Implicit motor learning within three trials

ObjectiveClinicians may use implicit or explicit motor learning approaches to facilitate motor learning of patients with stroke. Implicit motor learning approaches have shown promising results in healthy populations. The purpose of this study was to assess whether an implicit motor learning walking intervention is more effective compared with an explicit motor learning walking intervention delivered at home regarding walking speed in people after stroke in the chronic phase of recovery.MethodsThis randomized, controlled, single-blind trial was conducted in the home environment. The 79 participants, who were in the chronic phase after stroke (age = 66.4 [SD = 11.0] years; time poststroke = 70.1 [SD = 64.3] months; walking speed = 0.7 [SD = 0.3] m/s; Berg Balance Scale score = 44.5 [SD = 9.5]), were randomly assigned to an implicit (n = 38) or explicit (n = 41) group. Analogy learning was used as the implicit motor learning walking intervention, whereas the explicit motor learning walking intervention consisted of detailed verbal instructions. Both groups received 9 training sessions (30 minutes each), for a period of 3 weeks, targeted at improving quality of walking. The primary outcome was walking speed measured by the 10-Meter Walk Test at a comfortable walking pace. Outcomes were assessed at baseline, immediately after intervention, and 1 month postintervention.ResultsNo statistically or clinically relevant differences between groups were obtained postintervention (between-group difference was estimated at 0.02 m/s [95% CI = −0.04 to 0.08] and at follow-up (between-group difference estimated at −0.02 m/s [95% CI = −0.09 to 0.05]).ConclusionImplicit motor learning was not superior to explicit motor learning to improve walking speed in people after stroke in the chronic phase of recovery.ImpactTo our knowledge, this is the first study to examine the effects of implicit compared with explicit motor learning on a functional task in people after stroke. Results indicate that physical therapists can use (tailored) implicit and explicit motor learning strategies to improve walking speed in people after stroke who are in the chronic phase of recovery.

It has been suggested that developmental dyslexia involves various literacy, sensory, motor skill, and processing speed deficits. Some recent studies have shown that individuals with developmental dyslexia exhibit implicit motor learning deficits, which may be related to cerebellar functioning. However, previous studies on implicit motor learning in developmental dyslexics have produced conflicting results. Findings from cerebellar lesion patients have shown that patients' implicit motor learning performance varied when different hands were used to complete tasks. This suggests that dyslexia may have different effects on implicit motor learning between the two hands if cerebellar dysfunction is involved. To specify this question, we used a one-handed version of a serial reaction time task to compare the performance of 27 Chinese children with developmental dyslexics with another 27 age-matched children without reading difficulties. All the subjects were students from two primary schools, Grades 4 to 6. The results showed that children with developmental dyslexic responded more slowly than nondyslexic children, and exhibited no implicit motor learning in the condition of left-hand response. In contrast, there was no significant difference in reaction time between two groups of children when they used the right hand to respond. This finding indicates that children with developmental dyslexia exhibited normal motor skill and implicit motor learning ability provided the right hand was used. Taken together, these results suggested that Chinese children with developmental dyslexia exhibit unilateral deficits in motor skill and implicit motor learning in the left hand. Our findings lend partial support to the cerebellar deficit theory of developmental dyslexia.

A large portion of the rehabilitation experience after stroke relies on implicit learning. However, our understanding of how best to facilitate motor learning after stroke is limited by a paucity of research that has explored the interaction between explicit information and implicit learning across various task domains. Previously we reported that the delivery of explicit instructions disrupted implicit motor learning after stroke that involved the sensorimotor cortical areas or basal ganglia. The purpose of this study was to determine the robustness of these findings by determining whether they could be replicated with 2 motor tasks, one discrete and one continuous, employed by the same group of participants. Ten individuals with stroke in the sensorimotor cortical areas (SMC), 10 with stroke in the basal ganglia (BG), and 10 age-matched healthy controls (HC) participated in this study. Each completed 3 days of practice of both a discrete implicit motor task (the serial reaction time task) and a continuous motor task (the continuous tracking task); all returned on a fourth day for retention tests. By random designation, participants were divided into either the explicit information (EI) or no explicit information (No-EI) groups. Consistent with previous results, we found that the response to explicit information after stroke was uniformly negative regardless of task or lesion location; both stroke groups demonstrated an interference effect of explicit information while the healthy control group did not. Strengthening these findings is the fact that the interference effect of explicit information was not task dependent. This point is particularly important for rehabilitation scientists as they instruct clients during various therapeutic tasks after stroke. Our data suggest that certain forms of explicit information delivered before task practice may not be as useful for learning as discovering the solution to the motor task with practice alone, and this is regardless of the type of task being learned.

Error-based learning underlies motor learning, but what specific motor error drives implicit learning, the procedural component of motor skill, is unclear. A typical action consists of a movement and a performance outcome, e.g., grabbing a coffee cup involves a reaching movement and its actual landing of the hand relative to the target cup. While performance error is fundamental for the cognitive component of motor learning, what error, either performance or movement prediction error, underlies implicit motor learning has not been resolved. These two errors are hard to disentangle as the performance outcome is an integral part of the movement. Here we used the classical visuomotor adaptation paradigm, in which people learn to counter visual perturbations by deliberately aiming off the target, to dissociate the performance error from the prediction error. Using a series of behavioral experiments and model comparisons, we revealed that movement prediction error, but not performance error, can parsimoniously explain diverse learning effects. Importantly, despite the perturbation being visual, the movement prediction error is not specified in visual terms, but determined by a perceptual estimate of the hand kinematics. In other words, contrary to the widely-held concept of sensory prediction error, a perceptual prediction error drives implicit motor learning.

The aim of this study was to assess the current state of evidence and methodological quality of studies on implicit and explicit motor learning in both typically developing children and children with developmental disorders. A systematic literature review was conducted on the experimental literature published up to April 2020. A total of 25 studies were included. Studies were evaluated on methodological quality, paradigm used, and level of evidence. The results showed that implicit paradigms are as effective as explicit paradigms in both groups of children. Studies are predominantly experimental in nature involving mostly upper limb aiming tasks. The few studies that were performed outside the lab (n = 5) suggest superior efficacy of the implicit paradigm. Methodological quality varied between studies and was not always of sufficient standard to allow conclusions. In particular, manipulation checks were only performed in 13 studies (52% of all studies), limiting conclusions. Further progress can be made by focussing on improving methodological quality through retention testing by the inclusion of a control group, by the inclusion of a manipulation check, and via assessment of relevant co-variables, such as working memory, age, and motor competence.

Cathodal Transcranial Direct Current Stimulation Over Left Dorsolateral Prefrontal Cortex Area Promotes Implicit Motor Learning in a Golf Putting Task.

Background/Study Context: It has been proposed that effects of aging are more pronounced for explicit than for implicit motor learning. The authors evaluated this claim by comparing the efficacy of explicit and implicit learning of a movement sequence in young and older adults, and by testing the resilience against fatigue and secondary tasking after learning. It was also examined whether explicit learning in older adults can be promoted by alleviating time constraints during learning.Methods: The alternating serial reaction time task (ASRTT) was used. Experiment 1 compared the benefits of receiving full instructions about the stimulus sequence relative to receiving no instructions in young (20–25 years) and older (50–65 years) adults during retention and during transfer to fatigue and secondary task conditions. Experiment 2 alleviated time constraints during the initial bouts of practice with full instructions.Results: Experiment 1 indicated that the older adults learned on the ASRTT and achieved similar performance as young adults when no instructions were given. In contrast to the young adults, learning was not superior in older adults who received full instructions compared with those who did not. Experiment 2 indicated that alleviating time constraints allowed some of the older adults to gain from instruction but only under relatively low time constraints, but there was no retention with rigorous time constraints.Conclusion: Explicit learning, but not implicit learning, declines in older adults. This is partly due to older adults difficulties to apply explicit knowledge. Less rigorous time constraints can help to ameliorate some of these difficulties and may induce levels of explicit learning in older adults that will result in superior performance compared with implicit learning. Implicit learning did occur under time constraints that prevented explicit learning.

BackgroundA significant part of neurological rehabilitation focuses on facilitating the learning of motor skills. Training can adopt either (more) explicit or (more) implicit forms of motor learning. Gait is one of the most practiced motor skills within rehabilitation in people after stroke because it is an important criterion for discharge and requirement for functioning at home.ObjectiveThe aim of this study was to describe the design of a randomized controlled study assessing the effects of implicit motor learning compared with the explicit motor learning in gait rehabilitation of people suffering from stroke.MethodsThe study adopts a randomized, controlled, single-blinded study design. People after stroke will be eligible for participation when they are in the chronic stage of recovery (>6 months after stroke), would like to improve walking performance, have a slow walking speed (<1 m/s), can communicate in Dutch, and complete a 3-stage command. People will be excluded if they cannot walk a minimum of 10 m or have other additional impairments that (severely) influence gait. Participants will receive 9 gait-training sessions over a 3-week period and will be randomly allocated to an implicit or explicit group. Therapists are aware of the intervention they provide, and the assessors are blind to the intervention participants receive. Outcome will be assessed at baseline (T0), directly after the intervention (T1), and after 1 month (T2). The primary outcome parameter is walking velocity. Walking performance will be assessed with the 10-meter walking test, Dynamic Gait Index, and while performing a secondary task (dual task). Self-reported measures are the Movement Specific Reinvestment Scale, verbal protocol, Stroke and Aphasia Quality of Life Scale, and the Global Perceived Effect scale. A process evaluation will take place to identify how the therapy was perceived and identify factors that may have influenced the effectiveness of the intervention. Repeated measures analyses will be conducted to determine significant and clinical relevant differences between groups and over time.ResultsData collection is currently ongoing and results are expected in 2019.ConclusionsThe relevance of the study as well as the advantages and disadvantages of several aspects of the chosen design are discussed, for example, the personalized approach and choice of measurements.Trial RegistrationNetherlands Trial Register NTR6272; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=6272 (Archived by WebCite http://www.webcitation.org/6ytA937m5)Registered Report IdentifierRR1-10.2196/9595

Solving the Credit Assignment Problem: The interaction of Explicit and Implicit learning with Internal and External State Information Wai-Tat Fu (wfu@cmu.edu) Human Factors Division and Beckman Institute University of Illinois at Urbana-Champaign 1 Airport Road, Savoy, IL 61874, USA John R. Anderson (ja+@cmu.edu) Department of Psychology Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213 USA recognition of external state information (signs on the walls). Indeed, many have argued that real-world skills often involve the interplay between cognition (internal), perception, and action (external) that the understanding of these interactive skills requires careful study of how internal (memory) and external information (cues in the environment) are processed in the learning processes (Ballard, 1997; Fu & Gray, 2000; 2004; Gray & Fu, 2004; Larkin, 1989; Gray, Sims, Fu, & Schoelles, in press). The navigation problem above is an example of one of the most difficult situations in skill learning: when the learner has to perform a sequence of actions but only gets feedback on their success at the end of the sequence (e.g., when the destination is reached). This creates a credit-assignment problem, in which the learner has to assign credits to earlier actions that are responsible for eventual success. When actions are interdependent, either memory of previous actions or recognition of the correct problem state in the external environment is required to properly assign credits to the appropriate actions. In this article, we present results from an experiment in which we study how people learn to solve the credit-assignment problem in a simple but challenging example of such a situation. Our focus is on the recent proposal that humans exhibit two distinct learning processes and we apply it to learning of action sequences with delayed feedback: an explicit process (with awareness) that requires memory for actions and outcomes, and an implicit process (without awareness) that does not require such memory. We will first review research in some related areas that informed the design of our experiment. Abstract In most problem-solving activities, feedback is received at the end of an action sequence. This creates a credit-assignment problem where the learner must associate the feedback with earlier actions, and the interdependencies of actions require the learner to either remember past choices of actions (internal state information) or rely on external cues in the environment (external state information) to select the right actions. We investigated the nature of explicit and implicit learning processes in the credit-assignment problem using a probabilistic sequential choice task with and without external state information. We found that when explicit memory encoding was dominant, subjects were faster to select the better option in their first choices than in the last choices; when implicit reinforcement learning was dominant subjects were faster to select the better option in their last choices than in their first choices. However, implicit reinforcement learning was only successful when distinct external state information was available. The results suggest the nature of learning in credit assignment: an explicit memory encoding process that keeps track of internal state information and a reinforcement-learning process that uses state information to propagate reinforcement backwards to previous choices. However, the implicit reinforcement learning process is effective only when the valences can be attributed to the appropriate states in the system – either internally generated states in the cognitive system or externally presented stimuli in the environment. Introduction Consider a person navigating in a large office building. The person has to decide when to turn left or right at various hallway intersections. The sequence of decisions is interdependent – e.g., turning left at a particular hallway intersection will affect the decisions at the next intersections. The person may therefore need to keep track of previous actions to inform what actions to take in the future. In reality, memory of previous actions (internal state information) may not be necessary as people can explicitly seek information in the environment (external state information) to know where one is located or which direction to go to reach a destination (Fu & Gray, 2006). Learning to navigate is therefore likely to involve both the retention of internal state information (memory) and the Explicit and Implicit Learning Probability Learning and Classification There have been numerous studies on the learning of the probabilistic relationship between choices and their consequences. The simplest situation is the probability- learning experiment in which subjects guess which of the alternatives occurs and then receives feedback on their guesses (e.g., Estes, 1964). One robust finding is that subjects often “probability match”; that is, they will choose a particular alternative with the same probability that it is reinforced (e.g., Friedman et al., 1964). This leads many to propose that probability matching is the result of an implicit

Two experiments were conducted to investigate whether physical and observational practice in task-sharing entail comparable implicit motor learning. To this end, the social-transfer-of-learning (SToL) effect was assessed when both participants performed the joint practice task (Experiment 1 – complete task-sharing), or when one participant observed the other performing half of the practice task (Experiment 2 – evocative task-sharing). Since the inversion of the spatial relations between responding agent and stimulus position has been shown to prevent SToL, in the present study we assessed it in both complete and evocative task-sharing conditions either when spatial relations were kept constant or changed from the practice to the transfer session. The same pattern of results was found for both complete and evocative task-sharing, thus suggesting that implicit motor learning in evocative task-sharing is equivalent to that obtained in complete task-sharing. We conclude that this motor learning originates from the simulation of the complementary (rather than the imitative) action.

Skills that are learnt implicitly (i.e., without the accumulation of task-related rules and knowledge) have been shown to result in performance that displays stability in conditions of psychological stress, fatigue, multi-tasking, and over prolonged periods of time. Despite the wealth of evidence supporting the use of implicit motor learning strategies, the majority of this evidence has been generated from studies of novice performers rather than of experts. The aim of this paper is to describe some of the challenges faced by high-performance coaches and athletes who may wish to use implicit motor learning and to frame potential solutions with respect to the elite Australian National Rugby League competition. Practical training activities and techniques (e.g., errorless learning, random practice, cues, dual-tasking, and analogies), designed to facilitate the development of implicit skills that transfer robustly to high-performance competition environments, are presented.

Toluene is a well-known neurotoxic organic solvent and a major component of many industrial and commercial products such as adhesives, paint thinners and gasoline. Many workers are regularly exposed to toluene in their working environment and occupational exposure limits (OELs) have been set to avoid adverse health effects. These OELs or short-term exposure limits vary from 14 to 300ppm across countries partly due to heterogeneity of the findings from animal and human studies about its neurotoxic effects and the evaluation of the adversity of the underlying mechanisms. Furthermore, its acute neurophysiological effects remain poorly understood in humans. The purpose of this study was to investigate the effects of acute exposure to toluene on cortical excitability, plasticity, and implicit motor learning in healthy volunteers. Seventeen subjects were assessed with different transcranial magnetic stimulation measurements: motor thresholds, short-latency intracortical inhibition and intracortical facilitation, and short-interval afferent inhibition before and after clean air or toluene (single peak of 200ppm) administration. Furthermore, we evaluated long-term potentiation-like neuroplasticity induced by anodal transcranial direct current stimulation (tDCS) over the motor cortex, and the participants conducted a motor sequence learning task, the serial reaction time task. Our findings revealed that toluene abolished the plasticity induced by anodal tDCS, attenuated intracortical facilitation, and increased inhibition in the short-latency afferent inhibition measure, while cortico-spinal excitability and intracortical inhibition were not affected. On the behavioural level, toluene did not alter performance of the motor learning task. These results suggest that toluene might act by modulating NMDA receptor activity, as well as cortical glutamatergic and cholinergic neurotransmission in the human brain. This study encourages further research to obtain more knowledge about mechanisms of action and effects of toluene on both naïve and chronically exposed populations.

Short sub-100ms visual feedback latencies are common in many types of human-computer interactions yet are known to markedly reduce performance in a wide variety of motor tasks from simple pointing to operating surgical robotics. These latencies are also present in the computer-based experiments used to study the sensorimotor learning that underlies the acquisition of motor performance. Inspired by neurophysiological findings showing that cerebellar LTD and cortical LTP would both be disrupted by sub-100ms latencies, we hypothesized that implicit sensorimotor learning may be particularly sensitive to these short latencies. Remarkably, we find that improving latency by just 60ms, from 85 to 25ms in latency-optimized experiments, increases implicit learning by 50% and proportionally decreases explicit learning, resulting in a dramatic reorganization of sensorimotor memory. We go on to show that implicit sensorimotor learning is considerably more sensitive to latencies in the sub-100ms range than at higher latencies, in line with the latency-specific neural plasticity that has been observed. This suggests a clear benefit for latency reduction in computer-based training that involves implicit sensorimotor learning and that across-study differences in implicit motor learning might often be explained by disparities in feedback latency.

We investigated the attention demands associated with implicit and explicit (motor) learning and performance using a probe reaction time paradigm. Two groups of participants learned a golf putting task over eight blocks of 50 trials performed from different distances. One group (errorless learning) began putting from the shortest distance (25 cm) and moved progressively back to the furthest distance (200 cm). A second group (errorful learning) began putting from the furthest distance (200 cm) and moved progressively closer (25 cm). Retention tests were used to assess learning in the two conditions, followed by transfer tests in which participants used either an unusual putter or a very unusual putter. Transfer to the unusual putters had an equivalent effect on the performance of both errorless and errorful learners, but probe reaction times were unaffected in the errorless learners, suggesting that execution of their movements was associated with reduced attention demands. Reducing errors during initial learning trials may encourage an implicit mode of learning and lower the demand for cognitive resources in subsequent performance.

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.