Active learning: learning a motor skill without a coach

The Effects of Different Motor Teaching Strategies on Learning a Complex Motor Task

During the learning of a new sensorimotor task, individuals are usually provided with instructional stimuli and relevant information about the target task. The inclusion of haptic devices in the study of this kind of learning has greatly helped in the understanding of how an individual can improve or acquire new skills. However, the way in which the information and stimuli are delivered has not been extensively explored. We have designed a challenging task with nonintuitive visuomotor perturbation that allows us to apply and compare different motor strategies to study the teaching process and to avoid the interference of previous knowledge present in the naïve subjects. Three subject groups participated in our experiment, where the learning by repetition without assistance, learning by repetition with assistance, and task Segmentation Learning techniques were performed with a haptic robot. Our results show that all the groups were able to successfully complete the task and that the subjects' performance during training and evaluation was not affected by modifying the teaching strategy. Nevertheless, our results indicate that the presented task design is useful for the study of sensorimotor teaching and that the presented metrics are suitable for exploring the evolution of the accuracy and precision during learning.

A Low-fidelity Simulator for the Development of Vascular Ligation Skills

Training simulators can facilitate the acquisition and development of basic surgical skills, and they constitute a safe and humane method that does not harm animals in the process. The objective of this work was to create and evaluate a low-cost simulator to help undergraduate students of veterinary medicine acquire and practise vascular ligation skills. A training model was made by using easily accessible and inexpensive materials. Fifteen students, without prior surgical experience, each performed the orchiectomy technique on the simulator seven times. The emphasis was on performing vascular ligatures in both testicles of the simulator (n = 14), in two different scenarios: firstly, with the contents of the blood vessels not under pressure; and secondly, with the syringe plunger depressed by 1 ml to pressurise the blood vessels. The outcomes of the procedure in the simulator were based on three qualitative criteria: Correct (no 'bleeding'), Sufficient (light 'bleeding') and Incorrect (heavy 'bleeding'). After the seventh attempt, all participants were able to perform vascular ligatures with at least a score of Sufficient in both scenarios. By the 10th ligature attempt, they were all able to perform the procedure with a score of Correct (p < 0.05). There was a trend toward a decrease in the time taken to carry out the procedure as learning progress was made during training, with this being significant from the 12th attempt (p < 0.05). The use of this low-cost simulator represents a useful didactic tool, which supports the acquisition and practise of manual skills by using methods that do not involve the use of animals. In addition, this training simulator could be useful in distance learning, in view of the ready accessibility of the materials required for its construction.

Using EEG to study sensorimotor adaptation

Sensorimotor adaptation, or the capacity to flexibly adapt movements to changes in the body or the environment, is crucial to our ability to move efficiently in a dynamic world. The field of sensorimotor adaptation is replete with rigorous behavioural and computational methods, which support strong conceptual frameworks. An increasing number of studies have combined these methods with electroencephalography (EEG) to unveil insights into the neural mechanisms of adaptation. We review these studies: discussing EEG markers of adaptation in the frequency and the temporal domain, EEG predictors for successful adaptation and how EEG can be used to unmask latent processes resulting from adaptation, such as the modulation of spatial attention. With its high temporal resolution, EEG can be further exploited to deepen our understanding of sensorimotor adaptation.

The recommendation for learners to be provided with control over their feedback schedule is questioned in a self-controlled learning paradigm

Evidence that self-controlled feedback schedules are more effective for motor learning than yoked or predetermined schedules has been used to forward the recommendation that practitioners should provide choice to learners over when they would like to receive feedback. This recommendation can be questioned because the typical comparison groups in such experimentation do not well represent the applied setting. Consequently, comparison groups that better map onto the applied setting are needed. To this end, three groups learned a golf putting task: (1) self-controlled, (2) traditional-yoked, and (3) a group who were led to believe their KR schedule was being controlled by a golf coach. Participants (N = 60) completed a pre-test, acquisition phase, and delayed post-tests (retention/transfer). No group differences during the post-tests for mean radial error, F(2, 54) = 2.71, p = .075, or bivariate variable error, F(2, 56) = 0.11, p = .896, were found. Thus, the typical self-controlled learning advantage was not observed. Given the failure to replicate self-controlled benefits, combined with the fact there is little research that has directly compared self-controlled feedback schedules to coach-controlled schedules, we argue more evidence is needed before advocating that learners be provided choice over their feedback schedule.

Now you see me, now you don't: Adapting practice through target exclusion negatively impacts motor learning

How to optimize practice through scheduling of different task components or skills is a question that has received a lot of attention in motor learning research. Consistently, schedules with high variability in the order that skills are practiced elicit better learning outcomes than schedules with low variability. Another idea is that learners should seek to reduce the uncertainty of practice outcomes, by avoiding well-learned, low error components in acquisition. To test this idea, we used a target exclusion method to prevent learners from returning to task components with low error and studied how individuals given choice over practice choose to allocate time to components of varying difficulty. In a multi-target adaptation paradigm, we compared exclusion with no exclusion methods in random-schedule, self-control and yoked, matched-schedule groups (6 groups total). To manipulate uncertainty, we excluded targets from practice once participants attained a criterion error score (mean < 5^o) from the last 5 trials to the same target. Contrary to our predictions, groups that practiced without target exclusion were more accurate in retention compared to exclusion groups; irrespective of practice schedule. Self-control groups adopted uncertainty-based practice, spending more time at difficult targets and less time at easier targets. However, there were no group differences in error, based on schedule-type (random, self-control and yoked). In conclusion, target exclusion was not an effective method for learning and did not support the efficacy of uncertainty-based practice for learning novel skills. There were benefits from keeping easier/low error skills in practice for later retention. These benefits did not appear to be related to the increased switching between skills, but could be related to increased task engagement and more optimal challenge associated with practice on a range of target difficulties, rather than the most difficult.

Surface Electromyographic Biofeedback and the Effortful Swallow Exercise for Stroke-Related Dysphagia and in Healthy Ageing

Dysphagia is common after stroke, leading to adverse outcome. The Effortful Swallow (ES) is recommended to improve swallowing but it is not known if dysphagic patients can increase muscle activity during the exercise or if age affects performance. Providing surface electromyographic (sEMG) biofeedback during dysphagia therapy may enhance exercise completion, but this has not been investigated and the technique’s acceptability to patients is not known. Aims: To determine if age or post-stroke dysphagia affect the ability to increase submental muscle activity during the ES, if sEMG biofeedback improves ES performance and if sEMG is an acceptable addition to therapy. In a Phase I study submental sEMG amplitudes were measured from 15 people with dysphagia < 3 months post-stroke and 85 healthy participants aged 18–89 years during swallowing (NS) and when they performed the ES with and without sEMG biofeedback. Participant feedback was collected via questionnaire. Measurements were compared with repeated measures ANOVA and age effects were examined with linear regression. Both groups produced significantly greater muscle activity for the ES than NS (p < 0.001) and significantly increased activity with biofeedback (p < 0.001) with no effect of age. Participant feedback about sEMG was very positive; over 98% would be happy to use it regularly. The ES is a physiologically beneficial dysphagia exercise, increasing muscle activity during swallowing. sEMG biofeedback further enhances performance and is considered an acceptable technique by patients. These findings support the potential application of sEMG biofeedback and the ES in dysphagia therapy in stroke, justifying further investigation of patient outcome.

Self-controlled practice and nudging during structural learning of a novel control interface

Self-controlled practice schedules have been shown to enhance motor learning in several contexts, but their effectiveness in structural learning tasks, where the goal is to eventually learn an underlying structure or rule, is not well known. Here we examined the use of self-controlled practice in a novel control interface requiring structural learning. In addition, we examined the effect of ‘nudging’–i.e., whether altering task difficulty could influence self-selected strategies, and hence facilitate learning. Participants wore four inertial measurement units (IMUs) on their upper body and the goal was to use motions of the upper body to move a screen cursor to different targets presented on the screen. The structure in this task that had to be learned was based on the fact that the signals from the IMUs were linearly mapped to the x- and y- position of the cursor. Participants (N = 62) were split into 3 groups (random, self-selected, nudge) based on whether they had control over the sequence in which they could practice the targets. To test whether participants learned the underlying structure, participants were tested both on the trained targets, as well as novel targets that were not practiced during training. Results showed that during training, the self-selected group showed shorter movement times relative to the random group, and both self-selected and nudge groups adopted a strategy of tending to repeat targets. However, in the test phase, we found no significant differences in task performance between groups, indicating that structural learning was not reliably affected by the type of practice. In addition, nudging participants by adjusting task difficulty did not show any significant benefits to overall learning. These results suggest that although self-controlled practice influenced practice structure and facilitated learning, it did not provide any additional benefits relative to practicing on a random schedule in this task.

A Knowledge-Based Arrangement of Prototypical Neural Representation Prior to Experience Contributes to Selectivity in Upcoming Knowledge Acquisition

Knowledge acquisition is a process in which one actively selects a piece of information from the environment and assimilates it with prior knowledge. However, little is known about the neural mechanism underlying selectivity in knowledge acquisition. Here we executed a 2-day human experiment to investigate the involvement of characteristic spontaneous activity resembling a so-called “preplay” in selectivity in sentence comprehension, an instance of knowledge acquisition. On day 1, we presented 10 sentences (prior sentences) that were difficult to understand on their own. On the following day, we first measured the resting-state functional magnetic resonance imaging (fMRI). Then, we administered a sentence comprehension task using 20 new sentences (posterior sentences). The posterior sentences were also difficult to understand on their own, but some could be associated with prior sentences to facilitate their understanding. Next, we measured the posterior sentence-induced fMRI to identify the neural representation. From the resting-state fMRI, we extracted the appearances of activity patterns similar to the neural representations for posterior sentences. Importantly, the resting-state fMRI was measured before giving the posterior sentences, and thus such appearances could be considered as preplay-like or prototypical neural representations. We compared the intensities of such appearances with the understanding of posterior sentences. This gave a positive correlation between these two variables, but only if posterior sentences were associated with prior sentences. Additional analysis showed the contribution of the entorhinal cortex, rather than the hippocampus, to the correlation. The present study suggests that prior knowledge-based arrangement of neural activity before an experience contributes to the active selection of information to be learned. Such arrangement prior to an experience resembles preplay activity observed in the rodent brain. In terms of knowledge acquisition, the present study leads to a new view of the brain (or more precisely of the brain’s knowledge) as an autopoietic system in which the brain (or knowledge) selects what it should learn by itself, arranges preplay-like activity as a position for the new information in advance, and actively reorganizes itself.

Optimal Schedules in Multitask Motor Learning

Although scheduling multiple tasks in motor learning to maximize long-term retention of performance is of great practical importance in sports training and motor rehabilitation after brain injury, it is unclear how to do so. We propose here a novel theoretical approach that uses optimal control theory and computational models of motor adaptation to determine schedules that maximize long-term retention predictively. Using Pontryagin's maximum principle, we derived a control law that determines the trial-by-trial task choice that maximizes overall delayed retention for all tasks, as predicted by the state-space model. Simulations of a single session of adaptation with two tasks show that when task interference is high, there exists a threshold in relative task difficulty below which the alternating schedule is optimal. Only for large differences in task difficulties do optimal schedules assign more trials to the harder task. However, over the parameter range tested, alternating schedules yield long-term retention performance that is only slightly inferior to performance given by the true optimal schedules. Our results thus predict that in a large number of learning situations wherein tasks interfere, intermixing tasks with an equal number of trials is an effective strategy in enhancing long-term retention.

Neuromechanical principles underlying movement modularity and their implications for rehabilitation

Neuromechanical principles define the properties and problems that shape neural solutions for movement. Although the theoretical and experimental evidence is debated, we present arguments for consistent structures in motor patterns, i.e., motor modules, that are neuromechanical solutions for movement particular to an individual and shaped by evolutionary, developmental, and learning processes. As a consequence, motor modules may be useful in assessing sensorimotor deficits specific to an individual and define targets for the rational development of novel rehabilitation therapies that enhance neural plasticity and sculpt motor recovery. We propose that motor module organization is disrupted and may be improved by therapy in spinal cord injury, stroke, and Parkinson's disease. Recent studies provide insights into the yet-unknown underlying neural mechanisms of motor modules, motor impairment, and motor learning and may lead to better understanding of the causal nature of modularity and its underlying neural substrates.

Motor learning principles for neurorehabilitation

Neurorehabilitation is based on the assumption that motor learning contributes to motor recovery after injury. However, little is known about how learning itself is affected by brain injury, how learning mechanisms interact with spontaneous biological recovery, and how best to incorporate learning principles into rehabilitation training protocols. Here we distinguish between two types of motor learning, adaptation and skill acquisition, and discuss how they relate to neurorehabilitation. Functional recovery can occur through resolution of impairment (reacquisition of premorbid movement patterns) and through compensation (use of alternative movements or effectors to accomplish the same goal); both these forms of recovery respond to training protocols. The emphasis in current neurorehabilitation practice is on the rapid establishment of independence in activities of daily living through compensatory strategies, rather than on the reduction of impairment. Animal models, however, show that after focal ischemic damage there is a brief, approximately 3-4-week, window of heightened plasticity, which in combination with training protocols leads to large gains in motor function. Analogously, almost all recovery from impairment in humans occurs in the first 3 months after stroke, which suggests that targeting impairment in this time-window with intense motor learning protocols could lead to gains in function that are comparable in terms of effect size to those seen in animal models.

Augmented dynamics and motor exploration as training for stroke

With chronic stroke survivors (n = 30), we investigated how upper extremity training with negative viscosity affects coordination under unperturbed conditions. Subjects trained with a planar robotic interface simulating 1) negative viscosity augmented to elbow and shoulder joints; 2) negative viscosity combined with inertia; or 3) a null-field condition. Two treatment groups practiced with both force conditions (cross-over design), while a control group practiced with a null-field condition. Training (exploratory movement) and evaluations (prescribed circular movement) alternated in several phases to facilitate transfer from forces to the null field. Negative viscosity expanded exploration especially in the sagittal axis, and resulted in significant within-day improvements. Both treatment groups exhibited next day retention unobserved in the control. Our results suggest enhanced learning from forces that induce a broader range of kinematics. This study supports the use of robot-assisted training that encourages active patient involvement by preserving efferent commands for driving movement.

Principles of sensorimotor learning

The exploits of Martina Navratilova and Roger Federer represent the pinnacle of motor learning. However, when considering the range and complexity of the processes that are involved in motor learning, even the mere mortals among us exhibit abilities that are impressive. We exercise these abilities when taking up new activities - whether it is snowboarding or ballroom dancing - but also engage in substantial motor learning on a daily basis as we adapt to changes in our environment, manipulate new objects and refine existing skills. Here we review recent research in human motor learning with an emphasis on the computational mechanisms that are involved.

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.

Review of control strategies for robotic movement training after neurologic injury

There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury. This paper reviews control strategies for robotic therapy devices. Several categories of strategies have been proposed, including, assistive, challenge-based, haptic simulation, and coaching. The greatest amount of work has been done on developing assistive strategies, and thus the majority of this review summarizes techniques for implementing assistive strategies, including impedance-, counterbalance-, and EMG- based controllers, as well as adaptive controllers that modify control parameters based on ongoing participant performance. Clinical evidence regarding the relative effectiveness of different types of robotic therapy controllers is limited, but there is initial evidence that some control strategies are more effective than others. It is also now apparent there may be mechanisms by which some robotic control approaches might actually decrease the recovery possible with comparable, non-robotic forms of training. In future research, there is a need for head-to-head comparison of control algorithms in randomized, controlled clinical trials, and for improved models of human motor recovery to provide a more rational framework for designing robotic therapy control strategies.

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.

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.