Learning redundant motor tasks with and without overlapping dimensions: facilitation and interference effects

Prior coordination solutions shape motor learning and transfer in redundant tasks

Motor learning does not occur on a 'blank slate', but in the context of prior coordination solutions. The role of prior coordination solutions is likely critical in redundant tasks where there are multiple solutions to achieve the task goal - yet their influence on subsequent learning is currently not well understood. Here we addressed this issue by having human participants learn a redundant virtual shuffleboard task, where they held a bimanual manipulandum and made a discrete throwing motion to slide a virtual puck towards a target. The task was redundant because the distance traveled by the puck was determined by the sum of the left- and right-hand speeds at the time of release. On the first day, 37 participants in different groups practiced symmetric or asymmetric solutions. On the second day, all participants transferred to a common criterion task, which required an asymmetric solution. Results showed that: (i) the symmetry of the practiced solution affected motor variability during practice, with more asymmetric solutions showing higher exploration of the null space, (ii) when transferring to the common criterion task, participants in the symmetric group showed much higher null space exploration, and (iii) when no constraints were placed on the solution, participants tended to return to the symmetric solution regardless of the solution originally practiced. Overall, these results suggest that the stability of prior coordination solutions plays an important role in shaping learning in redundant motor tasks.

Ethnokinesiology: towards a neuromechanical understanding of cultural differences in movement

Each individual's movements are sculpted by constant interactions between sensorimotor and sociocultural factors. A theoretical framework grounded in motor control mechanisms articulating how sociocultural and biological signals converge to shape movement is currently missing. Here, we propose a framework for the emerging field of ethnokinesiology aiming to provide a conceptual space and vocabulary to help bring together researchers at this intersection. We offer a first-level schema for generating and testing hypotheses about cultural differences in movement to bridge gaps between the rich observations of cross-cultural movement variations and neurophysiological and biomechanical accounts of movement. We explicitly dissociate two interacting feedback loops that determine culturally relevant movement: one governing sensorimotor tasks regulated by neural signals internal to the body, the other governing ecological tasks generated through actions in the environment producing ecological consequences. A key idea is the emergence of individual-specific and culturally influenced motor concepts in the nervous system, low-dimensional functional mappings between sensorimotor and ecological task spaces. Motor accents arise from perceived differences in motor concept topologies across cultural contexts. We apply the framework to three examples: speech, gait and grasp. Finally, we discuss how ethnokinesiological studies may inform personalized motor skill training and rehabilitation, and challenges moving forward.This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.

De novo sensorimotor learning through reuse of movement components

From tying one's shoelaces to driving a car, complex skills involving the coordination of multiple muscles are common in everyday life; yet relatively little is known about how these skills are learned. Recent studies have shown that new sensorimotor skills involving re-mapping familiar body movements to unfamiliar outputs cannot be learned by adjusting pre-existing controllers, and that new task-specific controllers must instead be learned "de novo". To date, however, few studies have investigated de novo learning in scenarios requiring continuous and coordinated control of relatively unpractised body movements. In this study, we used a myoelectric interface to investigate how a novel controller is learned when the task involves an unpractised combination of relatively untrained continuous muscle contractions. Over five sessions on five consecutive days, participants learned to trace a series of trajectories using a computer cursor controlled by the activation of two muscles. The timing of the generated cursor trajectory and its shape relative to the target improved for conditions trained with post-trial visual feedback. Improvements in timing transferred to all untrained conditions, but improvements in shape transferred less robustly to untrained conditions requiring the trained order of muscle activation. All muscle outputs in the final session could already be generated during the first session, suggesting that participants learned the new task by improving the selection of existing motor commands. These results suggest that the novel controllers acquired during de novo learning can, in some circumstances, be constructed from components of existing controllers.

Double dissociation of visuomotor interaction mediated by visual feedback during continuous de novo motor learning

While the sensorimotor cortices are central neural substrates for motor control and learning, how the interaction between their subregions with visual cortices contributes to acquiring de novo visuomotor skills is poorly understood. We design a continuous visuomotor task in fMRI where participants control a cursor using their fingers while learning an arbitrary finger-to-cursor mapping. To investigate visuomotor interaction in the de novo motor task, we manipulate visual feedback of a cursor such that they learn to control using fingers under two alternating conditions: online cursor feedback is available or unavailable except when a target is reached. As a result, we find double dissociation of fMRI activity in subregions of the sensorimotor and visual cortices. Specifically, motor and late visual cortices are more active with online cursor feedback, and somatosensory and early visual cortices are more active without online cursor feedback. We also find a significant reduction in functional connectivity between somatosensory cortices and early visual cortices, which is highly correlated with performance improvement. These findings support the distinct interaction between subregions of sensorimotor cortices and visual cortices, while the connectivity analysis highlights the critical role of somatosensory cortices during de novo motor learning.

Learning to Control Complex Robots Using High-Dimensional Body-Machine Interfaces

When individuals are paralyzed from injury or damage to the brain, upper body movement and function can be compromised. While the use of body motions to interface with machines has shown to be an effective noninvasive strategy to provide movement assistance and to promote physical rehabilitation, learning to use such interfaces to control complex machines is not well understood. In a five session study, we demonstrate that a subset of an uninjured population is able to learn and improve their ability to use a high-dimensional Body-Machine Interface (BoMI), to control a robotic arm. We use a sensor net of four inertial measurement units, placed bilaterally on the upper body, and a BoMI with the capacity to directly control a robot in six dimensions. We consider whether the way in which the robot control space is mapped from human inputs has any impact on learning. Our results suggest that the space of robot control does play a role in the evolution of human learning: specifically, though robot control in joint space appears to be more intuitive initially, control in task space is found to have a greater capacity for longer-term improvement and learning. Our results further suggest that there is an inverse relationship between control dimension couplings and task performance.

Generalization in de novo learning of virtual upper limb movements is influenced by motor exploration

The acquisition of new motor skills from scratch, also known as de novo learning, is an essential aspect of motor development. In de novo learning, the ability to generalize skills acquired under one condition to others is crucial because of the inherently limited range of motor experiences available for learning. However, the presence of generalization in de novo learning and its influencing factors remain unclear. This study aimed to elucidate the generalization of de novo motor learning by examining the motor exploration process, which is the accumulation of motor experiences. To this end, we manipulated the exploration process during practice by changing the target shape using either a small circular target or a bar-shaped target. Our findings demonstrated that the amount of learning during practice was generalized across different conditions. Furthermore, the extent of generalization is influenced by movement variability in the control space, which is irrelevant to the task, rather than the target shapes themselves. These results confirmed the occurrence of generalization in de novo learning and suggest that the exploration process within the control space plays a significant role in facilitating this generalization.

Goalkeepers' plasticity during learning of a whole-body visuomotor rotation in a stable or variable environment

Postural adjustments performed in anticipation of uncertain visual events is a common sensorimotor control problem in open sport skills. In this study, we examined how expert soccer goalkeepers and non-athletes learn a whole body visuomotor rotation during postural tracking of constant and variable visual target motions. Twenty-one (21) soccer goalkeepers (18 ± 15 years, 75 ± 12 kg) and 25 age-matched non-athletes (18 ± 12 years, 75 ± 15 kg) practiced lateral weight shifting on a dual force platform while tracking the motion of a constant (11 goalkeepers and 12 non-athletes) or a variable (10 goalkeepers and 13 non-athletes) visual target with provision of online visual feedback (VF). After 40s of tracking (baseline), the visual presentation of the VF signal reversed direction relative to the participant's motion (180° visuo-motor rotation) for 60s (adaptation) and then returned to its veridical direction for another 20s (washout). During adaptation, goalkeepers reduced the spatiotemporal error to baseline levels at an earlier time block (3rd block) compared to non-athletes (6th block), but this difference was significant only for groups tracking of the constant and not the variable target motion. Only the groups tracking the constant target increased the spatiotemporal error during the 1st washout block demonstrating a significant aftereffect. It is concluded that goalkeepers adapt faster to the feedback rotation due to their prior field knowledge of relevant visuomotor transformations in anticipation of deceptive visual cues. This expertise advantage however is present only in a stable visual environment possibly because learning is compromised when tracking uncertain motion cues requiring closed loop control.HighlightsWe examined how expert goalkeepers and non-athletes adopt to a novel whole body visuomotor rotation when tracking a constantly or variably moving targetGoalkeepers adopted faster to the visuomotor rotation than non-athletesExpertise related differences were evident only for groups tracking the constant target motionGroups tracking the variable target motion did not learn the visuomotor rotation.

An Exploratory Multi-Session Study of Learning High-Dimensional Body-Machine Interfacing for Assistive Robot Control

Individuals who suffer from severe paralysis often lose the capacity to perform fundamental body movements and everyday activities. Empowering these individuals with the ability to operate robotic arms, in high degrees-of-freedom (DoFs), can help to maximize both functional utility and independence. However, robot teleoperation in high DoFs currently lacks accessibility due to the challenge in capturing high-dimensional control signals from the human, especially in the face of motor impairments. Body-machine interfacing is a viable option that offers the necessary high-dimensional motion capture, and it moreover is noninvasive, affordable, and promotes movement and motor recovery. Nevertheless, to what extent body-machine interfacing is able to scale to high-DoF robot control, and whether it is feasible for humans to learn, remains an open question. In this exploratory multi-session study, we demonstrate the feasibility of human learning to operate a body-machine interface to control a complex, assistive robotic arm. We use a sensor net of four inertial measurement unit sensors, bilaterally placed on the scapulae and humeri. Ten uninjured participants are familiarized, trained, and evaluated in reaching and Activities of Daily Living tasks, using the body- machine interface. Our results suggest the manner of control space mapping (joint-space control versus task-space control), from interface to robot, plays a critical role in the evolution of human learning. Though joint-space control shows to be more intuitive initially, task-space control is found to have a greater capacity for longer-term improvement and learning.

Evaluating transfer prediction using machine learning for skill acquisition study under various practice conditions

Recent research highlighted the interest in 1) investigating the effect of variable practice on the dynamics of learning and 2) modeling the dynamics of motor skill learning to enhance understanding of individual pathways learners. Such modeling has not been suitable for predicting future performance, both in terms of retention and transfer to new tasks. The present study attempted to quantify, by means of a machine learning algorithm, the prediction of skill transfer for three practice conditions in a climbing task: constant practice (without any modifications applied during learning), imposed variable practice (with graded contextual modifications, i.e., the variants of the climbing route), and self-controlled variable practice (participants were given some control over their variant practice schedule). The proposed pipeline allowed us to measure the fitness of the test to the dataset, i.e., the ability of the dataset to be predictive of the skill transfer test. Behavioral data are difficult to model with statistical learning and tend to be 1) scarce (too modest data sample in comparison with the machine learning standards) and 2) flawed (data tend to contain voids in measurements). Despite these adversities, we were nevertheless able to develop a machine learning pipeline for behavioral data. The main findings demonstrate that the level of learning transfer varies, according to the type of practice that the dynamics pertain: we found that the self-controlled condition is more predictive of generalization ability in learners than the constant condition.

Sequential motor learning transfers from real to virtual environment

Background

Skill acquisition of motor learning between virtual environments (VEs) and real environments (REs) may be related. Although studies have previously examined the transfer of motor learning in VEs and REs through the same tasks, only a small number of studies have focused on studying the transfer of motor learning in VEs and REs by using different tasks. Thus, detailed effects of the transfer of motor skills between VEs and REs remain controversial. Here, we investigated the transfer of sequential motor learning between VEs and REs conditions.

Methods

Twenty-seven healthy volunteers performed two types of sequential motor learning tasks; a visually cued button-press task in RE (RE task) and a virtual reaching task in VE (VE task). Participants were randomly assigned to two groups in the task order; the first group was RE task followed by VE task and the second group was VE task followed by RE task. Subsequently, the response time in RE task and VE task was compared between the two groups respectively.

Results

The results showed that the sequential reaching task in VEs was facilitated after the sequential finger task in REs.

Conclusions

These findings suggested that the sequential reaching task in VEs can be facilitated by a motor learning task comprising the same sequential finger task in REs, even when a different task is applied.

A Framework for Optimizing Co-adaptation in Body-Machine Interfaces

The operation of a human-machine interface is increasingly often referred to as a two-learners problem, where both the human and the interface independently adapt their behavior based on shared information to improve joint performance over a specific task. Drawing inspiration from the field of body-machine interfaces, we take a different perspective and propose a framework for studying co-adaptation in scenarios where the evolution of the interface is dependent on the users' behavior and that do not require task goals to be explicitly defined. Our mathematical description of co-adaptation is built upon the assumption that the interface and the user agents co-adapt toward maximizing the interaction efficiency rather than optimizing task performance. This work describes a mathematical framework for body-machine interfaces where a naïve user interacts with an adaptive interface. The interface, modeled as a linear map from a space with high dimension (the user input) to a lower dimensional feedback, acts as an adaptive “tool” whose goal is to minimize transmission loss following an unsupervised learning procedure and has no knowledge of the task being performed by the user. The user is modeled as a non-stationary multivariate Gaussian generative process that produces a sequence of actions that is either statistically independent or correlated. Dependent data is used to model the output of an action selection module concerned with achieving some unknown goal dictated by the task. The framework assumes that in parallel to this explicit objective, the user is implicitly learning a suitable but not necessarily optimal way to interact with the interface. Implicit learning is modeled as use-dependent learning modulated by a reward-based mechanism acting on the generative distribution. Through simulation, the work quantifies how the system evolves as a function of the learning time scales when a user learns to operate a static vs. an adaptive interface. We show that this novel framework can be directly exploited to readily simulate a variety of interaction scenarios, to facilitate the exploration of the parameters that lead to optimal learning dynamics of the joint system, and to provide an empirical proof for the superiority of human-machine co-adaptation over user adaptation.

Building an adaptive interface via unsupervised tracking of latent manifolds

In human-machine interfaces, decoder calibration is critical to enable an effective and seamless interaction with the machine. However, recalibration is often necessary as the decoder off-line predictive power does not generally imply ease-of-use, due to closed loop dynamics and user adaptation that cannot be accounted for during the calibration procedure. Here, we propose an adaptive interface that makes use of a non-linear autoencoder trained iteratively to perform online manifold identification and tracking, with the dual goal of reducing the need for interface recalibration and enhancing human-machine joint performance. Importantly, the proposed approach avoids interrupting the operation of the device and it neither relies on information about the state of the task, nor on the existence of a stable neural or movement manifold, allowing it to be applied in the earliest stages of interface operation, when the formation of new neural strategies is still on-going. In order to more directly test the performance of our algorithm, we defined the autoencoder latent space as the control space of a body-machine interface. After an initial offline parameter tuning, we evaluated the performance of the adaptive interface versus that of a static decoder in approximating the evolving low-dimensional manifold of users simultaneously learning to perform reaching movements within the latent space. Results show that the adaptive approach increased the representational efficiency of the interface decoder. Concurrently, it significantly improved users' task-related performance, indicating that the development of a more accurate internal model is encouraged by the online co-adaptation process.

The Effect of in-Service Methodology on Learning Transfer for School Personnel Managing Students following Concussion

Background

It is essential to increase the knowledge base of teachers involved in facilitating return to learning in middle school students following a concussion. However, the best method to enhance the transfer of learning for teachers remains to be elucidated. Application of Adult Learning Theory (ALT) is a plausible solution to this problem.

Purpose

The purpose of this randomized post-test study was to examine the effects of ALT on the transfer of learning in teachers who work with individuals with concussion.

Methods

A convenience sample of 169 teachers at four middle schools were randomized to receive an in-service regarding concussion management either in ALT or traditional lecture format. Vignettes approximating classroom practice evaluated learning transfer.

Results

one-way between subjects ANOVA revealed no significant difference between the methods of educational delivery on group assessment scores (p = .22). Additionally, a regression analysis did not identify any demographic variables that predicted learning transfer (p = .65). A statistically significant difference existed for four questions (1, 4, 7, 25) between the groups (p = .03, .02, .01, .00, respectively). These vignettes were those that assessed information that was likely novel to the learner.

Discussion

The current study demonstrated that ALT applied to teacher in-service did not impact transfer of learning immediately post training compared to a traditional lecture format. Future research should continue to examine the effects of various educational strategies to enhance learning transfer for teachers managing students in the classroom after concussion.

Clinical Application of Virtual Reality for Upper Limb Motor Rehabilitation in Stroke: Review of Technologies and Clinical Evidence

Neurorehabilitation for stroke is important for upper limb motor recovery. Conventional rehabilitation such as occupational therapy has been used, but novel technologies are expected to open new opportunities for better recovery. Virtual reality (VR) is a technology with a set of informatics that provides interactive environments to patients. VR can enhance neuroplasticity and recovery after a stroke by providing more intensive, repetitive, and engaging training due to several advantages, including: (1) tasks with various difficulty levels for rehabilitation, (2) augmented real-time feedback, (3) more immersive and engaging experiences, (4) more standardized rehabilitation, and (5) safe simulation of real-world activities of daily living. In this comprehensive narrative review of the application of VR in motor rehabilitation after stroke, mainly for the upper limbs, we cover: (1) the technologies used in VR rehabilitation, including sensors; (2) the clinical application of and evidence for VR in stroke rehabilitation; and (3) considerations for VR application in stroke rehabilitation. Meta-analyses for upper limb VR rehabilitation after stroke were identified by an online search of Ovid-MEDLINE, Ovid-EMBASE, the Cochrane Library, and KoreaMed. We expect that this review will provide insights into successful clinical applications or trials of VR for motor rehabilitation after stroke.

Guiding functional reorganization of motor redundancy using a body-machine interface

Background

Body-machine interfaces map movements onto commands to external devices. Redundant motion signals derived from inertial sensors are mapped onto lower-dimensional device commands. Then, the device users face two problems, a) the structural problem of understanding the operation of the interface and b) the performance problem of controlling the external device with high efficiency. We hypothesize that these problems, while being distinct are connected in that aligning the space of body movements with the space encoded by the interface, i.e. solving the structural problem, facilitates redundancy resolution towards increasing efficiency, i.e. solving the performance problem.

Methods

Twenty unimpaired volunteers practiced controlling the movement of a computer cursor by moving their arms. Eight signals from four inertial sensors were mapped onto the two cursor’s coordinates on a screen. The mapping matrix was initialized by asking each user to perform free-form spontaneous upper-limb motions and deriving the two main principal components of the motion signals. Participants engaged in a reaching task for 18 min, followed by a tracking task. One group of 10 participants practiced with the same mapping throughout the experiment, while the other 10 with an adaptive mapping that was iteratively updated by recalculating the principal components based on ongoing movements.

Results

Participants quickly reduced reaching time while also learning to distribute most movement variance over two dimensions. Participants with the fixed mapping distributed movement variance over a subspace that did not match the potent subspace defined by the interface map. In contrast, participant with the adaptive map reduced the difference between the two subspaces, resulting in a smaller amount of arm motions distributed over the null space of the interface map. This, in turn, enhanced movement efficiency without impairing generalization from reaching to tracking.

Conclusions

Aligning the potent subspace encoded by the interface map to the user’s movement subspace guides redundancy resolution towards increasing movement efficiency, with implications for controlling assistive devices. In contrast, in the pursuit of rehabilitative goals, results would suggest that the interface must change to drive the statistics of user’s motions away from the established pattern and toward the engagement of movements to be recovered.

Trial registration

ClinicalTrials.gov, NCT01608438, Registered 16 April 2012.

Learning and transfer of complex motor skills in virtual reality: a perspective review

The development of more effective rehabilitative interventions requires a better understanding of how humans learn and transfer motor skills in real-world contexts. Presently, clinicians design interventions to promote skill learning by relying on evidence from experimental paradigms involving simple tasks, such as reaching for a target. While these tasks facilitate stringent hypothesis testing in laboratory settings, the results may not shed light on performance of more complex real-world skills. In this perspective, we argue that virtual environments (VEs) are flexible, novel platforms to evaluate learning and transfer of complex skills without sacrificing experimental control. Specifically, VEs use models of real-life tasks that afford controlled experimental manipulations to measure and guide behavior with a precision that exceeds the capabilities of physical environments. This paper reviews recent insights from VE paradigms on motor learning into two pressing challenges in rehabilitation research: 1) Which training strategies in VEs promote complex skill learning? and 2) How can transfer of learning from virtual to real environments be enhanced? Defining complex skills by having nested redundancies, we outline findings on the role of movement variability in complex skill acquisition and discuss how VEs can provide novel forms of guidance to enhance learning. We review the evidence for skill transfer from virtual to real environments in typically developing and neurologically-impaired populations with a view to understanding how differences in sensory-motor information may influence learning strategies. We provide actionable suggestions for practicing clinicians and outline broad areas where more research is required. Finally, we conclude that VEs present distinctive experimental platforms to understand complex skill learning that should enable transfer from therapeutic practice to the real world.

Age-related deficits in motor learning are associated with altered motor exploration strategies

How is motor learning affected by aging? Although several experimental paradigms have been used to address this question, there has been limited focus on the early phase of motor learning, which involves motor exploration and the need to coordinate multiple degrees of freedom in the body. Here, we examined motor learning in a body-machine interface where we measured both age-related differences in task performance as well as the coordination strategies underlying this performance. Participants (N = 65; age range 18-72 years) wore wireless inertial measurement units on the upper body, and learned to control a cursor on a screen, which was controlled by motions of the trunk. Results showed that, consistent with prior studies, there was an age-related effect on movement time, with middle-aged and older adults taking longer to perform the task than young adults. However, we also found that these changes were associated with limited exploration in older adults. Moreover, when considering data across a majority of the lifespan (including children), longer movement times were associated with greater inefficiency of the coordination pattern, producing more task-irrelevant motion. These results suggest exploration behaviors during motor learning are affected with aging, and highlight the need for different practice strategies with aging.

Age-dependent differences in learning to control a robot arm using a body-machine interface

Body-machine interfaces, i.e. interfaces that rely on body movements to control external assistive devices, have been proposed as a safe and robust means of achieving movement and mobility; however, how children learn these novel interfaces is poorly understood. Here we characterized the learning of a body-machine interface in young unimpaired adults, two groups of typically developing children (9-year and 12-year olds), and one child with congenital limb deficiency. Participants had to control the end-effector of a robot arm in 2D using movements of the shoulder and torso. Results showed a striking effect of age - children had much greater difficulty in learning the task compared to adults, with a majority of the 9-year old group unable to even complete the task. The 12-year olds also showed poorer task performance compared to adults (as measured by longer movement times and greater path lengths), which were associated with less effective search strategies. The child with congenital limb deficiency showed superior task performance compared to age-matched children, but had qualitatively distinct coordination strategies from the adults. Taken together, these results imply that children have difficulty learning non-intuitive interfaces and that the design of body-machine interfaces should account for these differences in pediatric populations.

Development of an EMG-Controlled Serious Game for Rehabilitation

A majority of the seven million stroke survivors in the U.S. have hand impairments, adversely affecting performance of a variety of activities of daily living, because of the fundamental role of the hand in performing functional tasks. Disability in stroke survivors is largely attributable to damaged neuronal pathways, which result in inappropriate activation of muscles, a condition prevalent in distal upper extremity muscles following stroke. While conventional rehabilitation methods focus on the amplification of existing muscle activation, the effectiveness of therapy targeting the reorganization of pathological activation patterns is often unexplored. To encourage modulation of activation level and exploration of the activation workspace, we developed a novel platform for playing a serious game through electromyographic control. This system was evaluated by a group of neurologically intact subjects over multiple sessions held on different days. Subjects were assigned to one of two groups, training either with their non-dominant hand only (unilateral) or with both hands (bilateral). Both groups of subjects displayed improved performance in controlling the cursor with their non-dominant hand, with retention from one session to the next. The system holds promise for rehabilitation of control of muscle activation patterns.

Developmentally Changing Attractor Dynamics of Manual Actions with Objects in Late Infancy

Human infants interact with the environment through a growing and changing body and their manual actions provide new opportunities for exploration and learning. In the current study, a dynamical systems approach was used to quantify and characterize the early motor development of limb effectors during bouts of manual activity. Many contemporary theories of motor development emphasize sources of order in movement over developmental time. However, little is known about the dynamics of manual actions during the first two years of life, a period of development with dramatic anatomical changes resulting in new opportunities for action. Here, we introduce a novel analytical protocol for estimating properties of attractor regions using motion capture. We apply this new analysis to a longitudinal corpus of manual actions during sessions of toy play across the first two years of life. Our results suggest that the size of attractor regions for manual actions increases across development and that infants spend more time inside the attractor region of their movements during bouts of manual actions with objects. The sources of order in manual actions are discussed in terms of changing attractor dynamics across development.

Coordination of hamstrings is individual specific and is related to motor performance

The torque-sharing strategies between synergistic muscles may have important functional consequences. This study involved two experiments. The first experiment ( n = 22) aimed 1) to determine the relationship between the distribution of activation and the distribution of torque-generating capacity among the heads of the hamstring, and 2) to describe individual torque-sharing strategies and to determine whether these strategies are similar between legs. The second experiment ( n = 35) aimed to determine whether the distribution of activation between the muscle heads affects endurance performance during a sustained submaximal knee flexion task. Surface electromyography (EMG) was recorded from biceps femoris (BF), semimembranosus (SM), and semitendinosus (ST) during submaximal isometric knee flexions. Torque-generating capacity was estimated by measuring muscle volume, fascicle length, pennation angle, and moment arm. The product of the normalized EMG amplitude and the torque-generating capacity was used as an index of muscle torque. The distributions of muscle activation and of torque-generating capacity were not correlated significantly (all P > 0.18). Thus, there was a torque imbalance between the muscle heads (ST torque > BF and SM torque; P < 0.001), the magnitude of which varied greatly between participants. A significant negative correlation was observed between the imbalance of activation across the hamstring muscles and the time to exhaustion ( P < 0.001); i.e., the larger the imbalance of activation across muscles, the lower the muscle endurance performance. Torque-sharing strategies between the heads of the hamstrings are individual specific and related to muscle endurance performance. Whether these individual strategies play a role in hamstring injury remains to be determined.

NEW & NOTEWORTHY The distribution of activation among the heads of the hamstring is not related to the distribution of torque-generating capacity. The torque-sharing strategies within hamstring muscles vary greatly between individuals but are similar between legs. Hamstring coordination affects endurance performance; i.e., the larger the imbalance of activation across the muscle heads, the lower the muscle endurance.

A single exercise bout and locomotor learning after stroke: physiological, behavioural, and computational outcomes

KEY POINTS

Previous work demonstrated an effect of a single high-intensity exercise bout coupled with motor practice on the retention of a newly acquired skilled arm movement, in both neurologically intact and impaired adults. In the present study, using behavioural and computational analyses we demonstrated that a single exercise bout, regardless of its intensity and timing, did not increase the retention of a novel locomotor task after stroke. Considering both present and previous work, we postulate that the benefits of exercise effect may depend on the type of motor learning (e.g. skill learning, sensorimotor adaptation) and/or task (e.g. arm accuracy-tracking task, walking).

ABSTRACT

Acute high-intensity exercise coupled with motor practice improves the retention of motor learning in neurologically intact adults. However, whether exercise could improve the retention of locomotor learning after stroke is still unknown. Here, we investigated the effect of exercise intensity and timing on the retention of a novel locomotor learning task (i.e. split-belt treadmill walking) after stroke. Thirty-seven people post stroke participated in two sessions, 24 h apart, and were allocated to active control (CON), treadmill walking (TMW), or total body exercise on a cycle ergometer (TBE). In session 1, all groups exercised for a short bout (∼5 min) at low (CON) or high (TMW and TBE) intensity and before (CON and TMW) or after (TBE) the locomotor learning task. In both sessions, the locomotor learning task was to walk on a split-belt treadmill in a 2:1 speed ratio (100% and 50% fast-comfortable walking speed) for 15 min. To test the effect of exercise on 24 h retention, we applied behavioural and computational analyses. Behavioural data showed that neither high-intensity group showed greater 24 h retention compared to CON, and computational data showed that 24 h retention was attributable to a slow learning process for sensorimotor adaptation. Our findings demonstrated that acute exercise coupled with a locomotor adaptation task, regardless of its intensity and timing, does not improve retention of the novel locomotor task after stroke. We postulate that exercise effects on motor learning may be context specific (e.g. type of motor learning and/or task) and interact with the presence of genetic variant (BDNF Val66Met).

Temporal exploration in sequential movements shapes efficient neuromuscular control

The interaction of early and deliberate practice with genetic predisposition endows experts with virtuosic motor performance. However, it has not been known whether ways of practicing shape motor virtuosity. Here, we addressed this issue by comparing the effects of rhythmic variation in motor practice on neuromuscular control of the finger movements in pianists. With the use of a novel electromyography system with miniature active electrodes, we recorded the activity of the intrinsic hand muscles of 27 pianists while they played the piano and analyzed it by using a nonnegative matrix factorization algorithm and cluster analysis. The result demonstrated that practicing a target movement sequence with various rhythms reduced muscular activity, whereas neither practicing a sequence with a single rhythm nor taking a rest without practicing changed the activity. In addition, practice with rhythmic variation changed the patterns of simultaneous activations across muscles. This alteration of muscular coordination was associated with decreased activation of muscles not only relevant to, but also irrelevant to the task performance. In contrast, piano practice improved the maximum speed of the performance, the amount of which was independent of whether rhythmic variation was present. These results suggest that temporal variation in movement sequences during practice co-optimizes both movement speed and neuromuscular efficiency, which emphasizes the significance of ways of practice in the acquisition of motor virtuosity. NEW & NOTEWORTHY A key question in motor neuroscience is whether "ways of practicing" contribute to shaping motor virtuosity. We found both attenuation of activities and alteration of coordination of the intrinsic hand muscles of pianists, specifically through practicing a movement sequence with various rhythms. The maximum speed of the finger movements was also enhanced following the practice. These results emphasize the importance of ways of practicing in facilitating multiple skills: efficiency and speed.

Rewiring the connectome: Evidence and effects

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Rewiring is a plasticity mechanism that alters connectivity between neurons.

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Evidence for rewiring has been difficult to obtain.

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New evidence indicates that local circuitry is rewired during learning.

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Harnessing rewiring offers new ways to treat psychiatric and neurological diseases.

Neuronal connections form the physical basis for communication in the brain. Recently, there has been much interest in mapping the “connectome” to understand how brain structure gives rise to brain function, and ultimately, to behaviour. These attempts to map the connectome have largely assumed that connections are stable once formed. Recent studies, however, indicate that connections in mammalian brains may undergo rewiring during learning and experience-dependent plasticity. This suggests that the connectome is more dynamic than previously thought. To what extent can neural circuitry be rewired in the healthy adult brain? The connectome has been subdivided into multiple levels of scale, from synapses and microcircuits through to long-range tracts. Here, we examine the evidence for rewiring at each level. We then consider the role played by rewiring during learning. We conclude that harnessing rewiring offers new avenues to treat brain diseases.

Neural Population Dynamics Underlying Motor Learning Transfer

Covert motor learning can sometimes transfer to overt behavior. We investigated the neural mechanism underlying transfer by constructing a two-context paradigm. Subjects performed cursor movements either overtly using arm movements, or covertly via a brain-machine interface that moves the cursor based on motor cortical activity (in lieu of arm movement). These tasks helped evaluate whether and how cortical changes resulting from "covert rehearsal" affect overt performance. We found that covert learning indeed transfers to overt performance and is accompanied by systematic population-level changes in motor preparatory activity. Current models of motor cortical function ascribe motor preparation to achieving initial conditions favorable for subsequent movement-period neural dynamics. We found that covert and overt contexts share these initial conditions, and covert rehearsal manipulates them in a manner that persists across context changes, thus facilitating overt motor learning. This transfer learning mechanism might provide new insights into other covert processes like mental rehearsal.

Reorganization of finger coordination patterns through motor exploration in individuals after stroke

BACKGROUND

Impairment of hand and finger function after stroke is common and affects the ability to perform activities of daily living. Even though many of these coordination deficits such as finger individuation have been well characterized, it is critical to understand how stroke survivors learn to explore and reorganize their finger coordination patterns for optimizing rehabilitation. In this study, I examine the use of a body-machine interface to assess how participants explore their movement repertoire, and how this changes with continued practice.

METHODS

Ten participants with chronic stroke wore a data glove and the finger joint angles were mapped on to the position of a cursor on a screen. The task of the participants was to move the cursor back and forth between two specified targets on a screen. Critically, the map between the finger movements and cursor motion was altered so that participants sometimes had to generate coordination patterns that required finger individuation. There were two phases to the experiment - an initial assessment phase on day 1, followed by a learning phase (days 2-5) where participants trained to reorganize their coordination patterns.

RESULTS

Participants showed difficulty in performing tasks which had maps that required finger individuation, and the degree to which they explored their movement repertoire was directly related to clinical tests of hand function. However, over four sessions of practice, participants were able to learn to reorganize their finger movement coordination pattern and improve their performance. Moreover, training also resulted in improvements in movement repertoire outside of the context of the specific task during free exploration.

CONCLUSIONS

Stroke survivors show deficits in movement repertoire in their paretic hand, but facilitating movement exploration during training can increase the movement repertoire. This suggests that exploration may be an important element of rehabilitation to regain optimal function.

Skill Learning and Skill Transfer Mediated by Cooperative Haptic Interaction

It is known that physical coupling between two subjects may be advantageous in joint tasks. However, little is known about how two people mutually exchange information to exploit the coupling. Therefore, we adopted a reversed, novel perspective to the standard one that focuses on the ability of physically coupled subjects to adapt to cooperative contexts that require negotiating a common plan: we investigated how training in pairs on a novel task affects the development of motor skills of each of the interacting partners. The task involved reaching movements in an unstable dynamic environment using a bilateral non-linear elastic tool that could be used bimanually or dyadically. The main result is that training with an expert leads to the greatest performance in the joint task. However, the performance in the individual test is strongly affected by the initial skill level of the partner. Moreover, practicing with a peer rather than an expert appears to be more advantageous for a naive; and motor skills can be transferred to a bimanual context, after training with an expert, only if the non-expert subject had prior experience of the dynamics of the novel task.

Uniform and Non-uniform Perturbations in Brain-Machine Interface Task Elicit Similar Neural Strategies

The neural mechanisms that take place during learning and adaptation can be directly probed with brain-machine interfaces (BMIs). We developed a BMI controlled paradigm that enabled us to enforce learning by introducing perturbations which changed the relationship between neural activity and the BMI's output. We introduced a uniform perturbation to the system, through a visuomotor rotation (VMR), and a non-uniform perturbation, through a decorrelation task. The controller in the VMR was essentially unchanged, but produced an output rotated at 30° from the neurally specified output. The controller in the decorrelation trials decoupled the activity of neurons that were highly correlated in the BMI task by selectively forcing the preferred directions of these cell pairs to be orthogonal. We report that movement errors were larger in the decorrelation task, and subjects needed more trials to restore performance back to baseline. During learning, we measured decreasing trends in preferred direction changes and cross-correlation coefficients regardless of task type. Conversely, final adaptations in neural tunings were dependent on the type controller used (VMR or decorrelation). These results hint to the similar process the neural population might engage while adapting to new tasks, and how, through a global process, the neural system can arrive to individual solutions.

Learning new gait patterns: Exploratory muscle activity during motor learning is not predicted by motor modules

The motor module hypothesis in motor control proposes that the nervous system can simplify the problem of controlling a large number of muscles in human movement by grouping muscles into a smaller number of modules. Here, we tested one prediction of the modular organization hypothesis by examining whether there is preferential exploration along these motor modules during the learning of a new gait pattern. Healthy college-aged participants learned a new gait pattern which required increased hip and knee flexion during the swing phase while walking in a lower-extremity robot (Lokomat). The new gait pattern was displayed as a foot trajectory in the sagittal plane and participants attempted to match their foot trajectory to this template. We recorded EMG from 8 lower-extremity muscles and we extracted motor modules during both baseline walking and target-tracking using non-negative matrix factorization (NMF). Results showed increased trajectory variability in the first block of learning, indicating that participants were engaged in exploratory behavior. Critically, when we examined the muscle activity during this exploratory phase, we found that the composition of motor modules changed significantly within the first few strides of attempting the new gait pattern. The lack of persistence of the motor modules under even short time scales suggests that motor modules extracted during locomotion may be more indicative of correlated muscle activity induced by the task constraints of walking, rather than reflecting a modular control strategy.

Brain-computer interfaces for dissecting cognitive processes underlying sensorimotor control

Sensorimotor control engages cognitive processes such as prediction, learning, and multisensory integration. Understanding the neural mechanisms underlying these cognitive processes with arm reaching is challenging because we currently record only a fraction of the relevant neurons, the arm has nonlinear dynamics, and multiple modalities of sensory feedback contribute to control. A brain-computer interface (BCI) is a well-defined sensorimotor loop with key simplifying advantages that address each of these challenges, while engaging similar cognitive processes. As a result, BCI is becoming recognized as a powerful tool for basic scientific studies of sensorimotor control. Here, we describe the benefits of BCI for basic scientific inquiries and review recent BCI studies that have uncovered new insights into the neural mechanisms underlying sensorimotor control.

Transfer as a function of exploration and stabilization in original practice

The identification of practice conditions that provide flexibility to perform successfully in transfer is a long-standing issue in motor learning but is still not well understood. Here we investigated the hypothesis that a search strategy that encompasses both exploration and stabilization of the perceptual-motor workspace will enhance performance in transfer. Twenty-two participants practiced a virtual projection task (120 trials on each of 3 days) and subsequently performed two transfer conditions (20 trials/condition) with different constraints in the angle to project the object. The findings revealed a quadratic relation between exploration in practice (indexed by autocorrelation and distribution of error) and subsequent performance error in transfer. The integration of exploration and stabilization of the perceptual-motor workspace enhances transfer to tasks with different constraints on the scaling of motor output.

Structure Learning in Bayesian Sensorimotor Integration

Previous studies have shown that sensorimotor processing can often be described by Bayesian learning, in particular the integration of prior and feedback information depending on its degree of reliability. Here we test the hypothesis that the integration process itself can be tuned to the statistical structure of the environment. We exposed human participants to a reaching task in a three-dimensional virtual reality environment where we could displace the visual feedback of their hand position in a two dimensional plane. When introducing statistical structure between the two dimensions of the displacement, we found that over the course of several days participants adapted their feedback integration process in order to exploit this structure for performance improvement. In control experiments we found that this adaptation process critically depended on performance feedback and could not be induced by verbal instructions. Our results suggest that structural learning is an important meta-learning component of Bayesian sensorimotor integration.

The human sensorimotor system has to process highly structured information that is affected by uncertainty and variability at all levels. Previously, it has been shown that sensorimotor processing is very efficient at extracting structure even in variable environments and it has also been shown how sensorimotor integration takes into account uncertainty when processing novel information. In particular, the latter integration process has been shown to be consistent with Bayesian theory. Here we show how the two processes of structure learning and sensorimotor integration work together in a single experiment. We find that when human participants learn a novel motor skill they not only successfully extract structural knowledge from variable data, but they also exploit this structural knowledge for near-optimal sensorimotor integration.

Computations in Sensorimotor Learning

Our cognitive abilities can only be expressed on the world through our actions. Here we review the computations underlying the way that the sensorimotor system converts both low-level sensory signals and high-level decisions into action, focusing on the behavioral evidence for the theoretical frameworks. We review recent work that determines how motor memories underlying sensorimotor learning are activated and protected from interference, the role of Bayesian decision theory in sensorimotor control including sources of suboptimality, the role of risk sensitivity in guiding action, and how rapid motor responses may underlie the robustness of the motor system to the vagaries of the world.

Structural constraints on learning in the neural network

Recent research suggests the brain can learn almost any brain-computer interface (BCI) configuration; however, contrasting behavioral evidence from structural learning theory argues that previous experience facilitates, or impedes, future learning. A study by Sadtler and colleagues (Nature 512: 423-426, 2014) used BCI to demonstrate that neural network structural characteristics constrain learning, a finding that might also provide insight into how the brain responds to and recovers after injury.

Neural constraints on learning

Motor, sensory, and cognitive learning require networks of neurons to generate new activity patterns. Because some behaviors are easier to learn than others^1,2, we wondered if some neural activity patterns are easier to generate than others. We asked whether the existing network constrains the patterns that a subset of its neurons is capable of exhibiting, and if so, what principles define the constraint. We employed a closed-loop intracortical brain-computer interface (BCI) learning paradigm in which Rhesus monkeys controlled a computer cursor by modulating neural activity patterns in primary motor cortex. Using the BCI paradigm, we could specify and alter how neural activity mapped to cursor velocity. At the start of each session, we observed the characteristic activity patterns of the recorded neural population. These patterns comprise a low-dimensional space (termed the intrinsic manifold, or IM) within the high-dimensional neural firing rate space. They presumably reflect constraints imposed by the underlying neural circuitry. We found that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the IM. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the IM. This result suggests that the existing structure of a network can shape learning. On the timescale of hours, it appears to be difficult to learn to generate neural activity patterns that are not consistent with the existing network structure. These findings offer a network-level explanation for the observation that we are more readily able to learn new skills when they are related to the skills that we already possess^3,4.