Contribution of explicit processes to reinforcement-based motor learning

Fundamental processes in sensorimotor learning: Reasoning, refinement, and retrieval

Motor learning is often viewed as a unitary process that operates outside of conscious awareness. This perspective has led to the development of sophisticated models designed to elucidate the mechanisms of implicit sensorimotor learning. In this review, we argue for a broader perspective, emphasizing the contribution of explicit strategies to sensorimotor learning tasks. Furthermore, we propose a theoretical framework for motor learning that consists of three fundamental processes: reasoning, the process of understanding action-outcome relationships; refinement, the process of optimizing sensorimotor and cognitive parameters to achieve motor goals; and retrieval, the process of inferring the context and recalling a control policy. We anticipate that this '3R' framework for understanding how complex movements are learned will open exciting avenues for future research at the intersection between cognition and action.

Motor adaptation is reduced by symbolic compared to sensory feedback

Motor adaptation - the process of reducing motor errors through feedback and practice - is an essential feature of human competence, allowing us to move accurately in dynamic and novel environments. Adaptation typically results from sensory feedback, with most learning driven by visual and proprioceptive feedback that arises with the movement. In humans, motor adaptation can also be driven by symbolic feedback. In the present study, we examine how implicit and explicit components of motor adaptation are modulated by symbolic feedback. We conducted three reaching experiments involving over 400 human participants to compare sensory and symbolic feedback using a task in which both types of learning processes could be operative (Experiment 1) or tasks in which learning was expected to be limited to only an explicit process (Experiments 2 and 3). Adaptation with symbolic feedback was dominated by explicit strategy use, with minimal evidence of implicit recalibration. Even when matched in terms of information content, adaptation to rotational and mirror reversal perturbations was slower in response to symbolic feedback compared to sensory feedback. Our results suggest that the abstract and indirect nature of symbolic feedback disrupts strategic reasoning and/or refinement, deepening our understanding of how feedback type influences the mechanisms of sensorimotor learning.

Reward actively engages both implicit and explicit components in dual force field adaptation

Motor learning occurs through multiple mechanisms, including unsupervised, supervised (error based), and reinforcement (reward based) learning. Although studies have shown that reward leads to an overall better motor adaptation, the specific processes by which reward influences adaptation are still unclear. Here, we examine how the presence of reward affects dual adaptation to novel dynamics and distinguish its influence on implicit and explicit learning. Participants adapted to two opposing force fields in an adaptation/deadaptation/error-clamp paradigm, where five levels of reward (a score and a digital face) were provided as participants reduced their lateral error. Both reward and control (no reward provided) groups simultaneously adapted to both opposing force fields, exhibiting a similar final level of adaptation, which was primarily implicit. Triple-rate models fit to the adaptation process found higher learning rates in the fast and slow processes and a slightly increased fast retention rate for the reward group. Whereas differences in the slow learning rate were only driven by implicit learning, the large difference in the fast learning rate was mainly explicit. Overall, we confirm previous work showing that reward increases learning rates, extending this to dual-adaptation experiments and demonstrating that reward influences both implicit and explicit adaptation. Specifically, we show that reward acts primarily explicitly on the fast learning rate and implicitly on the slow learning rates.NEW & NOTEWORTHY Here we show that rewarding participants' performance during dual force field adaptation primarily affects the initial rate of learning and the early timescales of adaptation, with little effect on the final adaptation level. However, reward affects both explicit and implicit components of adaptation. Whereas the learning rate of the slow process is increased implicitly, the fast learning and retention rates are increased through both implicit components and the use of explicit strategies.

Reconfigurations of cortical manifold structure during reward-based motor learning

Adaptive motor behavior depends on the coordinated activity of multiple neural systems distributed across the brain. While the role of sensorimotor cortex in motor learning has been well established, how higher-order brain systems interact with sensorimotor cortex to guide learning is less well understood. Using functional MRI, we examined human brain activity during a reward-based motor task where subjects learned to shape their hand trajectories through reinforcement feedback. We projected patterns of cortical and striatal functional connectivity onto a low-dimensional manifold space and examined how regions expanded and contracted along the manifold during learning. During early learning, we found that several sensorimotor areas in the dorsal attention network exhibited increased covariance with areas of the salience/ventral attention network and reduced covariance with areas of the default mode network (DMN). During late learning, these effects reversed, with sensorimotor areas now exhibiting increased covariance with DMN areas. However, areas in posteromedial cortex showed the opposite pattern across learning phases, with its connectivity suggesting a role in coordinating activity across different networks over time. Our results establish the neural changes that support reward-based motor learning and identify distinct transitions in the functional coupling of sensorimotor to transmodal cortex when adapting behavior.

Reinforcement Motor Learning After Cerebellar Damage Is Related to State Estimation

Recent work showed that individuals with cerebellar degeneration could leverage intact reinforcement learning (RL) to alter their movement. However, there was marked inter-individual variability in learning, and the factors underlying it were unclear. Cerebellum-dependent sensory prediction may contribute to RL in motor contexts by enhancing body state estimates, which are necessary to solve the credit-assignment problem. The objective of this study was to test the relationship between the predictive component of state estimation and RL in individuals with cerebellar degeneration. Individuals with cerebellar degeneration and neurotypical control participants completed two tasks: an RL task that required them to alter the angle of reaching movements and a state estimation task that tested the somatosensory perception of active and passive movement. The state estimation task permitted the calculation of the active benefit shown by each participant, which is thought to reflect the cerebellum-dependent predictive component of state estimation. We found that the cerebellar and control groups showed similar magnitudes of learning with reinforcement and active benefit on average, but there was substantial variability across individuals. Using multiple regression, we assessed potential predictors of RL. Our analysis included active benefit, somatosensory acuity, clinical ataxia severity, movement variability, movement speed, and age. We found a significant relationship in which greater active benefit predicted better learning with reinforcement in the cerebellar, but not the control group. No other variables showed significant relationships with learning. Overall, our results support the hypothesis that the integrity of sensory prediction is a strong predictor of RL after cerebellar damage.

Simulated operant reflex conditioning environment reveals effects of feedback parameters

Operant conditioning of neural activation has been researched for decades in humans and animals. Many theories suggest two parallel learning processes, implicit and explicit. The degree to which feedback affects these processes individually remains to be fully understood and may contribute to a large percentage of non-learners. Our goal is to determine the explicit decision-making processes in response to feedback representing an operant conditioning environment. We developed a simulated operant conditioning environment based on a feedback model of spinal reflex excitability, one of the simplest forms of neural operant conditioning. We isolated the perception of the feedback signal from self-regulation of an explicit unskilled visuomotor task, enabling us to quantitatively examine feedback strategy. Our hypothesis was that feedback type, biological variability, and reward threshold affect operant conditioning performance and operant strategy. Healthy individuals (N = 41) were instructed to play a web application game using keyboard inputs to rotate a virtual knob representative of an operant strategy. The goal was to align the knob with a hidden target. Participants were asked to "down-condition" the amplitude of the virtual feedback signal, which was achieved by placing the knob as close as possible to the hidden target. We varied feedback type (knowledge of performance, knowledge of results), biological variability (low, high), and reward threshold (easy, moderate, difficult) in a factorial design. Parameters were extracted from real operant conditioning data. Our main outcomes were the feedback signal amplitude (performance) and the mean change in dial position (operant strategy). We observed that performance was modulated by variability, while operant strategy was modulated by feedback type. These results show complex relations between fundamental feedback parameters and provide the principles for optimizing neural operant conditioning for non-responders.

Punishment Leads to Greater Sensorimotor Learning But Less Movement Variability Compared to Reward

When a musician practices a new song, hitting a correct note sounds pleasant while striking an incorrect note sounds unpleasant. Such reward and punishment feedback has been shown to differentially influence the ability to learn a new motor skill. Recent work has suggested that punishment leads to greater movement variability, which causes greater exploration and faster learning. To further test this idea, we collected 102 participants over two experiments. Unlike previous work, in Experiment 1 we found that punishment did not lead to faster learning compared to reward (n = 68), but did lead to a greater extent of learning. Surprisingly, we also found evidence to suggest that punishment led to less movement variability, which was related to the extent of learning. We then designed a second experiment that did not involve adaptation, allowing us to further isolate the influence of punishment feedback on movement variability. In Experiment 2, we again found that punishment led to significantly less movement variability compared to reward (n = 34). Collectively our results suggest that punishment feedback leads to less movement variability. Future work should investigate whether punishment feedback leads to a greater knowledge of movement variability and or increases the sensitivity of updating motor actions.

Reinforcement Learning during Locomotion

When learning a new motor skill, people often must use trial and error to discover which movement is best. In the reinforcement learning framework, this concept is known as exploration and has been linked to increased movement variability in motor tasks. For locomotor tasks, however, increased variability decreases upright stability. As such, exploration during gait may jeopardize balance and safety, making reinforcement learning less effective. Therefore, we set out to determine if humans could acquire and retain a novel locomotor pattern using reinforcement learning alone. Young healthy male and female participants walked on a treadmill and were provided with binary reward feedback (indicated by a green checkmark on the screen) that was tied to a fixed monetary bonus, to learn a novel stepping pattern. We also recruited a comparison group who walked with the same novel stepping pattern but did so by correcting for target error, induced by providing real-time veridical visual feedback of steps and a target. In two experiments, we compared learning, motor variability, and two forms of motor memories between the groups. We found that individuals in the binary reward group did, in fact, acquire the new walking pattern by exploring (increasing motor variability). Additionally, while reinforcement learning did not increase implicit motor memories, it resulted in more accurate explicit motor memories compared with the target error group. Overall, these results demonstrate that humans can acquire new walking patterns with reinforcement learning and retain much of the learning over 24 h.

NSF DARE-transforming modeling in neurorehabilitation: a patient-in-the-loop framework

In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.

Dopamine Increases Accuracy and Lengthens Deliberation Time in Explicit Motor Skill Learning

Although animal research implicates a central role for dopamine in motor skill learning, a direct causal link has yet to be established in neurotypical humans. Here, we tested if a pharmacological manipulation of dopamine alters motor learning, using a paradigm which engaged explicit, goal-directed strategies. Participants (27 females; 11 males; aged 18-29 years) first consumed either 100 mg of levodopa (n = 19), a dopamine precursor that increases dopamine availability, or placebo (n = 19). Then, during training, participants learnt the explicit strategy of aiming away from presented targets by instructed angles of varying sizes. Targets jumped mid-movement by the instructed aiming angle. Task success was thus contingent upon aiming accuracy and not speed. The effect of the dopamine manipulations on skill learning was assessed during training and after an overnight follow-up. Increasing dopamine availability at training improved aiming accuracy and lengthened reaction times, particularly for larger, more difficult aiming angles, both at training and, importantly, at follow-up, despite prominent session-by-session performance improvements in both accuracy and speed. Exogenous dopamine thus seems to result in a learnt, persistent propensity to better adhere to task goals. Results support the proposal that dopamine is important in engagement of instrumental motivation to optimize adherence to task goals, particularly when learning to execute goal-directed strategies in motor skill learning.

Explicit learning based on reward prediction error facilitates agile motor adaptations

Error based motor learning can be driven by both sensory prediction error and reward prediction error. Learning based on sensory prediction error is termed sensorimotor adaptation, while learning based on reward prediction error is termed reward learning. To investigate the characteristics and differences between sensorimotor adaptation and reward learning, we adapted a visuomotor paradigm where subjects performed arm movements while presented with either the sensory prediction error, signed end-point error, or binary reward. Before each trial, perturbation indicators in the form of visual cues were presented to inform the subjects of the presence and direction of the perturbation. To analyse the interconnection between sensorimotor adaptation and reward learning, we designed a computational model that distinguishes between the two prediction errors. Our results indicate that subjects adapted to novel perturbations irrespective of the type of prediction error they received during learning, and they converged towards the same movement patterns. Sensorimotor adaptations led to a pronounced aftereffect, while adaptation based on reward consequences produced smaller aftereffects suggesting that reward learning does not alter the internal model to the same degree as sensorimotor adaptation. Even though all subjects had learned to counteract two different perturbations separately, only those who relied on explicit learning using reward prediction error could timely adapt to the randomly changing perturbation. The results from the computational model suggest that sensorimotor and reward learning operate through distinct adaptation processes and that only sensorimotor adaptation changes the internal model, whereas reward learning employs explicit strategies that do not result in aftereffects. Additionally, we demonstrate that when humans learn motor tasks, they utilize both learning processes to successfully adapt to the new environments.

Differential effects of reward and punishment on reinforcement-based motor learning and generalization

Reward and punishment have long been recognized as potent modulators of human behavior. Although reinforcement learning is a significant motor learning process, the exact mechanisms underlying how the brain learns movements through reward and punishment are not yet fully understood. Beyond the memory of specific examples, investigating the ability to generalize to new situations offers a better understanding of motor learning. This study hypothesizes that reward and punishment engage qualitatively different motivational systems with different neurochemical and neuroanatomical substrates, which would have differential effects on reinforcement-based motor learning and generalization. To test this hypothesis, two groups of participants learn a motor task in one direction and then relearn the same task in a new direction, receiving only performance-based reward or punishment score feedback. Our findings support our hypothesis, showing that reward led to slower learning but promoted generalization. On the other hand, punishment led to faster learning but impaired generalization. These behavioral differences may be due to different tendencies of movement variability in each group. The punishment group tended to explore more actively than the reward group during the initial learning phase, possibly due to loss aversion. In contrast, the reward group tended to explore more actively than the initial learning phase during the generalization test phase, seemingly recalling the strategy that led to the reward. These results suggest that reward and punishment may engage different neural mechanisms during reinforcement-based motor learning and generalization, with important implications for practical applications such as sports training and motor rehabilitation.NEW & NOTEWORTHY Although reinforcement learning is a significant motor learning process, the mechanisms underlying how the brain learns movements through reward and punishment are not fully understood. We modified a well-established motor adaptation task and used savings (faster relearning) to measure generalization. We found reward led to slower learning but promoted generalization, whereas punishment led to faster learning but impaired generalization, suggesting that reward and punishment may engage different neural mechanisms during reinforcement-based motor learning and generalization.

Reinforcement-based processes actively regulate motor exploration along redundant solution manifolds

From a baby's babbling to a songbird practising a new tune, exploration is critical to motor learning. A hallmark of exploration is the emergence of random walk behaviour along solution manifolds, where successive motor actions are not independent but rather become serially dependent. Such exploratory random walk behaviour is ubiquitous across species' neural firing, gait patterns and reaching behaviour. The past work has suggested that exploratory random walk behaviour arises from an accumulation of movement variability and a lack of error-based corrections. Here, we test a fundamentally different idea-that reinforcement-based processes regulate random walk behaviour to promote continual motor exploration to maximize success. Across three human reaching experiments, we manipulated the size of both the visually displayed target and an unseen reward zone, as well as the probability of reinforcement feedback. Our empirical and modelling results parsimoniously support the notion that exploratory random walk behaviour emerges by utilizing knowledge of movement variability to update intended reach aim towards recently reinforced motor actions. This mechanism leads to active and continuous exploration of the solution manifold, currently thought by prominent theories to arise passively. The ability to continually explore muscle, joint and task redundant solution manifolds is beneficial while acting in uncertain environments, during motor development or when recovering from a neurological disorder to discover and learn new motor actions.

Implicit reward-based motor learning

Binary feedback, providing information solely about task success or failure, can be sufficient to drive motor learning. While binary feedback can induce explicit adjustments in movement strategy, it remains unclear if this type of feedback also induces implicit learning. We examined this question in a center-out reaching task by gradually moving an invisible reward zone away from a visual target to a final rotation of 7.5° or 25° in a between-group design. Participants received binary feedback, indicating if the movement intersected the reward zone. By the end of the training, both groups modified their reach angle by about 95% of the rotation. We quantified implicit learning by measuring performance in a subsequent no-feedback aftereffect phase, in which participants were told to forgo any adopted movement strategies and reach directly to the visual target. The results showed a small, but robust (2-3°) aftereffect in both groups, highlighting that binary feedback elicits implicit learning. Notably, for both groups, reaches to two flanking generalization targets were biased in the same direction as the aftereffect. This pattern is at odds with the hypothesis that implicit learning is a form of use-dependent learning. Rather, the results suggest that binary feedback can be sufficient to recalibrate a sensorimotor map.

Exploring the role of task success in implicit motor adaptation

Although implicit motor adaptation is driven by sensory-prediction errors (SPEs), recent work has shown that task success modulates this process. Task success has typically been defined as hitting a target, which signifies the goal of the movement. Visuomotor adaptation tasks are uniquely situated to experimentally manipulate task success independently from SPE by changing the target size or the location of the target. These two, distinct manipulations may influence implicit motor adaptation in different ways, so, over four experiments, we sought to probe the efficacy of each manipulation. We found that changes in target size, which caused the target to fully envelop the cursor, only affected implicit adaptation for a narrow range of SPE sizes, while jumping the target to overlap with the cursor more reliably and robustly affected implicit adaptation. Taken together, our data indicate that, while task success exerts a small effect on implicit adaptation, these effects are susceptible to methodological variations. Future investigations of the effect of task success on implicit adaptation could benefit from employing target jump manipulations instead of target size manipulations.NEW & NOTEWORTHY Recent work has suggested that task success, namely, hitting a target, influences implicit motor adaptation. Here, we observed that implicit adaptation is modulated by target jump manipulations, where the target abruptly "jumps" to meet the cursor; however, implicit adaptation was only weakly modulated by target size manipulations, where a static target either envelops or excludes the cursor. We discuss how these manipulations may exert their effects through different mechanisms.

A novel video game for remote studies of motor adaptation in children

Here we designed a motor adaptation video game that could be played remotely (at home) through a web browser. This required the child to adapt to a visuomotor rotation between their hand movement and a ball displayed in the game. The task had several novel features, specifically designed to allow the study of the developmental trajectory of adaptation across a wide range of ages. We test the concurrent validity by comparing children's performance on our remote task to the same task performed in the laboratory. All participants remained engaged and completed the task. We quantified feedforward and feedback control during this task. Feedforward control, a key measure of adaptation, was similar at home and in the laboratory. All children could successfully use feedback control to guide the ball to a target. Traditionally, motor learning studies are performed in a laboratory to obtain high quality kinematic data. However, here we demonstrate concurrent validity of kinematic behavior when conducted at home. Our online platform provides the flexibility and ease of collecting data that will enable future studies with large sample sizes, longitudinal experiments, and the study of children with rare diseases.

Implicit reward-based motor learning

Binary feedback, providing information solely about task success or failure, can be sufficient to drive motor learning. While binary feedback can induce explicit adjustments in movement strategy, it remains unclear if this type of feedback also induce implicit learning. We examined this question in a center-out reaching task by gradually moving an invisible reward zone away from a visual target to a final rotation of 7.5° or 25° in a between-group design. Participants received binary feedback, indicating if the movement intersected the reward zone. By the end of the training, both groups modified their reach angle by about 95% of the rotation. We quantified implicit learning by measuring performance in a subsequent no-feedback aftereffect phase, in which participants were told to forgo any adopted movement strategies and reach directly to the visual target. The results showed a small, but robust (2-3°) aftereffect in both groups, highlighting that binary feedback elicits implicit learning. Notably, for both groups, reaches to two flanking generalization targets were biased in the same direction as the aftereffect. This pattern is at odds with the hypothesis that implicit learning is a form of use-dependent learning. Rather, the results suggest that binary feedback can be sufficient to recalibrate a sensorimotor map.

Exploring the role of task success in implicit motor adaptation

We learn to improve our motor skills using different forms of feedback: sensory-prediction error, task success, and reward/punishment. While implicit motor adaptation is driven by sensory-prediction errors, recent work has shown that task success modulates this process. Task success is often confounded with reward, so we sought to determine if the effects of these two signals on adaptation can be dissociated. To address this question, we conducted five experiments that isolated implicit learning using error-clamp visuomotor reach adaptation paradigms. Task success was manipulated by changing the size and position of the target relative to the cursor providing visual feedback, and reward expectation was established using monetary cues and auditory feedback. We found that neither monetary cues nor auditory feedback affected implicit adaptation, suggesting that task success influences implicit adaptation via mechanisms distinct from conventional reward-related processes. Additionally, we found that changes in target size, which caused the target to either exclude or fully envelop the cursor, only affected implicit adaptation for a narrow range of error sizes, while jumping the target to overlap with the cursor more reliably and robustly affected implicit adaptation. Taken together, our data indicate that, while task success exerts a small effect on implicit adaptation, these effects are susceptible to methodological variations and unlikely to be mediated by reward.

Trial-to-trial Variability and Cortical Processing Depend on Recent Outcomes During Human Reinforcement Motor Learning

The history of our actions and their outcomes represent important information, informing choices and efficiently guiding future behavior. While unsuccessful (S-) outcomes are expected to lead to more explorative motor states and increased behavioral variability, successful (S+) outcomes are expected to reinforce the use of the previous action. Here, we show that humans attribute different values to previous actions during reinforcement motor learning when they experience S- compared to S+ outcomes. Behavioral variability after an S- outcome is influenced more by the previous outcome than after S+ outcomes. Using electroencephalography, we show that theta band oscillations of the prefrontal cortex are most prominent during changes in two consecutive outcomes, potentially reflecting the need for enhanced cognitive control. Our results suggest that S+ experiences 'overwrite' previous motor states to a greater extent than S- experiences and that modulations in neural oscillations in the prefrontal cortex play a potential role in encoding changes in movement variability state during reinforcement motor learning.

The dissociable effects of reward on sequential motor behavior

Reward has consistently been shown to enhance motor behavior; however, its beneficial effects appear to be largely unspecific. For example, reward is associated with both rapid and training-dependent improvements in performance, with a mechanistic account of these effects currently lacking. Here we tested the hypothesis that these distinct reward-based improvements are driven by dissociable reward types: monetary incentive and performance feedback. Whereas performance feedback provides information on how well a motor task has been completed (knowledge of performance), monetary incentive increases the motivation to perform optimally without providing a performance-based learning signal. Experiment 1 showed that groups who received monetary incentive rapidly improved movement times (MTs), using a novel sequential reaching task. In contrast, only groups with correct performance-based feedback showed learning-related improvements. Importantly, pairing both maximized MT performance gains and accelerated movement fusion. Fusion describes an optimization process during which neighboring sequential movements blend together to form singular actions. Results from experiment 2 served as a replication and showed that fusion led to enhanced performance speed while also improving movement efficiency through increased smoothness. Finally, experiment 3 showed that these improvements in performance persist for 24 h even without reward availability. This highlights the dissociable impact of monetary incentive and performance feedback, with their combination maximizing performance gains and leading to stable improvements in the speed and efficiency of sequential actions.NEW & NOTEWORTHY Our work provides a mechanistic framework for how reward influences motor behavior. Specifically, we show that rapid improvements in speed and accuracy are driven by reward presented in the form of money, whereas knowledge of performance through performance feedback leads to training-based improvements. Importantly, combining both maximized performance gains and led to improvements in movement quality through fusion, which describes an optimization process during which sequential movements blend into a single action.

Reach adaption to a visuomotor gain with terminal error feedback involves reinforcement learning

Motor adaptation can be achieved through error-based learning, driven by sensory prediction errors, or reinforcement learning, driven by reward prediction errors. Recent work on visuomotor adaptation has shown that reinforcement learning leads to more persistent adaptation when visual feedback is removed, compared to error-based learning in which continuous visual feedback of the movement is provided. However, there is evidence that error-based learning with terminal visual feedback of the movement (provided at the end of movement) may be driven by both sensory and reward prediction errors. Here we examined the influence of feedback on learning using a visuomotor adaptation task in which participants moved a cursor to a single target while the gain between hand and cursor movement displacement was gradually altered. Different groups received either continuous error feedback (EC), terminal error feedback (ET), or binary reinforcement feedback (success/fail) at the end of the movement (R). Following adaptation we tested generalization to targets located in different directions and found that generalization in the ET group was intermediate between the EC and R groups. We then examined the persistence of adaptation in the EC and ET groups when the cursor was extinguished and only binary reward feedback was provided. Whereas performance was maintained in the ET group, it quickly deteriorated in the EC group. These results suggest that terminal error feedback leads to a more robust form of learning than continuous error feedback. In addition our findings are consistent with the view that error-based learning with terminal feedback involves both error-based and reinforcement learning.

Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks

Humans learn movements naturally, but it takes a lot of time and training to achieve expert performance in motor skills. In this review, we show how modern technologies can support people in learning new motor skills. First, we introduce important concepts in motor control, motor learning and motor skill learning. We also give an overview about the rapid expansion of machine learning algorithms and sensor technologies for human motion analysis. The integration between motor learning principles, machine learning algorithms and recent sensor technologies has the potential to develop AI-guided assistance systems for motor skill training. We give our perspective on this integration of different fields to transition from motor learning research in laboratory settings to real world environments and real world motor tasks and propose a stepwise approach to facilitate this transition.

Reward and plasticity: Implications for neurorehabilitation

Neuroplasticity follows nervous system injury in the presence or absence of rehabilitative treatments. Rehabilitative interventions can be used to modulate adaptive neuroplasticity, reducing motor impairment and improving activities of daily living in patients with brain lesions. Learning principles guide some rehabilitative interventions. While basic science research has shown that reward combined with training enhances learning, this principle has been only recently explored in the context of neurorehabilitation. Commonly used reinforcers may be more or less rewarding depending on the individual or the context in which the task is performed. Studies in healthy humans showed that both reward and punishment can enhance within-session motor performance; but reward, and not punishment, improves consolidation and retention of motor skills. On the other hand, neurorehabilitative training after brain lesions involves complex tasks (e.g., walking and activities of daily living). The contribution of reward to neurorehabilitation is incompletely understood. Here, we discuss recent research on the role of reward in neurorehabilitation and the needed directions of future research.

Mechanisms of Human Motor Learning Do Not Function Independently

Human motor learning is governed by a suite of interacting mechanisms each one of which modifies behavior in distinct ways and rely on different neural circuits. In recent years, much attention has been given to one type of motor learning, called motor adaptation. Here, the field has generally focused on the interactions of three mechanisms: sensory prediction error SPE-driven, explicit (strategy-based), and reinforcement learning. Studies of these mechanisms have largely treated them as modular, aiming to model how the outputs of each are combined in the production of overt behavior. However, when examined closely the results of some studies also suggest the existence of additional interactions between the sub-components of each learning mechanism. In this perspective, we propose that these sub-component interactions represent a critical means through which different motor learning mechanisms are combined to produce movement; understanding such interactions is critical to advancing our knowledge of how humans learn new behaviors. We review current literature studying interactions between SPE-driven, explicit, and reinforcement mechanisms of motor learning. We then present evidence of sub-component interactions between SPE-driven and reinforcement learning as well as between SPE-driven and explicit learning from studies of people with cerebellar degeneration. Finally, we discuss the implications of interactions between learning mechanism sub-components for future research in human motor learning.

Fluid Cognition Relates to Locomotor Switching in Neurotypical Adults, Not Individuals After Stroke

BACKGROUND AND PURPOSE

The ability to switch between walking patterns (ie, locomotor switching) is vital for successful community navigation and may be impacted by poststroke impairments. Thus, the purpose of this work was to examine locomotor switching and the relationship between locomotor switching and fluid cognition in individuals after stroke compared with neurotypical adults.

METHODS

Twenty-nine individuals more than 6 months after stroke and 18 neurotypical adults participated in a 2-day study. On day 1, participants were taught a new walking pattern on the treadmill and then locomotor switching was assessed by instructing participants to switch between the new walking pattern and their usual walking pattern. The change between these 2 patterns was calculated as the switching index. On day 2, the NIH Toolbox Cognition Battery was administered to obtain the Fluid Cognition Composite Score (FCCS), which reflected fluid cognition. The switching index was compared between groups using an analysis of covariance, and the relationship between locomotor switching and fluid cognition was assessed with regression.

RESULTS

Individuals after stroke had significantly lower switching indexes compared with neurotypical adults (P = 0.03). The regression showed a significant interaction between group and FCCS (P = 0.002), with the FCCS predicting the switching index in neurotypical adults but not in individuals after stroke.

DISCUSSION AND CONCLUSIONS

Individuals after stroke appear to have deficits in locomotor switching compared with neurotypical adults. The relationship between fluid cognition and locomotor switching was significant in neurotypical adults but not in individuals after stroke. Future work to understand the relationship between specific cognitive domains and locomotor switching is needed (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A361).

Human Variation in Error-Based and Reinforcement Motor Learning Is Associated With Entorhinal Volume

Error-based and reward-based processes are critical for motor learning and are thought to be mediated via distinct neural pathways. However, recent behavioral work in humans suggests that both learning processes can be bolstered by the use of cognitive strategies, which may mediate individual differences in motor learning ability. It has been speculated that medial temporal lobe regions, which have been shown to support motor sequence learning, also support the use of cognitive strategies in error-based and reinforcement motor learning. However, direct evidence in support of this idea remains sparse. Here we first show that better overall learning during error-based visuomotor adaptation is associated with better overall learning during the reward-based shaping of reaching movements. Given the cognitive contribution to learning in both of these tasks, these results support the notion that strategic processes, associated with better performance, drive intersubject variation in both error-based and reinforcement motor learning. Furthermore, we show that entorhinal cortex volume is larger in better learning individuals-characterized across both motor learning tasks-compared with their poorer learning counterparts. These results suggest that individual differences in learning performance during error and reinforcement learning are related to neuroanatomical differences in entorhinal cortex.

How Working Memory and Reinforcement Learning Are Intertwined: A Cognitive, Neural, and Computational Perspective

Reinforcement learning and working memory are two core processes of human cognition and are often considered cognitively, neuroscientifically, and algorithmically distinct. Here, we show that the brain networks that support them actually overlap significantly and that they are less distinct cognitive processes than often assumed. We review literature demonstrating the benefits of considering each process to explain properties of the other and highlight recent work investigating their more complex interactions. We discuss how future research in both computational and cognitive sciences can benefit from one another, suggesting that a key missing piece for artificial agents to learn to behave with more human-like efficiency is taking working memory's role in learning seriously. This review highlights the risks of neglecting the interplay between different processes when studying human behavior (in particular when considering individual differences). We emphasize the importance of investigating these dynamics to build a comprehensive understanding of human cognition.

Clustering analysis of movement kinematics in reinforcement learning

Reinforcement learning has been used as an experimental model of motor skill acquisition, where at times movements are successful and thus reinforced. One fundamental problem is to understand how humans select exploration over exploitation during learning. The decision could be influenced by factors such as task demands and reward availability. In this study, we applied a clustering algorithm to examine how a change in the accuracy requirements of a task affected the choice of exploration over exploitation. Participants made reaching movements to an unseen target using a planar robot arm and received reward after each successful movement. For one group of participants, the width of the hidden target decreased after every other training block. For a second group, it remained constant. The clustering algorithm was applied to the kinematic data to characterize motor learning on a trial-to-trial basis as a sequence of movements, each belonging to one of the identified clusters. By the end of learning, movement trajectories across all participants converged primarily to a single cluster with the greatest number of successful trials. Within this analysis framework, we defined exploration and exploitation as types of behavior in which two successive trajectories belong to different or similar clusters, respectively. The frequency of each mode of behavior was evaluated over the course of learning. It was found that by reducing the target width, participants used a greater variety of different clusters and displayed more exploration than exploitation. Excessive exploration relative to exploitation was found to be detrimental to subsequent motor learning.

NEW & NOTEWORTHY The choice of exploration versus exploitation is a fundamental problem in learning new motor skills through reinforcement. In this study, we employed a data-driven approach to characterize movements on a trial-by-trial basis with an unsupervised clustering algorithm. Using this technique, we found that changes in task demands and, in particular, in the required accuracy of movements, influenced the ratio of exploration to exploitation. This analysis framework provides an attractive tool to investigate mechanisms of explorative and exploitative behavior while studying motor learning.

Cerebellar Dysfunction in Multiple Sclerosis: Considerations for Research and Rehabilitation Therapy

Introduction. Cerebellar pathology is common among persons with multiple sclerosis (PwMS). The cerebellum is well recognized for its role in motor control and motor learning and cerebellar pathology in multiple sclerosis is associated with enhanced motor impairment and disability progression. The Problem. To mitigate motor disability progression, PwMS are commonly prescribed exercise and task-specific rehabilitation training. Yet, whether cerebellar dysfunction differentially affects rehabilitation outcomes in this population remains unknown. Furthermore, we lack rehabilitation interventions targeting cerebellar dysfunction. The Solution. Here, we summarize the current understanding of the impact of cerebellar dysfunction on motor control, motor training, and rehabilitation in persons with multiple sclerosis. Recommendations. Additionally, we highlight critical knowledge gaps and propose that these guide future research studying cerebellar dysfunction in persons with multiple sclerosis.

Updates in Motor Learning: Implications for Physical Therapist Practice and Education

Over the past 3 decades, the volume of human motor learning research has grown enormously. As such, the understanding of motor learning (ie, sustained change in motor behavior) has evolved. It has been learned that there are multiple mechanisms through which motor learning occurs, each with distinctive features. These mechanisms include use-dependent, instructive, reinforcement, and sensorimotor adaptation-based motor learning. It is now understood that these different motor learning mechanisms contribute in parallel or in isolation to drive desired changes in movement, and each mechanism is thought to be governed by distinct neural substrates. This expanded understanding of motor learning mechanisms has important implications for physical therapy. It has the potential to facilitate the development of new, more precise treatment approaches that physical therapists can leverage to improve human movement. This Perspective describes scientific advancements related to human motor learning mechanisms and discusses the practical implications of this work for physical therapist practice and education.

Punishments and rewards both modestly impair visuomotor memory retention

While the effects of rewards on memory appear well documented, the effects of punishments remain uncertain. Based on neuroimaging data, this study tested the hypothesis that, as compared to a neutral condition, a context allowing successful punishment avoidance would enhance memory to a similar extent as rewards. In a fully within-subject and counter-balanced design, participants (n = 18) took part in 3 distinct learning sessions during which the delivery of performance-contingent monetary punishments and rewards was manipulated. Specifically, participants had to reach towards visual targets while compensating for a gradually introduced visual deviation. Accuracy at achieving targets was either punished (Hit: "+0$"; Miss: "-0.5$), rewarded (Hit: "+0.5$"; Miss: "-0$"), or associated with neutral binary feedback (Hit: "Hit"; Miss: "Miss"). Retention was assessed through reach aftereffects both immediately and 24 h after initial acquisition. The results disconfirmed the hypothesis by showing that the punishment and reward learning sessions both impaired retention as compared to the neutral session, suggesting that both types of incentives similarly impaired memory formation and consolidation. Two alternative but complementary interpretations are discussed. One interpretation is that the presence of punishments and rewards induced a negative learning context, which - based on neurobiological data - could have been sufficient to interfere with memory formation and consolidation. Another interpretation is that punishments and rewards emphasized the disrupting effects of target hits on implicit learning processes, therefore yielding retention impairments. Altogether, these results suggest that incentives can have counter-productive effects on memory.

Unique behavioral strategies in visuomotor learning: Hope for the non-learner

The existence of individual differences in motor learning capability is well known but the behaviors or strategies that contribute to this variability have been vastly understudied. What performance characteristics distinguish an expert level performer from individuals who experience little to no success, those labeled non-learners? We designed a rule-based visuomotor task which requires identification (discovery) and then exploitation of specific explicit and implicit task components that requires a specific movement pattern, the task rule, for goal achievement. When participants first attempt the task, they are informed about the goal, but are naïve to the task rule. Therefore, the purpose of this experiment is to determine how acquisition of both implicit and explicit task components, the inherent elements of the task rule, reveals differing strategies associated with performance and task success. We test the hypothesis that an examination of performance will reveal sub-groups with varying levels of success. Further, for each subgroup, we expect to find a unique relationship between visual Time-in-Target feedback (a measure of success) and subsequent updating of each task component. Out of 32 non-disabled adults, we identified three distinct sub-groups: (Low Performer/Non-Learner (LP, N = 9), Moderate Performer (MP, N = 12) and High Performer (HP, N = 11)). A quantitative analysis of behavioral patterns reveals three findings: First, the LP sub-group demonstrated significantly lower task success which was associated with difficulty identifying the explicit component of the task. Second, the HP sub-group acquired the two task components in parallel over practice. Third, when both explicit and implicit component performance is plotted across sub-groups, a task component continuum emerges that seamlessly progresses from low to moderate to high performer groups. An exploratory analysis reveals that self-reported level of prior lifetime accumulation of video game and physical activity experience is a significant predictor of individual task performance (R² = 0.50). In summary, what appears to be a key distinction between varying levels of human rule-based motor learning is the process by which feedback is used to update performance of inherent elements of the task rule. Evidence of a performance continuum and limited prior experience suggests that Low Performer/Non-Learners are generally inexperienced with these kinds of tasks, although the role of genetics and other innate learning capabilities in visuomotor learning is still largely unknown. These findings provoke new research directions toward probing the differential performance strategies associated with expertise and the development of interventions aimed to convert non-learners into learners.

Pitfalls in quantifying exploration in reward-based motor learning and how to avoid them

When learning a movement based on binary success information, one is more variable following failure than following success. Theoretically, the additional variability post-failure might reflect exploration of possibilities to obtain success. When average behavior is changing (as in learning), variability can be estimated from differences between subsequent movements. Can one estimate exploration reliably from such trial-to-trial changes when studying reward-based motor learning? To answer this question, we tried to reconstruct the exploration underlying learning as described by four existing reward-based motor learning models. We simulated learning for various learner and task characteristics. If we simply determined the additional change post-failure, estimates of exploration were sensitive to learner and task characteristics. We identified two pitfalls in quantifying exploration based on trial-to-trial changes. Firstly, performance-dependent feedback can cause correlated samples of motor noise and exploration on successful trials, which biases exploration estimates. Secondly, the trial relative to which trial-to-trial change is calculated may also contain exploration, which causes underestimation. As a solution, we developed the additional trial-to-trial change (ATTC) method. By moving the reference trial one trial back and subtracting trial-to-trial changes following specific sequences of trial outcomes, exploration can be estimated reliably for the three models that explore based on the outcome of only the previous trial. Since ATTC estimates are based on a selection of trial sequences, this method requires many trials. In conclusion, if exploration is a binary function of previous trial outcome, the ATTC method allows for a model-free quantification of exploration.

Between Limb Muscle Co-activation Patterns in the Paretic Arm During Non-paretic Arm Tasks in Hemiparetic Cerebral Palsy

Tasks of daily life require the independent use of the arms and hands. Individuals with hemiparetic cerebral palsy (HCP) often experience difficulty with fine motor tasks demonstrating mirrored movements between the arms. In this study, bilateral muscle activations were quantified during single arm isometric maximum efforts and submaximal reaching tasks. The magnitude and direction of mirrored activation was examined in 14 individuals with HCP and 9 age-matched controls. Participants generated maximum voluntary torques (MVTs) in five different directions and completed ballistic reaches while producing up to 80% of shoulder abduction MVT. Electromyography (EMG) signals were recorded from six upper extremity muscles bilaterally. Participants with HCP demonstrated more mirrored activation when volitionally contracting the non-paretic (NP) arm than the paretic arm (F = 83.543, p < 0.001) in isometric efforts. Increased EMG activation during reach acceleration resulted in a larger increase in rest arm co-activation when reaching with the NP arm compared to the paretic arm in the HCP group (t = 8.425, p < 0.001). Mirrored activation is more pronounced when driving the NP arm and scales with effort level. This directionality of mirroring is indicative of the use of ipsilaterally terminating projections of the corticospinal tract (CST) originating in the non-lesioned hemisphere. Peripheral measures of muscle activation provide insight into the descending pathways available for control of the upper extremity after early unilateral brain injury.

Adaptive control of movement deceleration during saccades

As you read this text, your eyes make saccades that guide your fovea from one word to the next. Accuracy of these movements require the brain to monitor and learn from visual errors. A current model suggests that learning is supported by two different adaptive processes, one fast (high error sensitivity, low retention), and the other slow (low error sensitivity, high retention). Here, we searched for signatures of these hypothesized processes and found that following experience of a visual error, there was an adaptive change in the motor commands of the subsequent saccade. Surprisingly, this adaptation was not uniformly expressed throughout the movement. Rather, after experience of a single error, the adaptive response in the subsequent trial was limited to the deceleration period. After repeated exposure to the same error, the acceleration period commands also adapted, and exhibited resistance to forgetting during set-breaks. In contrast, the deceleration period commands adapted more rapidly, but suffered from poor retention during these same breaks. State-space models suggested that acceleration and deceleration periods were supported by a shared adaptive state which re-aimed the saccade, as well as two separate processes which resembled a two-state model: one that learned slowly and contributed primarily via acceleration period commands, and another that learned rapidly but contributed primarily via deceleration period commands.

Measures of explicit and implicit in motor learning: what we know and what we don't

Adaptation tasks are a key tool in characterizing the contribution of explicit and implicit processes to sensorimotor learning. However, different assumptions and ideas underlie methods used to measure these processes, leading to inconsistencies between studies. For instance, it is still unclear explicit and implicit combine additively. Cognitive studies of explicit and implicit processes show how non-additivity and bias in measurement can distort results. We argue that to understand explicit and implicit processes in visuomotor adaptation, we need a stronger characterization of the phenomenology and a richer set of models to test it on.

De novo learning versus adaptation of continuous control in a manual tracking task

How do people learn to perform tasks that require continuous adjustments of motor output, like riding a bicycle? People rely heavily on cognitive strategies when learning discrete movement tasks, but such time-consuming strategies are infeasible in continuous control tasks that demand rapid responses to ongoing sensory feedback. To understand how people can learn to perform such tasks without the benefit of cognitive strategies, we imposed a rotation/mirror reversal of visual feedback while participants performed a continuous tracking task. We analyzed behavior using a system identification approach, which revealed two qualitatively different components of learning: adaptation of a baseline controller and formation of a new, task-specific continuous controller. These components exhibited different signatures in the frequency domain and were differentially engaged under the rotation/mirror reversal. Our results demonstrate that people can rapidly build a new continuous controller de novo and can simultaneously deploy this process with adaptation of an existing controller.

Null effects of levodopa on reward- and error-based motor adaptation, savings, and anterograde interference

Dopamine signaling is thought to mediate reward-based learning. We tested for a role of dopamine in motor adaptation by administering the dopamine precursor levodopa to healthy participants in two experiments involving reaching movements. Levodopa has been shown to impair reward-based learning in cognitive tasks. Thus, we hypothesized that levodopa would selectively impair aspects of motor adaptation that depend on the reinforcement of rewarding actions. In the first experiment, participants performed two separate tasks in which adaptation was driven either by visual error-based feedback of the hand position or binary reward feedback. We used EEG to measure event-related potentials evoked by task feedback. We hypothesized that levodopa would specifically diminish adaptation and the neural responses to feedback in the reward learning task. However, levodopa did not affect motor adaptation in either task nor did it diminish event-related potentials elicited by reward outcomes. In the second experiment, participants learned to compensate for mechanical force field perturbations applied to the hand during reaching. Previous exposure to a particular force field can result in savings during subsequent adaptation to the same force field or interference during adaptation to an opposite force field. We hypothesized that levodopa would diminish savings and anterograde interference, as previous work suggests that these phenomena result from a reinforcement learning process. However, we found no reliable effects of levodopa. These results suggest that reward-based motor adaptation, savings, and interference may not depend on the same dopaminergic mechanisms that have been shown to be disrupted by levodopa during various cognitive tasks.NEW & NOTEWORTHY Motor adaptation relies on multiple processes including reinforcement of successful actions. Cognitive reinforcement learning is impaired by levodopa-induced disruption of dopamine function. We administered levodopa to healthy adults who participated in multiple motor adaptation tasks. We found no effects of levodopa on any component of motor adaptation. This suggests that motor adaptation may not depend on the same dopaminergic mechanisms as cognitive forms or reinforcement learning that have been shown to be impaired by levodopa.

Learning a reach trajectory based on binary reward feedback

Binary reward feedback on movement success is sufficient for learning some simple sensorimotor mappings in a reaching task, but not for some other tasks in which multiple kinematic factors contribute to performance. The critical condition for learning in more complex tasks remains unclear. Here, we investigate whether reward-based motor learning is possible in a multi-dimensional trajectory matching task and whether simplifying the task by providing feedback on one factor at a time ('factorized feedback') can improve learning. In two experiments, participants performed a trajectory matching task in which learning was measured as a reduction in the error. In Experiment 1, participants matched a straight trajectory slanted in depth. We factorized the task by providing feedback on the slant error, the length error, or on their composite. In Experiment 2, participants matched a curved trajectory, also slanted in depth. In this experiment, we factorized the feedback by providing feedback on the slant error, the curvature error, or on the integral difference between the matched and target trajectory. In Experiment 1, there was anecdotal evidence that participants learnt the multidimensional task. Factorization did not improve learning. In Experiment 2, there was anecdotal evidence the multidimensional task could not be learnt. We conclude that, within a complexity range, multiple kinematic factors can be learnt in parallel.

Prolonged response time helps eliminate residual errors in visuomotor adaptation

One persistent curiosity in visuomotor adaptation tasks is that participants often do not reach maximal performance. This incomplete asymptote has been explained as a consequence of obligatory computations within the implicit adaptation system, such as an equilibrium between learning and forgetting. A body of recent work has shown that in standard adaptation tasks, cognitive strategies operate alongside implicit learning. We reasoned that incomplete learning in adaptation tasks may primarily reflect a speed-accuracy tradeoff on time-consuming motor planning. Across three experiments, we find evidence supporting this hypothesis, showing that hastened motor planning may primarily lead to under-compensation. When an obligatory waiting period was administered before movement start, participants were able to fully counteract imposed perturbations (Experiment 1). Inserting the same delay between trials – rather than during movement planning – did not induce full compensation, suggesting that the motor planning interval influences the learning asymptote (Experiment 2). In the last experiment (Experiment 3), we asked participants to continuously report their movement intent. We show that emphasizing explicit re-aiming strategies (and concomitantly increasing planning time) also lead to complete asymptotic learning. Findings from all experiments support the hypothesis that incomplete adaptation is, in part, the result of an intrinsic speed-accuracy tradeoff, perhaps related to cognitive strategies that require parametric attentional reorienting from the visual target to the goal.

Use of explicit processes during a visually guided locomotor learning task predicts 24-h retention after stroke

Implicit and explicit processes can occur within a single locomotor learning task. The combination of these learning processes may impact how individuals acquire/retain the task. Because these learning processes rely on distinct neural pathways, neurological conditions may selectively impact the processes that occur, thus, impacting learning and retention. Thus, our purpose was to examine the contribution of implicit and explicit processes during a visually guided walking task and characterize the relationship between explicit processes and performance/retention in stroke survivors and age-matched healthy adults. Twenty chronic stroke survivors and twenty healthy adults participated in a 2-day treadmill study. Day 1 included baseline, acquisition1, catch, acquisition2, and immediate retention phases, and day 2 included 24-h retention. During acquisition phases, subjects learned to take a longer step with one leg through distorted visual feedback. During catch and retention phases, visual feedback was removed and subjects were instructed to walk normally (catch) or how they walked during the acquisition phases (retention). Change in step length from baseline to catch represented implicit processes. Change in step length from catch to the end of acquisition2 represented explicit processes. A mixed ANOVA found no difference in the type of learning between groups (P = 0.74). There was a significant relationship between explicit processes and 24-h retention in stroke survivors (r = 0.47, P = 0.04) but not in healthy adults (r = 0.34, P = 0.15). These results suggest that stroke may not affect the underlying learning mechanisms used during locomotor learning, but that these mechanisms impact how well stroke survivors retain the new walking pattern.NEW & NOTEWORTHY This study found that stroke survivors used implicit and explicit processes similar to age-matched healthy adults during a visually guided locomotion learning task. The amount of explicit processes was related to how well stroke survivors retained the new walking pattern but not to how well they performed during the task. This work illustrates the importance of understanding the underlying learning mechanisms to maximize retention of a newly learned motor behavior.

A tale of too many tasks: task fragmentation in motor learning and a call for model task paradigms

Motor learning encompasses a broad set of phenomena that requires a diverse set of experimental paradigms. However, excessive variation in tasks across studies creates fragmentation that can adversely affect the collective advancement of knowledge. Here, we show that motor learning studies tend toward extreme fragmentation in the choice of tasks, with almost no overlap between task paradigms across studies. We argue that this extreme level of task fragmentation poses serious theoretical and methodological barriers to advancing the field. To address these barriers, we propose the need for developing common 'model' task paradigms which could be widely used across labs. Combined with the open sharing of methods and data, we suggest that these model task paradigms could be an important step in increasing the robustness of the motor learning literature and facilitate the cumulative process of science.

Exposure to Auditory Feedback Delay while Speaking Induces Perceptual Habituation but does not Mitigate the Disruptive Effect of Delay on Speech Auditory-motor Learning

Perceiving the sensory consequences of our actions with a delay alters the interpretation of these afferent signals and impacts motor learning. For reaching movements, delayed visual feedback of hand position reduces the rate and extent of visuomotor adaptation, but substantial adaptation still occurs. Moreover, the detrimental effect of visual feedback delay on reach motor learning-selectively affecting its implicit component-can be mitigated by prior habituation to the delay. Auditory-motor learning for speech has been reported to be more sensitive to feedback delay, and it remains unknown whether habituation to auditory delay reduces its negative impact on learning. We investigated whether 30 min of exposure to auditory delay during speaking (a) affects the subjective perception of delay, and (b) mitigates its disruptive effect on speech auditory-motor learning. During a speech adaptation task with real-time perturbation of vowel spectral properties, participants heard this frequency-shifted feedback with no delay, 75 ms delay, or 115 ms delay. In the delay groups, 50% of participants had been exposed to the delay throughout a preceding 30-minute block of speaking whereas the remaining participants completed this block without delay. Although habituation minimized awareness of the delay, no improvement in adaptation to the spectral perturbation was observed. Thus, short-term habituation to auditory feedback delays is not effective in reducing the negative impact of delay on speech auditory-motor adaptation. Combined with previous findings, the strong negative effect of delay and the absence of an influence of delay awareness suggest the involvement of predominantly implicit learning mechanisms in speech.

The Psychology of Reaching: Action Selection, Movement Implementation, and Sensorimotor Learning

The study of motor planning and learning in humans has undergone a dramatic transformation in the 20 years since this journal's last review of this topic. The behavioral analysis of movement, the foundational approach for psychology, has been complemented by ideas from control theory, computer science, statistics, and, most notably, neuroscience. The result of this interdisciplinary approach has been a focus on the computational level of analysis, leading to the development of mechanistic models at the psychological level to explain how humans plan, execute, and consolidate skilled reaching movements. This review emphasizes new perspectives on action selection and motor planning, research that stands in contrast to the previously dominant representation-based perspective of motor programming, as well as an emerging literature highlighting the convergent operation of multiple processes in sensorimotor learning.

Reinforcement Signaling Can Be Used to Reduce Elements of Cerebellar Reaching Ataxia

Damage to the cerebellum causes a disabling movement disorder called ataxia, which is characterized by poorly coordinated movement. Arm ataxia causes dysmetria (over- or under-shooting of targets) with many corrective movements. As a result, people with cerebellar damage exhibit reaching movements with highly irregular and prolonged movement paths. Cerebellar patients are also impaired in error-based motor learning, which may impede rehabilitation interventions. However, we have recently shown that cerebellar patients can learn a simple reaching task using a binary reinforcement paradigm, in which feedback is based on participants' mean performance. Here, we present a pilot study that examined whether patients with cerebellar damage can use this reinforcement training to learn a more complex motor task-to decrease the path length of their reaches. We compared binary reinforcement training to a control condition of massed practice without reinforcement feedback. In both conditions, participants made target-directed reaches in 3-dimensional space while vision of their movement was occluded. In the reinforcement training condition, reaches with a path length below participants' mean were reinforced with an auditory stimulus at reach endpoint. We found that patients were able to use reinforcement signaling to significantly reduce their reach paths. Massed practice produced no systematic change in patients' reach performance. Overall, our results suggest that binary reinforcement training can improve reaching movements in patients with cerebellar damage and the benefit cannot be attributed solely to repetition or reduced visual control.

Punishment Feedback Impairs Memory and Changes Cortical Feedback-Related Potentials During Motor Learning

Reward and punishment have demonstrated dissociable effects on motor learning and memory, which suggests that these reinforcers are differently processed by the brain. To test this possibility, we use electroencephalography to record cortical neural activity after the presentation of reward and punishment feedback during a visuomotor rotation task. Participants were randomly placed into Reward, Punishment, or Control groups and performed the task under different conditions to assess the adaptation (learning) and retention (memory) of the motor task. These conditions featured an incongruent position between the cursor and the target, with the cursor trajectory, rotated 30° counterclockwise, requiring the participant to adapt their movement to hit the target. Feedback based on error magnitude was provided during the Adaptation condition in the form of a positive number (Reward) or negative number (Punishment), each representing a monetary gain or loss, respectively. No reinforcement or visual feedback was provided during the No Vision condition (retention). Performance error and event-related potentials (ERPs) time-locked to feedback presentation were calculated for each participant during both conditions. Punishment feedback reduced performance error and promoted faster learning during the Adaptation condition. In contrast, punishment feedback increased performance error during the No Vision condition compared to Control and Reward groups, which suggests a diminished motor memory. Moreover, the Punishment group showed a significant decrease in the amplitude of ERPs during the No Vision condition compared to the Adaptation condition. The amplitude of ERPs did not change in the other two groups. These results suggest that punishment feedback impairs motor retention by altering the neural processing involved in memory encoding. This study provides a neurophysiological underpinning for the dissociative effects of punishment feedback on motor learning.

Intermittent theta burst stimulation of the right dorsolateral prefrontal cortex accelerates visuomotor adaptation with delayed feedback

Implicit adaptation to visual rotations during fast reaching is a well-recognized function of the cerebellum. However, there is still no well-established understanding of the neural underpinnings that support explicit processes during visuomotor adaptation. We tested the causative involvement of dorsolateral prefrontal cortex (DLPFC) in an adaptive reaching task by employing excitatory intermittent theta burst stimulation (iTBS) to left or right DLPFC during learning to adapt to a sudden large visual rotation with delayed terminal feedback. Spontaneous resting-state electroencephalography (EEG) signals were recorded before and immediately after the administration of iTBS. iTBS to right DLPFC, compared to left DLPFC or control, induced faster adaptation to the rotation and had a greater adjustment of aiming directions in early adaptation trials. Moreover, resting-state functional connectivity of EEG of the frontal cortex after iTBS predicted subsequent adaptation rate. These results suggest a critical role of right DLPFC in supporting explicit learning in the adaptive reaching task.

Intention to learn modulates the impact of reward and punishment on sequence learning

In real-world settings, learning is often characterised as intentional: learners are aware of the goal during the learning process, and the goal of learning is readily dissociable from the awareness of what is learned. Recent evidence has shown that reward and punishment (collectively referred to as valenced feedback) are important factors that influence performance during learning. Presently, however, studies investigating the impact of valenced feedback on skill learning have only considered unintentional learning, and therefore the interaction between intentionality and valenced feedback has not been systematically examined. The present study investigated how reward and punishment impact behavioural performance when participants are instructed to learn in a goal-directed fashion (i.e. intentionally) rather than unintentionally. In Experiment 1, participants performed the serial response time task with reward, punishment, or control feedback and were instructed to ignore the presence of the sequence, i.e., learn unintentionally. Experiment 2 followed the same design, but participants were instructed to intentionally learn the sequence. We found that punishment significantly benefitted performance during learning only when participants learned unintentionally, and we observed no effect of punishment when participants learned intentionally. Thus, the impact of feedback on performance may be influenced by goal of the learner.

Assessing explicit strategies in force field adaptation

In recent years, it has become increasingly clear that a number of learning processes are at play in visuomotor adaptation tasks. In addition to implicitly adapting to a perturbation, learners can develop explicit knowledge allowing them to select better actions in responding to it. Advances in visuomotor rotation experiments have underscored the important role of such "explicit learning" in shaping adaptation to kinematic perturbations. Yet, in adaptation to dynamic perturbations, its contribution has been largely overlooked. We therefore sought to approach the assessment of explicit learning in adaptation to dynamic perturbations, by developing two novel modifications of a force field experiment. First, we asked learners to abandon any cognitive strategy before selected force channel trials to expose consciously accessible parts of overall learning. Here, learners indeed reduced compensatory force compared with standard Catch channels. Second, we instructed a group of learners to mimic their right hand's adaptation by moving with their naïve left hand. While a control group displayed negligible left hand force compensation, the mimicking group reported forces that approximated right hand adaptation but appeared to under-report the velocity component of the force field in favor of a more position-based component. Our results highlight the viability of explicit learning as a potential contributor to force field adaptation, though the fraction of learning under participants' deliberate control on average remained considerably smaller than that of implicit learning, despite task conditions favoring explicit learning. The methods we employed provide a starting point for investigating the contribution of explicit strategies to force field adaptation.NEW & NOTEWORTHY While the contribution of explicit learning has been increasingly studied in visuomotor adaptation, its contribution to force field adaptation has not been studied extensively. We employed two novel methods to assay explicit learning in a force field adaptation task and found that learners can voluntarily control aspects of compensatory force production and manually report it with their untrained limb. This supports the general viability of the contribution of explicit learning also in force field adaptation.

Domain-Specific Working Memory, But Not Dopamine-Related Genetic Variability, Shapes Reward-Based Motor Learning

The addition of rewarding feedback to motor learning tasks has been shown to increase the retention of learning, spurring interest in its possible utility for rehabilitation. However, motor tasks using rewarding feedback have repeatedly been shown to lead to great interindividual variability in performance. Understanding the causes of such variability is vital for maximizing the potential benefits of reward-based motor learning. Thus, using a large human cohort of both sexes (n = 241), we examined whether spatial (SWM), verbal, and mental rotation (RWM) working memory capacity and dopamine-related genetic profiles were associated with performance in two reward-based motor tasks. The first task assessed the participant's ability to follow a slowly shifting reward region based on hit/miss (binary) feedback. The second task investigated the participant's capacity to preserve performance with binary feedback after adapting to the rotation with full visual feedback. Our results demonstrate that higher SWM is associated with greater success and an enhanced capacity to reproduce a successful motor action, measured as change in reach angle following reward. In contrast, higher RWM was predictive of an increased propensity to express an explicit strategy when required to make large reach angle adjustments. Therefore, SWM and RWM were reliable, but dissociable, predictors of success during reward-based motor learning. Change in reach direction following failure was also a strong predictor of success rate, although we observed no consistent relationship with working memory. Surprisingly, no dopamine-related genotypes predicted performance. Therefore, working memory capacity plays a pivotal role in determining individual ability in reward-based motor learning.SIGNIFICANCE STATEMENT Reward-based motor learning tasks have repeatedly been shown to lead to idiosyncratic behaviors that cause varying degrees of task success. Yet, the factors determining an individual's capacity to use reward-based feedback are unclear. Here, we assessed a wide range of possible candidate predictors, and demonstrate that domain-specific working memory plays an essential role in determining individual capacity to use reward-based feedback. Surprisingly, genetic variations in dopamine availability were not found to play a role. This is in stark contrast with seminal work in the reinforcement and decision-making literature, which show strong and replicated effects of the same dopaminergic genes in decision-making. Therefore, our results provide novel insights into reward-based motor learning, highlighting a key role for domain-specific working memory capacity.