Interactions between sensory prediction error and task error during implicit motor learning

Attenuation of implicit motor learning with consecutive exposure to visual errors

Visual errors induced by movement drive implicit corrections of that movement. When similar errors are experienced consecutively, does sensitivity to the error remain consistent each time? This study aimed to investigate the modulation of implicit error sensitivity through continuous exposure to the same errors. In the reaching task using visual error-clamp feedback, participants were presented with the same error in direction and magnitude for four consecutive trials. We found that implicit error sensitivity decreased after exposure to the second error. These results indicate that when visual errors occur consecutively, the sensorimotor system exhibits different responses, even for identical errors. The continuity of errors may be a factor that modulates error sensitivity.

Effects of motor imagery training on generalization and retention for different task difficulties

Although previous studies have suggested that motor adaptation through motor imagery training of similar tasks can improve retention and generalization of motor learning, the benefits of mental and physical training remain unclear for different task difficulties. Two experiments were conducted in this study. The first experiment aimed to determine whether there were differences in movement time (MT) when drawing circles based on three conditions in accordance with Fitts' law. The results showed significant differences in MT among the three conditions (p < 0.001), with MT becoming long as the width of the circle line (which indicated different difficulty level) narrowed. The second experiment aimed to determine whether the task difficulty influenced immediate generalization and retention at 24 h after mental vs. physical training. Participants in both training groups practiced the task with the medium-sized circle, which indicated medium difficulty. The posttest results revealed that mental training leads to considerable performance improvement than physical training, as demonstrated by a shorter MT regardless of the task difficulty level. Meanwhile, the retention test results showed no difference in generalization between mental and physical training. However, generalization of an easier task was more effectively retained than more difficult tasks. These results suggest that mental training can improve performance during the adaptation phase and that difficulty level can influence the degree of retention.

A systematic review of observational practice for adaptation of reaching movements

Observational practice is discussed as a substitute for physical practice for motor learning and adaptation. We systematically reviewed the literature on observational practice in reaching and aiming tasks. Our objectives were to identify (i) performance differences between observational and physical practice; (ii) factors that contribute to adaptation following observational practice; and (iii) the neural correlates of observational practice. We found 18 studies, all investigated adaptation of reaching in visuomotor rotations or force-field perturbations. Results of the studies showed that observational practice led to adaptation in both, visuomotor rotation and force-field paradigms (d = -2.16 as compared to no practice). However, direct effects were considerably smaller as compared to physical practice (d = 4.38) and aftereffects were absent, suggesting that observational practice informed inverse, but not forward modes. Contrarily, neurophysiological evidence in this review showed that observational and physical practice involved similar brain regions.

Pupillary responses to directional uncertainty while intercepting a moving target

Pupillary responses serve as sensitive indicators of cognitive processes, attentional shifts and decision-making dynamics. Our study investigates how directional uncertainty and target speed (V T) influence pupillary responses in a foveal tracking task involving the interception of a moving dot. Directional uncertainty, reflecting the unpredictability of the target's direction changes, was manipulated by altering the angular range (AR) from which random directions for the moving dot were extracted. Higher AR values were associated with reduced pupillary diameters, indicating that heightened uncertainty led to smaller pupil sizes. Additionally, an inverse U-shaped relationship between V T and pupillary responses suggested maximal diameters at intermediate speeds. Analysis of saccade-triggered responses showed a negative correlation between pupil diameter and directional uncertainty. Dynamic linear modelling revealed the influence of past successful collisions and other behavioural parameters on pupillary responses, emphasizing the intricate interaction between task variables and cognitive processing. Our results highlight the dynamic interplay between the directional uncertainty of a single moving target, V T and pupillary responses, with implications for understanding attentional mechanisms, decision-making processes and potential applications in emerging technologies.

Roles and interplay of reinforcement-based and error-based processes during reaching and gait in neurotypical adults and individuals with Parkinson's disease

From a game of darts to neurorehabilitation, the ability to explore and fine tune our movements is critical for success. Past work has shown that exploratory motor behaviour in response to reinforcement (reward) feedback is closely linked with the basal ganglia, while movement corrections in response to error feedback is commonly attributed to the cerebellum. While our past work has shown these processes are dissociable during adaptation, it is unknown how they uniquely impact exploratory behaviour. Moreover, converging neuroanatomical evidence shows direct and indirect connections between the basal ganglia and cerebellum, suggesting that there is an interaction between reinforcement-based and error-based neural processes. Here we examine the unique roles and interaction between reinforcement-based and error-based processes on sensorimotor exploration in a neurotypical population. We also recruited individuals with Parkinson's disease to gain mechanistic insight into the role of the basal ganglia and associated reinforcement pathways in sensorimotor exploration. Across three reaching experiments, participants were given either reinforcement feedback, error feedback, or simultaneously both reinforcement & error feedback during a sensorimotor task that encouraged exploration. Our reaching results, a re-analysis of a previous gait experiment, and our model suggests that in isolation, reinforcement-based and error-based processes respectively boost and suppress exploration. When acting in concert, we found that reinforcement-based and error-based processes interact by mutually opposing one another. Finally, we found that those with Parkinson's disease had decreased exploration when receiving reinforcement feedback, supporting the notion that compromised reinforcement-based processes reduces the ability to explore new motor actions. Understanding the unique and interacting roles of reinforcement-based and error-based processes may help to inform neurorehabilitation paradigms where it is important to discover new and successful motor actions.

Implicit sensorimotor learning in ballistic movement for transporting an object to a target

To enhance and sustain movement accuracy, humans make corrections in subsequent trials based on previous errors. Trial-by-trial learning occurs unconsciously and has mostly been studied using reaching movements. Goal-directed projection movements, such as archery, have an inherent delay between releasing an object and observing an outcome (e.g. the arrival position of the object), and this delay may prevent trial-by-trial implicit learning. We aimed to investigate the learning in the projection movement and the impacts of the inherent delay. During the experiment, a joystick was flicked once to transport a cursor from the starting location to a target. To manipulate the length of the delay between the cursor release and outcome observation, the speed of the cursor movement was varied: a fast speed can lead to a short delay. We found trial-by-trial implicit learning under all speed conditions, and the error sensitivity was not significantly different across speed conditions. Furthermore, the error sensitivity depended on the target location, that is, the movement direction. The results indicate that trial-by-trial implicit learning occurred in goal-directed projection movement, despite the length of the inherent delay. Additionally, the degree of this learning was affected by the movement direction.

Minimal impact of chronic proprioceptive loss on implicit sensorimotor adaptation and perceived movement outcome

Implicit sensorimotor adaptation keeps our movements well calibrated amid changes in the body and environment. We have recently postulated that implicit adaptation is driven by a perceptual error: the difference between the desired and perceived movement outcome. According to this perceptual realignment model, implicit adaptation ceases when the perceived movement outcome-a multimodal percept determined by a prior belief conveying the intended action, the motor command, and feedback from proprioception and vision-is aligned with the desired movement outcome. Here, we examined the role of proprioception in implicit motor adaptation and perceived movement outcome by examining individuals who experience deafferentation (i.e., individuals with impaired proprioception and touch). We used a modified visuomotor rotation task designed to isolate implicit adaptation and probe perceived movement outcomes throughout the experiment. Surprisingly, both implicit adaptation and perceived movement outcome were minimally impacted by chronic deafferentation, posing a challenge to the perceptual realignment model of implicit adaptation.NEW & NOTEWORTHY We tested six individuals with chronic somatosensory deafferentation on a novel task that isolates implicit sensorimotor adaptation and probes perceived movement outcome. Strikingly, both implicit motor adaptation and perceptual movement outcome were not significantly impacted by chronic deafferentation, posing a challenge for theoretical models of adaptation that involve proprioception.

Perceptual error based on Bayesian cue combination drives implicit motor adaptation

The sensorimotor system can recalibrate itself without our conscious awareness, a type of procedural learning whose computational mechanism remains undefined. Recent findings on implicit motor adaptation, such as over-learning from small perturbations and fast saturation for increasing perturbation size, challenge existing theories based on sensory errors. We argue that perceptual error, arising from the optimal combination of movement-related cues, is the primary driver of implicit adaptation. Central to our theory is the increasing sensory uncertainty of visual cues with increasing perturbations, which was validated through perceptual psychophysics (Experiment 1). Our theory predicts the learning dynamics of implicit adaptation across a spectrum of perturbation sizes on a trial-by-trial basis (Experiment 2). It explains proprioception changes and their relation to visual perturbation (Experiment 3). By modulating visual uncertainty in perturbation, we induced unique adaptation responses in line with our model predictions (Experiment 4). Overall, our perceptual error framework outperforms existing models based on sensory errors, suggesting that perceptual error in locating one's effector, supported by Bayesian cue integration, underpins the sensorimotor system's implicit adaptation.

Ouvrai opens access to remote virtual reality studies of human behavioural neuroscience

Modern virtual reality (VR) devices record six-degree-of-freedom kinematic data with high spatial and temporal resolution and display high-resolution stereoscopic three-dimensional graphics. These capabilities make VR a powerful tool for many types of behavioural research, including studies of sensorimotor, perceptual and cognitive functions. Here we introduce Ouvrai, an open-source solution that facilitates the design and execution of remote VR studies, capitalizing on the surge in VR headset ownership. This tool allows researchers to develop sophisticated experiments using cutting-edge web technologies such as WebXR to enable browser-based VR, without compromising on experimental design. Ouvrai's features include easy installation, intuitive JavaScript templates, a component library managing front- and backend processes and a streamlined workflow. It integrates with Firebase, Prolific and Amazon Mechanical Turk and provides data processing utilities for analysis. Unlike other tools, Ouvrai remains free, with researchers managing their web hosting and cloud database via personal Firebase accounts. Ouvrai is not limited to VR studies; researchers can also develop and run desktop or touchscreen studies using the same streamlined workflow. Through three distinct motor learning experiments, we confirm Ouvrai's efficiency and viability for conducting remote VR studies.

Implicit Adaptation Is Modulated by the Relevance of Feedback

Given that informative and relevant feedback in the real world is often intertwined with distracting and irrelevant feedback, we asked how the relevancy of visual feedback impacts implicit sensorimotor adaptation. To tackle this question, we presented multiple cursors as visual feedback in a center-out reaching task and varied the task relevance of these cursors. In other words, participants were instructed to hit a target with a specific task-relevant cursor, while ignoring the other cursors. In Experiment 1, we found that reach aftereffects were attenuated by the mere presence of distracting cursors, compared with reach aftereffects in response to a single task-relevant cursor. The degree of attenuation did not depend on the position of the distracting cursors. In Experiment 2, we examined the interaction between task relevance and attention. Participants were asked to adapt to a task-relevant cursor/target pair, while ignoring the task-irrelevant cursor/target pair. Critically, we jittered the location of the relevant and irrelevant target in an uncorrelated manner, allowing us to index attention via how well participants tracked the position of target. We found that participants who were better at tracking the task-relevant target/cursor pair showed greater aftereffects, and interestingly, the same correlation applied to the task-irrelevant target/cursor pair. Together, these results highlight a novel role of task relevancy on modulating implicit adaptation, perhaps by giving greater attention to informative sources of feedback, increasing the saliency of the sensory prediction error.

Abnormalities in motor adaptation to different types of perturbations in schizophreniaperturbations in schizophrenia

Schizophrenia is a mental health disorder that often includes psychomotor disturbances, impacting how individuals adjust their motor output based on the cause of motor errors. While previous motor adaptation studies on individuals with schizophrenia have largely focused on large and consistent perturbations induced by abrupt experimental manipulations, such as donning prism goggles, the adaptation process to random perturbations, either caused by intrinsic motor noise or external disturbances, has not been examined - despite its ecological relevance. Here, we used a unified behavioral task paradigm to examine motor adaptation to perturbations of three causal structures among individuals in the remission stage of schizophrenia, youth with ultra-high risk of psychosis, adults with active symptoms, and age-matched controls. Results showed that individuals with schizophrenia had reduced trial-by-trial adaptation and large error variance when adapting to their own motor noise. When adapting to random but salient perturbations, they showed intact adaptation and normal causal inference of errors. This contrasted with reduced adaptation to large yet consistent perturbations, which could reflect difficulties in forming cognitive strategies rather than the often-assumed impairments in procedural learning or sense of agency. Furthermore, the observed adaptation effects were correlated with the severity of positive symptoms across the diagnosis groups. Our findings suggest that individuals with schizophrenia face challenges in accommodating intrinsic perturbations when motor errors are ambiguous but adapt with intact causal attribution when errors are salient.

Multisensory perception depends on the reliability of the type of judgment

The brain engages the processes of multisensory integration and recalibration to deal with discrepant multisensory signals. These processes consider the reliability of each sensory input, with the more reliable modality receiving the stronger weight. Sensory reliability is typically assessed via the variability of participants' judgments, yet these can be shaped by factors both external and internal to the nervous system. For example, motor noise and participant's dexterity with the specific response method contribute to judgment variability, and different response methods applied to the same stimuli can result in different estimates of sensory reliabilities. Here we ask how such variations in reliability induced by variations in the response method affect multisensory integration and sensory recalibration, as well as motor adaptation, in a visuomotor paradigm. Participants performed center-out hand movements and were asked to judge the position of the hand or rotated visual feedback at the movement end points. We manipulated the variability, and thus the reliability, of repeated judgments by asking participants to respond using either a visual or a proprioceptive matching procedure. We find that the relative weights of visual and proprioceptive signals, and thus the asymmetry of multisensory integration and recalibration, depend on the reliability modulated by the judgment method. Motor adaptation, in contrast, was insensitive to this manipulation. Hence, the outcome of multisensory binding is shaped by the noise introduced by sensorimotor processing, in line with perception and action being intertwined.NEW & NOTEWORTHY Our brain tends to combine multisensory signals based on their respective reliability. This reliability depends on sensory noise in the environment, noise in the nervous system, and, as we show here, variability induced by the specific judgment procedure.

Theoretical limits on the speed of learning inverse models explain the rate of adaptation in arm reaching tasks

An essential aspect of human motor learning is the formation of inverse models, which map desired actions to motor commands. Inverse models can be learned by adjusting parameters in neural circuits to minimize errors in the performance of motor tasks through gradient descent. However, the theory of gradient descent establishes limits on the learning speed. Specifically, the eigenvalues of the Hessian of the error surface around a minimum determine the maximum speed of learning in a task. Here, we use this theoretical framework to analyze the speed of learning in different inverse model learning architectures in a set of isometric arm-reaching tasks. We show theoretically that, in these tasks, the error surface and, thus the speed of learning, are determined by the shapes of the force manipulability ellipsoid of the arm and the distribution of targets in the task. In particular, rounder manipulability ellipsoids generate a rounder error surface, allowing for faster learning of the inverse model. Rounder target distributions have a similar effect. We tested these predictions experimentally in a quasi-isometric reaching task with a visuomotor transformation. The experimental results were consistent with our theoretical predictions. Furthermore, our analysis accounts for the speed of learning in previous experiments with incompatible and compatible virtual surgery tasks, and with visuomotor rotation tasks with different numbers of targets. By identifying aspects of a task that influence the speed of learning, our results provide theoretical principles for the design of motor tasks that allow for faster learning.

Motion state-dependent motor learning based on explicit visual feedback has limited spatiotemporal properties compared with adaptation to physical perturbations

We recently showed that subjects can learn motion state-dependent changes to motor output (temporal force patterns) based on explicit visual feedback of the equivalent force field (i.e., without the physical perturbation). Here, we examined the spatiotemporal properties of this learning compared with learning based on physical perturbations. There were two human subject groups and two experimental paradigms. One group (n = 40) experienced physical perturbations (i.e., a velocity-dependent force field, vFF), whereas the second (n = 40) was given explicit visual feedback (EVF) of the force-velocity relationship. In the latter, subjects moved in force channels and we provided visual feedback of the lateral force exerted during the movement, as well as the required force pattern based on movement velocity. In the first paradigm (spatial generalization), following vFF or EVF training, generalization of learning was tested by requiring subjects to move to 14 untrained target locations (0° to ±135° around the trained location). In the second paradigm (temporal stability), following training, we examined the decay of learning over eight delay periods (0 to 90 s). Results showed that learning based on EVF did not generalize to untrained directions, whereas the generalization for the vFF was significant for targets ≤ 45° away. In addition, the decay of learning for the EVF group was significantly faster than the FF group (a time constant of 2.72 ± 1.74 s vs. 12.53 ± 11.83 s). Collectively, our results suggest that recalibrating motor output based on explicit motion state information, in contrast to physical disturbances, uses learning mechanisms with limited spatiotemporal properties.NEW & NOTEWORTHY Adjustment of motor output based on limb motion state information can be achieved based on explicit information or from physical perturbations. Here, we investigated the spatiotemporal characteristics of short-term motor learning to determine the properties of the respective learning mechanisms. Our results suggest that adjustments based on physical perturbations are more temporally stable and applied over a greater spatial range than the learning based on explicit visual feedback, suggesting largely separate learning mechanisms.

Dorsomedial frontal cortex damage impairs error-based, but not reinforcement-based motor learning in humans

We adapt our movements to new and changing environments through multiple processes. Sensory error-based learning counteracts environmental perturbations that affect the sensory consequences of movements. Sensory errors also cause the upregulation of reflexes and muscle co-contraction. Reinforcement-based learning enhances the selection of movements that produce rewarding outcomes. Although some findings have identified dissociable neural substrates of sensory error- and reinforcement-based learning, correlative methods have implicated dorsomedial frontal cortex in both. Here, we tested the causal contributions of dorsomedial frontal to adaptive motor control, studying people with chronic damage to this region. Seven human participants with focal brain lesions affecting the dorsomedial frontal and 20 controls performed a battery of arm movement tasks. Three experiments tested: (i) the upregulation of visuomotor reflexes and muscle co-contraction in response to unpredictable mechanical perturbations, (ii) sensory error-based learning in which participants learned to compensate predictively for mechanical force-field perturbations, and (iii) reinforcement-based motor learning based on binary feedback in the absence of sensory error feedback. Participants with dorsomedial frontal damage were impaired in the early stages of force field adaptation, but performed similarly to controls in all other measures. These results provide evidence for a specific and selective causal role for the dorsomedial frontal in sensory error-based learning.

Brain-behavior analysis of transcranial direct current stimulation effects on a complex surgical motor task

Transcranial Direct Current Stimulation (tDCS) has demonstrated its potential in enhancing surgical training and performance compared to sham tDCS. However, optimizing its efficacy requires the selection of appropriate brain targets informed by neuroimaging and mechanistic understanding. Previous studies have established the feasibility of using portable brain imaging, combining functional near-infrared spectroscopy (fNIRS) with tDCS during Fundamentals of Laparoscopic Surgery (FLS) tasks. This allows concurrent monitoring of cortical activations. Building on these foundations, our study aimed to explore the multi-modal imaging of the brain response using fNIRS and electroencephalogram (EEG) to tDCS targeting the right cerebellar (CER) and left ventrolateral prefrontal cortex (PFC) during a challenging FLS suturing with intracorporeal knot tying task. Involving twelve novices with a medical/premedical background (age: 22-28 years, two males, 10 females with one female with left-hand dominance), our investigation sought mechanistic insights into tDCS effects on brain areas related to error-based learning, a fundamental skill acquisition mechanism. The results revealed that right CER tDCS applied to the posterior lobe elicited a statistically significant (q < 0.05) brain response in bilateral prefrontal areas at the onset of the FLS task, surpassing the response seen with sham tDCS. Additionally, right CER tDCS led to a significant (p < 0.05) improvement in FLS scores compared to sham tDCS. Conversely, the left PFC tDCS did not yield a statistically significant brain response or improvement in FLS performance. In conclusion, right CER tDCS demonstrated the activation of bilateral prefrontal brain areas, providing valuable mechanistic insights into the effects of CER tDCS on FLS peformance. These insights motivate future investigations into the effects of CER tDCS on error-related perception-action coupling through directed functional connectivity studies.

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.

A Case Study of the Validity of Web-based Visuomotor Rotation Experiments

Web-based experiments are gaining momentum in motor learning research because of the desire to increase statistical power, decrease overhead for human participant experiments, and utilize a more demographically inclusive sample population. However, there is a vital need to understand the general feasibility and considerations necessary to shift tightly controlled human participant experiments to an online setting. We developed and deployed an online experimental platform modeled after established in-laboratory visuomotor rotation experiments to serve as a case study examining remotely collected data quality for an 80-min experiment. Current online motor learning experiments have thus far not exceeded 60 min, and current online crowdsourced studies have a median duration of approximately 10 min. Thus, the impact of a longer-duration, web-based experiment is unknown. We used our online platform to evaluate perturbation-driven motor adaptation behavior under three rotation sizes (±10°, ±35°, and ±65°) and two sensory uncertainty conditions. We hypothesized that our results would follow predictions by the relevance estimation hypothesis. Remote execution allowed us to double (n = 49) the typical participant population size from similar studies. Subsequently, we performed an in-depth examination of data quality by analyzing single-trial data quality, participant variability, and potential temporal effects across trials. Results replicated in-laboratory findings and provided insight on the effect of induced sensory uncertainty on the relevance estimation hypothesis. Our experiment also highlighted several specific challenges associated with online data collection including potentially smaller effect sizes, higher data variability, and lower recommended experiment duration thresholds. Overall, online paradigms present both opportunities and challenges for future motor learning research.

A software tool for at-home measurement of sensorimotor adaptation

Sensorimotor adaptation is traditionally studied in well-controlled laboratory settings with specialized equipment. However, recent public health concerns such as the COVID-19 pandemic, as well as a desire to recruit a more diverse study population, have led the motor control community to consider at-home study designs. At-home motor control experiments are still rare because of the requirement to write software that can be easily used by anyone on any platform. To this end, we developed software that runs locally on a personal computer. The software provides audiovisual instructions and measures the ability of the subject to control the cursor in the context of visuomotor perturbations. We tested the software on a group of at-home participants and asked whether the adaptation principles inferred from in-lab measurements were reproducible in the at-home setting. For example, we manipulated the perturbations to test whether there were changes in adaptation rates (savings and interference), whether adaptation was associated with multiple timescales of memory (spontaneous recovery), and whether we could selectively suppress subconscious learning (delayed feedback, perturbation variability) or explicit strategies (limited reaction time). We found remarkable similarity between in-lab and at-home behaviors across these experimental conditions. Thus, we developed a software tool that can be used by research teams with little or no programming experience to study mechanisms of adaptation in an at-home setting.

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.

Error-independent effect of sensory uncertainty on motor learning when both feedforward and feedback control processes are engaged

Integrating sensory information during movement and adapting motor plans over successive movements are both essential for accurate, flexible motor behaviour. When an ongoing movement is off target, feedback control mechanisms update the descending motor commands to counter the sensed error. Over longer timescales, errors induce adaptation in feedforward planning so that future movements become more accurate and require less online adjustment from feedback control processes. Both the degree to which sensory feedback is integrated into an ongoing movement and the degree to which movement errors drive adaptive changes in feedforward motor plans have been shown to scale inversely with sensory uncertainty. However, since these processes have only been studied in isolation from one another, little is known about how they are influenced by sensory uncertainty in real-world movement contexts where they co-occur. Here, we show that sensory uncertainty may impact feedforward adaptation of reaching movements differently when feedback integration is present versus when it is absent. In particular, participants gradually adjust their movements from trial-to-trial in a manner that is well characterised by a slow and consistent envelope of error reduction. Riding on top of this slow envelope, participants exhibit large and abrupt changes in their initial movement vectors that are strongly correlated with the degree of sensory uncertainty present on the previous trial. However, these abrupt changes are insensitive to the magnitude and direction of the sensed movement error. These results prompt important questions for current models of sensorimotor learning under uncertainty and open up new avenues for future exploration in the field.

Strategy-based motor learning decreases the post-movement β power

Motor learning depends on the joint contribution of several processes including cognitive strategies aiming at goal achievement and prediction error-driven implicit adaptation. Understanding this functional interplay and its clinical implications requires insight into the individual learning processes, including at a neural level. Here, we set out to examine the impact of learning a cognitive strategy, over and above implicit adaptation, on the oscillatory post-movement β rebound (PMBR), which typically decreases in power following (visuo)motor perturbations. Healthy participants performed reaching movements towards a target, with online visual feedback replacing the view of their moving hand. The feedback was sometimes rotated, either relative to their movements (visuomotor rotation) or invariant to their movements (and relative to the target; clamped feedback), always for two consecutive trials interspersed between non-rotated trials. In both conditions, the first trial with a rotation was unpredictable. On the second trial, the task was either to re-aim, and thereby compensate for the rotation experienced in the first trial (visuomotor rotation; Compensate condition), or to ignore the rotation and keep on aiming at the target (clamped feedback; Ignore condition). After-effects did not differ between conditions, indicating that the amount of implicit learning was similar, while large differences in movement direction in the second rotated trial between conditions indicated that participants successfully acquired re-aiming strategies. Importantly, PMBR power following the first rotated trial was modulated differently in the two conditions. Specifically, it decreased in both conditions, but this effect was larger when participants had to acquire a cognitive strategy and prepare to re-aim. Our results therefore suggest that the PMBR is modulated by cognitive demands of motor learning, possibly reflecting the evaluation of a behaviourally significant goal achievement error.

Sensory prediction error drives subconscious motor learning outside of the laboratory

Sensorimotor adaptation is supported by at least two parallel learning systems: an intentionally controlled explicit strategy and an involuntary implicit learning system. Past work focused on constrained reaches or finger movements in laboratory environments has shown subconscious learning systems to be driven in part by sensory prediction error (SPE), i.e., the mismatch between the realized and expected outcome of an action. We designed a ball rolling task to explore whether SPEs can drive implicit motor adaptation during complex whole body movements that impart physical motion on external objects. After applying a visual shift, participants rapidly adapted their rolling angles to reduce the error between the ball and the target. We removed all visual feedback and told participants to aim their throw directly toward the primary target, revealing an unintentional 5.06° implicit adjustment to reach angles that decayed over time. To determine whether this implicit adaptation was driven by SPE, we gave participants a second aiming target that would "solve" the visual shift, as in the study by Mazzoni and Krakauer (Mazzoni P, Krakauer JW. J Neurosci 26: 3642-3645, 2006). Remarkably, after rapidly reducing ball-rolling error to zero (due to enhancements in strategic aiming), the additional aiming target caused rolling angles to deviate beyond the primary target by 3.15°. This involuntary overcompensation, which worsened task performance, is a hallmark of SPE-driven implicit learning. These results show that SPE-driven implicit processes, previously observed within simplified finger or planar reaching movements, actively contribute to motor adaptation in more complex naturalistic skill-based tasks.NEW & NOTEWORTHY Implicit and explicit learning systems have been detected using simple, constrained movements inside the laboratory. How these systems impact movements during complex whole body, skill-based tasks has not been established. Here, we demonstrate that sensory prediction errors significantly impact how a person updates their movements, replicating findings from the laboratory in an unconstrained ball-rolling task. This real-world validation is an important step toward explaining how subconscious learning helps humans execute common motor skills in dynamic environments.

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.

Implicit Sensorimotor Adaptation Proceeds in Absence of Movement Execution

In implicit sensorimotor adaptation, a mismatch between the predicted and actual sensory feedback results in a sensory prediction error (SPE). Sensory predictions have long been thought to be linked to descending motor commands, implying a necessary contribution of movement execution to adaptation. However, recent work has shown that mere motor imagery (MI) also engages predictive mechanisms, opening up the possibility that MI might be sufficient to drive implicit adaptation. In a within-subject design in humans (n = 30), implicit adaptation was assessed in a center-out reaching task, following a single exposure to a visuomotor rotation. It was hypothesized that performing MI of a reaching movement while being provided with an animation of rotated visual feedback (MI condition) would lead to postrotation biases (PRBs) similar to those observed when the movement is executed (Execution condition). Results revealed that both the MI and Execution conditions led to significant directional biases following rotated trials. Yet the magnitude of these biases was significantly larger in the Execution condition. To further probe the contribution of MI to adaptation, a Control condition was conducted in which participants were presented with the same rotated visual animation as in the MI condition, but in which they were prevented from performing MI. Surprisingly, significant biases were also observed in the Control condition, suggesting that MI per se may not have accounted for adaptation. Overall, these results suggest that implicit adaptation can be partially supported by processes other than those that strictly pertain to generating motor commands, although movement execution does potentiate it.

Implicit motor adaptation driven by intermittent and invariant errors

Our movements and movement outcomes are disturbed by environmental changes, leading to errors. During ongoing environmental changes, people should correct their movement using sensory feedback. However, when the changes are momentary, corrections based on sensory feedback are undesirable. Previous studies have suggested that implicit motor adaptation takes place despite the realization that the presented visual feedback should be ignored. Although these studies created experimental situations in which participants had to continuously ignore the presented visual feedback, in daily lives, people intermittently encounter opportunities to ignore sensory feedback. In this study, by intermittently presenting visual error clamp feedback, always offset from a target by 16° counterclockwise, regardless of the actual movement in a reaching experiment, we provided intermittent opportunities to ignore the visual feedback. We found that in the trials conducted immediately after presenting the visual error clamp feedback, reaching movements shifted in the direction opposite to the feedback, which is a hallmark of implicit motor adaptation. Moreover, the magnitude of the shift was significantly correlated with the rate of motor adaptation to gradual changes in the environment. Therefore, the results suggest that people unintentionally react to momentary environmental changes, which should be ignored. In addition, the sensitivity to momentary changes is greater in people who can quickly adapt to gradual environmental changes.

Updating of Implicit Adaptation Processes through Erroneous Numeric Feedback

There is debate about how implicit and explicit processes interact in sensorimotor adaptation, implicating how error signals drive learning. Target error information is thought to primarily influence explicit processes, therefore manipulations to the veracity of this information should impact adaptation but not implicit recalibration (i.e. after-effects). Thirty participants across three groups initially adapted to rotated cursor feedback. Then we manipulated numeric target error through knowledge of results (KR) feedback, where groups practised with correct or incorrect (+/-15°) numeric KR. Participants adapted to erroneous KR, but only the KR + 15 group showed augmented implicit recalibration, evidenced by larger after-effects than before KR exposure. In the presence of sensory prediction errors, target errors modulated after-effects, suggesting an interaction between implicit and explicit processes.

Reinforcement learning establishes a minimal metacognitive process to monitor and control motor learning performance

Humans and animals develop learning-to-learn strategies throughout their lives to accelerate learning. One theory suggests that this is achieved by a metacognitive process of controlling and monitoring learning. Although such learning-to-learn is also observed in motor learning, the metacognitive aspect of learning regulation has not been considered in classical theories of motor learning. Here, we formulated a minimal mechanism of this process as reinforcement learning of motor learning properties, which regulates a policy for memory update in response to sensory prediction error while monitoring its performance. This theory was confirmed in human motor learning experiments, in which the subjective sense of learning-outcome association determined the direction of up- and down-regulation of both learning speed and memory retention. Thus, it provides a simple, unifying account for variations in learning speeds, where the reinforcement learning mechanism monitors and controls the motor learning process.

Evidence of Implicit and Explicit Motor Learning during Gait Training with Distorted Rhythmic Auditory Cues

Gait training with rhythmic auditory cues contains motor learning mechanisms that are weighted more explicitly than implicitly. However, various clinical populations may benefit from a shift to gait training with greater implicit motor learning mechanisms. To investigate the ability to incorporate more implicit-weighted motor learning processes during rhythmic auditory cueing, we attempted to induce error-based recalibration using a subtly varying metronome cue for naïve unimpaired young adults. We assessed the extent of implicit and explicit retention after both an isochronous metronome and subtly varying metronome frequency during treadmill and overground walking. Despite 90% of participants remaining unaware of the changing metronome frequency, participants adjusted their cadence and step length to the subtly changing metronome, both on a treadmill and overground (p < 0.05). However, despite evidence of both implicit and explicit processes involved with each metronome (i.e., isochronous and varying), there were no between-condition differences in implicit or explicit retention for cadence, step length, or gait speed, and thus no increased implicit learning advantage with the addition of error-based recalibration for young, unimpaired adults.

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.

Bridging event-related potentials with behavioral studies in motor learning

Behavioral approaches and electrophysiology in understanding human sensorimotor systems have both yielded substantial advancements in past decades. In fact, behavioral neuroscientists have found that motor learning involves the two distinct processes of the implicit and the explicit. Separately, they have also distinguished two kinds of errors that drive motor learning: sensory prediction error and task error. Scientists in electrophysiology, in addition, have discovered two motor-related, event-related potentials (ERPs): error-related negativity (ERN), and feedback-related negativity (FRN). However, there has been a lack of interchange between the two lines of research. This article, therefore, will survey through the literature in both directions, attempting to establish a bridge between these two fruitful lines of research.

Low Vision Impairs Implicit Sensorimotor Adaptation in Response to Small Errors, But Not Large Errors

Successful goal-directed actions require constant fine-tuning of the motor system. This fine-tuning is thought to rely on an implicit adaptation process that is driven by sensory prediction errors (e.g., where you see your hand after reaching vs. where you expected it to be). Individuals with low vision experience challenges with visuomotor control, but whether low vision disrupts motor adaptation is unknown. To explore this question, we assessed individuals with low vision and matched controls with normal vision on a visuomotor task designed to isolate implicit adaptation. We found that low vision was associated with attenuated implicit adaptation only for small visual errors, but not for large visual errors. This result highlights important constraints underlying how low-fidelity visual information is processed by the sensorimotor system to enable successful implicit adaptation.

Adapting to visuomotor rotations in stepped increments increases implicit motor learning

Human motor adaptation relies on both explicit conscious strategies and implicit unconscious updating of internal models to correct motor errors. Implicit adaptation is powerful, requiring less preparation time before executing adapted movements, but recent work suggests it is limited to some absolute magnitude regardless of the size of a visuomotor perturbation when the perturbation is introduced abruptly. It is commonly assumed that gradually introducing a perturbation should lead to improved implicit learning beyond this limit, but outcomes are conflicting. We tested whether introducing a perturbation in two distinct gradual methods can overcome the apparent limit and explain past conflicting findings. We found that gradually introducing a perturbation in a stepped manner, where participants were given time to adapt to each partial step before being introduced to a larger partial step, led to ~ 80% higher implicit aftereffects of learning, but introducing it in a ramped manner, where participants adapted larger rotations on each subsequent reach, did not. Our results clearly show that gradual introduction of a perturbation can lead to substantially larger implicit adaptation, as well as identify the type of introduction that is necessary to do so.

Error Size Shape Relationships between Motor Variability and Implicit Motor Adaptation

Previous studies have demonstrated the effects of motor variability on motor adaptation. However, their findings have been inconsistent, suggesting that various factors affect the relationship between motor variability and adaptation. This study focused on the size of errors driving motor adaptation as one of the factors and examined the relationship between different error sizes. Thirty-one healthy young adults participated in a visuomotor task in which they made fast-reaching movements toward a target. Motor variability was measured in the baseline phase when a veridical feedback cursor was presented. In the adaptation phase, the feedback cursor was sometimes not reflected in the hand position and deviated from the target by 0°, 3°, 6°, or 12° counterclockwise or clockwise (i.e., error-clamp feedback). Movements during trials following trials with error-clamp feedback were measured to quantify implicit adaptation. Implicit adaptation was driven by errors presented through error-clamp feedback. Moreover, motor variability significantly correlated with implicit adaptation driven by a 12° error. The results suggested that motor variability accelerates implicit adaptation when a larger error occurs. As such a trend was not observed when smaller errors occurred, the relationship between motor variability and motor adaptation might have been affected by the error size driving implicit adaptation.

Abrupt, but not gradual, motor adaptation biases saccadic target selection

Motor costs influence movement selection. These costs could change when movements are adapted in response to errors. When the motor system attributes the encountered errors to an external cause, appropriate movement selection requires an update of the movement goal, which prompts the selection of a different control policy. However, when errors are attributed to an internal cause, the initially selected control policy could remain unchanged, but the internal forward model of the body needs to be updated, resulting in an online correction of the movement. We hypothesized that external attribution of errors leads to the selection of a different control policy, and thus to a change in the expected cost of movements. This should also affect subsequent motor decisions. Conversely, internal attribution of errors may (initially) only evoke online corrections, and thus is expected to leave the motor decision process unchanged. We tested this hypothesis using a saccadic adaptation paradigm, designed to change the relative motor cost of two targets. Motor decisions were measured using a target selection task between the two saccadic targets before and after adaptation. Adaptation was induced by either abrupt or gradual perturbation schedules, which are thought to induce more external or internal attribution of errors, respectively. By taking individual variability into account, our results show that saccadic decisions shift toward the least costly target after adaptation, but only when the perturbation is abruptly, and not gradually, introduced. We suggest that credit assignment of errors not only influences motor adaptation but also subsequent motor decisions.NEW & NOTEWORTHY Decisions between potential motor actions are influenced by their costs, but costs change when movements are adapted. Using a saccadic target selection task, we show that target preference shifts after abrupt, but not after gradual adaptation. We suggest that this difference emerges because abrupt adaptation results in target remapping, and thus directly influences cost calculations, whereas gradual adaptation is mainly driven by corrections to a forward model that is not involved in cost calculations.

Interaction of dynamic error signals in saccade adaptation

Motor adaptation maintains movement accuracy. To evaluate movement accuracy, motor adaptation relies on an error signal, generated by the movement target, while suppressing error signals from irrelevant objects in the vicinity. Previous work used static testing environments, where all information required to evaluate movement accuracy was available simultaneously. Using saccadic eye movements as a model for motor adaptation, we tested how movement accuracy is maintained in dynamic environments, where the availability of conflicting error signals varied over time. Participants made a vertical saccade toward a target (either a small square or a large ring). Upon saccade detection, two candidate stimuli were shown left and right of the target, and participants were instructed to discriminate a feature on one of the candidates. Critically, candidate stimuli were presented sequentially, and saccade adaptation, thus, had to resolve a conflict between a task-relevant and a task-irrelevant error signal that were separated in space and time. We found that the saccade target influenced several aspects of oculomotor learning. In presence of a small target, saccade adaptation evaluated movement accuracy based on the first available error signal after the saccade, irrespective of its task relevance. However, a large target not only allowed for greater flexibility when evaluating movement accuracy, but it also promoted a stronger contribution of strategic behavior when compensating inaccurate saccades. Our results demonstrate how motor adaptation maintains movement accuracy in dynamic environments, and how properties of the visual environment modulate the relative contribution of different learning processes.NEW & NOTEWORTHY Motor adaptation is typically studied in static environments, where all information that is required to evaluate movement accuracy is available simultaneously. Here, using saccadic eye movements as a model, we studied motor adaptation in a dynamic environment, where the availability of conflicting information about movement accuracy varied over time. We demonstrate that properties of the visual environment determine how dynamic movement errors are corrected.

Signatures of contextual interference in implicit sensorimotor adaptation

Contextual interference refers to the phenomenon whereby a blocked practice schedule results in faster acquisition but poorer retention of new motor skills compared to a random practice schedule. While contextual interference has been observed under a broad range of tasks, it remains unclear if this effect generalizes to the implicit and automatic recalibration of an overlearned motor skill. To address this question, we compared blocked and random practice schedules in a visuomotor rotation task that isolates implicit adaptation. In experiment 1, we found robust signatures of contextual interference in implicit adaptation: compared to participants tested under a blocked training schedule, participants tested under a random training schedule exhibited a reduced rate of learning during the training phase but better retention during a subsequent no-feedback assessment phase. In experiment 2, we again observed an advantage in retention following random practice and showed that this result was not due to a change in context between the training and assessment phases (e.g. a blocked training schedule followed by a random assessment schedule). Taken together, these results indicate that contextual interference is not limited to the acquisition of new motor skills but also applies to the implicit adaptation of established motor skills.

Short-term effects of visuomotor discrepancies on multisensory integration, proprioceptive recalibration, and motor adaptation

Information about the position of our hand is provided by multisensory signals that are often not perfectly aligned. Discrepancies between the seen and felt hand position or its movement trajectory engage the processes of 1) multisensory integration, 2) sensory recalibration, and 3) motor adaptation, which adjust perception and behavioral responses to apparently discrepant signals. To foster our understanding of the coemergence of these three processes, we probed their short-term dependence on multisensory discrepancies in a visuomotor task that has served as a model for multisensory perception and motor control previously. We found that the well-established integration of discrepant visual and proprioceptive signals is tied to the immediate discrepancy and independent of the outcome of the integration of discrepant signals in immediately preceding trials. However, the strength of integration was context dependent, being stronger in an experiment featuring stimuli that covered a smaller range of visuomotor discrepancies (±15°) compared with one covering a larger range (±30°). Both sensory recalibration and motor adaptation for nonrepeated movement directions were absent after two bimodal trials with same or opposite visuomotor discrepancies. Hence our results suggest that short-term sensory recalibration and motor adaptation are not an obligatory consequence of the integration of preceding discrepant multisensory signals.NEW & NOTEWORTHY The functional relation between multisensory integration and recalibration remains debated. We here refute the notion that they coemerge in an obligatory manner and support the hypothesis that they serve distinct goals of perception.

Implicit sensorimotor adaptation is preserved in Parkinson's disease

Our ability to enact successful goal-directed actions involves multiple learning processes. Among these processes, implicit motor adaptation ensures that the sensorimotor system remains finely tuned in response to changes in the body and environment. Whether Parkinson's disease impacts implicit motor adaptation remains a contentious area of research: whereas multiple reports show impaired performance in this population, many others show intact performance. While there is a range of methodological differences across studies, one critical issue is that performance in many of the studies may reflect a combination of implicit adaptation and strategic re-aiming. Here, we revisited this controversy using a visuomotor task designed to isolate implicit adaptation. In two experiments, we found that adaptation in response to a wide range of visual perturbations was similar in Parkinson's disease and matched control participants. Moreover, in a meta-analysis of previously published and unpublished work, we found that the mean effect size contrasting Parkinson's disease and controls across 16 experiments involving over 200 participants was not significant. Together, these analyses indicate that implicit adaptation is preserved in Parkinson's disease, offering a fresh perspective on the role of the basal ganglia in sensorimotor learning.

Understanding implicit sensorimotor adaptation as a process of proprioceptive re-alignment

Multiple learning processes contribute to successful goal-directed actions in the face of changing physiological states, biomechanical constraints, and environmental contexts. Amongst these processes, implicit sensorimotor adaptation is of primary importance, ensuring that movements remain well-calibrated and accurate. A large body of work on reaching movements has emphasized how adaptation centers on an iterative process designed to minimize visual errors. The role of proprioception has been largely neglected, thought to play a passive role in which proprioception is affected by the visual error but does not directly contribute to adaptation. Here, we present an alternative to this visuo-centric framework, outlining a model in which implicit adaptation acts to minimize a proprioceptive error, the distance between the perceived hand position and its intended goal. This proprioceptive re-alignment model (PReMo) is consistent with many phenomena that have previously been interpreted in terms of learning from visual errors, and offers a parsimonious account of numerous unexplained phenomena. Cognizant that the evidence for PReMo rests on correlational studies, we highlight core predictions to be tested in future experiments, as well as note potential challenges for a proprioceptive-based perspective on implicit adaptation.

Motor learning without movement

Prediction errors guide many forms of learning, providing teaching signals that help us improve our performance. Implicit motor adaptation, for instance, is thought to be driven by sensory prediction errors (SPEs), which occur when the expected and observed consequences of a movement differ. Traditionally, SPE computation is thought to require movement execution. However, recent work suggesting that the brain can generate sensory predictions based on motor imagery or planning alone calls this assumption into question. Here, by measuring implicit motor adaptation during a visuomotor task, we tested whether motor planning and well-timed sensory feedback are sufficient for adaptation. Human participants were cued to reach to a target and were, on a subset of trials, rapidly cued to withhold these movements. Errors displayed both on trials with and without movements induced single-trial adaptation. Learning following trials without movements persisted even when movement trials had never been paired with errors and when the direction of movement and sensory feedback trajectories were decoupled. These observations indicate that the brain can compute errors that drive implicit adaptation without generating overt movements, leading to the adaptation of motor commands that are not overtly produced.

Visuomotor Adaptation of Lower Extremity Movements During Virtual Ball-Kicking Task

Sophisticated soccer players can skillfully manipulate a ball with their feet depending on the external environment. This ability of goal-directed control in the lower limbs has not been fully elucidated, although upper limb movements have been studied extensively using motor adaptation tasks. The purpose of this study was to clarify how the goal-directed movements of the lower limbs is acquired by conducting an experiment of visuomotor adaptation in ball-kicking movements. In this study, healthy young participants with and without experience playing soccer or futsal performed ball-kicking movements. They were instructed to move a cursor representing the right foot position and shoot a virtual ball to a target on a display in front of them. During the learning trials, the trajectories of the virtual ball were rotated by 15° either clockwise or counterclockwise relative to the actual ball direction. As a result, participants adapted their lower limb movements to novel visuomotor perturbation regardless of the soccer playing experience, and changed their whole trajectories not just the kicking position during adaptation. These results indicate that the goal-directed lower limb movements can be adapted to the novel environment. Moreover, it was suggested that fundamental structure of visuomotor adaptation is common between goal-directed movements in the upper and lower limbs.

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.