Switching in Feedforward Control of Grip Force During Tool-Mediated Interaction With Elastic Force Fields.

Switched systems are common in artificial control systems. Here, we suggest that the brain adopts a switched feedforward control of grip forces during manipulation of objects. We measured how participants modulated grip force when interacting with soft and rigid virtual objects when stiffness varied continuously between trials. We identified a sudden phase transition between two forms of feedforward control that differed in the timing of the synchronization between the anticipated load force and the applied grip force. The switch occurred several trials after a threshold stiffness level in the range 100-200 N/m. These results suggest that in the control of grip force, the brain acts as a switching control system. This opens new research questions as to the nature of the discrete state variables that drive the switching.

The Mechanical Representation of Temporal Delays.

When we knock on a door, we perceive the impact as a collection of simultaneous events, combining sound, sight, and tactile sensation. In reality, information from different modalities but from a single source is flowing inside the brain along different pathways, reaching processing centers at different times. Therefore, interpreting different sensory modalities which seem to occur simultaneously requires information processing that accounts for these different delays. As in a computer-based robotic system, does the brain use some explicit estimation of the time delay, to realign the sensory flows? Or does it compensate for temporal delays by representing them as changes in the body/environment mechanics? Using delayed-state or an approximation for delayed-state manipulations between visual and proprioceptive feedback during a tracking task, we show that tracking errors, grip forces, and learning curves are consistent with predictions of a representation that is based on approximation for delay, refuting an explicit delayed-state representation. Delayed-state representations are based on estimating the time elapsed between the movement commands and their observed consequences. In contrast, an approximation for delay representations result from estimating the instantaneous relation between the expected and observed motion variables, without explicit reference to time.

Representing delayed force feedback as a combination of current and delayed states.

To adapt to deterministic force perturbations that depend on the current state of the hand, internal representations are formed to capture the relationships between forces experienced and motion. However, information from multiple modalities travels at different rates, resulting in intermodal delays that require compensation for these internal representations to develop. To understand how these delays are represented by the brain, we presented participants with delayed velocity-dependent force fields, i.e., forces that depend on hand velocity either 70 or 100 ms beforehand. We probed the internal representation of these delayed forces by examining the forces the participants applied to cope with the perturbations. The findings showed that for both delayed forces, the best model of internal representation consisted of a delayed velocity and current position and velocity. We show that participants relied initially on the current state, but with adaptation, the contribution of the delayed representation to adaptation increased. After adaptation, when the participants were asked to make movements with a higher velocity for which they had not previously experienced with the delayed force field, they applied forces that were consistent with current position and velocity as well as delayed velocity representations. This suggests that the sensorimotor system represents delayed force feedback using current and delayed state information and that it uses this representation when generalizing to faster movements.NEW & NOTEWORTHY The brain compensates for forces in the body and the environment to control movements, but it is unclear how it does so given the inherent delays in information transmission and processing. We examined how participants cope with delayed forces that depend on their arm velocity 70 or 100 ms beforehand. After adaptation, participants applied opposing forces that revealed a partially correct representation of the perturbation using the current and the delayed information.

State-Based Delay Representation and Its Transfer from a Game of Pong to Reaching and Tracking.

To accurately estimate the state of the body, the nervous system needs to account for delays between signals from different sensory modalities. To investigate how such delays may be represented in the sensorimotor system, we asked human participants to play a virtual pong game in which the movement of the virtual paddle was delayed with respect to their hand movement. We tested the representation of this new mapping between the hand and the delayed paddle by examining transfer of adaptation to blind reaching and blind tracking tasks. These blind tasks enabled to capture the representation in feedforward mechanisms of movement control. A Time Representation of the delay is an estimation of the actual time lag between hand and paddle movements. A State Representation is a representation of delay using current state variables: the distance between the paddle and the ball originating from the delay may be considered as a spatial shift; the low sensitivity in the response of the paddle may be interpreted as a minifying gain; and the lag may be attributed to a mechanical resistance that influences paddle's movement. We found that the effects of prolonged exposure to the delayed feedback transferred to blind reaching and tracking tasks and caused participants to exhibit hypermetric movements. These results, together with simulations of our representation models, suggest that delay is not represented based on time, but rather as a spatial gain change in visuomotor mapping.

Stimulation of PPC Affects the Mapping between Motion and Force Signals for Stiffness Perception But Not Motion Control.

How motion and sensory inputs are combined to assess an object's stiffness is still unknown. Here, we provide evidence for the existence of a stiffness estimator in the human posterior parietal cortex (PPC). We showed previously that delaying force feedback with respect to motion when interacting with an object caused participants to underestimate its stiffness. We found that applying theta-burst transcranial magnetic stimulation (TMS) over the PPC, but not the dorsal premotor cortex, enhances this effect without affecting movement control. We explain this enhancement as an additional lag in force signals. This is the first causal evidence that the PPC is not only involved in motion control, but also has an important role in perception that is disassociated from action. We provide a computational model suggesting that the PPC integrates position and force signals for perception of stiffness and that TMS alters the synchronization between the two signals causing lasting consequences on perceptual behavior. SIGNIFICANCE STATEMENT: When selecting an object such as a ripe fruit or sofa, we need to assess the object's stiffness. Because we lack dedicated stiffness sensors, we rely on an as yet unknown mechanism that generates stiffness percepts by combining position and force signals. Here, we found that the posterior parietal cortex (PPC) contributes to combining position and force signals for stiffness estimation. This finding challenges the classical view about the role of the PPC in regulating position signals only for motion control because we highlight a key role of the PPC in perception that is disassociated from action. Altogether this sheds light on brain mechanisms underlying the interaction between action and perception and may help in the development of better teleoperation systems and rehabilitation of patients with sensory impairments.

Increased Adaptation Rates and Reduction in Trial-by-Trial Variability in Subjects with Cerebral Palsy Following a Multi-session Locomotor Adaptation Training.

Cerebral Palsy (CP) results from an insult to the developing brain and is associated with deficits in locomotor and manual skills and in sensorimotor adaptation. We hypothesized that the poor sensorimotor adaptation in persons with CP is related to their high execution variability and does not reflect a general impairment in adaptation learning. We studied the interaction between performance variability and adaptation deficits using a multi-session locomotor adaptation design in persons with CP. Six adolescents with diplegic CP were exposed, during a period of 15 weeks, to a repeated split-belt treadmill perturbation spread over 30 sessions and were tested again 6 months after the end of training. Compared to age-matched healthy controls, subjects with CP showed poor adaptation and high execution variability in the first exposure to the perturbation. Following training they showed marked reduction in execution variability and an increase in learning rates. The reduction in variability and the improvement in adaptation were highly correlated in the CP group and were retained 6 months after training. Interestingly, despite reducing their variability in the washout phase, subjects with CP did not improve learning rates during washout phases that were introduced only four times during the experiment. Our results suggest that locomotor adaptation in subjects with CP is related to their execution variability. Nevertheless, while variability reduction is generalized to other locomotor contexts, the development of savings requires both reduction in execution variability and multiple exposures to the perturbation.

Target size matters: target errors contribute to the generalization of implicit visuomotor learning.

The process of sensorimotor adaptation is considered to be driven by errors. While sensory prediction errors, defined as the difference between the planned and the actual movement of the cursor, drive implicit learning processes, target errors (e.g., the distance of the cursor from the target) are thought to drive explicit learning mechanisms. This distinction was mainly studied in the context of arm reaching tasks where the position and the size of the target were constant. We hypothesize that in a dynamic reaching environment, where subjects have to hit moving targets and the targets' dynamic characteristics affect task success, implicit processes will benefit from target errors as well. We examine the effect of target errors on learning of an unnoticed perturbation during unconstrained reaching movements. Subjects played a Pong game, in which they had to hit a moving ball by moving a paddle controlled by their hand. During the game, the movement of the paddle was gradually rotated with respect to the hand, reaching a final rotation of 25°. Subjects were assigned to one of two groups: The high-target error group played the Pong with a small ball, and the low-target error group played with a big ball. Before and after the Pong game, subjects performed open-loop reaching movements toward static targets with no visual feedback. While both groups adapted to the rotation, the postrotation reaching movements were directionally biased only in the small-ball group. This result provides evidence that implicit adaptation is sensitive to target errors.

The effect of force feedback delay on stiffness perception and grip force modulation during tool-mediated interaction with elastic force fields.

During interaction with objects, we form an internal representation of their mechanical properties. This representation is used for perception and for guiding actions, such as in precision grip, where grip force is modulated with the predicted load forces. In this study, we explored the relationship between grip force adjustment and perception of stiffness during interaction with linear elastic force fields. In a forced-choice paradigm, participants probed pairs of virtual force fields while grasping a force sensor that was attached to a haptic device. For each pair, they were asked which field had higher level of stiffness. In half of the pairs, the force feedback of one of the fields was delayed. Participants underestimated the stiffness of the delayed field relatively to the nondelayed, but their grip force characteristics were similar in both conditions. We analyzed the magnitude of the grip force and the lag between the grip force and the load force in the exploratory probing movements within each trial. Right before answering which force field had higher level of stiffness, both magnitude and lag were similar between delayed and nondelayed force fields. These results suggest that an accurate internal representation of environment stiffness and time delay was used for adjusting the grip force. However, this representation did not help in eliminating the bias in stiffness perception. We argue that during performance of a perceptual task that is based on proprioceptive feedback, separate neural mechanisms are responsible for perception and action-related computations in the brain.

Duration and patterns of habitual physical activity in adolescents and young adults with cerebral palsy.

AIM: Adolescents and young adults with cerebral palsy (CP) show reduced motor function and gait efficiency, and lower levels of habitual physical activity (HPA), than adolescents with typical development and children with CP. This study examined activity duration and patterns in this population in the Middle East through long-term monitoring of a large sample using accelerometers. METHOD: Adolescents and young adults with bilateral CP at Gross Motor Function Classification System (GMFCS) levels II, III, and IV, were monitored in their habitual environment for four consecutive days with ActivPAL3 monitors. Time spent in sedentary, standing, and walking activities, and frequency of walking steps and transitions, were analysed for each GMFCS level. RESULTS: Measurements were made on 222 participants (132 males, 90 females; mean age 16 y 9 mo SD 2y, range 13 y 4 mo-22 y). The Mann-Whitney U test demonstrated significant differences (p<0.05) between GMFCS levels, showing reduced walking and standing activity and increased sedentary duration at higher GMFCS levels (p<0.001), except for increased standing time between GMFCS levels II and III (p=0.07). Participants in educational facilities exhibited less sedentary behaviour than those who were homebound (p<0.05). INTERPRETATION: These descriptions of duration and frequency of active and sedentary behaviours may serve as a basis for recommendations to minimize inactivity in this population. Adolescents and young adults with CP in the Middle East demonstrate similar patterns of HPA to their peers in other regions.

Savings in locomotor adaptation explained by changes in learning parameters following initial adaptation.

Faster relearning of an external perturbation, savings, offers a behavioral linkage between motor learning and memory. To explain savings effects in reaching adaptation experiments, recent models suggested the existence of multiple learning components, each shows different learning and forgetting properties that may change following initial learning. Nevertheless, the existence of these components in rhythmic movements with other effectors, such as during locomotor adaptation, has not yet been studied. Here, we study savings in locomotor adaptation in two experiments; in the first, subjects adapted to speed perturbations during walking on a split-belt treadmill, briefly adapted to a counter-perturbation and then readapted. In a second experiment, subjects readapted after a prolonged period of washout of initial adaptation. In both experiments we find clear evidence for increased learning rates (savings) during readaptation. We show that the basic error-based multiple timescales linear state space model is not sufficient to explain savings during locomotor adaptation. Instead, we show that locomotor adaptation leads to changes in learning parameters, so that learning rates are faster during readaptation. Interestingly, we find an intersubject correlation between the slow learning component in initial adaptation and the fast learning component in the readaptation phase, suggesting an underlying mechanism for savings. Together, these findings suggest that savings in locomotion and in reaching may share common computational and neuronal mechanisms; both are driven by the slow learning component and are likely to depend on cortical plasticity.

Feedback and feedforward adaptation to visuomotor delay during reaching and slicing movements.

It has been suggested that the brain and in particular the cerebellum and motor cortex adapt to represent the environment during reaching movements under various visuomotor perturbations. It is well known that significant delay is present in neural conductance and processing; however, the possible representation of delay and adaptation to delayed visual feedback has been largely overlooked. Here we investigated the control of reaching movements in human subjects during an imposed visuomotor delay in a virtual reality environment. In the first experiment, when visual feedback was unexpectedly delayed, the hand movement overshot the end-point target, indicating a vision-based feedback control. Over the ensuing trials, movements gradually adapted and became accurate. When the delay was removed unexpectedly, movements systematically undershot the target, demonstrating that adaptation occurred within the vision-based feedback control mechanism. In a second experiment designed to broaden our understanding of the underlying mechanisms, we revealed similar after-effects for rhythmic reversal (out-and-back) movements. We present a computational model accounting for these results based on two adapted forward models, each tuned for a specific modality delay (proprioception or vision), and a third feedforward controller. The computational model, along with the experimental results, refutes delay representation in a pure forward vision-based predictor and suggests that adaptation occurred in the forward vision-based predictor, and concurrently in the state-based feedforward controller. Understanding how the brain compensates for conductance and processing delays is essential for understanding certain impairments concerning these neural delays as well as for the development of brain-machine interfaces.

The minimum transition hypothesis for intermittent hierarchical motor control.

In intermittent control, instead of continuously calculating the control signal, the controller occasionally changes this signal at certain sparse points in time. The control law may include feedback, adaptation, optimization, or any other control strategies. When, where, and how does the brain employ intermittency as it controls movement? These are open questions in motor neuroscience. Evidence for intermittency in human motor control has been repeatedly observed in the neural control of movement literature. Moreover, some researchers have provided theoretical models to address intermittency. Even so, the vast majority of current models, and I would dare to say the dogma in most of the current motor neuroscience literature involves continuous control. In this paper, I focus on an area in which intermittent control has not yet been thoroughly considered, the structure of muscle synergies. A synergy in the muscle space is a group of muscles activated together by a single neural command. Under the assumption that the motor control is intermittent, I present the minimum transition hypothesis (MTH) and its predictions with regards to the structure of muscle synergies. The MTH asserts that the purpose of synergies is to minimize the effort of the higher level in the hierarchy by minimizing the number of transitions in an intermittent control signal. The implications of the MTH are not only for the structure of the muscle synergies but also to the intermittent and hierarchical nature of the motor system, with various predictions as to the process of skill learning, and important implications to the design of brain machine interfaces and human robot interaction.

Kinetic adaptation during locomotion on a split-belt treadmill.

It has been suggested that a feedforward control mechanism drives the adaptation of the spatial and temporal interlimb locomotion variables. However, the internal representation of limb kinetics during split-belt locomotion has not yet been studied. In hand movements, it has been suggested that kinetic and kinematic parameters are controlled by separate neural processes; therefore, it is possible that separate neural processes are responsible for kinetic and kinematic locomotion parameters. In the present study, we assessed the adaptation of the limb kinetics by analyzing the ground reaction forces (GRFs) as well as the center of pressure (COP) during adaptation to speed perturbation, using a split-belt treadmill with an integrated force plate. We found that both the GRF of each leg at initial contact and the COP changed gradually and showed motor aftereffects during early postadaptation, suggesting the use of a feedforward predictive mechanism. However, the GRF of each leg in the single-support period used a feedback control mechanism. It changed rapidly during the adaptation phase and showed no motor aftereffect when the speed perturbation was removed. Finally, we found that the motor adaptation of the GRF and the COP are mediated by a dual-rate process. Our results suggest two important contributions to neural control of locomotion. First, different control mechanisms are responsible for forces at single- and double-support periods, as previously reported for kinematic variables. Second, our results suggest that motor adaptation during split-belt locomotion is mediated by fast and slow adaptation processes.

The effects of rhythmicity and amplitude on transfer of motor learning.

We perform rhythmic and discrete arm movements on a daily basis, yet the motor control literature is not conclusive regarding the mechanisms controlling these movements; does a single mechanism generate both movement types, or are they controlled by separate mechanisms? A recent study reported partial asymmetric transfer of learning from discrete movements to rhythmic movements. Other studies have shown transfer of learning between large-amplitude to small-amplitude movements. The goal of this study is to explore which aspect is important for learning to be transferred from one type of movement to another: rhythmicity, amplitude or both. We propose two hypotheses: (1) Rhythmic and discrete movements are generated by different mechanisms; therefore we expect to see a partial or no transfer of learning between the two types of movements; (2) Within each movement type (rhythmic/discrete), there will be asymmetric transition of learning from larger movements to smaller ones. We used a learning-transfer paradigm, in which 70 participants performed flexion/extension movements with their forearm, and switched between types of movement, which differed in amplitude and/or rhythmicity. We found partial transfer of learning between discrete and rhythmic movements, and an asymmetric transfer of learning from larger movements to smaller movements (within the same type of movement). Our findings suggest that there are two different mechanisms underlying the generation of rhythmic and discrete arm movements, and that practicing on larger movements helps perform smaller movements; the latter finding might have implications for rehabilitation.

Adaptation to sequence force perturbation during vertical and horizontal reaching movement-averaging the past or predicting the future?

Several studies conducted during the past decade have suggested that episodic memory is better equipped to handle the future than the past. Here, we consider this premise in the context of motor memory. State-of-the-art computational models for trial-by-trial motor adaptation to constant and stochastic force field perturbations in a horizontal reaching paradigm have shown that motor memory registers a weighted sum of past experiences to predict force perturbation in a subsequent trial. In the current study, we used the standard horizontal reaching movement paradigm and a novel vertical reaching movement paradigm to test motor memory function during adaptation to force fields increasing in magnitude in a simple predictable linear series. We found that adaptation to constant and sequence force fields are similar in vertical and horizontal reaching. For both horizontal and vertical reaching, we found that the expectation in a particular trial was the average of the previous few trials rather than an expectation of a larger perturbation, as would be expected from a simple extrapolation. These findings are not consistent with those of our previous studies on lifting and grasping tasks, in which we found that the grip force is correctly adjusted to the next weight in a series of tasks with gradually increasing weights, thus predicting the future rather than averaging the past. The results of the current study devoted to reaching movements and of our previous study addressing a lifting task suggest that the brain can generate at least two different types of motor representation, either addressing the past in reaching or predicting the future in lifting. We propose that prior experience and the effect of environment's variability are the reasons for the observed differences in expectation during lifting and reaching. Finally, we discuss these two types of memory mechanisms with respect to the distinct neural circuits responsible for lifting and reaching.

Minimum acceleration with constraints of center of mass: a unified model for arm movements and object manipulation.

Daily interaction with the environment consists of moving with or without objects. Increasing interest in both types of movements drove the creation of computational models to describe reaching movements and, later, to describe a simplified version of object manipulation. The previously suggested models for object manipulation rely on the same optimization criteria as models for reaching movements, yet there is no single model accounting for both tasks that does not require reminimization of the criterion for each environment. We suggest a unified model for both cases: minimum acceleration with constraints for the center of mass (MACM). For point-to-point reaching movement, the model predicts the typical rectilinear path and bell-shaped speed profile as previous criteria. We have derived the predicted trajectories for the case of manipulating a mass-on-spring and show that the predicted trajectories match the observations of a few independent previous experimental studies of human arm movement during a mass-on-spring manipulation. Moreover, the previously reported "unusual" trajectories are also well accounted for by the proposed MACM. We have tested the predictions of the MACM model in 3 experiments with 12 subjects, where we demonstrated that the MACM model is equal or better (Wilcoxon sign-rank test, P < 0.001) in accounting for the data than three other previously proposed models in the conditions tested. Altogether, the MACM model is currently the only model accounting for reaching movements with or without external degrees of freedom. Moreover, it provides predictions about the intermittent nature of the neural control of movements and about the dominant control variable.

Knowledge of performance is insufficient for implicit visuomotor rotation adaptation.

The ability to adapt is a fundamental and vital characteristic of the motor system. The authors altered the visual environment and focused on the ability of humans to adapt to a rotated environment in a reaching task, in the absence of continuous visual information about their hand location. Subjects could not see their arm but were provided with post trial knowledge of performance depicting hand path from movement onset to final position. Subjects failed to adapt under these conditions. The authors sought to find out whether the lack of adaptation is related to the number of target directions presented in the task, and planned 2 protocols in which subjects were gradually exposed to 22.5° visuomotor rotation. These protocols differed only in the number of target directions: 8 and 4 targets. The authors found that subjects had difficulty adapting without the existence of continuous visual feedback of their performance regardless of the number of targets presented in task. In the 4-target protocol, some of the subjects noticed the rotation and explicitly aimed to the correct direction. The results suggest that real-time feedback is required for motor adaptation to visual rotation during reaching movements.

Perception of stiffness in laparoscopy - the fulcrum effect.

We explored how the perception of stiffness can be distorted in Minimally Invasive Surgery. We combined a mechanical simulator with a haptic device, and implemented linear springs at the tip of the simulated laparoscopic device. To explore the influence of mechanical advantage on perception, we set different values of the ratio between internal and external length of the tool. We found that a nonsymmetrical ratio causes bias in the perceived stiffness when novice tangential probing is compared to radial probing. In contrast, haptic experts did not show similar perceptual bias.

Open questions in computational motor control.

Computational motor control covers all applications of quantitative tools for the study of the biological movement control system. This paper provides a review of this field in the form of a list of open questions. After an introduction in which we define computational motor control, we describe: a Turing-like test for motor intelligence; internal models, inverse model, forward model, feedback error learning and distal teacher; time representation, and adaptation to delay; intermittence control strategies; equilibrium hypotheses and threshold control; the spatiotemporal hierarchy of wide sense adaptation, i.e., feedback, learning, adaptation, and evolution; optimization based models for trajectory formation and optimal feedback control; motor memory, the past and the future; and conclude with the virtue of redundancy. Each section in this paper starts with a review of the relevant literature and a few more specific studies addressing the open question, and ends with speculations about the possible answer and its implications to motor neuroscience. This review is aimed at concisely covering the topic from the author's perspective with emphasis on learning mechanisms and the various structures and limitations of internal models.