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

Task-dependent coarticulation of movement sequences

Combining individual actions into sequences is a hallmark of everyday activities. Classical theories propose that the motor system forms a single specification of the sequence as a whole, leading to the coarticulation of the different elements. In contrast, recent neural recordings challenge this idea and suggest independent execution of each element specified separately. Here, we show that separate or coarticulated sequences can result from the same task-dependent controller, without implying different representations in the brain. Simulations show that planning for multiple reaches simultaneously allows separate or coarticulated sequences depending on instructions about intermediate goals. Human experiments in a two-reach sequence task validated this model. Furthermore, in co-articulated sequences, the second goal influenced long-latency stretch responses to external loads applied during the first reach, demonstrating the involvement of the sensorimotor network supporting fast feedback control. Overall, our study establishes a computational framework for sequence production that highlights the importance of feedback control in this essential motor skill.

Parkinsonian Tremor as Unstable Feedback in a Physiologically Consistent Control Framework

Parkinson's disease (PD) is characterized by decreased dopamine in the basal ganglia that causes excessive tonic inhibition of the thalamus. This excessive inhibition seems to explain inhibitory motor symptoms in PD, but the source of tremor remains unclear. This paper investigates how neural inhibition may change the closed-loop characteristics of the human motor control system to determine how this established pathophysiology could produce tremor. The rate-coding model of neural signals suggests increased inhibition decreases signal amplitude, which could create a mismatch between the closed-loop dynamics and the internal models that overcome proprioceptive feedback delays. This paper aims to identify a candidate model structure with decreased-amplitude-induced tremor in PD that also agrees with previously recorded movements of healthy and cerebellar patients. The optimal feedback control theory of human motor control forms the basis of the model. Key additional elements include gating of undesired movements via the basal ganglia-thalamus-motor cortex circuit and the treatment of the efferent copy of the control input as a measurement in the state estimator. Simulations confirm the model's ability to capture tremor in PD and also demonstrate how disease progression could affect tremor and other motor symptoms, providing insight into the existence of tremor and non-tremor phenotypes. Altogether, the physiological underpinnings of the model structure and the agreement of model predictions with clinical observations provides support for the hypothesis that unstable feedback produces parkinsonian tremor. Consequently, these results also support the associated framework for the neuroanatomy of human motor control.

Rapid Online Corrections for Proprioceptive and Visual Perturbations Recruit Similar Circuits in Primary Motor Cortex

An important aspect of motor function is our ability to rapidly generate goal-directed corrections for disturbances to the limb or behavioral goal. The primary motor cortex (M1) is a key region involved in processing feedback for rapid motor corrections, yet we know little about how M1 circuits are recruited by different sources of sensory feedback to make rapid corrections. We trained two male monkeys (Macaca mulatta) to make goal-directed reaches and on random trials introduced different sensory errors by either jumping the visual location of the goal (goal jump), jumping the visual location of the hand (cursor jump), or applying a mechanical load to displace the hand (proprioceptive feedback). Sensory perturbations evoked a broad response in M1 with ∼73% of neurons (n = 257) responding to at least one of the sensory perturbations. Feedback responses were also similar as response ranges between the goal and cursor jumps were highly correlated (range of r = [0.91, 0.97]) as were the response ranges between the mechanical loads and the visual perturbations (range of r = [0.68, 0.86]). Lastly, we identified the neural subspace each perturbation response resided in and found a strong overlap between the two visual perturbations (range of overlap index, 0.73-0.89) and between the mechanical loads and visual perturbations (range of overlap index, 0.36-0.47) indicating each perturbation evoked similar structure of activity at the population level. Collectively, our results indicate rapid responses to errors from different sensory sources target similar overlapping circuits in M1.

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.

Continuous evaluation of cost-to-go for flexible reaching control and online decisions

Humans consider the parameters linked to movement goal during reaching to adjust their control strategy online. Indeed, rapid changes in target structure or disturbances interfering with their initial plan elicit rapid changes in behavior. Here, we hypothesize that these changes could result from the continuous use of a decision variable combining motor and cognitive components. We combine an optimal feedback controller with a real-time evaluation of the expected cost-to-go, which considers target- and movement-related costs, in a common theoretical framework. This model reproduces human behaviors in presence of changes in the target structure occurring during movement and of online decisions to flexibly change target following external perturbations. It also predicts that the time taken to decide to select a novel goal after a perturbation depends on the amplitude of the disturbance and on the rewards of the different options, which is a direct result of the continuous monitoring of the cost-to-go. We show that this result was present in our previously collected dataset. Together our developments point towards a continuous evaluation of the cost-to-go during reaching to update control online and make efficient decisions about movement goal.

Measuring complexity of muscle force control: Theoretical principles and clinical relevance in musculoskeletal research and practice

Musculoskeletal conditions affect bones, joints, and muscles of the locomotor system and are a leading cause of disability worldwide. This suggests that current musculoskeletal rehabilitation techniques fail to target the characteristics (e.g., physiological/physical/psychological) most influential for long-term musculoskeletal health. To identify whether a physiological characteristic is impaired, it must be measured. In neuromuscular control, traditional research approaches use magnitude-based measurements (e.g., peak force/standard deviation of force/coefficient of variation of force). However, magnitude-based measurements miss 'hidden information' regarding a physiological system's status across time. To better identify physiological characteristics that are clinically-important for long-term musculoskeletal health, other measurement approaches currently less applied in musculoskeletal research may be helpful. The purpose of this article is to present an introduction to technical and measurement principles for quantifying the 'complexity' of muscle force control as one representation of peripheral joint neuromuscular control. Complexity measurements are time-based and consider the irregular temporal structure of physiological signals. We review theoretical principles underlying measuring complexity of muscle force control and explain its clinical relevance for musculoskeletal scientists and clinicians. The principles include sensorimotor control of peripheral joints, muscle force signal construction and features, muscle force control measurement procedures, and variability and complexity variables. We propose the potential utility of measuring the complexity of muscle force control for diagnosing sensorimotor system impairment and prognosis following musculoskeletal disease or injury. This article will serve as an educational asset and a scientific resource that will inform future research directions to optimise rehabilitation for people with peripheral joint disease and injury.

Human Stabilization of Delay-Induced Instability of Haptic Rendering in a Stiffness Discrimination Task

Towards developing a coupled stability theory for haptic systems, we study the interaction of operators with time-delayed force feedback. In this work, we analyzed and validated experimentally the stability boundaries of an uncoupled system - without considering the human. We then designed an experiment in which the participants used a haptic device to interact with virtual elastic force fields in a stiffness discrimination task. We compared the performance and kinematics of users in uncoupled-unstable and uncoupled-stable conditions and characterized the stabilizing contribution of the users. We found that the users were able to perform the task regardless of the uncoupled-stability conditions. In addition, in uncoupled-unstable conditions, users maintained movement characteristics that were important for exploratory mediation, such as depth and duration of the movement, whereas other characteristics were not preserved. The results were reproduced in a simulation of the human controller that combined an inverse model and an optimal feedback controller. Adequate performance under the uncoupled-unstable yet coupled-stable conditions supports the potential benefit of designing for coupled stability of haptic systems. This could lead to the use of less conservative controllers than state-of-the-art solutions in haptic and teleoperation systems, and advance the fidelity of haptic feedback.

Separability of Human Motor Memories during reaching adaptation with force cues

Judging by the breadth of our motor repertoire during daily activities, it is clear that learning different tasks is a hallmark of the human motor system. However, for reaching adaptation to different force fields, the conditions under which this is possible in laboratory settings have remained a challenging question. Previous work has shown that independent movement representations or goals enabled dual adaptation. Considering the importance of force feedback during limb control, here we hypothesised that independent cues delivered by means of background loads could support simultaneous adaptation to various velocity-dependent force fields, for identical kinematic plan and movement goal. We demonstrate in a series of experiments that indeed healthy adults can adapt to opposite force fields, independently of the direction of the background force cue. However, when the cue and force field were in the same direction but differed by heir magnitude, the formation of different motor representations was still observed but the associated mechanism was subject to increased interference. Finally, we highlight that this paradigm allows dissociating trial-by-trial adaptation from online feedback adaptation, as these two mechanisms are associated with different time scales that can be identified reliably and reproduced in a computational model.

What is the contribution of voluntary and reflex processes to sensorimotor control of balance?

The contribution to balance of spinal and transcortical processes including the long-latency reflex is well known. The control of balance has been modelled previously as a continuous, state feedback controller representing, long-latency reflexes. However, the contribution of slower, variable delay processes has not been quantified. Compared with fixed delay processes (spinal, transcortical), we hypothesize that variable delay processes provide the largest contribution to balance and are sensitive to historical context as well as current states. Twenty-two healthy participants used a myoelectric control signal from their leg muscles to maintain balance of their own body while strapped to an actuated, inverted pendulum. We study the myoelectric control signal (u) in relation to the independent disturbance (d) comprising paired, discrete perturbations of varying inter-stimulus-interval (ISI). We fit the closed loop response, u from d, using one linear and two non-linear non-parametric (many parameter) models. Model M1 (ARX) is a generalized, high-order linear-time-invariant (LTI) process with fixed delay. Model M1 is equivalent to any parametric, closed-loop, continuous, linear-time-invariant (LTI), state feedback model. Model M2, a single non-linear process (fixed delay, time-varying amplitude), adds an optimized response amplitude to each stimulus. Model M3, two non-linear processes (one fixed delay, one variable delay, each of time-varying amplitude), add a second process of optimized delay and optimized response amplitude to each stimulus. At short ISI, the myoelectric control signals deviated systematically both from the fixed delay LTI process (M1), and also from the fixed delay, time-varying amplitude process (M2) and not from the two-process model (M3). Analysis of M3 (all fixed delay and variable delay response amplitudes) showed the variable (compared with fixed) delay process 1) made the largest contribution to the response, 2) exhibited refractoriness (increased delay related to short ISI) and 3) was sensitive to stimulus history (stimulus direction 2 relative to stimulus 1). For this whole-body balance task and for these impulsive stimuli, non-linear processes at variable delay are central to control of balance. Compared with fixed delay processes (spinal, transcortical), variable delay processes provided the largest contribution to balance and were sensitive to historical context as well as current states.

Interpersonal synchronization of movement intermittency

Most animal species group together and coordinate their behavior in quite sophisticated manners for mating, hunting, or defense purposes. In humans, coordination at a macroscopic level (the pacing of movements) is evident both in daily life (e.g., walking) and skilled (e.g., music and dance) behaviors. By examining the fine structure of movement, we here show that interpersonal coordination is established also at a microscopic – submovement – level. Natural movements appear as marked by recurrent (2–3 Hz) speed breaks, i.e., submovements, that are traditionally considered the result of intermittency in (visuo)motor feedback-based control. In a series of interpersonal coordination tasks, we show that submovements produced by interacting partners are not independent but alternate tightly over time, reflecting online mutual adaptation. These findings unveil a potential core mechanism for behavioral coordination that is based on between-persons synchronization of the intrinsic dynamics of action-perception cycles.

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Movements show intermittent speed pulses occurring at 2–3 Hz, called submovements

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Submovements are actively coordinated in counter-phase by interacting partners

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Submovements coordination depends on spatial alignment but not movement congruency

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Behavioral coordination occurs both at macro- and microscopic movement scales

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.

Rotational dynamics in motor cortex are consistent with a feedback controller

Recent studies have identified rotational dynamics in motor cortex (MC), which many assume arise from intrinsic connections in MC. However, behavioral and neurophysiological studies suggest that MC behaves like a feedback controller where continuous sensory feedback and interactions with other brain areas contribute substantially to MC processing. We investigated these apparently conflicting theories by building recurrent neural networks that controlled a model arm and received sensory feedback from the limb. Networks were trained to counteract perturbations to the limb and to reach toward spatial targets. Network activities and sensory feedback signals to the network exhibited rotational structure even when the recurrent connections were removed. Furthermore, neural recordings in monkeys performing similar tasks also exhibited rotational structure not only in MC but also in somatosensory cortex. Our results argue that rotational structure may also reflect dynamics throughout the voluntary motor system involved in online control of motor actions.

Principal Components Analysis Using Data Collected From Healthy Individuals on Two Robotic Assessment Platforms Yields Similar Behavioral Patterns

BACKGROUND

Kinarm Standard Tests (KSTs) is a suite of upper limb tasks to assess sensory, motor, and cognitive functions, which produces granular performance data that reflect spatial and temporal aspects of behavior (>100 variables per individual). We have previously used principal component analysis (PCA) to reduce the dimensionality of multivariate data using the Kinarm End-Point Lab (EP). Here, we performed PCA using data from the Kinarm Exoskeleton Lab (EXO), and determined agreement of PCA results across EP and EXO platforms in healthy participants. We additionally examined whether further dimensionality reduction was possible by using PCA across behavioral tasks.

METHODS

Healthy participants were assessed using the Kinarm EXO (N = 469) and EP (N = 170-200). Four behavioral tasks (six assessments in total) were performed that quantified arm sensory and motor function, including position sense [Arm Position Matching (APM)] and three motor tasks [Visually Guided Reaching (VGR), Object Hit (OH), and Object Hit and Avoid (OHA)]. The number of components to include per task was determined from scree plots and parallel analysis, and rotation type (orthogonal vs. oblique) was decided on a per-task basis. To assess agreement, we compared principal components (PCs) across platforms using distance correlation. We additionally considered inter-task interactions in EXO data by performing PCA across all six behavioral assessments.

RESULTS

By applying PCA on a per task basis to data collected using the EXO, the number of behavioral parameters were substantially reduced by 58-75% while accounting for 76-87% of the variance. These results compared well to the EP analysis, and we found good-to-excellent agreement values (0.75-0.99) between PCs from the EXO and those from the EP. Finally, we were able to reduce the dimensionality of the EXO data across tasks down to 16 components out of a total of 76 behavioral parameters, which represents a reduction of 79% while accounting for 73% of the total variance.

CONCLUSION

PCA of Kinarm robotic assessment appears to capture similar relationships between kinematic features in healthy individuals and is agnostic to the robotic platform used for collection. Further work is needed to investigate the use of PCA-based data reduction for the characterization of neurological deficits in clinical populations.

Efference copy in kinesthetic perception: a copy of what is it?

A number of notions in the fields of motor control and kinesthetic perception have been used without clear definitions. In this review, we consider definitions for efference copy, percept, and sense of effort based on recent studies within the physical approach, which assumes that the neural control of movement is based on principles of parametric control and involves defining time-varying profiles of spatial referent coordinates for the effectors. The apparent redundancy in both motor and perceptual processes is reconsidered based on the principle of abundance. Abundance of efferent and afferent signals is viewed as the means of stabilizing both salient action characteristics and salient percepts formalized as stable manifolds in high-dimensional spaces of relevant elemental variables. This theoretical scheme has led recently to a number of novel predictions and findings. These include, in particular, lower accuracy in perception of variables produced by elements involved in a multielement task compared with the same elements in single-element tasks, dissociation between motor and perceptual effects of muscle coactivation, force illusions induced by muscle vibration, and errors in perception of unintentional drifts in performance. Taken together, these results suggest that participation of efferent signals in perception frequently involves distorted copies of actual neural commands, particularly those to antagonist muscles. Sense of effort is associated with such distorted efferent signals. Distortions in efference copy happen spontaneously and can also be caused by changes in sensory signals, e.g., those produced by muscle vibration.

Rapid Changes in Movement Representations during Human Reaching Could Be Preserved in Memory for at Least 850 ms

Humans adapt to mechanical perturbations such as forcefields (FFs) during reaching within tens of trials. However, recent findings suggested that this adaptation may start within one single trial, i.e., online corrective movements can become tuned to the unanticipated perturbations within a trial. This was highlighted in previous works with a reaching experiment in which participants had to stop at a via-point (VP) located between the start and the goal. An FF was applied during the first and second parts of the movement and then occasionally unexpectedly switched off at the VP during catch trials. The results showed an after-effect during the second part of the movement when participants exited the VP. This behavioral result was interpreted as a standard after-effect, but it remained unclear how it was related to conventional trial-by-trial learning. The current study aimed to investigate how long do such changes in movement representations last in memory. For this, we have studied the same reaching task with VP in two situations: one with very short residing time in the VP and the second with an imposed minimum 500 ms dwell time in the VP. In both situations, during the unexpected absence of the FF after VP, after-effects were observed. This suggests that online corrections to the internal representation of reach dynamics can be preserved in memory for around 850 ms of resting time on average. Therefore, rapid changes occurring within movements can thus be preserved in memory long enough to influence trial-by-trial motor adaptation.

Multiple strategies to correct errors in foot placement and control speed in human walking

Neural feedback plays a key role in maintaining locomotor stability in the face of perturbations. In this study, we systematically identified properties of neural feedback that contribute to stabilizing human walking by examining how the nervous system responds to small kinematic deviations away from the desired gait pattern. We collected data from 20 participants (9 men and 11 women). We simultaneously applied (1) small continuous mechanical perturbations, forces at the ankles that affected foot placement, and (2) small continuous sensory perturbations, movement of a virtual visual scene that produced the illusion of change in walking speed, to compare how neural feedback responds to actual and illusory kinematic deviations. We computed phase-dependent impulse response functions that describe kinematic and muscular responses to small brief perturbations to identify critical phases of the gait cycle when the nervous system modulates muscle activity. In addition to the known foot-placement strategies that counteract kinematic displacement, such as the modulation of the hamstring muscle group during swing, we identified phase-specific muscle modulations that compensated for the perturbations. In particular, our results suggested that an early-stance modulation of anterior leg muscles (i.e., dorsiflexors and quadriceps) is a general control mechanism that serves to control forward body propulsion and compensates for errors in foot placement. Another detected general compensatory strategy was the late-stance modulation of the rectus femoris and gastrocnemius muscles, which controls walking speed in the next cycle.

Balance perturbation-evoked cortical N1 responses are larger when stepping and not influenced by motor planning

The cortical N1 response to balance perturbation is observed in electroencephalography recordings simultaneous to automatic balance-correcting muscle activity. We recently observed larger cortical N1s in individuals who had greater difficulty resisting compensatory steps, suggesting the N1 may be influenced by stepping or changes in response strategy. Here, we test whether the cortical N1 response is influenced by stepping (planned steps versus feet-in-place) or prior planning (planned vs. unplanned steps). We hypothesized that prior planning of a step would reduce the amplitude of the cortical N1 response to balance perturbations. In 19 healthy young adults (ages 19-38; 8 men and 11 women), we measured the cortical N1 amplitude evoked by 48 backward translational support-surface perturbations of unpredictable timing and amplitude in a single experimental session. Participants were asked to plan a stepping reaction on half of perturbations, but to resist stepping otherwise. Perturbations included an easy (8 cm, 16 cm/s) perturbation that was identical across participants and did not naturally elicit compensatory steps, and a height-adjusted difficult (18-22 cm, 38-42 cm/s) perturbation that frequently elicited compensatory steps despite instructions to resist stepping. In contrast to our hypothesis, cortical N1 response amplitudes did not differ between planned and unplanned stepping reactions, but cortical responses were 11% larger with the execution of planned compensatory steps compared with nonstepping responses to difficult perturbations. These results suggest a possible role for the cortical N1 in the execution of compensatory steps for balance recovery, and this role is not influenced by whether the compensatory step was planned before the perturbation.NEW & NOTEWORTHY The cortical N1 response to balance perturbation is larger when executing compensatory steps, suggesting a relationship between the cortical N1 and subsequent motor behavior. Additionally, the cortical N1 response is not impacted by prior planning of the stepping reaction, suggesting that predictability of the motor outcome does not impact the N1 in the same way as predictability of the perturbation stimulus.

Optimal Control Perspective on Parkinson's Disease: Increased Delay Between State Estimator and Controller Produces Tremor

Parkinson's disease produces tremor in a large subset of patients despite generally inhibiting movement. The pathophysiology of parkinsonian tremor is unclear, leading to uncertainty in how and why treatments reduce tremor with varying effectiveness. Models for parkinsonian tremor attempt to explain the underlying principles of tremor generation in the central nervous system, often focusing on neural activity of specific substructures. In contrast, control system approaches to modeling the human motor system provide qualitative results that help inform conclusions from clinical studies. This article uses an optimal control approach to investigate the hypothesis that an increased delay in the central nervous system-unaccounted by delay compensation mechanisms-produces parkinsonian tremor. This hypothesis is motivated by the excessive inhibition projected from the basal ganglia to the thalamus in Parkinson's disease. The thalamus relays signals from the cerebellum to the primary motor cortex: previous mapping of optimal control components indicates this prospective delay exists between the estimator (cerebellum) and controller (primary motor cortex). Simulations demonstrate realistic tremor in a neuromuscular model of the wrist. In addition, changes to effort sensitivity in the optimal controller may account for some clinical features of parkinsonian tremor, including the characteristics of re-emergent tremor and the time-varying amplitude and frequency of tremor. Contextualization of the optimal control model with physiological models and clinical observations provides insight into the potential role of the basal ganglia and cerebello-thalamo-cortical circuit and how treatments like dopaminergic medications and deep brain stimulation reduce tremor.

The gravitational imprint on sensorimotor planning and control

Humans excel at learning complex tasks, and elite performers such as musicians or athletes develop motor skills that defy biomechanical constraints. All actions require the movement of massive bodies. Of particular interest in the process of sensorimotor learning and control is the impact of gravitational forces on the body. Indeed, efficient control and accurate internal representations of the body configuration in space depend on our ability to feel and anticipate the action of gravity. Here we review studies on perception and sensorimotor control in both normal and altered gravity. Behavioral and modeling studies together suggested that the nervous system develops efficient strategies to take advantage of gravitational forces across a wide variety of tasks. However, when the body was exposed to altered gravity, the rate and amount of adaptation exhibited substantial variation from one experiment to another and sometimes led to partial adjustment only. Overall, these results support the hypothesis that the brain uses a multimodal and flexible representation of the effect of gravity on our body and movements. Future work is necessary to better characterize the nature of this internal representation and the extent to which it can adapt to novel contexts.

Feedback Adaptation to Unpredictable Force Fields in 250 ms

Motor learning and adaptation are important functions of the nervous system. Classical studies have characterized how humans adapt to changes in the environment during tasks such as reaching, and have documented improvements in behavior across movements. However, little is known about how quickly the nervous system adapts to such disturbances. In particular, recent work has suggested that adaptation could be sufficiently fast to alter the control strategies of an ongoing movement. To further address the possibility that learning occurred within a single movement, we designed a series of human reaching experiments to extract from muscles recordings the latency of feedback adaptation. Our results confirmed that participants adapted their feedback responses to unanticipated force fields applied randomly. In addition, our analyses revealed that the feedback response was specifically and finely tuned to the ongoing perturbation not only across trials with the same force field, but also across different kinds of force fields. Finally, changes in muscle activity consistent with feedback adaptation occurred in ∼250 ms following reach onset. The adaptation that we observed across trials presented in a random context was similar to the one observed when the force fields could be anticipated, suggesting that these two adaptive processes may be closely linked to each other. In such case, our measurement of 250 ms may correspond to the latency of motor adaptation in the nervous system.

A Very Fast Time Scale of Human Motor Adaptation: Within Movement Adjustments of Internal Representations during Reaching

Humans and other animals adapt motor commands to predictable disturbances within tens of trials in laboratory conditions. A central question is how does the nervous system adapt to disturbances in natural conditions when exactly the same movements cannot be practiced several times. Because motor commands and sensory feedback together carry continuous information about limb dynamics, we hypothesized that the nervous system could adapt to unexpected disturbances online.

Humans and other animals adapt motor commands to predictable disturbances within tens of trials in laboratory conditions. A central question is how does the nervous system adapt to disturbances in natural conditions when exactly the same movements cannot be practiced several times. Because motor commands and sensory feedback together carry continuous information about limb dynamics, we hypothesized that the nervous system could adapt to unexpected disturbances online. We tested this hypothesis in two reaching experiments during which velocity-dependent force fields (FFs) were randomly applied. We found that within-movement feedback corrections gradually improved, despite the fact that the perturbations were unexpected. Moreover, when participants were instructed to stop at a via-point, the application of a FF prior to the via-point induced mirror-image after-effects after the via-point, consistent with within-trial adaptation to the unexpected dynamics. These findings suggest a fast time-scale of motor learning, which complements feedback control and supports adaptation of an ongoing movement.

Robust Control in Human Reaching Movements: A Model-Free Strategy to Compensate for Unpredictable Disturbances

Current models of motor learning suggest that multiple timescales support adaptation to changes in visual or mechanical properties of the environment. These models capture patterns of learning and memory across a broad range of tasks, yet do not consider the possibility that rapid changes in behavior may occur without adaptation. Such changes in behavior may be desirable when facing transient disturbances, or when unpredictable changes in visual or mechanical properties of the task make it difficult to form an accurate model of the perturbation. Whether humans can modulate control strategies without an accurate model of the perturbation remains unknown. Here we frame this question in the context of robust control (H _∞-control), a control strategy that specifically considers unpredictable disturbances by increasing initial movement speed and feedback gains. Correspondingly, we demonstrate in two human reaching experiments including males and females that the occurrence of a single unpredictable disturbance led to an increase in movement speed and in the gain of rapid feedback responses to mechanical disturbances on subsequent movements. This strategy reduced perturbation-related motion regardless of the direction of the perturbation. Furthermore, we found that changes in the control strategy were associated with co-contraction, which amplified the gain of muscle responses to both lengthening and shortening perturbations. These results have important implications for studies on motor adaptation because they highlight that trial-by-trial changes in limb motion also reflected changes in control strategies dissociable from error-based adaptation.SIGNIFICANCE STATEMENT Humans and animals use internal representations of movement dynamics to anticipate the impact of predictable disturbances. However, we are often confronted with transient or unpredictable disturbances, and it remains unknown whether and how the nervous system handles these disturbances over fast time scales. Here we hypothesized that humans can modulate their control strategy to make reaching movements less sensitive to perturbations. We tested this hypothesis in the framework of robust control, and found changes in movement speed and feedback gains consistent with the model predictions. These changes impacted participants' behavior on a trial-by-trial basis. We conclude that compensation for disturbances over fast time scales involves a robust control strategy, which potentially plays a key role in motor planning and execution.

Contribution of the Cerebellum to Predictive Motor Control and Its Evaluation in Ataxic Patients

Goal-directed movements are predictive and multimodal in nature, especially for moving targets. For instance, during a reaching movement for a moving target, humans need to predict both motion of the target and movement of the limb. Recent computational studies show that the cerebellum predicts current and future states of the body and its environment using internal forward models. Sensory feedback signals from the periphery have delays in reaching the central nervous system, ranging between tens to hundreds of milliseconds. It is well known in engineering that feedback control based on time-delayed inputs can result in oscillatory and often unstable movements. In contrast, the brain predicts a current state from a previous state using forward models. This predictive mechanism most likely underpins stable and dexterous control of reaching movements. Although the cerebro-cerebellum has long been suggested as loci of various forward models, few methods are available to evaluate accuracy of the forward models in patients with cerebellar ataxia. Recently, we developed a non-invasive method to analyze receipt of motor commands in terms of movement kinematics for the wrist joint (B_r/K_r ratio). In the present study, we have identified two components (F1 and F2) of the smooth pursuit movement. We found that the two components were in different control modes with different B_r/K_r ratios. The major F1 component in a lower frequency range encodes both velocity and position of the moving target (higher B_r/K_r ratio) to synchronize movement of the wrist joint with motion of the target in a predictive manner. The minor F2 component in a higher frequency range is biased to position control in order to generate intermittent small step-wise movements. In cerebellar patients, the F1 component shows a selective decrease in the B_r/K_r ratio, which is correlated with decrease in accuracy of the pursuit movement. We conclude that the B_r/K_r ratio of the F1 component provides a unique parameter to evaluate accuracy of the predictive control. We also discuss the pathophysiological and clinical implications for clinical ataxiology.

Filtering Compensation for Delays and Prediction Errors during Sensorimotor Control

Compensating for sensorimotor noise and for temporal delays has been identified as a major function of the nervous system. Although these aspects have often been described separately in the frameworks of optimal cue combination or motor prediction during movement planning, control-theoretic models suggest that these two operations are performed simultaneously, and mounting evidence supports that motor commands are based on sensory predictions rather than sensory states. In this letter, we study the benefit of state estimation for predictive sensorimotor control. More precisely, we combine explicit compensation for sensorimotor delays and optimal estimation derived in the context of Kalman filtering. We show, based on simulations of human-inspired eye and arm movements, that filtering sensory predictions improves the stability margin of the system against prediction errors due to low-dimensional predictions or to errors in the delay estimate. These simulations also highlight that prediction errors qualitatively account for a broad variety of movement disorders typically associated with cerebellar dysfunctions. We suggest that adaptive filtering in cerebellum, instead of often-assumed feedforward predictions, may achieve simple compensation for sensorimotor delays and support stable closed-loop control of movements.

Do sensorimotor perturbations to standing balance elicit an error-related negativity?

Detecting and correcting errors is essential to successful action. Studies on response monitoring have examined scalp ERPs following the commission of motor slips in speeded-response tasks, focusing on a frontocentral negativity (i.e., error-related negativity or ERN). Sensorimotor neurophysiologists investigating cortical monitoring of reactive balance recovery behavior observe a strikingly similar pattern of scalp ERPs following externally imposed postural errors, including a brief frontocentral negativity that has been referred to as the balance N1. We integrate and review relevant literature from these discrepant fields to suggest shared underlying mechanisms and potential benefits of collaboration across fields. Unlike the cognitive tasks leveraged to study the ERN, balance perturbations afford precise experimental control of postural errors to elicit balance N1s that are an order of magnitude larger than the ERN and drive robust and well-characterized adaptation of behavior within an experimental session. Many factors that modulate the ERN, including motivation, perceived consequences, perceptual salience, expectation, development, and aging, are likewise known to modulate the balance N1. We propose that the ERN and balance N1 reflect common neural activity for detecting errors. Collaboration across fields could help clarify the functional significance of the ERN and poorly understood interactions between motor and cognitive impairments.

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.