On-line processing of uncertain information in visuomotor control

Modulations of stretch reflex by altering visuomotor contexts

Various functional modulations of the stretch reflex help to stabilize actions, but the computational mechanism behind its context-dependent tuning remains unclear. While many studies have demonstrated that motor contexts associated with the task goal cause functional modulation of the stretch reflex of upper limbs, it is not well understood how visual contexts independent of the task requirements affect the stretch reflex. To explore this issue, we conducted two experiments testing 20 healthy human participants (age range 20-45, average 31.3 ± 9.0), in which visual contexts were manipulated in a visually guided reaching task. During wrist flexion movements toward a visual target, a mechanical load was applied to the wrist joint to evoke stretch reflex of wrist flexor muscle (flexor carpi radialis). The first experiment (n = 10) examined the effect of altering the visuomotor transformation on the stretch reflex that was evaluated with surface electromyogram. We found that the amplitude of the stretch reflex decreased (p = 0.024) when a rotational transformation of 90° was introduced between the hand movement and the visual cursor, whereas the amplitude did not significantly change (p = 0.26) when the rotational transformation was accompanied by a head rotation so that the configuration of visual feedback was maintained in visual coordinates. The results suggest that the stretch reflex was regulated depending on whether the visuomotor mapping had already been acquired or not. In the second experiment (n = 10), we examined how uncertainty in the visual target or hand cursor affects the stretch reflex by removing these visual stimuli. We found that the reflex amplitude was reduced by the disappearance of the hand cursor (p = 0.039), but was not affected by removal of the visual target (p = 0.27), suggesting that the visual state of the body and target contribute differently to the reflex tuning. These findings support the idea that visual updating of the body state is crucial for regulation of quick motor control driven by proprioceptive signals.

Interaction between decision-making and motor learning when selecting reach targets in the presence of bias and noise

Motor errors can have both bias and noise components. Bias can be compensated for by adaptation and, in tasks in which the magnitude of noise varies across the environment, noise can be reduced by identifying and then acting in less noisy regions of the environment. Here we examine how these two processes interact when participants reach under a combination of an externally imposed visuomotor bias and noise. In a center-out reaching task, participants experienced noise (zero-mean random visuomotor rotations) that was target-direction dependent with a standard deviation that increased linearly from a least-noisy direction. They also experienced a constant bias, a visuomotor rotation that varied (across groups) from 0 to 40 degrees. Critically, on each trial, participants could select one of three targets to reach to, thereby allowing them to potentially select targets close to the least-noisy direction. The group who experienced no bias (0 degrees) quickly learned to select targets close to the least-noisy direction. However, groups who experienced a bias often failed to identify the least-noisy direction, even though they did partially adapt to the bias. When noise was introduced after participants experienced and adapted to a 40 degrees bias (without noise) in all directions, they exhibited an improved ability to find the least-noisy direction. We developed two models-one for reach adaptation and one for target selection-that could explain participants' adaptation and target-selection behavior. Our data and simulations indicate that there is a trade-off between adaptation and selection. Specifically, because bias learning is local, participants can improve performance, through adaptation, by always selecting targets that are closest to a chosen direction. However, this comes at the expense of improving performance, through selection, by reaching toward targets in different directions to find the least-noisy direction.

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.

Evoking natural thermal perceptions using a thin-film thermoelectric device with high cooling power density and speed

Multimodal sensory feedback from upper-limb prostheses can increase their function and usability. Here we show that intuitive thermal perceptions during cold-object grasping with a prosthesis can be restored in a phantom hand through targeted nerve stimulation via a wearable thin-film thermoelectric device with high cooling power density and speed. We found that specific regions of the residual limb, when thermally stimulated, elicited thermal sensations in the phantom hand that remained stable beyond 48 weeks. We also found stimulation sites that selectively elicited sensations of temperature, touch or both, depending on whether the stimulation was thermal or mechanical. In closed-loop functional tasks involving the identification of cold objects by amputees and by non-amputee participants, and compared with traditional bulk thermoelectric devices, the wearable thin-film device reliably elicited cooling sensations that were up to 8 times faster and up to 3 times greater in intensity while using half the energy and 1/600th the mass of active thermoelectric material. Wearable thin-film thermoelectric devices may allow for the non-invasive restoration of thermal perceptions during touch.

The sensorimotor system modulates muscular co-contraction relative to visuomotor feedback responses to regulate movement variability

The naturally occurring variability in our movements often poses a significant challenge when attempting to produce precise and accurate actions, which is readily evident when playing a game of darts. Two differing, yet potentially complementary, control strategies that the sensorimotor system may use to regulate movement variability are impedance control and feedback control. Greater muscular co-contraction leads to greater impedance that acts to stabilize the hand, while visuomotor feedback responses can be used to rapidly correct for unexpected deviations when reaching toward a target. Here, we examined the independent roles and potential interplay of impedance control and visuomotor feedback control when regulating movement variability. Participants were instructed to perform a precise reaching task by moving a cursor through a narrow visual channel. We manipulated cursor feedback by visually amplifying movement variability and/or delaying the visual feedback of the cursor. We found that participants decreased movement variability by increasing muscular co-contraction, aligned with an impedance control strategy. Participants displayed visuomotor feedback responses during the task but, unexpectedly, there was no modulation between conditions. However, we did find a relationship between muscular co-contraction and visuomotor feedback responses, suggesting that participants modulated impedance control relative to feedback control. Taken together, our results highlight that the sensorimotor system modulates muscular co-contraction, relative to visuomotor feedback responses, to regulate movement variability and produce accurate actions.NEW & NOTEWORTHY The sensorimotor system has the constant challenge of dealing with the naturally occurring variability in our movements. Here, we investigated the potential roles of muscular co-contraction and visuomotor feedback responses to regulate movement variability. When we visually amplified movements, we found that the sensorimotor system primarily uses muscular co-contraction to regulate movement variability. Interestingly, we found that muscular co-contraction was modulated relative to inherent visuomotor feedback responses, suggesting an interplay between impedance and feedback control.

Perception of effort and the allocation of physical resources: A generalization to upper-limb motor tasks

Purpose

The perception of effort (PE) is widely used to prescribe and monitor exercise during locomotor and resistance tasks. The present study examines the validity of PE to prescribe and monitor exercise during upper-limb motor tasks under various loads and speed requirements.

Methods

Forty participants volunteered in two experiments. In experiment 1, we used four PE intensities to prescribe exercise on a modified version of the box and block test (BBT) and a pointing task. We investigated the possibility of monitoring the exercise intensity by tracking changes in PE rating in response to three different tempos or additional weights. Experiment 2 replicated the possibility of prescribing the exercise with the PE intensity during the BBT and explored the impact of additional weights on performance and PE during the standardized version of the BBT. Muscle activation, heart rate, and respiratory frequencies were recorded.

Results

In experiment 1, increasing the PE intensity to prescribe exercise induced an increased performance between each intensity. Increasing task difficulty with faster movement tempo and adding weight on the forearm increased the rating of PE. Experiment 2 replicated the possibility to use PE intensity for exercise prescription during the BBT. When completing the BBT with an additional weight on the forearm, participants maintained performance at the cost of a higher PE. In both experiments, changes in PE were associated with changes in muscle activation.

Conclusion

Our results suggest that PE is a valid tool to prescribe and monitor exercise during upper-limb motor tasks.

Behavioral and neural representation of expected reward and risk

When faced with uncertainty, individuals' value-based decisions are influenced by the expected rewards and risks. Understanding how reward and risk are processed and integrated at the behavioral and neural levels is essential for building up utility theories. Using a modified monetary incentive delay task in which the mean of two possible outcomes (expected reward) and the standard deviation (SD) of the possible outcomes (risk) were parametrically manipulated and orthogonalized, we measured eye movements, response times (RTs), and brain activity when participants seek to secure a reward. We found that RTs varied as a function of the mean but not the SD of the potential reward, suggesting that expected rewards are the main driver of RTs. Moreover, the difference between gazes focused on high vs. low value rewards became smaller when the magnitude of the potential reward (mean of possible outcomes) was larger and when risk (SD of possible outcomes) became smaller, highlighting that reward and risk have different effects on attention deployment. Processing the mean reward activated the striatum. The positive striatal connectivity to the amygdala and negative striatal connectivity to the superior frontal gyrus were correlated with individuals' sensitivity to the expected reward. In contrast, processing risk activated the anterior insula. Its positive connectivity to the ventromedial prefrontal cortex and negative connectivity to the anterior midcingulate cortex were correlated with individual differences in risk sensitivity, further suggesting the functional dissociation of reward and risk at the neural level. Our findings, based on several different measures, delineate the distinct representations of reward and risk in non-decision contexts and provide insight into how these utility parameters modulate attention, motivation, and brain networks.

A Motor Adaptation Model Assuming Update of Internal Model in the Motor Cortex

When considering the human motor-adaptation mechanism from the perspective of the motor control theory, updating the internal model constitutes a critical component. The learning curve at each trial of motion can be explained by a state-space model; however, the model cannot reproduce the time-series data for the hand’s position, velocity, and acceleration (motion profiles). There is no internal model-updating rule for optimal feedback control, a plausible model for reproducing motion profiles. In this paper, we propose an adaptation model that incorporates an internal model-updating rule which modeled after Hebb’s rule into optimal feedback control. Also, we examine the neural substrate of the internal model. To validate the proposed adaptation model, we conducted behavioral experiments with humans that reflected changes in the internal model and reproduced the changes in the internal model as well as the motion profiles using the proposed adaptation model. In addition, we analyzed the data for a visuomotor rotation task performed by a monkey and checked for changes in the output characteristics of neurons in the motor cortex before and after adaptation. According to the above-mentioned validation and analysis results, the motor cortex constitutes the neural substrate of the internal model.

Contextual cues are not unique for motor learning: Task-dependant switching of feedback controllers

The separation of distinct motor memories by contextual cues is a well known and well studied phenomenon of feedforward human motor control. However, there is no clear evidence of such context-induced separation in feedback control. Here we test both experimentally and computationally if context-dependent switching of feedback controllers is possible in the human motor system. Specifically, we probe visuomotor feedback responses of our human participants in two different tasks—stop and hit—and under two different schedules. The first, blocked schedule, is used to measure the behaviour of stop and hit controllers in isolation, showing that it can only be described by two independent controllers with two different sets of control gains. The second, mixed schedule, is then used to compare how such behaviour evolves when participants regularly switch from one task to the other. Our results support our hypothesis that there is contextual switching of feedback controllers, further extending the accumulating evidence of shared features between feedforward and feedback control.

Extensive evidence has demonstrated that humans can learn distinct motor memories (i.e. independent feedforward controllers) using contextual cues. However, there is little evidence that such contextual cues produce similar separation of feedback controllers. As accumulating evidence highlights the connection between feedforward and feedback control, we propose that context may be used to separate feedback controllers as well. It has not been trivial to test experimentally whether a change in context also modulates the feedback control, as the controller output is affected by other non-contextual factors such as movement kinematics, time-to-target or the properties of the perturbation used to probe the control. Here we present a computational approach based on normative modelling where we separate the effects of the context from other non-contextual effects on the visuomotor feedback system. We then show experimentally that task context independently modulates the feedback control in a particular manner that can be reliably predicted using optimal feedback control.

The influence of the motor command accuracy on the prediction error and the automatic corrective response

The online control system allows for automatic corrective response to unexpected perturbation. This corrective response may involve a prediction error between the sensory prediction by the motor command and the actual feedback signal. Therefore, we attempted to investigate the effect of motor command accuracy on the automatic corrective response. Participants were asked to move a cursor displayed on a monitor and required to reach the center of a Gaussian blob target as accurately as possible for small and large Gaussian blob conditions. The accuracy of the motor command was manipulated by the size of the Gaussian blob. In half of the trials, a perturbation occurred in which the cursor position jumped 10 mm to either the left or right from the actual position, which induced an automatic corrective response. This corrective response was detected by the acceleration signal on the lateral axis. In addition, the prediction error was estimated by the amplitude of the N1 event-related potential (ERP) of the EEG signal. We found that the automatic response and N1 ERP were significantly larger in the small Gaussian blob conditions than in the large one. This result indicates that the automatic corrective response is affected by the certainty of the motor command manipulated by the Gaussian blob. Furthermore, the linear mixed-effect model (LME) indicated that the response is associated with the N1 ERP. Therefore, we suggest that the motor command accuracy affects the prediction error, which in turn modulates the automatic corrective response.

The Effect of Target Velocity on the Fast Corrective Response during Reaching Movement

Online motor control is often required to correct errors in rapid adjustments during reaching movements. It has been established that the initial arm trajectory during reaching is corrected by a target displacement. Since this corrective response occurs without perception of target perturbation, this is regarded as an automatic response. However, an object rarely "jumps" in daily life, rather it often "moves" as a chronological change of the position that causes visual motion. Therefore, the purpose of this study was to investigate whether the implicit visuomotor response is induced by target motion stimuli and to clarify the effects of target motion velocity on initial arm trajectory. Participants were asked to move a cursor from a start circle to a visual target. The target moved either leftward or rightward when the cursor passed 20 mm from the start circle. Four target velocities (10, 20, 30, 40 deg/s) were randomly presented. Our results showed that the initial velocity (first 50 ms) of the fast corrective response increased with the target velocity. Therefore, it is indicated that the fast corrective response is induced by the target motion stimulus with a short latency and its amplitude is dependent on the target velocity.

Eye-hand coordination during online reach corrections is task dependent

To produce accurate movements, the human motor system needs to deal with errors that can occur due to inherent noise, changes in the body, or disturbances in the environment. Here, we investigated the temporal coupling of rapid corrections of the eye and hand in response to a change in visual target location during the movement. In addition to a "classic" double-step task in which the target stepped to a new position, participants performed a set of modified double-step tasks in which the change in movement goal was indicated by the appearance of an additional target, or by a spatial or symbolic cue. We found that both the absolute correction latencies of the eye and hand and the relative eye-hand correction latencies were dependent on the visual characteristics of the target change, with increasingly longer latencies in tasks that required more visual and cognitive processing. Typically, the hand started correcting slightly earlier than the eye, especially when the target change was indicated by a symbolic cue, and in conditions where visual feedback of the hand position was provided during the reach. Our results indicate that the oculomotor and limb-motor system can be differentially influenced by processing requirements of the task and emphasize that temporal eye-hand coupling is flexible rather than rigid.NEW & NOTEWORTHY Eye movements support hand movements in many situations. Here, we used variations of a double-step task to investigate temporal coupling of corrective hand and eye movements in response to target displacements. Correction latency coupling depended on the visual and cognitive processing demands of the task. The hand started correcting before the eye, especially when the task required decoding a symbolic cue. These findings highlight the flexibility and task dependency of eye-hand coordination.

Parallel and hierarchical neural mechanisms for adaptive and predictive behavioral control

Our brain can be recognized as a network of largely hierarchically organized neural circuits that operate to control specific functions, but when acting in parallel, enable the performance of complex and simultaneous behaviors. Indeed, many of our daily actions require concurrent information processing in sensorimotor, associative, and limbic circuits that are dynamically and hierarchically modulated by sensory information and previous learning. This organization of information processing in biological organisms has served as a major inspiration for artificial intelligence and has helped to create in silico systems capable of matching or even outperforming humans in several specific tasks, including visual recognition and strategy-based games. However, the development of human-like robots that are able to move as quickly as humans and respond flexibly in various situations remains a major challenge and indicates an area where further use of parallel and hierarchical architectures may hold promise. In this article we review several important neural and behavioral mechanisms organizing hierarchical and predictive processing for the acquisition and realization of flexible behavioral control. Then, inspired by the organizational features of brain circuits, we introduce a multi-timescale parallel and hierarchical learning framework for the realization of versatile and agile movement in humanoid robots.

Rapid Feedback Responses Parallel the Urgency of Voluntary Reaching Movements

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

Stochastic optimal feedforward-feedback control determines timing and variability of arm movements with or without vision

Human movements with or without vision exhibit timing (i.e. speed and duration) and variability characteristics which are not well captured by existing computational models. Here, we introduce a stochastic optimal feedforward-feedback control (SFFC) model that can predict the nominal timing and trial-by-trial variability of self-paced arm reaching movements carried out with or without online visual feedback of the hand. In SFFC, movement timing results from the minimization of the intrinsic factors of effort and variance due to constant and signal-dependent motor noise, and movement variability depends on the integration of visual feedback. Reaching arm movements data are used to examine the effect of online vision on movement timing and variability, and test the model. This modelling suggests that the central nervous system predicts the effects of sensorimotor noise to generate an optimal feedforward motor command, and triggers optimal feedback corrections to task-related errors based on the available limb state estimate.

Online modification of goal-directed control in human reaching movements

Humans are able to perform very sophisticated reaching movements in a myriad of contexts based on flexible control strategies influenced by the task goal and environmental constraints such as obstacles. However, it remains unknown whether these control strategies can be adjusted online. The objective of this study was to determine whether the factors that determine control strategies during planning also modify the execution of an ongoing movement following sudden changes in task demand. More precisely, we investigated whether, and at which latency, feedback responses to perturbation loads followed the change in the structure of the goal target or environment. We changed the target width (square or rectangle) to alter the task redundancy, or the presence of obstacles to induce different constraints on the reach path, and assessed based on surface electromyography (EMG) recordings when the change in visual display altered the feedback response to mechanical perturbations. Task-related EMG responses were detected within 150 ms of a change in target shape. Considering visuomotor delays of ∼ 100 ms, these results suggest that it takes 50 ms to change control policy within a trial. An additional 30-ms delay was observed when the change in context involved sudden appearance or disappearance of obstacles. Overall, our results demonstrate that the control policy within a reaching movement is not static: contextual factors that influence movement planning also influence movement execution at surprisingly short latencies. Moreover, the additional 30 ms associated with obstacles suggests that these two types of changes may be mediated via distinct processes.NEW & NOTEWORTHY The present work demonstrates that the control strategies used to perform reaching movements are adjusted online when the structure of the target or the presence of obstacles are altered during movements. Thus, the properties of goal-directed reaching control are not simply selected during the planning stage of a movement prior to execution. Rather, they are dynamically and rapidly adjusted online, within ∼150 ms, according to changes in environment.

Interlimb differences in visuomotor and dynamic adaptation during targeted reaching in children

While a number of studies have focused on movement (a)symmetries between the arms in adults, less is known about movement asymmetries in typically developing children. The goal of this study was to examine interlimb differences in children when adapting to novel visuomotor and dynamic conditions while performing a center-out reaching task. We tested 13 right-handed children aged 9-11 years old. Prior to movement, one of eight targets arranged radially around the start position was randomly displayed. Movements were made either with the right (dominant) arm or the left (nondominant) arm. The children participated in two experiments separated by at least one week. In one experiment, subjects were exposed to a rotated visual display (30° about the start circle); and in the other, a 1 kg mass (attached eccentrically to the forearm axis). Each experiment consisted of three blocks: pre-exposure, exposure and post-exposure. Three measures of task performance were calculated from hand trajectory data: hand-path deviation from the straight target line, direction error at peak velocity and final position error. Results showed that during visuomotor adaptation, no interlimb differences were observed for any of the three measures. During dynamic adaptation, however, a significant difference between the arms was observed at the first cycle during dynamic adaptation. With regard to the aftereffects observed during the post-exposure block, direction error data indicate considerably large aftereffects for both arms during visuomotor adaptation; and there was a significant difference between the arms, resulting in substantially larger aftereffects for the right arm. Similarly, dynamic adaptation results also showed a significant difference between the arms; and post hoc analyses indicated that aftereffects were present only for the right arm. Collectively, these findings indicate that the dominant arm advantage for developing an internal model associated with a novel visuomotor or dynamic transform, as previously shown in young adults, may already be apparent at 9 to 11-year old children.

Kinematic Gait Adjustments to Virtual Environments on Different Surface Conditions: Do Treadmill and Over-Ground Walking Exhibit Different Adaptations to Passive Virtual Immersion?

BACKGROUND

The aim of this study was to examine the kinematic gait adjustments performed in response to passive and photorealistic virtual reality environment (VRE) demands during over-ground and treadmill walking conditions and determine whether the surface presentation order affects the gait adjustments in response to different VREs.

METHODS

Twenty young participants divided into two groups performed two virtual reality (VR) walking protocols which included two different VREs (snowy and crowded conditions). Group A performed the VR over-ground protocol (four natural walking (NW), seven VR snowy, and seven VR crowded trials) followed by the VR treadmill protocol (four NW, one VR snowy, and one VR crowded trials); Group B performed the VR treadmill protocol (four NW, seven VR snowy, and seven VR crowded trials) followed by the VR over-ground protocol (four NW, one VR snowy, and one VR crowded trials). Center of mass (COM) excursion angles and mediolateral (ML) COM excursions were analyzed and used as outcome measures.

RESULTS

Group A showed higher COM excursion angles and ML-COM excursion on over-ground VR trials compared to NW trials (p < 0.05), while Group B only showed kinematic changes for the crowded VRE compared to NW trials during the treadmill walking protocol (p < 0.05). Post over-ground exposure, Group A showed greater COM excursion angle and ML-COM excursions on VR trials compared to NW trials during the treadmill walking protocol (p < 0.05). Post treadmill exposure, Group B only showed higher COM excursion angles for the snowy VRE compared to NW trials during the over-ground walking protocol (p < 0.01).

CONCLUSION

Results showed that higher kinematic gait adjustments in response to VRE demands were observed during over-ground walking. Additionally, higher sensorimotor responses to VRE demands were observed when the VR protocol was first performed on the over-ground surface and followed by the treadmill walking condition (Group A) compared to the opposite (Group B).

Cerebellar patients have intact feedback control that can be leveraged to improve reaching

It is thought that the brain does not simply react to sensory feedback, but rather uses an internal model of the body to predict the consequences of motor commands before sensory feedback arrives. Time-delayed sensory feedback can then be used to correct for the unexpected—perturbations, motor noise, or a moving target. The cerebellum has been implicated in this predictive control process. Here, we show that the feedback gain in patients with cerebellar ataxia matches that of healthy subjects, but that patients exhibit substantially more phase lag. This difference is captured by a computational model incorporating a Smith predictor in healthy subjects that is missing in patients, supporting the predictive role of the cerebellum in feedback control. Lastly, we improve cerebellar patients’ movement control by altering (phase advancing) the visual feedback they receive from their own self movement in a simplified virtual reality setup.

Reaching movements are automatically redirected to nearby options during target split

Motor behavior often occurs in environments with multiple goal options that can vary during the ongoing action. We explored this situation by requiring subjects to select between different target options during an ongoing reach. During split trials the original target was replaced with a left and a right flanking target, and participants had to select between them. This contrasted with the standard jump trials, where the original target would be replaced with a single flanking target, left or right. When participants were instructed to follow their natural tendency, they all tended to select the split target nearest the original. The near-target preference was more prominent with increased spatial disparity between the options and when participants could preview the potential options. Moreover, explicit instruction to obtain the "far" target during split trials resulted many errors compared with a "near" instruction, ~50% vs. ~15%. Online reaction times to target change were delayed in split trials compared with jump trials, ~200 ms vs. ~150 ms, but also highly automatic. Trials in which the instructed far target was correctly obtained were delayed by a further ~50 ms, unlike those in which the near target was incorrectly obtained. We also observed nonspecific responses from arm muscles at the jump trial latency during split trials. Taken together, our results indicate that online selection of reach targets is automatically linked to the spatial distribution of the options, though at greater delays than redirecting to a single target.NEW & NOTEWORTHY This work demonstrates that target selection during an ongoing reach is automatically linked to the option nearest a voided target. Online reaction times for two options are longer than redirection to a single option. Attempts to override the near-target tendency result in a high number of errors at the normal delay and further delays when the attempt is successful.

Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices

Biological neural networks outperform current computer technology in terms of power consumption and computing speed while performing associative tasks, such as pattern recognition. The analogue and massive parallel in-memory computing in biology differs strongly from conventional transistor electronics that rely on the von Neumann architecture. Therefore, novel bio-inspired computing architectures have been attracting a lot of attention in the field of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are employed to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology to neuromorphic systems. However, dealing with the inherent stochasticity of resistive switching can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two different RRAM technologies with different device variabilities are investigated in detail, and their potential applications in stochastic artificial neural networks (StochANNs) capable of solving MNIST pattern recognition tasks is examined. A mixed-signal implementation with hardware synapses and software neurons combined with numerical simulations shows that the proposed concept of stochastic computing is able to process analogue data with binary memory cells.

Passive training with upper extremity exoskeleton robot affects proprioceptive acuity and performance of motor learning

Sports trainers often grasp and move trainees' limbs to give instructions on desired movements, and a merit of this passive training is the transferring of instructions via proprioceptive information. However, it remains unclear how passive training affects the proprioceptive system and improves learning. This study examined changes in proprioceptive acuity due to passive training to understand the underlying mechanisms of upper extremity training. Participants passively learned a trajectory of elbow-joint movement as per the instructions of a single-arm upper extremity exoskeleton robot, and the performance of the target movement and proprioceptive acuity were assessed before and after the training. We found that passive training improved both the reproduction performance and proprioceptive acuity. We did not identify a significant transfer of the training effect across arms, suggesting that the learning effect is specific to the joint space. Furthermore, we found a significant improvement in learning performance in another type of movement involving the trained elbow joint. These results suggest that participants form a representation of the target movement in the joint space during the passive training, and intensive use of proprioception improves proprioceptive acuity.

Visually-updated hand state estimates modulate the proprioceptive reflex independently of motor task requirements

Fast signaling from vision and proprioception to muscle activation plays essential roles in quickly correcting movement. Though many studies have demonstrated modulation of the quick sensorimotor responses as depending on context in each modality, the contribution of multimodal information has not been established. Here, we examined whether state estimates contributing to stretch reflexes are represented solely by proprioceptive information or by multimodal information. Unlike previous studies, we newly found a significant stretch-reflex attenuation by the distortion and elimination of visual-feedback without any change in motor tasks. Furthermore, the stretch-reflex amplitude reduced with increasing elimination durations which would degrade state estimates. By contrast, even though a distortion was introduced in the target-motor-mapping, the stretch reflex was not simultaneously attenuated with visuomotor reflex. Our results therefore indicate that the observed stretch-reflex attenuation is specifically ascribed to uncertainty increase in estimating hand states, suggesting multimodal contributions to the generation of stretch reflexes.

"Two sides of the same coin": constant motor learning speeds up, whereas variable motor learning stabilizes, speed-accuracy movements

PURPOSE

The aim of this study was to determine the time course of the trade-off between speed and accuracy, intraindividual variability, and movement transfer and retention (4 weeks after learning) of speed-accuracy tasks.

METHODS

The participants in this study were healthy adults randomly divided into three groups (control versus constant versus variable). They were aged 19-24 years, and 30 (15 men and 15 women) were in each group. Participants had to perform various tasks with the right dominant hand: (a) simple reaction test; (b) maximal velocity measurement; and (c) a speed-accuracy task.

RESULTS

During constant and variable learning, the trade-off in a speed-accuracy task in specific situations shifted toward improved motor planning and motor execution speed, and to reduced intraindividual variability. However, during variable learning, the maximal velocity and variability of motor planning time did not change. Constant learning effectively transferred into variable tasks in terms of reaction time, average velocity and maximal velocity, and these effects were greater than those associated with variable learning. However, the effects of constant learning did not transfer fully into the performance variability of variable movements. Variable learning effectively transferred into constant tasks for the coefficient of variation of the path of movement, average velocity, maximal velocity and reaction time. The retention effect depended neither on learning nor task specificity (constant versus variable tasks).

CONCLUSION

Constant learning speeds up but does not stabilize speed-accuracy movements in variable tasks; whereas, variable learning stabilizes but does not speed up speed-accuracy movements in constant tasks.

Feedback Delay Changes the Control of an Inverted Pendulum

We recently developed a simulated inverted pendulum in order to examine human sensorimotor control strategies for stabilization. This simulated system allows us to manipulate the visual and haptic feedback independently from the physical dynamics of the task. Here we examine the effect of sensory delay in a balancing task. Human participants attempted to balance an inverted pendulum (simulated on a robotic manipulandum) with three different added delays (25, 50, and 75 ms), where the same delay was added to both the visual and haptic feedback. Increasing sensory delays decreased the ability of the participants to stabilize the pendulum. Investigation into the online control of the pendulum showed that with longer delays participants reduced their movement frequency but increased the amplitudes of their corrections.

Split-Belt walking induces changes in active, but not passive, perception of step length

Successful motor control requires accurate estimation of our body in space for planning, executing, and evaluating the outcome of our actions. It has been shown that the estimation of limb position is susceptible to motor adaptation. However, a similar effect has not been found in locomotion, possibly due to how it was tested. We hypothesized that split-belt walking with the legs moving at different speeds changes the estimation of the legs' position when taking a step. Thus, we assessed young subjects' perception of step length (i.e., inter-feet distance at foot landing) when they moved their legs (active perception) or when the legs were moved by the experimenter (passive perception). We found that the active perception of step length was substantially altered following split-belt walking, whereas passive perception exhibited minor changes. This suggests that split-belt walking induced the adaptation of efferent signals, without altering sensory signals. We also found that active perceptual shifts were sensitive to how they were tested: they were most salient in the trailing leg and at short step lengths. Our results suggest that split-belt walking could modulate the deficient perception of step length post-stroke, which may contribute to gait asymmetries impairing patients' mobility.

Visual Feedback Processing of the Limb Involves Two Distinct Phases

Muscle responses to mechanical disturbances exhibit two distinct phases: a response starting at ~20 ms that is fairly stereotyped, and a response starting at ~60 ms modulated by many behavioral contexts including goal-redundancy and environmental obstacles. Muscle responses to disturbances of visual feedback of the hand arise within ~90 ms. However, little is known whether these muscle responses are sensitive to behavioral contexts. We had 49 human participants (27 male) execute goal-directed reaches with visual feedback of their hand presented as a cursor. On random trials, the cursor jumped laterally to the reach direction, and thus, required a correction to attain the goal. The first experiment demonstrated that the response amplitude starting at 90 ms scaled with jump magnitude, but only for jumps <2 cm. For larger jumps, the duration of the muscle response scaled with the jump size starting after 120 ms. The second experiment demonstrated that the early response was sensitive to goal redundancy as wider targets evoked a smaller corrective response. The third experiment demonstrated that the early response did not consider the presence of obstacles, as this response routinely drove participants directly to the goal even though this path was blocked by an obstacle. Instead, the appropriate muscle response to navigate around the obstacle started after 120 ms. Our findings highlight that visual feedback of the limb involves two distinct phases: a response starting at 90 ms with limited sensitivity to jump magnitude and sensitive to goal-redundancy, and a response starting at 120 ms with increased sensitivity to jump magnitude and environmental factors.SIGNIFICANCE STATEMENT The motor system can integrate proprioceptive feedback to guide an ongoing action in ~60 ms and is flexible to a broad range of behavioral contexts. In contrast, the present study identified that the motor response to a visual disturbance exhibits two distinct phases: an early response starting at 90 ms with limited scaling with disturbance size and sensitivity to goal-redundancy, and a slower response starting after 120 ms with increased sensitivity to disturbance size and sensitive to environmental obstacles. These data suggest visual feedback of the hand is processed through two distinct feedback processes.

Contextual Interference Effect Is Independent of Retroactive Inhibition but Variable Practice Is Not Always Beneficial

Positive effects of variable practice conditions on subsequent motor memory consolidation and generalization are widely accepted and described as the contextual interference effect (CIE). However, the general benefits of CIE are low and these benefits might even depend on decreased retest performances in the blocked-practicing control group, caused by retroactive inhibition. The aim of this study was to investigate if CIE represents a true learning phenomenon or possibly reflects confounding effects of retroactive inhibition. We tested 48 healthy human participants adapting their reaching movements to three different force field magnitudes. Subjects practiced the force fields in either a Blocked (B), Random (R), or Constant (C) schedule. In addition, subjects of the Blocked group performed either a retest schedule that did (Blocked-Matched; BM) or did not (Blocked-Unmatched; BU) control for retroactive inhibition. Results showed that retroactive inhibition did not affect the results of the BU group much and that the Random group showed a better consolidation performance compared to both Blocked groups. However, compared to the Constant group, the Random group showed only slight benefits in its memory consolidation of the mean performance across all force field magnitudes and no benefits in absolute performance values. This indicates that CIE reflects a true motor learning phenomenon, which is independent of retroactive inhibition. However, random practice is not always beneficial over constant practice.

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.

Neuroanatomical Basis of Individuality in Muscle Tuning Function: Neural Correlates of Muscle Tuning

In a conventional view of motor control, the human brain might employ an optimization principle that leads a stereotypical motor behavior which we observe as an averaged behavioral data over subjects. In this scenario, the inter-individual motor variability is considered as an observation noise. Here, we challenged this view. We considered a motor control task where the human participants manipulated arm force by coordinating shoulder and elbow torques and investigated the muscle-tuning function that represents how the brain distributed the ideal joint torques to multiple muscles. In the experimental data, we observed large inter-individual variability in the profile of a muscle-tuning function. This contradicts with a well-established optimization theory that is based on minimization of muscle energy consumption and minimization of motor variability. We then hypothesized the inter-subject differences in the structure of the motor cortical areas might be the source of the across-subjects variability of the motor behavior. This was supported by a voxel-based morphometry analysis of magnetic resonance imaging; The inter-individual variability of the muscle tuning profile was correlated with that of the gray matter volume in the premotor cortex which is ipsilateral to the used arm (i.e., right hemisphere for the right arm). This study suggests that motor individuality may originate from inter-individual variation in the cortical structure.

Adaptive Gaze Strategies to Reduce Environmental Uncertainty During a Sequential Visuomotor Behaviour

People must decide where, when, and for how long to allocate gaze to perform different motor behaviours. However, the factors guiding gaze during these ongoing, natural behaviours are poorly understood. Gaze shifts help acquire information, suggesting that people should direct gaze to locations where environmental details most relevant to the task are uncertain. To explore this, human subjects stepped on a series of targets as they walked. We used different levels of target uncertainty, and through instruction, altered the importance of (or subjective value assigned to) foot-placement accuracy. Gaze time on targets increased with greater target uncertainty when precise foot placement was more important, and these longer gaze times associated with reduced foot-placement error. Gaze times as well as the gaze shifts to and from targets relative to stepping differed depending on the target's position in the sequence and uncertainty level. Overall, we show that gaze is allocated to reduce uncertainty about target locations, and this depends on the value of this information gain for successful task performance. Furthermore, we show that the spatial-temporal pattern of gaze to resolve uncertainty changes with the evolution of the motor behaviour, indicating a flexible strategy to plan and control movement.

Visuomotor feedback gains are modulated by gaze position

During goal-directed reaching, people typically direct their gaze to the target before the start of the hand movement and maintain fixation until the hand arrives. This gaze strategy improves reach accuracy in two ways. It enables the use of central vision at the end of movement, and it allows the use of extraretinal information in guiding the hand to the target. Here we tested whether fixating the reach target further facilitates reach accuracy by optimizing the use of peripheral vision in detecting, and rapidly responding to, reach errors during the ongoing movement. We examined automatic visuomotor corrections in response to displacements of the cursor representing the hand position as a function of gaze fixation location during unimanual goal-directed reaching. Eight fixation targets were positioned either in line with, or at different angles relative to, the straight-ahead movement direction (manipulation of fixation angle), and at different distances from the location of the visual perturbation (manipulation of fixation distance). We found that corrections were fastest and strongest when gaze was directed at the reach target compared with when gaze was directed to a different location in the workspace. We found that the gain of the visuomotor response was strongly affected by fixation angle, and to a smaller extent by fixation distance, with lower gains as the angle or distance increased. We submit that fixating the reach target improves reach accuracy by facilitating rapid visuomotor responses to reach errors viewed in peripheral vision. NEW & NOTEWORTHY It is well known that directing gaze to the reach target allows the use of foveal visual feedback and extraretinal information to improve the accuracy of reaching movements. Here we demonstrate that target fixation also optimizes rapid visuomotor corrections to reach errors viewed in peripheral vision, with the angle of gaze relative to the hand movement being a critical determinant in the gain of the visuomotor response.

Interlimb Generalization of Learned Bayesian Visuomotor Prior Occurs in Extrinsic Coordinates

Recent work suggests that the brain represents probability distributions and performs Bayesian integration during sensorimotor learning. However, our understanding of the neural representation of this learning remains limited. To begin to address this, we performed two experiments. In the first experiment, we replicated the key behavioral findings of Körding and Wolpert (2004), demonstrating that humans can perform in a Bayes-optimal manner by combining information about their own sensory uncertainty and a statistical distribution of lateral shifts encountered in a visuomotor adaptation task. In the second experiment, we extended these findings by testing whether visuomotor learning occurring during the same task generalizes from one limb to the other, and relatedly, whether this learning is represented in an extrinsic or intrinsic reference frame. We found that the learned mean of the distribution of visuomotor shifts generalizes to the opposite limb only when the perturbation is congruent in extrinsic coordinates, indicating that the underlying representation of learning acquired during training is available to the untrained limb and is coded in an extrinsic reference frame.

Weighted integration of short-term memory and sensory signals in the oculomotor system

Oculomotor behaviors integrate sensory and prior information to overcome sensory-motor delays and noise. After much debate about this process, reliability-based integration has recently been proposed and several models of smooth pursuit now include recurrent Bayesian integration or Kalman filtering. However, there is a lack of behavioral evidence in humans supporting these theoretical predictions. Here, we independently manipulated the reliability of visual and prior information in a smooth pursuit task. Our results show that both smooth pursuit eye velocity and catch-up saccade amplitude were modulated by visual and prior information reliability. We interpret these findings as the continuous reliability-based integration of a short-term memory of target motion with visual information, which support modeling work. Furthermore, we suggest that saccadic and pursuit systems share this short-term memory. We propose that this short-term memory of target motion is quickly built and continuously updated, and constitutes a general building block present in all sensorimotor systems.

Movement vigor as a traitlike attribute of individuality

A common aspect of individuality is our subjective preferences in evaluation of reward and effort. The neural circuits that evaluate these commodities influence circuits that control our movements, raising the possibility that vigor differences between individuals may also be a trait of individuality, reflecting a willingness to expend effort. In contrast, classic theories in motor control suggest that vigor differences reflect a speed-accuracy trade-off, predicting that those who move fast are sacrificing accuracy for speed. Here we tested these contrasting hypotheses. We measured motion of the eyes, head, and arm in healthy humans during various elementary movements (saccades, head-free gaze shifts, and reaching). For each person we characterized their vigor, i.e., the speed with which they moved a body part (peak velocity) with respect to the population mean. Some moved with low vigor, while others moved with high vigor. Those with high vigor tended to react sooner to a visual stimulus, moving both their eyes and arm with a shorter reaction time. Arm and head vigor were tightly linked: individuals who moved their head with high vigor also moved their arm with high vigor. However, eye vigor did not correspond strongly with arm or head vigor. In all modalities, vigor had no impact on end-point accuracy, demonstrating that differences in vigor were not due to a speed-accuracy trade-off. Our results suggest that movement vigor may be a trait of individuality, not reflecting a willingness to accept inaccuracy but demonstrating a propensity to expend effort. NEW & NOTEWORTHY A common aspect of individuality is how we evaluate economic variables like reward and effort. This valuation affects not only decision making but also motor control, raising the possibility that vigor may be distinct between individuals but conserved across movements within an individual. Here we report conservation of vigor across elementary skeletal movements, but not eye movements, raising the possibility that the individuality of our movements may be driven by a common neural mechanism of effort evaluation across modalities of skeletal motor control.

Simulating the impact of sensorimotor deficits on reaching performance

The healthy human nervous system accurately and robustly controls movements despite nonlinear dynamics, noise, and delays. After a stroke, motor ability frequently becomes impaired. To provide insight into the relative impact of specific sensorimotor deficits on motor performance, we modeled neural control of reaching with the human upper limb as a near-optimally feedback-controlled two-degree-of-freedom system with biologically based parameters. We added three sensorimotor impairments commonly associated with post-stroke hemiparesis - abnormal joint coupling, increased noise on internally modeled dynamics, and muscular weakness - and examined the impact on reaching performance. We found that abnormal joint coupling unknown to the system's internal model caused systematic perturbations to trajectories, longer reach durations, and target overshoot. Increasing internal model noise and muscular weakness had little impact on motor performance unless model noise was increased by several orders of magnitude. Many reaches performed by our perturbed models replicate features commonly observed in reaches by hemiparetic stroke survivors. The sensitivity to unmodeled abnormal joint coupling agrees with experimental findings that abnormal coupling (possibly related to internal model errors) is the main cause of post-stroke motor impairment.

The effect of proprioceptive acuity variability on motor adaptation in older adults

Motor adaptation requires efficient integration of sensory information with predicted sensory consequences of one's own action. However, the effect of reduced sensory acuity on motor adaptation in humans remains to be further investigated. Here, we examined the variability of proprioceptive acuity during an arm-position matching task and the pattern of visuomotor adaptation in older and young adults, and determined the relationship between the two variables. The older adults, a known example of impaired proprioceptive acuity, exhibited greater trial-to-trial variability during the arm-position matching task as compared with the young adults. Furthermore, the older adults showed a slower rate of adaptation to a 30° visuomotor rotation during targeted reaching movements, as well as larger movement errors in the later phase of adaptation, than the young adults. Our correlation analyses indicated a negative association between the variability in proprioceptive acuity and the rate of visuomotor adaptation in the older adults; and no association was observed in the young adults. These findings point to a possibility that an increase in the variability of proprioceptive acuity due to aging may weaken the integration of predicted and actual sensory feedback, which in turn may result in poor visuomotor adaptation in older adults.

Diverse coordinate frames on sensorimotor areas in visuomotor transformation

The visuomotor transformation during a goal-directed movement may involve a coordinate transformation from visual 'extrinsic' to muscle-like 'intrinsic' coordinate frames, which might be processed via a multilayer network architecture composed of neural basis functions. This theory suggests that the postural change during a goal-directed movement task alters activity patterns of the neurons in the intermediate layer of the visuomotor transformation that recieves both visual and proprioceptive inputs, and thus influence the multi-voxel pattern of the blood oxygenation level dependent signal. Using a recently developed multi-voxel pattern decoding method, we found extrinsic, intrinsic and intermediate coordinate frames along the visuomotor cortical pathways during a visuomotor control task. The presented results support the hypothesis that, in human, the extrinsic coordinate frame was transformed to the muscle-like frame over the dorsal pathway from the posterior parietal cortex and the dorsal premotor cortex to the primary motor cortex.

Anterior cingulate is a source of valence-specific information about value and uncertainty

Anterior cingulate cortex (ACC) is thought to control a wide range of reward, punishment, and uncertainty-related behaviors. However, how it does so is unclear. Here, in a Pavlovian procedure in which monkeys displayed a diverse repertoire of reward-related, punishment-related, and uncertainty-related behaviors, we show that many ACC-neurons represent expected value and uncertainty in a valence-specific manner, signaling value or uncertainty predictions about either rewards or punishments. Other ACC-neurons signal prediction information about rewards and punishments by displaying excitation to both (rather than excitation to one and inhibition to the other). This diversity in valence representations may support the role of ACC in many behavioral states that are either enhanced by reward and punishment (e.g., vigilance) or specific to either reward or punishment (e.g., approach and avoidance). Also, this first demonstration of punishment-uncertainty signals in the brain suggests that ACC could be a target for the treatment of uncertainty-related disorders of mood.

Rewards or punishments elicit diverse behavioral responses; however, the neural circuits underlying such flexibility are unclear. Here Monosov shows that this diversity could be supported by neurons in the anterior cingulate that represent expected value and uncertainty in a valence-specific manner.

The Role of Variability in Motor Learning

Trial-to-trial variability in the execution of movements and motor skills is ubiquitous and widely considered to be the unwanted consequence of a noisy nervous system. However, recent studies have suggested that motor variability may also be a feature of how sensorimotor systems operate and learn. This view, rooted in reinforcement learning theory, equates motor variability with purposeful exploration of motor space that, when coupled with reinforcement, can drive motor learning. Here we review studies that explore the relationship between motor variability and motor learning in both humans and animal models. We discuss neural circuit mechanisms that underlie the generation and regulation of motor variability and consider the implications that this work has for our understanding of motor learning.

Dynamic Multisensory Integration: Somatosensory Speed Trumps Visual Accuracy during Feedback Control

Recent advances in movement neuroscience have consistently highlighted that the nervous system performs sophisticated feedback control over very short time scales (<100 ms for upper limb). These observations raise the important question of how the nervous system processes multiple sources of sensory feedback in such short time intervals, given that temporal delays across sensory systems such as vision and proprioception differ by tens of milliseconds. Here we show that during feedback control, healthy humans use dynamic estimates of hand motion that rely almost exclusively on limb afferent feedback even when visual information about limb motion is available. We demonstrate that such reliance on the fastest sensory signal during movement is compatible with dynamic Bayesian estimation. These results suggest that the nervous system considers not only sensory variances but also temporal delays to perform optimal multisensory integration and feedback control in real-time.

SIGNIFICANCE STATEMENT

Numerous studies have demonstrated that the nervous system combines redundant sensory signals according to their reliability. Although very powerful, this model does not consider how temporal delays may impact sensory reliability, which is an important issue for feedback control because different sensory systems are affected by different temporal delays. Here we show that the brain considers not only sensory variability but also temporal delays when integrating vision and proprioception following mechanical perturbations applied to the upper limb. Compatible with dynamic Bayesian estimation, our results unravel the importance of proprioception for feedback control as a consequence of the shorter temporal delays associated with this sensory modality.

Optimal feedback control to describe multiple representations of primary motor cortex neurons

Primary motor cortex (M1) neurons are tuned in response to several parameters related to motor control, and it was recently reported that M1 is important in feedback control. However, it remains unclear how M1 neurons encode information to control the musculoskeletal system. In this study, we examined the underlying computational mechanisms of M1 based on optimal feedback control (OFC) theory, which is a plausible hypothesis for neuromotor control. We modelled an isometric torque production task that required joint torque to be regulated and maintained at desired levels in a musculoskeletal system physically constrained by muscles, which act by pulling rather than pushing. Then, a feedback controller was computed using an optimisation approach under the constraint. In the presence of neuromotor noise, known as signal-dependent noise, the sensory feedback gain is tuned to an extrinsic motor output, such as the hand force, like a population response of M1 neurons. Moreover, a distribution of the preferred directions (PDs) of M1 neurons can be predicted via feedback gain. Therefore, we suggest that neural activity in M1 is optimised for the musculoskeletal system. Furthermore, if the feedback controller is represented in M1, OFC can describe multiple representations of M1, including not only the distribution of PDs but also the response of the neuronal population.

Saccadic suppression as a perceptual consequence of efficient sensorimotor estimation

Humans perform saccadic eye movements two to three times per second. When doing so, the nervous system strongly suppresses sensory feedback for extended periods of time in comparison to movement time. Why does the brain discard so much visual information? Here we suggest that perceptual suppression may arise from efficient sensorimotor computations, assuming that perception and control are fundamentally linked. More precisely, we show theoretically that a Bayesian estimator should reduce the weight of sensory information around the time of saccades, as a result of signal dependent noise and of sensorimotor delays. Such reduction parallels the behavioral suppression occurring prior to and during saccades, and the reduction in neural responses to visual stimuli observed across the visual hierarchy. We suggest that saccadic suppression originates from efficient sensorimotor processing, indicating that the brain shares neural resources for perception and control.

Parallel Specification of Visuomotor Feedback Gains during Bimanual Reaching to Independent Goals

During goal-directed reaching, rapid visuomotor feedback processes enable the human motor system to quickly correct for errors in the trajectory of the hand that arise from motor noise and, in some cases, external perturbations. To date, these visuomotor responses, the gain of which is sensitive to features of the task and environment, have primarily been examined in the context of unimanual reaching movements toward a single target. However, many natural tasks involve moving both hands together, often to separate targets, such that errors can occur in parallel and at different spatial locations. Here, we examined the resource capacity of automatic visuomotor corrective mechanisms by comparing feedback gains during bimanual reaches, toward two targets, to feedback gains during unimanual reaches toward single targets. To investigate the sensitivity of the feedback gains and their relation to visual-spatial processing, we manipulated the widths of the targets and participants’ gaze location. We found that the gain of corrective responses to cursor displacements, while strongly modulated by target width and gaze position, were only slightly reduced during bimanual control. Our results show that automatic visuomotor corrective mechanisms can efficiently operate in parallel across multiple spatial locations.

Target Uncertainty Mediates Sensorimotor Error Correction

Human movements are prone to errors that arise from inaccuracies in both our perceptual processing and execution of motor commands. We can reduce such errors by both improving our estimates of the state of the world and through online error correction of the ongoing action. Two prominent frameworks that explain how humans solve these problems are Bayesian estimation and stochastic optimal feedback control. Here we examine the interaction between estimation and control by asking if uncertainty in estimates affects how subjects correct for errors that may arise during the movement. Unbeknownst to participants, we randomly shifted the visual feedback of their finger position as they reached to indicate the center of mass of an object. Even though participants were given ample time to compensate for this perturbation, they only fully corrected for the induced error on trials with low uncertainty about center of mass, with correction only partial in trials involving more uncertainty. The analysis of subjects’ scores revealed that participants corrected for errors just enough to avoid significant decrease in their overall scores, in agreement with the minimal intervention principle of optimal feedback control. We explain this behavior with a term in the loss function that accounts for the additional effort of adjusting one’s response. By suggesting that subjects’ decision uncertainty, as reflected in their posterior distribution, is a major factor in determining how their sensorimotor system responds to error, our findings support theoretical models in which the decision making and control processes are fully integrated.

Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other's Actions by Humans

The question of how humans predict outcomes of observed motor actions by others is a fundamental problem in cognitive and social neuroscience. Previous theoretical studies have suggested that the brain uses parts of the forward model (used to estimate sensory outcomes of self-generated actions) to predict outcomes of observed actions. However, this hypothesis has remained controversial due to the lack of direct experimental evidence. To address this issue, we analyzed the behavior of darts experts in an understanding learning paradigm and utilized computational modeling to examine how outcome prediction of observed actions affected the participants’ ability to estimate their own actions. We recruited darts experts because sports experts are known to have an accurate outcome estimation of their own actions as well as prediction of actions observed in others. We first show that learning to predict the outcomes of observed dart throws deteriorates an expert’s abilities to both produce his own darts actions and estimate the outcome of his own throws (or self-estimation). Next, we introduce a state-space model to explain the trial-by-trial changes in the darts performance and self-estimation through our experiment. The model-based analysis reveals that the change in an expert’s self-estimation is explained only by considering a change in the individual’s forward model, showing that an improvement in an expert’s ability to predict outcomes of observed actions affects the individual’s forward model. These results suggest that parts of the same forward model are utilized in humans to both estimate outcomes of self-generated actions and predict outcomes of observed actions.

Using noise to shape motor learning

Each of our movements is selected from any number of alternative movements. Some studies have shown evidence that the central nervous system (CNS) chooses to make the specific movements that are least affected by motor noise. Previous results showing that the CNS has a natural tendency to minimize the effects of noise make the direct prediction that if the relationship between movements and noise were to change, the specific movements people learn to make would also change in a predictable manner. Indeed, this has been shown for well-practiced movements such as reaching. Here, we artificially manipulated the relationship between movements and visuomotor noise by adding noise to a motor task in a novel redundant geometry such that there arose a single control policy that minimized the noise. This allowed us to see whether, for a novel motor task, people could learn the specific control policy that minimized noise or would need to employ other compensation strategies to overcome the added noise. As predicted, subjects were able to learn movements that were biased toward the specific ones that minimized the noise, suggesting not only that the CNS can learn to minimize the effects of noise in a novel motor task but also that artificial visuomotor noise can be a useful tool for teaching people to make specific movements. Using noise as a teaching signal promises to be useful for rehabilitative therapies and movement training with human-machine interfaces.

NEW & NOTEWORTHY

Many theories argue that we choose to make the specific movements that minimize motor noise. Here, by changing the relationship between movements and noise, we show that people actively learn to make movements that minimize noise. This not only provides direct evidence for the theories of noise minimization but presents a way to use noise to teach specific movements to improve rehabilitative therapies and human-machine interface control.

Visuomotor Map Determines How Visually Guided Reaching Movements are Corrected Within and Across Trials

When a visually guided reaching movement is unexpectedly perturbed, it is implicitly corrected in two ways: immediately after the perturbation by feedback control (online correction) and in the next movement by adjusting feedforward motor commands (offline correction or motor adaptation). Although recent studies have revealed a close relationship between feedback and feedforward controls, the nature of this relationship is not yet fully understood. Here, we show that both implicit online and offline movement corrections utilize the same visuomotor map for feedforward movement control that transforms the spatial location of visual objects into appropriate motor commands. First, we artificially distorted the visuomotor map by applying opposite visual rotations to the cursor representing the hand position while human participants reached for two different targets. This procedure implicitly altered the visuomotor map so that changes in the movement direction to the target location were more insensitive or more sensitive. Then, we examined how such visuomotor map distortion influenced online movement correction by suddenly changing the target location. The magnitude of online movement correction was altered according to the shape of the visuomotor map. We also examined offline movement correction; the aftereffect induced by visual rotation in the previous trial was modulated according to the shape of the visuomotor map. These results highlighted the importance of the visuomotor map as a foundation for implicit motor control mechanisms and the intimate relationship between feedforward control, feedback control, and motor adaptation.