instructed to respond to the perturbation, yet the neural pathways responsible for this modulation remain unclear. The goal of this study was to determine whether instruction-dependent modulation is associated with activity in brainstem pathways contributing to startle. Our hypothesis was that elbow perturbations can evoked startle, indicated by activity in the sternocleidomastoid muscle (SCM). Perturbation responses were compared to those elicited by a loud acoustic stimulus, known to elicit startle. Postural perturbations and startling acoustic stimuli both evoked SCM activity, but only when a ballistic elbow extension movement was planned. Both stimuli triggered SCM activity with the same probability. When SCM activity was present, there was an associated early onset of triceps electromyographic (EMG), as required for the planned movement. This early EMG onset occurred at a time often attributed to long-latency stretch reflexes (75-100 ms). The nature of the perturbation-triggered EMG (excitatory or inhibitory) was independent of the perturbation direction (flexion or extension) indicating that it was not a feedback response appropriate for returning the limb to its original position. The net EMG response to perturbations delivered after a movement had been planned could be explained as the sum of a stretch reflex opposing the perturbation and a startle-evoked response associated with the prepared movement. These results demonstrate that rapid perturbations can trigger early release of a planned ballistic movement, and that this release is associated with activity in the brainstem pathways contributing to startle reflexes.

Cerebellar damage diminishes long-latency responses to multijoint perturbations

Damage to the cerebellum can cause significant problems in the coordination of voluntary arm movements. One prominent idea is that incoordination stems from an inability to predictively account for the complex mechanical interactions between the arm's several joints. Motivated by growing evidence that corrective feedback control shares important capabilities and neural substrates with feedforward control, we asked whether cerebellar damage impacts feedback stabilization of the multijoint arm appropriate for the arm's intersegmental dynamics. Specifically, we tested whether cerebellar dysfunction impacts the ability of posterior deltoid to incorporate elbow motion in its long-latency response (R2 = 45-75 ms and R3 = 75-100 ms after perturbation) to an unexpected torque perturbation. Healthy and cerebellar-damaged subjects were exposed to a selected pattern of shoulder-elbow displacements to probe the response pattern from this shoulder extensor muscle. The healthy elderly subjects expressed a long-latency response linked to both shoulder and elbow motion, including an increase/decrease in shoulder extensor activity with elbow flexion/extension. Critically, cerebellar-damaged subjects displayed the normal pattern of activity in the R3 period indicating an intact ability to rapidly integrate multijoint motion appropriate to the arm's intersegmental dynamics. However, cerebellar-damaged subjects had a lower magnitude of activity that was specific to the long-latency period (both R2 and R3) and a slightly delayed onset of multijoint sensitivity. Taken together, our results suggest that the basic motor pattern of the long-latency response is housed outside the cerebellum and is scaled by processes within the cerebellum.

Rapid feedback corrections during a bimanual postural task

An important observation in motor physiology is that even the fastest feedback responses can be modified in a task-dependent manner. However, whether or not such responses in one limb can be modulated based on online sensory feedback from other limbs is still unknown. We tested this using a bimanual postural control task, in which the two hands either controlled two separate cursors (double-cursor task) or a single cursor displayed at the spatial average between the hands (single-cursor task). In the first experiment, the two hands were symmetrically perturbed outwards. In the double-cursor task, the participants therefore had to return their hands to the targets, whereas in the single-cursor task no correction was necessary. Within 50 ms, the electromyographic activity showed significantly smaller responses in the single- compared with the double-cursor task. In the second experiment, the perturbation direction of the left hand (inward/outward) was randomized, such that participants could not preplan their response before perturbation onset. Results show that the behavior of the right arm in the one-cursor task depended on online feedback coming from the left arm: the muscular response was modulated within 75 ms based on directionally specific information of the left arm. These results suggest that sensory feedback from one limb can quickly modify the perturbation response of another limb in a task-dependent manner.

Preparing to reach: selecting an adaptive long-latency feedback controller

In a voluntary movement, the nervous system specifies not only the motor commands but also the gains associated with reaction to sensory feedback. For example, suppose that, during reaching, a perturbation tends to push the hand to the left. With practice, the brain not only learns to produce commands that predictively compensate for the perturbation but also increases the long-latency reflex gain associated with leftward displacements of the arm. That is, the brain learns a feedback controller. Here, we wondered whether, during the preparatory period before the reach, the brain engaged this feedback controller in anticipation of the upcoming movement. If so, its signature might be present in how the motor system responds to perturbations in the preparatory period. Humans trained on a reach task in which they adapted to a force field. During the preparatory period before the reach, we measured how the arm responded to a pulse to the hand that was either in the direction of the upcoming field, or in the opposite direction. Reach adaptation produced an increase in the long-latency (45-100 ms delay) feedback gains with respect to baseline, but only for perturbations that were in the same direction as the force field that subjects expected to encounter during the reach. Therefore, as the brain prepares for a reach, it loads a feedback controller specific to the upcoming reach. With adaptation, this feedback controller undergoes a change, increasing the gains for the expected sensory feedback.

Human eye-head gaze shifts preserve their accuracy and spatiotemporal trajectory profiles despite long-duration torque perturbations that assist or oppose head motion

Humans routinely use coordinated eye-head gaze saccades to rapidly and accurately redirect the line of sight (Land MF. Vis Neurosci 26: 51–62, 2009). With a fixed body, the gaze control system combines visual, vestibular, and neck proprioceptive sensory information and coordinates two moving platforms, the eyes and head. Classic engineering tools have investigated the structure of motor systems by testing their ability to compensate for perturbations. When a reaching movement of the hand is subjected to an unexpected force field of random direction and strength, the trajectory is deviated and its final position is inaccurate. Here, we found that the gaze control system behaves differently. We perturbed horizontal gaze shifts with long-duration torques applied to the head that unpredictably either assisted or opposed head motion and very significantly altered the intended head trajectory. We found, as others have with brief head perturbations, that gaze accuracy was preserved. Unexpectedly, we found also that the eye compensated well—with saccadic and rollback movements—for long-duration head perturbations such that resulting gaze trajectories remained close to that when the head was not perturbed. However, the ocular compensation was best when torques assisted, compared with opposed, head motion. If the vestibuloocular reflex (VOR) is suppressed during gaze shifts, as currently thought, what caused invariant gaze trajectories and accuracy, early eye-direction reversals, and asymmetric compensations? We propose three mechanisms: a gaze feedback loop that generates a gaze-position error signal; a vestibular-to-oculomotor signal that dissociates self-generated from passively imposed head motion; and a saturation element that limits orbital eye excursion.

Influence of the behavioral goal and environmental obstacles on rapid feedback responses

The motor system must consider a variety of environmental factors when executing voluntary motor actions, such as the shape of the goal or the possible presence of intervening obstacles. It remains unknown whether rapid feedback responses to mechanical perturbations also consider these factors. Our first experiment quantified how feedback corrections were altered by target shape, which was either a circular dot or a bar. Unperturbed movements to each target were qualitatively similar on average but with greater dispersion of end point positions when reaching to the bar. On random trials, multijoint torque perturbations deviated the hand left or right. When reaching to a circular target, perturbations elicited corrective movements that were directed straight to the location of the target. In contrast, corrective movements when reaching to a bar were redirected to other locations along the bar axis. Our second experiment quantified whether the presence of obstacles could interfere with feedback corrections. We found that hand trajectories after the perturbations were altered to avoid obstacles in the environment. Importantly, changes in muscle activity reflecting the different target shapes (bar vs. dot) or the presence of obstacles were observed in as little as 70 ms. Such changes in motor responses were qualitatively consistent with simulations based on optimal feedback control. Taken together, these results highlight that long-latency motor responses consider spatial properties of the goal and environment.

Fast corrective responses are evoked by perturbations approaching the natural variability of posture and movement tasks

A wealth of studies highlight the importance of rapid corrective responses during voluntary motor tasks. These studies used relatively large perturbations to evoke robust muscle activity. Thus it remains unknown whether these corrective responses (latency 20–100 ms) are evoked at perturbation levels approaching the inherent variability of voluntary control. To fill this gap, we examined responses for large to small perturbations applied while participants either performed postural or reaching tasks. To address multijoint corrective responses, we induced various amounts of single-joint elbow motion with scaled amounts of combined elbow and shoulder torques. Indeed, such perturbations are known to elicit a response at the unstretched shoulder muscle, which reflects an internal model of arm intersegmental dynamics. Significant muscle responses were observed during both postural control and reaching, even when perturbation-related joint angle, velocity, and acceleration overlapped in distribution with deviations encountered in unperturbed trials. The response onsets were consistent across the explored range of perturbation loads, with short-latency onset for the muscles spanning the elbow joints (20–40 ms), and long-latency for shoulder muscles (onset > 45 ms). In addition, the evoked activity was strongly modulated by perturbation magnitude. These results suggest that multijoint responses are not specifically engaged to counter motor errors that exceed a certain threshold. Instead, we suggest that these corrective processes operate continuously during voluntary motor control.

Visuomotor feedback gains upregulate during the learning of novel dynamics

At an early stage of learning novel dynamics, changes in muscle activity are mainly due to corrective feedback responses. These feedback contributions to the overall motor command are gradually reduced as feedforward control is learned. The temporary increased use of feedback could arise simply from the large errors in early learning with either unaltered gains or even slightly downregulated gains, or from an upregulation of the feedback gains when feedforward prediction is insufficient. We therefore investigated whether the sensorimotor control system alters feedback gains during adaptation to a novel force field generated by a robotic manipulandum. To probe the feedback gains throughout learning, we measured the magnitude of involuntary rapid visuomotor responses to rapid shifts in the visual location of the hand during reaching movements. We found large increases in the magnitude of the rapid visuomotor response whenever the dynamics changed: both when the force field was first presented, and when it was removed. We confirmed that these changes in feedback gain are not simply a byproduct of the change in background load, by demonstrating that this rapid visuomotor response is not load sensitive. Our results suggest that when the sensorimotor control system experiences errors, it increases the gain of the visuomotor feedback pathways to deal with the unexpected disturbances until the feedforward controller learns the appropriate dynamics. We suggest that these feedback gains are upregulated with increased uncertainty in the knowledge of the dynamics to counteract any errors or disturbances and ensure accurate and skillful movements.

Optimal feedback control and the long-latency stretch response

There has traditionally been a separation between voluntary control processes and the fast feedback responses which follow mechanical perturbations (i.e., stretch "reflexes"). However, a recent theory of motor control, based on optimal control, suggests that voluntary motor behavior involves the sophisticated manipulation of sensory feedback. We have recently proposed that one implication of this theory is that the long-latency stretch "reflex", like voluntary control, should support a rich assortment of behaviors because these two processes are intimately linked through shared neural circuitry including primary motor cortex. In this review, we first describe the basic principles of optimal feedback control related to voluntary motor behavior. We then explore the functional properties of upper-limb stretch responses, with a focus on how the sophistication of the long-latency stretch response rivals voluntary control. And last, we describe the neural circuitry that underlies the long-latency stretch response and detail the evidence that primary motor cortex participates in sophisticated feedback responses to mechanical perturbations.

Computational mechanisms of sensorimotor control

In order to generate skilled and efficient actions, the motor system must find solutions to several problems inherent in sensorimotor control, including nonlinearity, nonstationarity, delays, redundancy, uncertainty, and noise. We review these problems and five computational mechanisms that the brain may use to limit their deleterious effects: optimal feedback control, impedance control, predictive control, Bayesian decision theory, and sensorimotor learning. Together, these computational mechanisms allow skilled and fluent sensorimotor behavior.

Suppression of proprioceptive feedback control in movement sequences through intermediate targets

Simple movements can be seen as building blocks for complex action sequences, and neural control of an action sequence can be expected to preserve some control features of its constituent blocks. It was previously found that during single-joint elbow movements to a single target, the proprioceptive feedback control is initially suppressed, and we tested this feedback suppression in a two-segment sequence during which subjects momentarily slowed down at an intermediate target at a 30° distance (first segment) and then immediately moved another 30° to the final target (second segment). Either the first or second segment was unexpectedly perturbed; the latency of the earliest response to the perturbation in the muscle surface electromyogram was analyzed. The perturbations were delivered either at the onset of each segment or about 0.1 s later. We found that in both segments, the response latency to the late perturbation was shorter than the latency to the early perturbation, which suggests that the proprioceptive feedback control is suppressed in the beginning of each segment. Next, we determined the latency of the response to unexpected perturbations in 30° movements to a single target. We found that the response latency was not significantly different in the movement to a single target and in each segment in the sequence. This result suggests that the initial suppression of the proprioceptive feedback control in movements to single targets is preserved in movements through intermediate targets and supports the idea of modular organization of neural control of movement sequences.