Evolution of motor memory during the seconds after observation of motor error

What is the nature of motor adaptation to dynamic perturbations?

When human participants repeatedly encounter a velocity-dependent force field that distorts their movement trajectories, they adapt their motor behavior to recover straight trajectories. Computational models suggest that adaptation to a force field occurs at the action selection level through changes in the mapping between goals and actions. The quantitative prediction from these models indicates that early perturbed trajectories before adaptation and late unperturbed trajectories after adaptation should have opposite curvature, i.e. one being a mirror image of the other. We tested these predictions in a human adaptation experiment and we found that the expected mirror organization was either absent or much weaker than predicted by the models. These results are incompatible with adaptation occurring at the action selection level but compatible with adaptation occurring at the goal selection level, as if adaptation corresponds to aiming toward spatially remapped targets.

Motor adaptation is a fundamental component of the acquisition and maintenance of skilled behaviors. Yet the nature of motor adaptation remains poorly understood: when we encounter forces which repeatedly perturb our movements, do we change our actions or our plans? Current computational models of motor control favor the former, but this assumption has not been thoroughly investigated. To address this issue, we compared predictions of a model of motor adaptation based on changes at the action level with observations obtained from a group of human participants involved in a motor adaptation task. The behavior of the participants clearly differed from the model’s predictions. These results challenge contemporary perspectives on motor adaptation.

Random Practice Enhances Retention and Spatial Transfer in Force Field Adaptation

The contextual-interference effect is a frequently examined phenomenon in motor skill learning but has not been extensively investigated in motor adaptation. Here, we first tested experimentally if the contextual-interference effect is detectable in force field adaptation regarding retention and spatial transfer, and then fitted state-space models to the data to relate the findings to the “forgetting-and-reconstruction hypothesis”. Thirty-two participants were divided into two groups with either a random or a blocked practice schedule. They practiced reaching to four targets and were tested 10 min and 24 h afterward for motor retention and spatial transfer on an interpolation and an extrapolation target, and on targets which were shifted 10 cm away. The adaptation progress was participant-specifically fitted with 4-slow-1-fast state-space models accounting for generalization and set breaks. The blocked group adapted faster (p = 0.007) but did not reach a better adaptation at practice end. We found better retention (10 min), interpolation transfer (10 min), and transfer to shifted targets (10 min and 24 h) for the random group (each p < 0.05). However, no differences were found for retention or for the interpolation target after 24 h. Neither group showed transfer to the extrapolation target. The extended state-space model could replicate the behavioral results with some exceptions. The study shows that the contextual-interference effect is partially detectable in practice, short-term retention, and spatial transfer in force field adaptation; and that state-space models provide explanatory descriptions for the contextual-interference effect in force field adaptation.

The role of motor memory dynamics in structuring bodily self-consciousness

Bodily self-consciousness has been considered a sensorimotor root of self-consciousness. If this is the case, how does sensorimotor memory, which is important for the prediction of sensory consequences of volitional actions, influence awareness of bodily self-consciousness? This question is essential for understanding the effective acquisition and recovery of self-consciousness following its impairment, but it has remained unexamined. Here, we investigated how body ownership and agency recovered following body schema distortion in a virtual reality environment along with two kinds of motor memories: memories that were rapidly updated and memories that were gradually updated. We found that, although agency and body ownership recovered in parallel, the recovery of body ownership was predicted by fast memories and that of agency was predicted by slow memories. Thus, the bodily self was represented in multiple motor memories with different dynamics. This finding demystifies the controversy about the causal relationship between body ownership and agency.

The nature of savings associated with a visuomotor adaptation task that involves one arm or both arms

The nature of savings in visuomotor adaptation is typically studied using a paradigm in which one arm experiences multiple conditions such as adaptation, washout and readaptation. It has seldom been studied, however, using a paradigm that involves both arms. Here, we examined the effect of (1) using different arms and (2) the availability of visual feedback during a washout session following visuomotor adaptation on savings. We first had healthy young adults adapt to a visuomotor rotation condition during reaching movements with the left arm. Following that, they experienced a washout session with either the left or right arm, with or without visual feedback, and then the readaptation session with the left arm again. We hypothesized that if savings occurred due to the explicit recall of cognitive strategies, the pattern of savings would be similar regardless of which arm was used during the washout session. Results showed that in terms of the percentage of savings, there was a significant difference between the conditions in which the left or right arm was used during the washout, but not between the conditions in which visual feedback was provided or absent. In terms of the rate of relearning, a significant difference was observed between the conditions in which the left or right arm was used during the washout, and also between the conditions in which visual feedback was provided or absent. These findings suggest that the explicit recall of strategies is not the only source for savings and further suggest that effector-specific, use-dependent learning can also contribute to savings.

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

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

Exploring disturbance as a force for good in motor learning

Disturbance forces facilitate motor learning, but theoretical explanations for this counterintuitive phenomenon are lacking. Smooth arm movements require predictions (inference) about the force-field associated with a workspace. The Free Energy Principle (FEP) suggests that such 'active inference' is driven by 'surprise'. We used these insights to create a formal model that explains why disturbance might help learning. In two experiments, participants undertook a continuous tracking task where they learned how to move their arm in different directions through a novel 3D force field. We compared baseline performance before and after exposure to the novel field to quantify learning. In Experiment 1, the exposure phases (but not the baseline measures) were delivered under three different conditions: (i) robot haptic assistance; (ii) no guidance; (iii) robot haptic disturbance. The disturbance group showed the best learning as our model predicted. Experiment 2 further tested our FEP inspired model. Assistive and/or disturbance forces were applied as a function of performance (low surprise), and compared to a random error manipulation (high surprise). The random group showed the most improvement as predicted by the model. Thus, motor learning can be conceptualised as a process of entropy reduction. Short term motor strategies (e.g. global impedance) can mitigate unexpected perturbations, but continuous movements require active inference about external force-fields in order to create accurate internal models of the external world (motor learning). Our findings reconcile research on the relationship between noise, variability, and motor learning, and show that information is the currency of motor learning.

Force field generalization and the internal representation of motor learning

When learning a new motor behavior, e.g. reaching in a force field, the nervous system builds an internal representation. Examining how subsequent reaches in unpracticed directions generalize reveals this representation. Although often studied, it is not known how this representation changes across training directions, or how changes in reach direction and the corresponding changes in limb impedance, influence these measurements. We ran a force field adaptation experiment using eight groups of subjects each trained on one of eight standard directions and then tested for generalization in the remaining seven directions. Generalization in all directions was local and asymmetric, providing limited and unequal transfer to the left and right side of the trained target. These asymmetries were not consistent in either magnitude or direction, even after correcting for changes in limb impedance. Relying on a standard model for generalization the inferred representations inconsistently shifted to one side or the other of their respective training direction. A second model that accounted for limb impedance and variations in baseline trajectories explained more data and the inferred representations were centered on their respective training directions. Our results highlight the influence of limb mechanics and impedance on psychophysical measurements and their interpretations for motor learning.

The temporal stability of visuomotor adaptation generalization

Movement adaptation in response to systematic motor perturbations exhibits distinct spatial and temporal properties. These characteristics are typically studied in isolation, leaving the interaction largely unknown. Here we examined how the temporal decay of visuomotor adaptation influences the spatial generalization of the motor recalibration. First, we quantified the extent to which adaptation decayed over time. Subjects reached to a peripheral target, and a rotation was applied to the visual feedback of the unseen motion. The retention of this adaptation over different delays (0-120 s) 1) decreased by 29.0 ± 6.8% at the longest delay and 2) was represented by a simple exponential, with a time constant of 22.5 ± 5.6 s. On the basis of this relationship we simulated how the spatial generalization of adaptation would change with delay. To test this directly, we trained additional subjects with the same perturbation and assessed transfer to 19 different locations (spaced 15° apart, symmetric around the trained location) and examined three delays (~4, 12, and 25 s). Consistent with the simulation, we found that generalization around the trained direction (±15°) significantly decreased with delay and distance, while locations >60° displayed near-constant spatiotemporal transfer. Intermediate distances (30° and 45°) showed a difference in transfer across space, but this amount was approximately constant across time. Interestingly, the decay at the trained direction was faster than that based purely on time, suggesting that the spatial transfer of adaptation is modified by concurrent passive (time dependent) and active (movement dependent) processes.NEW & NOTEWORTHY Short-term motor adaptation exhibits distinct spatial and temporal characteristics. Here we investigated the interaction of these features, utilizing a simple motor adaptation paradigm (recalibration of reaching arm movements in response to rotated visual feedback). We examined the changes in the spatial generalization of motor adaptation for different temporal manipulations and report that the spatiotemporal generalization of motor adaptation is generally local and is influenced by both passive (time dependent) and active (movement dependent) learning processes.

Decomposition of a sensory prediction error signal for visuomotor adaptation

To accomplish effective motor control, the brain contains an internal forward model that predicts the expected sensory consequence of a motor command. When this prediction is inaccurate, a sensory prediction error is produced which adapts the forward model to make more accurate predictions of future movements. Other types of errors, such as task performance errors or reward, play less of a role in adapting a forward model. This raises the following question: What unique information is conveyed by the sensory prediction error that results in forward model adaptation? sensory prediction errors typically contain both the magnitude and direction of the error, but it is unclear if both components are necessary for adaptation or a single component is sufficient. In this article, we address this by having participants learn to counter a visuomotor rotation, which induces an angular mismatch between movements of the hand and visual feedback. We manipulated the information content of the visual feedback, in the form of a line, which accurately represented only the magnitude (distance), direction, or both magnitude and direction, of the virtual cursor relative to the target. We demonstrate that sensorimotor adaptation does not occur, or is minimal, when feedback is limited to information about the magnitude of an error. In contrast, sensorimotor adaptation is present when feedback is limited only to the direction of an error or when it contains combined direction and magnitude information. This result stands in contrast to current computational models of cerebellar-based sensorimotor adaptation that use error magnitude to drive adaptation. (PsycINFO Database Record

Long-Term Predictive and Feedback Encoding of Motor Signals in the Simple Spike Discharge of Purkinje Cells

Most hypotheses of cerebellar function emphasize a role in real-time control of movements. However, the cerebellum’s use of current information to adjust future movements and its involvement in sequencing, working memory, and attention argues for predicting and maintaining information over extended time windows. The present study examines the time course of Purkinje cell discharge modulation in the monkey (Macaca mulatta) during manual, pseudo-random tracking. Analysis of the simple spike firing from 183 Purkinje cells during tracking reveals modulation up to 2 s before and after kinematics and position error. Modulation significance was assessed against trial shuffled firing, which decoupled simple spike activity from behavior and abolished long-range encoding while preserving data statistics. Position, velocity, and position errors have the most frequent and strongest long-range feedforward and feedback modulations, with less common, weaker long-term correlations for speed and radial error. Position, velocity, and position errors can be decoded from the population simple spike firing with considerable accuracy for even the longest predictive (-2000 to -1500 ms) and feedback (1500 to 2000 ms) epochs. Separate analysis of the simple spike firing in the initial hold period preceding tracking shows similar long-range feedforward encoding of the upcoming movement and in the final hold period feedback encoding of the just completed movement, respectively. Complex spike analysis reveals little long-term modulation with behavior. We conclude that Purkinje cell simple spike discharge includes short- and long-range representations of both upcoming and preceding behavior that could underlie cerebellar involvement in error correction, working memory, and sequencing.

Between-Trial Forgetting Due to Interference and Time in Motor Adaptation

Learning a motor task with temporally spaced presentations or with other tasks intermixed between presentations reduces performance during training, but can enhance retention post training. These two effects are known as the spacing and contextual interference effect, respectively. Here, we aimed at testing a unifying hypothesis of the spacing and contextual interference effects in visuomotor adaptation, according to which forgetting between trials due to either spaced presentations or interference by another task will promote between-trial forgetting, which will depress performance during acquisition, but will promote retention. We first performed an experiment with three visuomotor adaptation conditions: a short inter-trial-interval (ITI) condition (SHORT-ITI); a long ITI condition (LONG-ITI); and an alternating condition with two alternated opposite tasks (ALT), with the same single-task ITI as in LONG-ITI. In the SHORT-ITI condition, there was fastest increase in performance during training and largest immediate forgetting in the retention tests. In contrast, in the ALT condition, there was slowest increase in performance during training and little immediate forgetting in the retention tests. Compared to these two conditions, in the LONG-ITI, we found intermediate increase in performance during training and intermediate immediate forgetting. To account for these results, we fitted to the data six possible adaptation models with one or two time scales, and with interference in the fast, or in the slow, or in both time scales. Model comparison confirmed that two time scales and some degree of interferences in either time scale are needed to account for our experimental results. In summary, our results suggest that retention following adaptation is modulated by the degree of between-trial forgetting, which is due to time-based decay in single adaptation task and interferences in multiple adaptation tasks.

Reward-dependent modulation of movement variability

Movement variability is often considered an unwanted byproduct of a noisy nervous system. However, variability can signal a form of implicit exploration, indicating that the nervous system is intentionally varying the motor commands in search of actions that yield the greatest success. Here, we investigated the role of the human basal ganglia in controlling reward-dependent motor variability as measured by trial-to-trial changes in performance during a reaching task. We designed an experiment in which the only performance feedback was success or failure and quantified how reach variability was modulated as a function of the probability of reward. In healthy controls, reach variability increased as the probability of reward decreased. Control of variability depended on the history of past rewards, with the largest trial-to-trial changes occurring immediately after an unrewarded trial. In contrast, in participants with Parkinson's disease, a known example of basal ganglia dysfunction, reward was a poor modulator of variability; that is, the patients showed an impaired ability to increase variability in response to decreases in the probability of reward. This was despite the fact that, after rewarded trials, reach variability in the patients was comparable to healthy controls. In summary, we found that movement variability is partially a form of exploration driven by the recent history of rewards. When the function of the human basal ganglia is compromised, the reward-dependent control of movement variability is impaired, particularly affecting the ability to increase variability after unsuccessful outcomes.

Behavioural and neural basis of anomalous motor learning in children with autism

Autism spectrum disorder is a developmental disorder characterized by deficits in social and communication skills and repetitive and stereotyped interests and behaviours. Although not part of the diagnostic criteria, individuals with autism experience a host of motor impairments, potentially due to abnormalities in how they learn motor control throughout development. Here, we used behavioural techniques to quantify motor learning in autism spectrum disorder, and structural brain imaging to investigate the neural basis of that learning in the cerebellum. Twenty children with autism spectrum disorder and 20 typically developing control subjects, aged 8-12, made reaching movements while holding the handle of a robotic manipulandum. In random trials the reach was perturbed, resulting in errors that were sensed through vision and proprioception. The brain learned from these errors and altered the motor commands on the subsequent reach. We measured learning from error as a function of the sensory modality of that error, and found that children with autism spectrum disorder outperformed typically developing children when learning from errors that were sensed through proprioception, but underperformed typically developing children when learning from errors that were sensed through vision. Previous work had shown that this learning depends on the integrity of a region in the anterior cerebellum. Here we found that the anterior cerebellum, extending into lobule VI, and parts of lobule VIII were smaller than normal in children with autism spectrum disorder, with a volume that was predicted by the pattern of learning from visual and proprioceptive errors. We suggest that the abnormal patterns of motor learning in children with autism spectrum disorder, showing an increased sensitivity to proprioceptive error and a decreased sensitivity to visual error, may be associated with abnormalities in the cerebellum.

Brain representations for acquiring and recalling visual-motor adaptations

Humans readily learn and remember new motor skills, a process that likely underlies adaptation to changing environments. During adaptation, the brain develops new sensory-motor relationships, and if consolidation occurs, a memory of the adaptation can be retained for extended periods. Considerable evidence exists that multiple brain circuits participate in acquiring new sensory-motor memories, though the networks engaged in recalling these and whether the same brain circuits participate in their formation and recall have less clarity. To address these issues, we assessed brain activation with functional MRI while young healthy adults learned and recalled new sensory-motor skills by adapting to world-view rotations of visual feedback that guided hand movements. We found cerebellar activation related to adaptation rate, likely reflecting changes related to overall adjustments to the visual rotation. A set of parietal and frontal regions, including inferior and superior parietal lobules, premotor area, supplementary motor area and primary somatosensory cortex, exhibited non-linear learning-related activation that peaked in the middle of the adaptation phase. Activation in some of these areas, including the inferior parietal lobule, intra-parietal sulcus and somatosensory cortex, likely reflected actual learning, since the activation correlated with learning after-effects. Lastly, we identified several structures having recall-related activation, including the anterior cingulate and the posterior putamen, since the activation correlated with recall efficacy. These findings demonstrate dynamic aspects of brain activation patterns related to formation and recall of a sensory-motor skill, such that non-overlapping brain regions participate in distinctive behavioral events.

Intermittent visual feedback can boost motor learning of rhythmic movements: evidence for error feedback beyond cycles

Movement error is a driving force behind motor learning. For motor learning with discrete movements, such as point-to-point reaching, it is believed that the brain uses error information of the immediately preceding movement only. However, in the case of continuous and repetitive movements (i.e., rhythmic movements), there is a ceaseless inflow of performance information. Thus, an accurate temporal association of the motor commands with the resultant movement errors is not necessarily guaranteed. We investigated how the brain overcomes this challenging situation. Human participants adapted rhythmic movements between two targets to visuomotor rotations, the amplitudes of which changed randomly from cycle to cycle (the duration of one cycle was ∼400 ms). A system identification technique revealed that the motor adaptation was affected not just by the preceding movement error, but also by a history of errors from the previous cycles. Error information obtained from more than one previous cycle tended to increase, rather than decrease, movement error. This result led to a counterintuitive prediction: providing visual error feedback for only a fraction of cycles should enhance visuomotor adaptation. As predicted, we observed that motor adaptation to a constant visual rotation (30°) was significantly enhanced by providing visual feedback once every fourth or fifth cycle rather than for every cycle. These results suggest that the brain requires a specific processing time to modify the motor command, based on the error information, and so is unable to deal appropriately with the overwhelming flow of error information generated during rhythmic movements.

Rethinking motor learning and savings in adaptation paradigms: model-free memory for successful actions combines with internal models

Although motor learning is likely to involve multiple processes, phenomena observed in error-based motor learning paradigms tend to be conceptualized in terms of only a single process: adaptation, which occurs through updating an internal model. Here we argue that fundamental phenomena like movement direction biases, savings (faster relearning), and interference do not relate to adaptation but instead are attributable to two additional learning processes that can be characterized as model-free: use-dependent plasticity and operant reinforcement. Although usually "hidden" behind adaptation, we demonstrate, with modified visuomotor rotation paradigms, that these distinct model-based and model-free processes combine to learn an error-based motor task. (1) Adaptation of an internal model channels movements toward successful error reduction in visual space. (2) Repetition of the newly adapted movement induces directional biases toward the repeated movement. (3) Operant reinforcement through association of the adapted movement with successful error reduction is responsible for savings.

Expressions of multiple neuronal dynamics during sensorimotor learning in the motor cortex of behaving monkeys

Previous studies support the notion that sensorimotor learning involves multiple processes. We investigated the neuronal basis of these processes by recording single-unit activity in motor cortex of non-human primates (Macaca fascicularis), during adaptation to force-field perturbations. Perturbed trials (reaching to one direction) were practiced along with unperturbed trials (to other directions). The number of perturbed trials relative to the unperturbed ones was either low or high, in two separate practice schedules. Unsurprisingly, practice under high-rate resulted in faster learning with more pronounced generalization, as compared to the low-rate practice. However, generalization and retention of behavioral and neuronal effects following practice in high-rate were less stable; namely, the faster learning was forgotten faster. We examined two subgroups of cells and showed that, during learning, the changes in firing-rate in one subgroup depended on the number of practiced trials, but not on time. In contrast, changes in the second subgroup depended on time and practice; the changes in firing-rate, following the same number of perturbed trials, were larger under high-rate than low-rate learning. After learning, the neuronal changes gradually decayed. In the first subgroup, the decay pace did not depend on the practice rate, whereas in the second subgroup, the decay pace was greater following high-rate practice. This group shows neuronal representation that mirrors the behavioral performance, evolving faster but also decaying faster at learning under high-rate, as compared to low-rate. The results suggest that the stability of a new learned skill and its neuronal representation are affected by the acquisition schedule.

Compensation for changing motor uncertainty

When movement outcome differs consistently from the intended movement, errors are used to correct subsequent movements (e.g., adaptation to displacing prisms or force fields) by updating an internal model of motor and/or sensory systems. Here, we examine changes to an internal model of the motor system under changes in the variance structure of movement errors lacking an overall bias. We introduced a horizontal visuomotor perturbation to change the statistical distribution of movement errors anisotropically, while monetary gains/losses were awarded based on movement outcomes. We derive predictions for simulated movement planners, each differing in its internal model of the motor system. We find that humans optimally respond to the overall change in error magnitude, but ignore the anisotropy of the error distribution. Through comparison with simulated movement planners, we found that aimpoints corresponded quantitatively to an ideal movement planner that updates a strictly isotropic (circular) internal model of the error distribution. Aimpoints were planned in a manner that ignored the direction-dependence of error magnitudes, despite the continuous availability of unambiguous information regarding the anisotropic distribution of actual motor errors.

Linear hypergeneralization of learned dynamics across movement speeds reveals anisotropic, gain-encoding primitives for motor adaptation

The ability to generalize learned motor actions to new contexts is a key feature of the motor system. For example, the ability to ride a bicycle or swing a racket is often first developed at lower speeds and later applied to faster velocities. A number of previous studies have examined the generalization of motor adaptation across movement directions and found that the learned adaptation decays in a pattern consistent with the existence of motor primitives that display narrow Gaussian tuning. However, few studies have examined the generalization of motor adaptation across movement speeds. Following adaptation to linear velocity-dependent dynamics during point-to-point reaching arm movements at one speed, we tested the ability of subjects to transfer this adaptation to short-duration higher-speed movements aimed at the same target. We found near-perfect linear extrapolation of the trained adaptation with respect to both the magnitude and the time course of the velocity profiles associated with the high-speed movements: a 69% increase in movement speed corresponded to a 74% extrapolation of the trained adaptation. The close match between the increase in movement speed and the corresponding increase in adaptation beyond what was trained indicates linear hypergeneralization. Computational modeling shows that this pattern of linear hypergeneralization across movement speeds is not compatible with previous models of adaptation in which motor primitives display isotropic Gaussian tuning of motor output around their preferred velocities. Instead, we show that this generalization pattern indicates that the primitives involved in the adaptation to viscous dynamics display anisotropic tuning in velocity space and encode the gain between motor output and motion state rather than motor output itself.

Generalization of Dynamics Learning Across Changes in Movement Amplitude

Studies on generalization show the nature of how learning is encoded in the brain. Previous studies have shown rather limited generalization of dynamics learning across changes in movement direction, a finding that is consistent with the idea that learning is primarily local. In contrast, studies show a broader pattern of generalization across changes in movement amplitude, suggesting a more general form of learning. To understand this difference, we performed an experiment in which subjects held a robotic manipulandum and made movements to targets along the body midline. Subjects were trained in a velocity-dependent force field while moving to a 15 cm target. After training, subjects were tested for generalization using movements to a 30 cm target. We used force channels in conjunction with movements to the 30 cm target to assess the extent of generalization. Force channels restricted lateral movements and allowed us to measure force production during generalization. We compared actual lateral forces to the forces expected if dynamics learning generalized fully. We found that, during the test for generalization, subjects produced reliably less force than expected. Force production was appropriate for the portion of the transfer movement in which velocities corresponded to those experienced with the 15 cm target. Subjects failed to produce the expected forces when velocities exceeded those experienced in the training task. This suggests that dynamics learning generalizes little beyond the range of one's experience. Consistent with this result, subjects who trained on the 30 cm target showed full generalization to the 15 cm target. We performed two additional experiments that show that interleaved trials to the 30 cm target during training on the 15 cm target can resolve the difference between the current results and those reported previously.

Asymmetric transfer of visuomotor learning between discrete and rhythmic movements

As long as we only focus on kinematics, rhythmic movement appears to be a concatenation of discrete movements or discrete movement appears to be a truncated rhythmic movement. However, whether or not the neural control processes of discrete and rhythmic movements are distinct has not yet been clearly understood. Here, we address this issue by examining the motor learning transfer between these two types of movements testing the hypothesis that distinct neural control processes should lead to distinct motor learning and transfer. First, we found that the adaptation to an altered visuomotor condition was almost fully transferred from the discrete out-and-back movements to the rhythmic out-and-back movements; however, the transfer from the rhythmic to discrete movements was very small. Second, every time a new set of rhythmic movements was started, a considerable amount of movement error reappeared in the first and the following several cycles although the error converged to a small level by the end of each set. Last, we observed that when the discrete movement training was performed with intertrial intervals longer than 4 s, a significantly larger error appeared, specifically for the second and third cycles of the subsequent rhythmic movements, despite a seemingly full transfer to the first cycle. These results provide strong behavioral evidence that different neuronal control processes are involved in the two types of movements and that discrete control processes contribute to the generation of the first cycle of the rhythmic movement.

Parallel explicit and implicit control of reaching

Background

Human movement can be guided automatically (implicit control) or attentively (explicit control). Explicit control may be engaged when learning a new movement, while implicit control enables simultaneous execution of multiple actions. Explicit and implicit control can often be assigned arbitrarily: we can simultaneously drive a car and tune the radio, seamlessly allocating implicit or explicit control to either action. This flexibility suggests that sensorimotor signals, including those that encode spatially overlapping perception and behavior, can be accurately segregated to explicit and implicit control processes.

Methodology/Principal Findings

We tested human subjects' ability to segregate sensorimotor signals to parallel control processes by requiring dual (explicit and implicit) control of the same reaching movement and testing for interference between these processes. Healthy control subjects were able to engage dual explicit and implicit motor control without degradation of performance compared to explicit or implicit control alone. We then asked whether segregation of explicit and implicit motor control can be selectively disrupted by studying dual-control performance in subjects with no clinically manifest neurologic deficits in the presymptomatic stage of Huntington's disease (HD). These subjects performed successfully under either explicit or implicit control alone, but were impaired in the dual-control condition.

Conclusion/Significance

The human nervous system can exert dual control on a single action, and is therefore able to accurately segregate sensorimotor signals to explicit and implicit control. The impairment observed in the presymptomatic stage of HD points to a possible crucial contribution of the striatum to the segregation of sensorimotor signals to multiple control processes.

Adaptation to visuomotor rotation through interaction between posterior parietal and motor cortical areas

Studying how motor adaptation to visuomotor rotation for one reach direction generalizes to other reach directions can provide insight into how visuomotor maps are represented and learned in the brain. Previous psychophysical studies have concluded that postadaptation generalization is restricted to a narrow range of directions around the training direction. A population-coding model that updates the weights between narrow Gaussian-tuned visual units and motor units on each trial reproduced experimental trial-by-trial learning curves for rotation adaptation and the generalization function measured postadaptation. These results suggest that the neurons involved in rotation adaptation have a relatively narrow directional tuning width ( approximately 23 degrees ). Population coding models with units having broader tuning functions (such as cosine tuning in motor cortex and Gaussian sum in the cerebellum) could not reproduce the narrow single-peaked generalization pattern. Visually selective neurons with narrow Gaussian tuning curves have been identified in posterior parietal cortex, making it a possible site of adaptation to visuomotor rotation. We propose that rotation adaptation proceeds through changes in synaptic weights between neurons in posterior parietal cortex and motor cortex driven by a prediction error computed by the cerebellum.

Persistence of motor memories reflects statistics of the learning event

Learning to control a new tool (i.e., a novel environment) produces an internal model, i.e., a motor memory that allows the brain to implicitly predict the behavior of the tool. Data from a wide array of experiments suggest that formation of motor memory is not a single process, but one that is due to multiple adaptive processes with different time constants. Here we asked whether these time constants are invariant or are they influenced by the statistics of the learning event. To measure the time constants, we controlled the statistics of the learning event in a reaching task and then assayed the decay rates of motor output in a set of trials in which errors were effectively removed. We found that prior experience with a rapid change in the environment increased the decay rate of memories acquired later in response to a gradual change in the same environment. Prior experience in an environment that changed gradually reduced the decay rates of memories acquired later in response to a rapid change in that same environment. Indeed we found that by manipulating the prior statistics of the learning experience, we could readily alter the decay rates of a given motor memory. This suggests that time scales of processes that support motor memory are not constant. Rather decay of motor memory is the brain's implicit estimate of how likely it is that the environment will change with time. During motor learning, prior statistics that suggest changes are likely to be permanent result in slowly decaying memories, whereas prior statistics that suggest changes are transient result in rapidly decaying memories.

Understanding sensorimotor adaptation and learning for rehabilitation

PURPOSE OF REVIEW

Understanding the behavioral mechanisms of sensorimotor adaptation and learning is essential for designing rational rehabilitation interventions.

RECENT FINDINGS

Adaptation is the trial-and-error process of adjusting movement to new demands and is now thought to be more than a simple error cancellation process. Instead, it may calibrate the brain's prediction of how the body will move and takes into account costs associated with the new task demand. Damage of the cerebellum systematically disrupts adaptation, but damage to other brain regions often does not. Adapting to perturbations driven by a device like a robot or a treadmill leads to only partial generalization to unconstrained 'real-world' movements. Repeated adaptation can lead to learning a new motor calibration, but process of consolidation of this type of learning is less understood in patients.

SUMMARY

Adaptation is inherently important for rehabilitation by making movement flexible, but can also be used to ascertain whether some patients can generate a more normal motor pattern. Repeated adaptation can lead to learning of a new, more permanent motor calibration. Though less understood, this type of learning is likely to be an important method for making long-term improvements in patients' movement patterns.

Robotic neurorehabilitation: a computational motor learning perspective

Conventional neurorehabilitation appears to have little impact on impairment over and above that of spontaneous biological recovery. Robotic neurorehabilitation has the potential for a greater impact on impairment due to easy deployment, its applicability across of a wide range of motor impairment, its high measurement reliability, and the capacity to deliver high dosage and high intensity training protocols. We first describe current knowledge of the natural history of arm recovery after stroke and of outcome prediction in individual patients. Rehabilitation strategies and outcome measures for impairment versus function are compared. The topics of dosage, intensity, and time of rehabilitation are then discussed. Robots are particularly suitable for both rigorous testing and application of motor learning principles to neurorehabilitation. Computational motor control and learning principles derived from studies in healthy subjects are introduced in the context of robotic neurorehabilitation. Particular attention is paid to the idea of context, task generalization and training schedule. The assumptions that underlie the choice of both movement trajectory programmed into the robot and the degree of active participation required by subjects are examined. We consider rehabilitation as a general learning problem, and examine it from the perspective of theoretical learning frameworks such as supervised and unsupervised learning. We discuss the limitations of current robotic neurorehabilitation paradigms and suggest new research directions from the perspective of computational motor learning.

Active learning: learning a motor skill without a coach

When we learn a new skill (e.g., golf) without a coach, we are "active learners": we have to choose the specific components of the task on which to train (e.g., iron, driver, putter, etc.). What guides our selection of the training sequence? How do choices that people make compare with choices made by machine learning algorithms that attempt to optimize performance? We asked subjects to learn the novel dynamics of a robotic tool while moving it in four directions. They were instructed to choose their practice directions to maximize their performance in subsequent tests. We found that their choices were strongly influenced by motor errors: subjects tended to immediately repeat an action if that action had produced a large error. This strategy was correlated with better performance on test trials. However, even when participants performed perfectly on a movement, they did not avoid repeating that movement. The probability of repeating an action did not drop below chance even when no errors were observed. This behavior led to suboptimal performance. It also violated a strong prediction of current machine learning algorithms, which solve the active learning problem by choosing a training sequence that will maximally reduce the learner's uncertainty about the task. While we show that these algorithms do not provide an adequate description of human behavior, our results suggest ways to improve human motor learning by helping people choose an optimal training sequence.

Neural mechanisms underlying auditory feedback control of speech

The neural substrates underlying auditory feedback control of speech were investigated using a combination of functional magnetic resonance imaging (fMRI) and computational modeling. Neural responses were measured while subjects spoke monosyllabic words under two conditions: (i) normal auditory feedback of their speech and (ii) auditory feedback in which the first formant frequency of their speech was unexpectedly shifted in real time. Acoustic measurements showed compensation to the shift within approximately 136 ms of onset. Neuroimaging revealed increased activity in bilateral superior temporal cortex during shifted feedback, indicative of neurons coding mismatches between expected and actual auditory signals, as well as right prefrontal and Rolandic cortical activity. Structural equation modeling revealed increased influence of bilateral auditory cortical areas on right frontal areas during shifted speech, indicating that projections from auditory error cells in posterior superior temporal cortex to motor correction cells in right frontal cortex mediate auditory feedback control of speech.

Impairment of retention but not acquisition of a visuomotor skill through time-dependent disruption of primary motor cortex

Learning a visuomotor skill involves a distributed network which includes the primary motor cortex (M1). Despite multiple lines of evidence supporting the role of M1 in motor learning and memory, it is unclear whether M1 plays distinct roles in different aspects of learning such as acquisition and retention. Here, we investigated the nature and chronometry of that processing through a temporally specific disruption of M1 activity using single-pulse transcranial magnetic stimulation (TMS). We applied single-pulse TMS to M1 or dorsal premotor cortex (PMd) during adaptation of rapid arm movements (approximately 150 ms duration) to a visuomotor rotation. When M1 was stimulated either immediately after the end of each trial or with a 700 ms delay, subjects exhibited normal adaptation. However, whereas the memory of the subjects who received delayed-TMS showed normal rates of forgetting during deadaptation, the memory of those who received immediate TMS was more fragile: in the deadaptation period, they showed a faster rate of forgetting. Stimulation of PMd with adjusted (reduced) intensity to rule out the possibility of coactivation of this structure caused by the current spread from M1 stimulation did not affect adaptation or retention. The data suggest that, during the short time window after detection of movement errors, neural processing in M1 plays a crucial role in formation of motor memories. This processing in M1 may represent a slow component of motor memory which plays a significant role in retention.

Modifiability of Generalization in Dynamics Learning

Studies on plasticity in motor function have shown that motor learning generalizes, such that movements in novel situations are affected by previous training. It has been shown that the pattern of generalization for visuomotor rotation learning changes when training movements are made to a wide distribution of directions. Here we have found that for dynamics learning, the shape of the generalization gradient is not similarly modifiable by the extent of training within the workspace. Subjects learned to control a robotic device during training and we measured how subsequent movements in a reference direction were affected. Our results show that as the angular separation between training and test directions increased, the extent of generalization was reduced. When training involved multiple targets throughout the workspace, the extent of generalization was no greater than following training to the nearest target alone. Thus a wide range of experience compensating for a dynamics perturbation provided no greater benefit than localized training. Instead, generalization was complete when training involved targets that bounded the reference direction. This suggests that broad generalization of dynamics learning to movements in novel directions depends on interpolation between instances of localized learning.