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Target reward influences motor planning strategies through modulation of movement vigor. Considering current theories of sensorimotor control suggesting that movement planning consists in selecting a goal-directed control strategy, we sought to investigate the influence of reward on feedback control. Here, we explored this question in three human reaching experiments. First, we altered the explicit reward associated with the goal target and found an overall increase in feedback gains for higher target rewards, highlighted by larger velocities, feedback responses to external loads, and background muscle activity. Then, we investigated whether the differences in target rewards across multiple goals impacted rapid motor decisions during movement. We observed idiosyncratic switching strategies dependent on both target rewards and, surprisingly, the feedback gains at perturbation onset: the more vigorous movements were less likely to switch to a new goal following perturbations. To gain further insight into a causal influence of the feedback gains on rapid motor decisions, we demonstrated that biasing the baseline activity and reflex gains by means of a background load evoked a larger proportion of target switches in the direction opposite to the background load associated with lower muscle activity. Together, our results demonstrate an impact of target reward on feedback control and highlight the competition between movement vigor and flexibility.

Savings have been described as the ability of healthy humans to relearn a previously acquired motor skill faster than the first time, which in the context of motor adaptation suggests that the learning rate in the brain could be adjusted when a perturbation is recognized. Alternatively, it has been argued that apparent savings were the consequence of a distinct process that instead of reflecting a change in the learning rate, revealed an explicit re-aiming strategy. Based on recent evidence that feedback adaptation may be central to both planning and control, we hypothesized that this component could genuinely accelerate relearning in human adaptation to force fields during reaching. Consistent with our hypothesis, we observed that upon re-exposure to a previously learned force field, the very first movement performed by healthy volunteers in the relearning context was better adapted to the external disturbance, and this occurred without any anticipation or cognitive strategy because the relearning session was started unexpectedly. We conclude that feedback adaptation is a medium by which the nervous system can genuinely accelerate learning across movements.Significance StatementSavings describe the ability of healthy humans to faster relearn a previously acquired motor skill. It is debated whether savings result from implicit changes in the learning rate or an explicit strategy. Given recent evidence that feedback control plays a key role in movement adaptation, we hypothesized that this component also contributed to savings in the context of force field adaptation. We confirm that relearning was faster and demonstrate that the very first relearning movement was better adapted to the external disturbance, i.e., the online corrections in the first re-exposure trial carried imprints of feedback adaptation from the previous sessions in the absence of any explicit strategy. We conclude that this non-explicit feedback component can genuinely accelerate motor learning.

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.

In humans, practically all movements are learnt and performed in a constant gravitational field. Yet, studies on arm movements and object manipulation in parabolic flight have highlighted very fast sensorimotor adaptations to altered gravity environments. Here, we wondered if the motor adjustments observed in those altered gravity environments could also be observed on Earth in a situation where the body is upside-down. To address this question, we asked participants to perform rhythmic arm movements in two different body postures (right-side-up and upside-down) while holding an object in precision grip. Analyses of grip-load force coordination and of movement kinematics revealed distinct adaptation patterns between grip and arm control. Grip force and load force were tightly synchronized from the first movements performed in upside-down posture, reflecting a malleable allocentric grip control. In contrast, velocity profiles showed a more progressive adaptation to the upside-down posture and reflected an egocentric planning of arm kinematics. In addition to suggesting distinct mechanisms between grip dynamics and arm kinematics for adaptation to novel contexts, these results also suggest the existence of general mechanisms underlying gravity-dependent motor adaptation that can be used for fast sensorimotor coordination across different postures on Earth and, incidentally, across different gravitational conditions in parabolic flights, in human centrifuges, or in Space. NEW

A hallmark of human reaching movements is that they are appropriately tuned to the task goal and to the environmental context. This was demonstrated by the way humans flexibly respond to mechanical and visual perturbations that happen during movement. Furthermore, it was previously showed that the properties of goal-directed control can change within a movement, following abrupt changes in the goal structure. Such online adjustment was characterized by a modulation of feedback gains following switches in target shape. However, it remains unknown whether the underlying mechanism merely switches between prespecified policies, or whether it results from continuous and potentially dynamic adjustments. Here, we address this question by investigating participants’ feedback control strategies in presence of various changes in target width during reaching. More specifically, we studied whether the feedback responses to mechanical perturbations were sensitive to the rate of change in target width, which would be inconsistent with the hypothesis of a single, discrete switch. Based on movement kinematics and surface EMG data, we observed a modulation of feedback response clearly dependent on dynamical changes in target width. Together, our results demonstrate a continuous and online transformation of task-related parameters into suitable control policies.Significance statementHumans can adjust their control policy online in response to changes in the goal structure. However, it was unknown whether this adjustment resulted from a switch between two policies, or from dynamic and continuous adjustments. To address this question, we investigated whether online adjustments were tuned to dynamic changes in goal target which varied at different rates. Our results demonstrated that online adjustments were tuned to the rate of change in target width, suggesting that human reaching control policies are derived based on continuous monitoring of task-related parameters supporting online and dynamic adjustments.

Many studies highlight that human movements are highly successful yet display a surprising amount of variability from trial to trial. There is a consistent pattern of variability throughout movement: initial motor errors are corrected by the end of movement, suggesting the presence of a powerful online control process. Here, we analyze the trial-by-trial variability of goal-directed reaching in nonhuman primates (five male Rhesus monkeys) and demonstrate that they display a similar pattern of variability during reaching, including a strong negative correlation between initial and late hand motion. We then demonstrate that trial-to-trial neural variability of primary motor cortex (M1) is positively correlated with variability of future hand motion (τ = ∼160 ms) during reaching. Furthermore, the variability of M1 activity is also correlated with variability of past hand motion (τ = ∼90 ms), but in the opposite polarity (i.e., negative correlation). Partial correlation analysis demonstrated that M1 activity independently reflects the variability of both past and future hand motions. These findings provide support for the hypothesis that M1 activity is involved in online feedback control of motor actions. SIGNIFICANCE STATEMENT Previous studies highlight that primary motor cortex (M1) rapidly responds to either visual or mechanical disturbances, suggesting its involvement in online feedback control. However, these studies required external disturbances to the motor system and it is not clear whether a similar feedback process addresses internal noise/errors generated by the motor system itself. Here, we introduce a novel analysis that evaluates how variations in the activity of M1 neurons covary with variations in hand motion on a trial-to-trial basis. The analyses demonstrate that M1 activity is correlated with hand motion in both the near future and the recent past, but with opposite polarity. These results suggest that M1 is involved in online feedback motor control to address errors/noise within the motor system.

Bimanual tasks involve the coordination of both arms, which often offers redundancy in the ways a task can be completed. The distribution of control across limbs is often considered from the perspective of handedness. In this context, although there are differences across dominant and nondominant arms during reaching control ( Sainburg 2002 ), previous studies have shown that the brain tends to favor the dominant arm when performing bimanual tasks ( Salimpour and Shadmehr 2014 ). However, biomechanical factors known to influence planning and control in unimanual tasks may also generate limb asymmetries in force generation, but their influence on bimanual control has remained unexplored. We investigated this issue in a series of experiments in which participants were instructed to generate a 20-N force with both arms, with or without perturbation of the target force during the trial. We modeled the task in the framework of optimal feedback control of a two-link model with six human-like muscles groups. The biomechanical model predicted a differential contribution of each arm dependent on the orientation of the target force and joint configuration that was quantitatively matched by the participants’ behavior, regardless of handedness. Responses to visual perturbations were strongly influenced by the perturbation direction, such that online corrections also reflected an optimal use of limb biomechanics. These results show that the nervous system takes biomechanical constraints into account when optimizing the distribution of forces generated across limbs during both movement planning and feedback control of a bimanual task. NEW & NOTEWORTHY Here, we studied a bimanual force production task to examine the effects of biomechanical constraints on the distribution of control across limbs. Our findings show that the central nervous system optimizes the distribution of force across the two arms according to the joint configuration of the upper limbs. We further show that the underlying mechanisms influence both movement planning and online corrective responses to sudden changes in the target force.

Tactile and muscle afferents provide critical sensory information for grasp control, yet the contribution of each sensory system during online control has not been clearly identified. More precisely, it is unknown how these two sensory systems participate in online control of digit forces following perturbations to held objects. To address this issue, we investigated motor responses in the context of fingertip loading, which parallels the impact of perturbations to held objects on finger motion and fingerpad deformation, and characterized surface recordings of intrinsic (first dorsal interosseous, FDI) and extrinsic (flexor digitorum superficialis, FDS) hand muscles based on statistical modeling. We designed a series of experiments probing the effects of peripheral stimulation with or without anesthesia of the finger, and of task instructions. Loading of the fingertip generated a motor response in FDI at ~60 ms following the perturbation onset, which was only driven by muscle stretch, as the ring-block anesthesia reduced the gain of the response occurring later than 90 ms, leaving responses occurring before this time unaffected. In contrast, the motor response in FDS was independent of the lateral motion of the finger. This response started at ~90 ms on average and was immediately adjusted to task demands. Altogether these results highlight how a rapid integration of partially distinct sensorimotor circuits supports rapid motor responses to fingertip loading. NEW & NOTEWORTHY To grasp and manipulate objects, the brain uses touch signals related to skin deformation as well as sensory information about motion of the fingers encoded in muscle spindles. Here we investigated how these two sensory systems contribute to feedback responses to perturbation applied to the fingertip. We found distinct response components, suggesting that each sensory system engages separate sensorimotor circuits with distinct functions and latencies.

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.

Recent advances in movement neuroscience have consistently highlighted that the nervous system performs sophisticated feedback control over very short time scales (

Humans adapt to mechanical perturbations such as force fields 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. A force field 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 behavioural 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 500ms dwell time in the VP. In both situations, during the unexpected absence of the force field 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 850ms 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. SIGNIFICANCE STATEMENT Recent studies suggested that adaptive feedback control happens within a reach movement and the feedback responses are tuned specifically to the single-trial perturbation. Here we show that these feedback mediated changes in movement representations can last for around 850ms and are available to reproduce the characteristics of the newly acquired correction process. Current data replicate previous studies showing that feedback corrections are associated with changes in online representations, and demonstrate that these changes are preserved in memory long enough to be an important component of standard trial-by-trial adaptation.

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 ;250ms 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 250ms may correspond to the latency of motor adaptation in the nervous system. Key words: adaptive control; feedback control; motor adaptation; reaching Significance Statement We measure the latency of feedback adaptation in a human reaching experiment by applying force field trials randomly. Despite the fact that these disturbances could not be anticipated, we measured improvement in feedback corrections that paralleled standard adaptation. Correlates in muscle recordings occurred within ;250ms following movement onset. Such a short timescale of adaptation suggested that rapid adaptation complements feedback control of an ongoing movement. To further test this hypothesis, we demonstrate that indeed participants are able to adapt their feedback responses to different kinds of force fields and directions applied randomly. These findings support the existence of very rapid, possibly online, adaptation in the nervous system.

In order to perform accurate movements, the nervous system must transform sensory feedback into motor commands that compensate for errors caused by motor variability and external disturbances. Recent studies focusing on the importance of sensory feedback in motor control have illustrated that the brain generates highly flexible responses to visual perturbations (hand-cursor or target jumps), or following mechanical loads applied to the limb. These parallel approaches have emphasized sophisticated, goal-directed feedback control, but also reveal that flexible perturbation responses are expressed at different latencies depending on what sensory system is engaged by the perturbation. Across studies, goal-directed visuomotor responses consistently emerge in muscle activity ∼100 ms after a perturbation, while mechanical perturbations evoke goal-directed muscle responses in as little as ∼60 ms (long-latency responses). We discuss the limitation of current models of multisensory integration in light of these asynchronous processing delays, and suggest that understanding how the brain performs real-time multisensory integration is an open question for future studies.

Manipulating a cup by the handle requires compensating for the torque induced by the moment of the mass of the cup relative to the location of the handle. In the present study, we investigated the control strategy of subjects asked to perform grip-lift movements with an object with center of mass located away from the grip axis. Participants were asked to lift the manipulandum with a two-fingers precision grip and stabilize it in front of a visual target. Subjects showed a gradual and slow adaptation of the grip-force scaling across trials: the grip force tended to decrease slowly, and the temporal coordination between grip-force and load-torque rates displayed gradually, better-coordinated patterns. Importantly, this adaptation was much slower than the stabilization of the same parameters measured either when no torque came into play or after previous adaptation to the presence of a torque. In contrast, the maximum rotation induced by the torque was controlled efficiently after only few trials, and an unexpected decrease in the tangential torque produced significant overcompensation. An unexpected increase in torque produced a consistent opposite effect. This shows that the compensation for the dynamic torque was based on an anticipatory, dynamic counter-torque produced by the arm and wrist motor commands. The comparatively slow stabilization of grip-force control suggests a specific adaptation process engaged by the presence of the torque. This paradigm, including tangential torques, clearly constitutes a powerful tool to extract the adaptive component of grip control during object manipulation.

Crevecoeur F, McIntyre J, Thonnard J-L, Lefevre P. Movement stability under uncertain internal models of dynamics. J Neurophysiol 104: 1301-1313, 2010. First published June 16, 2010; doi:10.1152/jn.00315.2010. Sensory noise and feedback delay are potential sources of instability and variability for the on-line control of movement. It is commonly assumed that predictions based on internal models allow the CNS to anticipate the consequences of motor actions and protect the movements from uncertainty and instability. However, during motor learning and exposure to unknown dynamics, these predictions can be inaccurate. Therefore a distinct strategy is necessary to preserve movement stability. This study tests the hypothesis that in such situations, subjects adapt the speed and accuracy constraints on the movement, yielding a control policy that is less prone to undesirable variability in the outcome. This hypothesis was tested by asking subjects to hold a manipulandum in precision grip and to perform single-joint, discrete arm rotations during short-term exposure to weightlessness (0 g), where the internal models of the limb dynamics must be updated. Measurements of grip force adjustments indicated that the internal predictions were altered during early exposure to the 0 g condition. Indeed, the grip force/load force coupling reflected that the grip force was less finely tuned to the load-force variations at the beginning of the exposure to the novel gravitational condition. During this learning period, movements were slower with asymmetric velocity profiles and target undershooting. This effect was compared with theoretical results obtained in the context of optimal feedback control, where changing the movement objective can be directly tested by adjusting the cost parameters. The effect on the simulated movements quantitatively supported the hypothesis of a change in cost function during early exposure to a novel environment. The modified optimization criterion reduces the trial-to-trial variability in spite of the fact that noise affects the internal prediction. These observations support the idea that the CNS adjusts the movement objective to stabilize the movement when internal models are uncertain.

Crevecoeur F, Thonnard J-L, Lefevre P. Sensorimotor mapping for anticipatory grip force modulation. J Neurophysiol 104: 1401-1408, 2010. First published June 23, 2010; doi:10.1152/jn.00114.2010. During object manipulation, predictive grip force modulation allows compensation for inertial forces induced by the object's acceleration. This coupling between grip force (GF) and load force (LF) during voluntary movements has demonstrated high levels of complexity, adaptability, and flexibility under many loading conditions in a broad range of experimental studies. The association between GF and LF indicates the presence of internal models underlying predictive GF control. The present experiment sought to identify the variables taken into account during GF modulation at the initiation of a movement. Twenty subjects performed discrete point-to-point movements under normal and hypergravity conditions induced by parabolic flights. Two control experiments performed under normal gravitational conditions compared the observed effect of the increase in gravity with the effects of a change in movement kinematics and a change in mass. In hypergravity, subjects responded accurately to the increase in weight during stationary holding but overestimated inertial loads. During dynamic phases, the relationship between GF and LF under hypergravity varied in a manner similar to the control test in which object mass was increased, whereas a change in movement kinematics could not reproduce this result. We suggest that the subjects' strategy for anticipatory GF modulation is based on sensorimotor mapping that combines the perception of the weight encoded during stationary holding with an internal representation of the movement kinematics. In particular, such a combination reflects a prior knowledge of the unequivocal relationship linking mass, weight, and loads under the invariant gravitational context experienced on Earth.

Series of motor outputs generated by cyclic movements are typically complex, suggesting that the correlation function of the time series spans over a large number of consecutive samples. Famous examples include inter-stride intervals, heartbeat variability, spontaneous neural firing patterns or motor synchronization with external pacing. Long-range correlations are potentially important for fundamental research, as the neural and biomechanical mechanisms generating these correlations remain unknown, and for clinical applications, given that the loss of long-range correlation may be a marker of disease. However, no systematic approach or robust analysis methods have yet been used to support the study of correlation functions in physiological series. This study investigates four selected methods (the Hurst exponent, the power spectral density analysis, the rate of moment convergence and the multi-scale entropy methods). We present the result of each analysis performed on artificial computer-generated series in which the autocorrelation function is known, and then on time series extracted from gait and upper limb rhythmic movements. Our results suggest that combined analysis using the Hurst exponent and the power spectral density is suitable for rather short series (512 points). The rate of moment convergence directly supports the power spectral density analysis, and the multiscale entropy further confirms the presence of long-range correlation, although this method seems more appropriate for longer series. The proposed methodology increases the level of confidence in the hypothesis that physiological series are long-memory processes, which is of prime importance for future fundamental and clinical research.

A hallmark of movement control expressed by healthy humans is the ability to gradually improve motor performance through learning. In the context of object manipulation, previous work has shown that the presence of a torque load has a direct impact on grip-force control, characterized by a significantly slower grip-force adjustment across lifting movements. The origin of this slower adaptation rate remains unclear. On the one hand, information about tangential constraints during stationary holding may be difficult to extract in the presence of a torque. On the other hand, inertial torque experienced during movement may also potentially disrupt the grip-force adjustments, as the dynamical constraints clearly differ from the situation when no torque load is present. To address the influence of inertial torque loads, we instructed healthy adults to perform visually guided reaching movements in weightlessness while holding an unbalanced object relative to the grip axis. Weightlessness offered the possibility to remove gravitational constraints and isolate the effect of movement-related feedback on grip force adjustments. Grip-force adaptation rates were compared with a control group who manipulated a balanced object without any torque load and also in weightlessness. Our results clearly show that grip-force adaptation in the presence of a torque load is significantly slower, which suggests that the presence of torque loads experienced during movement may alter our internal estimates of how much force is required to hold an unbalanced object stable. This observation may explain why grasping objects around the expected location of the center of mass is such an important component of planning and control of manipulation tasks.

Moving requires handling gravitational and inertial constraints pulling on our body and on the objects that we manipulate. Although previous work emphasized that the brain uses internal models of each type of mechanical load, little is known about their interaction during motor planning and execution. In this report, we examine visually guided reaching movements in the horizontal plane performed by naive participants exposed to changes in gravity during parabolic flight. This approach allowed us to isolate the effect of gravity because the environmental dynamics along the horizontal axis remained unchanged. We show that gravity has a direct effect on movement kinematics, with faster movements observed after transitions from normal gravity to hypergravity (1.8g), followed by significant movement slowing after the transition from hypergravity to zero gravity. We recorded finger forces applied on an object held in precision grip and found that the coupling between grip force and inertial loads displayed a similar effect, with an increase in grip force modulation gain under hypergravity followed by a reduction of modulation gain after entering the zero-gravity environment. We present a computational model to illustrate that these effects are compatible with the hypothesis that participants partially attribute changes in weight to changes in mass and scale incorrectly their motor commands with changes in gravity. These results highlight a rather direct internal mapping between the force generated during stationary holding against gravity and the estimation of inertial loads that limb and hand motor commands must overcome.

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.

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

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.

In this experiment, we investigated whether the Central Nervous System uses internal forward models of inertial loads to maintain the stability of a precision grip when manipulating objects in the absence of gravity. The micro-gravity condition causes profound changes in the profile of tangential constraints at the finger-object interface. In order to assess the ability to predict the micro-gravity-specific variation of inertial loads, we analyzed the grip force adjustments that occurred when naive subjects held an object in a precision grip and performed point-to-point movements under the weightless condition induced by parabolic flight. Such movements typically presented static and dynamic phases, which permitted distinction between a static component of the grip force (measured before the movement) and a dynamic component of the grip force (measured during the movement). The static component tended to gradually decrease across the parabolas, whereas the dynamic component was rapidly modulated with the micro-gravity-specific inertial loads. In addition, the amplitude of the modulation significantly correlated with the amplitude of the tangential constraints for the dynamic component. These results strongly support the hypothesis that the internal representation of arm and object dynamics adapts to new gravitational contexts. In addition, the difference in timescales of adaptation of static and dynamic components suggests that they can be processed independently. The prediction of self-induced variation of inertial loads permits fine modulation of grip force, which ensures a stable grip during manipulation of an object in a new environment.