Biology subjects

Gribble, P. L.

Publications and source records attributed to Gribble, P. L..

At least 19 recordsLinked to original sources

bioRxiv2026

Reaching movements in virtual reality depend on the seen avatar arm and not just the acting arm

Virtual reality (VR) offers unique opportunities to study behaviour by allowing the visual environment to be manipulated in ways otherwise impossible in the real world. A central theoretical question is how body-related visual information shapes movement control. Given right-handers' greater dexterity with the right arm, perhaps even seeing a right arm vs. a left arm may engage sensorimotor processes associated with more precise motor control. We examined reaching performance while participants used their own right or left arm to move the manipulandum of a KINARM robot to intercept static targets. Participants wore a VR head-mounted display, calibrated to the robot's physical workspace, in which they saw either the right or left arm holding and moving the manipulandum. The 2 x 2 design allowed for independent manipulation of the controlling arm and virtual arm displayed. As expected, reaches were faster and more accurate when controlled by the right arm. More interestingly, performance was also affected by the seen arm. Participants made straighter reaches when they saw a virtual right arm than a left arm, especially for the right controlling arm. These findings show that precise motor control relies not only on the arm that is actually moved and provides proprioceptive feedback, but also on the visual identity of the arm. The ability to modulate performance using visual features in VR opens new theoretical questions and opportunities for applications such as rehabilitation.

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bioRxiv2025

A Context-Free Model of Savings in Motor Learning

Learning to adapt voluntary movements to an external perturbation, whether mechanical or visual, is faster during a second encounter than during the first. The mechanisms underlying this phenomenon, known as savings, remain unclear. Recent studies propose that the high dimensionality of neural control enables the retention of learning traces that may facilitate savings. To test this idea we used MotorNet, a framework for training recurrent neural networks (RNNs) to control biomechanical models of the human upper limb. RNNs were trained to perform reaching movements with a velocity-dependent force field (FF) and without (NF) in the sequence NF1 (baseline), FF1 (adaptation), NF2 (washout), and FF2 (re-adaptation). RNNs showed behavioural signatures of savings in the absence of any explicit contextual input signalling the presence or absence of the FF. Savings was more robust in RNNs with larger numbers of units. We identified a component of RNN activity associated with savings--a shift in preparatory activity that persisted even after washout. Displacing this preparatory activity in the direction of the shift enhanced savings, whereas perturbations in the opposite direction reduced or eliminated savings. These findings suggest a potential neural basis for motor memory retention underlying savings that is reliant on the high dimensionality of neural circuits for control, and is independent of cognitive or strategic learning.

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bioRxiv2024

Sensory expectations shape neural population dynamics in motor circuits

The neural basis of movement preparation has been extensively studied during self-initiated actions where motor cortical activity during preparation shows a lawful relationship to the parameters of the subsequent action1,2. However, movements are regularly triggered and constantly corrected based on sensory inputs caused by disturbances to the body or environment. Since such disturbances are often predictable and since preparing for disturbances would make movements better, we hypothesized that expectations about sensory inputs also influence preparatory activity in motor circuits. Here we show that when humans and monkeys are probabilistically cued about the direction of a future mechanical perturbation, they incorporate sensory expectations into their movement preparation and improve their corrective responses. Using high-density neural recordings, we establish that sensory expectations are widespread across the brain, including the motor cortical areas involved in preparing self-initiated actions. The geometry of these preparatory signals in the neural population state is simple, scaling directly with the probability of each perturbation direction. After perturbation onset, a condition-independent perturbation signal shifts the neural state leading to rapid responses that initially reflect sensory expectations. Based on neural networks coupled to a biomechanical model of the arm3, we show that this neural geometry emerges through training, but only when the incoming sensory information indicating perturbation direction coincides with - or is preceded by - a condition-independent signal indicating that a perturbation has occurred. Thus, motor circuit dynamics are shaped by future sensory inputs, providing clear empirical support for the idea that movement is governed by the sophisticated manipulation of sensory feedback4.

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bioRxiv2023

Durability of Motor Learning by Observing

Information about another persons movement kinematics obtained through visual observation activates brain regions involved in motor learning. Observation-related changes in these brain areas are associated with adaptive changes to feedforward neural control of muscle activation and behavioural improvements in limb movement control. However, little is known about the stability of these observation-related effects over time. Here we used force channel trials to probe changes in lateral force production at various time points (1 min, 10 min, 30 min, 60 min, 24 h) after participants either physically performed, or observed another individual performing upper limb reaching movements that were perturbed by novel, robot-generated forces (a velocity-dependent force-field). Observers learned to predictively generate directionally and temporally specific compensatory forces during reaching, consistent with the idea that they acquired an internal representation of the novel dynamics. Participants who physically practiced in the force-field showed adaptation that was detectable at all time points, with some decay detected after 24 h. Observation-related adaptation was less temporally stable in comparison, decaying slightly after 1 h and undetectable at 24 h. Observation induced less adaptation overall than physical practice, which could explain differences in temporal stability. Visually acquired representations of movement dynamics are retained and continue to influence behavior for at least one hour after observation. SignificanceWe used force channel probes in an upper limb force-field reaching task in humans to compare the durability of learning-related changes that occured through visual observation to those after physical movement practice. Visually acquired representations of movement dynamics continued to influence behavior for at least one hour after observation. The significance of the findings is two-fold. First, they broaden our understanding of the potential role of visual observation in naturalistic motor skill acquisition. Our findings point to a (at least) 1-hour window during which visual observation of another person (e.g. a tutor or a conspecific learner) could play a role in motor learning, especially in early learning, presumably (as shown in our previous work) through activation of the neural circuitry that underlies motor skill acquisition. Second, this one-hour window represents an opportunity for observation-based interventions in neurorehabilitation (e.g. interleaving observation with physical practice) to aid in driving cortical reorganization for recovery of motor function.

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bioRxiv2023

MotorNet: a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks

Artificial neural networks (ANNs) are a powerful class of computational models for unravelling neural mechanisms of brain function. However, for neural control of movement, they currently must be integrated with software simulating biomechanical effectors, leading to limiting impracticalities: (1) researchers must rely on two different platforms and (2) biomechanical effectors are not generally differentiable, constraining researchers to reinforcement learning algorithms despite the existence and potential biological relevance of faster training methods. To address these limitations, we developed MotorNet, an open-source Python toolbox for creating arbitrarily complex, differentiable, and biomechanically realistic effectors that can be trained on user-defined motor tasks using ANNs. MotorNet is designed to meet several goals: ease of installation, ease of use, a high-level user-friendly API, and a modular architecture to allow for flexibility in model building. MotorNet requires no dependencies outside Python, making it easy to get started with. For instance, it allows training ANNs on typically used motor control models such as a two joint, six muscle, planar arm within minutes on a typical desktop computer. MotorNet is built on PyTorch and therefore can implement any network architecture that is possible using the PyTorch framework. Consequently, it will immediately benefit from advances in artificial intelligence through PyTorch updates. Finally, it is open source, enabling users to create and share their own improvements, such as new effector and network architectures or custom task designs. MotorNets focus on higher order model and task design will alleviate overhead cost to initiate computational projects for new researchers by providing a standalone, ready-to-go framework, and speed up efforts of established computational teams by enabling a focus on concepts and ideas over implementation.

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bioRxiv2021

The role of feedback in the production of skilled finger sequences

Actions involving fine control of the hand, for example grasping an object, rely heavily on sensory information from the fingertips. While the integration of feedback during execution of individual movements is well understood, less is known about the use of sensory feedback in the control of skilled movement sequences. To address this gap, we trained participants to produce sequences of finger movements on a keyboard-like device over a four-day training period. Participants received haptic, visual, and auditory feedback indicating the occurrence of each finger press. We then either transiently delayed or advanced the feedback for a single press by a small amount of time (30 or 60 ms). We observed that participants rapidly adjusted their ongoing finger press by either accelerating or prolonging the ongoing press, in accordance with the direction of the perturbation. Furthermore, we could show that this rapid behavioural modulation was driven by haptic feedback. While these feedback-driven adjustments reduced in size with practice, they were still clearly present at the end of training. In contrast to the directionally-specific effect we observed on the perturbed press, a feedback perturbation resulted in a delayed onset of the subsequent presses irrespective of perturbation direction or feedback modality. This observation is consistent with a hierarchical organization of skilled movement sequences, with different levels reacting distinctly to sensory perturbations.

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bioRxiv2020

The role of dopamine in reward-based motor adaptation, savings, and interference

Dopamine signaling is thought to mediate reward-based learning. We tested for a role of dopamine in motor adaptation by administering the dopamine precursor levodopa to healthy participants in two experiments involving reaching movements. Levodopa has been shown to impair reward-based learning in cognitive tasks. Thus, we hypothesized that levodopa would selectively impair aspects of motor adaptation that depend on reinforcement of rewarding actions. In the first experiment, participants performed two separate tasks in which adaptation was driven either by visual error-based feedback of the hand position or binary reward feedback. We used EEG to measure event-related potentials evoked by task feedback. We hypothesized that levodopa would specifically diminish adaptation and the neural responses to feedback in the reward learning task. However, levodopa did not affect motor adaptation in either task nor did it diminish event-related potentials elicited by reward outcomes. In the second experiment, participants learned to compensate for mechanical force field perturbations applied to the hand during reaching. Previous exposure to a particular force field can result in savings during subsequent adaptation to the same force field or interference during adaptation to an opposite force field. We hypothesized that levodopa would diminish savings and anterograde interference, as previous work suggests that these phenomena result from a reinforcement learning process. However, we found no reliable effects of levodopa. These results suggest that reward-based motor adaptation, savings, and interference may not depend on the same dopaminergic mechanisms that have been shown to be disrupted by levodopa during various cognitive tasks. New and NoteworthyMotor adaptation relies on multiple processes including reinforcement of successful actions. Cognitive reinforcement learning is impaired by levodopa-induced disruption of dopamine function. We administered levodopa to healthy adults who participated in multiple motor adaptation tasks. We found no effects of levodopa on any component of motor adaptation. This suggests that motor adaptation may not depend on the same dopaminergic mechanisms as cognitive forms or reinforcement learning that have been shown to be impaired by levodopa.

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bioRxiv2020

Differential dopamine receptor-dependent sensitivity improves action selection in the basal ganglia

The problem of selecting one action from a set of different possible actions, simply referred to as the problem of action selection, is a ubiquitous challenge in the animal world. For vertebrates, the basal ganglia (BG) are widely thought to implement the core computation to solve this problem, as the anatomy and physiology of the BG are well-suited to this end. However, the BG still displays physiological features whose role in achieving efficient action selection remains unclear. In particular, it is known that the two types of dopaminergic receptors (D1 and D2) present in the BG give rise to mechanistically different responses. The overall effect will be a difference in sensitivity to dopamine which may have ramifications for action selection. However, which receptor type leads to a stronger response is, a priori, unclear, due to the complexity of the intracellular mechanisms involved. In this study, we use the action selection hypothesis to predict which of D1 or D2 has the greater sensitivity. Thus, we ask - what sensitivity ratio would result in enhanced action selection functionality in the basal ganglia? To do this, we incorporated differential D1 and D2 sensitivity in an existing, high level computational model of the macro-architecture of the basal ganglia, via a simple weighting variable. We then quantitatively assessed the models capacity to perform action selection as we parametrically manipulated the new feature. We show that differential (rather than equal) D1 and D2 sensitivity to dopaminergic input improves action selection, and specifically, that greater D1 sensitivity (compared to that for D2) leads to these improvements.

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bioRxiv2020

Time course of changes in the long latency feedback response parallels the fast process of short term motor adaptation

Adapting to novel dynamics involves modifying both feedforward and feedback control. We investigated whether the motor system alters feedback responses during adaptation to a novel force field in a manner similar to adjustments in feedforward control. We simultaneously tracked the time course of both feedforward and feedback systems via independent probes during a force field adaptation task. Participants (n=35) grasped the handle of a robotic manipulandum and performed reaches to a visual target while the hand and arm were occluded. We introduced an abrupt counter-clockwise velocity-dependent force field during a block of reaching trials. We measured movement kinematics and shoulder and elbow muscle activity with surface EMG electrodes. We tracked the feedback stretch response throughout the task. Using force channel trials we measured overall learning, which was later decomposed into a fast and slow process. We found that the long-latency feedback response (LLFR) was upregulated in the early stages of learning and was correlated with the fast component of feedforward adaptation. The change in feedback response was specific to the long-latency epoch (50-100 ms after muscle stretch) and was observed only in the triceps muscle, which was the muscle required to counter the force field during adaptation. The similarity in time course for the LLFR and the estimated time course of the fast process suggests both are supported by common neural circuits. While some propose that the fast process reflects an explicit strategy, we argue instead that it may be a proxy for the feedback controller. New & NoteworthyWe investigated whether changes in the feedback stretch response were related to the proposed fast and slow processes of motor adaptation. We found that the long latency component of the feedback stretch response was upregulated in the early stages of learning, and the time course was correlated with the fast process. While some propose that the fast process reflects an explicit strategy, we argue instead that it may be a proxy for the feedback controller.

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bioRxiv2020

Explicit feedback and instruction do not change shoulder muscle activity reduction after shoulder fixation

Generating pure elbow rotation requires contracting muscles at both the shoulder and elbow joints to counter torques that arise at the shoulder when the forearm rotates (i.e., intersegmental dynamics). Previous work has shown that human participants learn to reduce their shoulder muscle activity if the same elbow movement is performed after the shoulder joint is mechanically locked, which is appropriate because locking the shoulder joint eliminates the torques that arise at the shoulder when the forearm rotates. However, this learning is slow (i.e., it unfolds over hundreds of trials) and incomplete (i.e., shoulder activity is not fully eliminated). Here we investigated whether and how the addition of explicit strategies and biofeedback modulate this type of learning. Three groups of human participants (N = 55) performed voluntary pure elbow rotations using a robotic exoskeleton that permits shoulder and elbow rotation in a horizontal plane. Participants did the task with the shoulder free to move (baseline), then with the shoulder joint locked by the robotic manipulandum (adaptation), and then with the shoulder free to move again (post-adaptation). The first group of participants performed this protocol and received no instructions about what to do after their shoulder was locked. The second group of participants received visual feedback about their shoulder muscle activity after each movement and was instructed to reduce their shoulder activity to zero. The third group of participants also received visual biofeedback, but it was removed part way through the experiment. We found that, although all groups learned, the rate and magnitude of learning was not reliably different across the groups. Taken together, our results suggest that learning new arm dynamics, unlike other motor learning paradigms, unfolds independent of explicit instructions, biofeedback and task instructions.

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bioRxiv2020

Spinal stretch reflexes support efficient control of reaching

Efficiently controlling the movement of our hand requires coordinating the motion of multiple joints of the arm. Although it is widely assumed that this type of efficient control is implemented by processing that occurs in the cerebral cortex and brain stem, recent work has shown that spinal circuits can generate efficient motor output that supports keeping the hand in a static location. Here, we show that a spinal pathway can also efficiently control the hand during reaching. In our first experiment we applied multi-joint mechanical perturbations to participants elbow and wrist as they began reaching towards a target. We found that spinal stretch reflexes evoked in elbow muscles were not proportional to how much the elbow muscles were stretched but instead were efficiently scaled to the hands distance from the target. In our second experiment we applied the same elbow and wrist perturbations but had participants change how they grasped the manipulandum, diametrically altering how the same wrist perturbation moved the hand relative to the reach target. We found that changing the arms orientation diametrically altered how spinal reflexes in the elbow muscles were evoked, and in such a way that were again efficiently scaled to the hands distance from the target. These findings demonstrate that spinal circuits can help efficiently control the hand during dynamic reaching actions, and show that efficient and flexible motor control is not exclusively dependent on processing that occurs within supraspinal regions of the nervous system.

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bioRxiv2019

Motor learning and transfer: from feedback to feedforward control

Previous work has demonstrated that when learning a new motor task, the nervous system modifies feedforward (ie. voluntary) motor commands and that such learning transfers to fast feedback (ie. reflex) responses evoked by mechanical perturbations. Here we show the inverse, that learning new feedback responses transfers to feedforward motor commands. Sixty human participants (34 females) used a robotic exoskeleton and either 1) received short duration mechanical perturbations (20 ms) that created pure elbow rotation or 2) generated self-initiated pure elbow rotations. They did so with the shoulder joint free to rotate (normal arm dynamics) or locked (altered arm dynamics) by the robotic manipulandum. With the shoulder unlocked, the perturbation evoked clear shoulder muscle activity in the long-latency stretch reflex epoch (50-100ms post-perturbation), as required for countering the imposed joint torques, but little muscle activity thereafter in the so-called voluntary response. After locking the shoulder joint, which alters the required joint torques to counter pure elbow rotation, we found a reliable reduction in the long-latency stretch reflex over many trials. This reduction transferred to feedforward control as we observed 1) a reduction in shoulder muscle activity during self-initiated pure elbow rotation trials and 2) kinematic errors (ie. aftereffects) in the direction predicted when failing to compensate for normal arm dynamics, even though participants never practiced self-initiated movements with the shoulder locked. 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.

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bioRxiv2019

Generalizing movement patterns following shoulder fixation

A common goal of motor learning is generalizing newly learned movement patterns beyond the training context. Here we tested whether learning a new physical property of the arm during self-initiated reaching generalizes to new arm configurations. One hundred human participants performed a single-joint elbow reaching task and/or countered mechanical perturbations that created pure elbow motion. Participants did so with the shoulder joint either free to rotate or locked by the robotic manipulandum. With the shoulder free, we found activation of shoulder extensor muscles for pure elbow extension trials, as required to counter the interaction torques that arise at the shoulder due to forearm rotation. After locking the shoulder joint, we found a substantial reduction in shoulder muscle activity that developed slowly over many trials. This reduction is appropriate because locking the shoulder joint cancels the interaction torques that arise at the shoulder to do forearm rotation and thus removes the need to activate shoulder muscles. In our first three experiments, we tested whether this reduction generalizes when reaching is self-initiated in (1) a different initial shoulder orientation, (2) a different initial elbow orientation and (3) for a different reach distance/speed. We found reliable generalization across initial shoulder orientation and reach distance/speed but not for initial elbow orientation. In our fourth experiment, we tested whether generalization is also transferred to feedback control by applying mechanical perturbations and observing reflex responses in a distinct shoulder orientation. We found robust transfer to feedback control.\n\nNew & NoteworthyHere we show that learning to reduce shoulder muscles activity following shoulder fixation generalizes to other movement conditions but does not generalize globally, indicating that the nervous system does not implement such learning by modifying a general internal model of arm dynamics.\n\nDisclosuresThe authors declare no conflict of interest.

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bioRxiv2018

Feedforward and feedback control share an internal model of the arm’s dynamics

Humans have a remarkable capacity to learn novel movement patterns in a wide variety of contexts. Recent work has shown that, when countering external forces, the nervous system adjusts not only voluntary (ie. feedforward) control but also reflex (ie. feedback) responses. Here we show that directly altering the physical properties of the arm (i.e. intersegmental dynamics) causes the nervous system to adjust feedforward control and that this learning also transfers to feedback responses even though they were never directly trained. In our first experiment, we altered intersegmental dynamics by asking participants to generate pure elbow movements with the shoulder joint either free to rotate or locked. Locking the shoulder joint cancels the interaction forces that arise at the shoulder during forearm rotation and thus removes the need to activate shoulder muscles to prevent shoulder joint rotation. We first asked whether the nervous system learns this altered mapping of intersegmental dynamics. In the baseline phase, we found robust activation of shoulder flexor muscles for pure elbow flexion trials prior to movement onset - as required to counter the intersegmental dynamics. After locking the shoulder joint in the adaptation phase, we found a substantial reduction in shoulder muscle activity over many trials. After unlocking the shoulder joint in the post-adaptation phase, we observed after-effects, as participants made systematic hand path errors. In our second experiment, we investigated whether such learning transfers to feedback control. Mechanical perturbations applied to the limb in the baseline phase revealed that feedback responses, like feedforward control, also appropriately countered intersegmental dynamics. In the adaptation phase, we found a substantial reduction in shoulder feedback responses - as appropriate for the altered intersegmental dynamics. We also found that this decay in shoulder feedback responses correlated across subjects with the amount of decay during feedforward control. Our work adds to the growing evidence that feedforward and feedback control share an internal model of the arms dynamics.

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bioRxiv2018

Spinal stretch reflexes exploit musculoskeletal redundancy to support postural hand control

Motor behaviour is most efficiently controlled by only correcting disturbances or deviations that influence task success. It is currently thought that such sophisticated control is computed within a transcortical feedback pathway. Here we show that even the fastest spinal feedback pathway can produce corrective responses that adhere to this control scheme. We first applied small mechanical perturbations that flexed the elbow joint - stretching the triceps muscle - and simultaneously flexed or extended the wrist joint, displacing the hand various distances away from a central target. We then changed the arms orientation and applied the same joint perturbations, which reversed the mapping between joint motion and hand displacement. In all cases, we found that the triceps spinal stretch reflex was tuned to the hands displacement relative to the target, and not how the triceps muscle was stretched. Our findings reveal that the fastest spinal feedback pathway is capable of integrating and modulating feedback from multiple muscles to produce efficient corrective responses, forcing a re-evaluation of the how the nervous system derives the sophisticated control laws that support natural motor behaviour.

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bioRxiv2017

Done in 100 ms: Path-dependent visuomotor transformation in the human upper limb

A core assumption underlying mental chronometry is that more complex tasks increase cortical processing, prolonging reaction times. Here we show that increases in task complexity alter the magnitude, rather than the latency, of the output for a circuit that rapidly transforms visual information into motor actions. We quantified visual stimulus-locked responses (SLRs), which are changes in upper limb muscle recruitment that evolve at a fixed latency [~]100 ms after novel visual stimulus onset. First, we studied the underlying reference frame of the SLR, by dissociating initial eye and hand position. Despite its quick latency, we found that the SLR was expressed in a hand-centric reference frame, suggesting that the circuit mediating the SLR integrated retinotopic visual information with body configuration. Next, we studied the influence of planned movement trajectory, requiring participants to prepare and generate curved or straight reaches in the presence of obstacles to attain the same visual stimulus. We found that SLR magnitude reflected the initial planned movement trajectory, regardless of the ensuing movement curvature. Based on these results, we suggest that the circuit mediating the SLR lies in parallel to other well-studied corticospinal pathways. Although the fixed latency of the SLR precludes extensive cortical processing, inputs conveying information relating to task complexity, such as body configuration and planned movement trajectory, can pre-set nodes within the circuit underlying to the SLR to modulate its magnitude.\n\nSIGNIFICANCE STATEMENTA core assumption underlying mental chronometry is that more complex tasks increase cortical processing, prolonging reaction times. Here, we showed that increases in task complexity altered the magnitude, rather than the latency, of a circuit that rapidly transforms visual information into motor actions. We focus on stimulus-locked responses (SLRs), which are changes in upper limb muscle recruitment that evolve at a fixed latency [~]100 ms after novel visual stimulus onset. We showed that despite is quick latency, the circuitry mediating the SLR transformed a retinotopic visual signal into a hand-centric motor command suitable to contribute to the initial movement trajectory. We suggest that this circuit lies in parallel to other well-studied corticospinal pathways.

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bioRxiv2017

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 point 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 cursors 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 wrists veridical movement.

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bioRxiv2017

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. Here, we investigated whether the nervous system accounts for intersegmental limb dynamics across the shoulder, elbow, and wrist joints during self-initiated reaching and when countering external mechanical perturbations. Our first experiment tested whether the timing and amplitude of shoulder muscle activity accounts 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 when countering 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.\n\nNEW & NOTEWORTHYIntersegmental dynamics complicate the mapping between applied joint torques and the resulting joint motions. Here, 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.

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