Feedback delay attenuates implicit but facilitates explicit adjustments to a visuomotor rotation

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

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

Subtle Visual Latency Can Profoundly Impair Implicit Sensorimotor Learning

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.

Advanced feedback enhances sensorimotor adaptation

It is widely recognized that sensorimotor adaptation is facilitated when feedback is provided throughout the movement compared with when it is provided at the end of the movement. However, the source of this advantage is unclear: continuous feedback is more ecological, dynamic, and available earlier than endpoint feedback. Here, we assess the relative merits of these factors using a method that allows us to manipulate feedback timing independent of actual hand position. By manipulating the onset time of "endpoint" feedback, we found that adaptation was modulated in a non-monotonic manner, with the peak of the function occurring in advance of the hand reaching the target. Moreover, at this optimal time, learning was of similar magnitude as that observed with continuous feedback. By varying movement duration, we demonstrate that this optimal time occurs at a relatively fixed time after movement onset, an interval we hypothesize corresponds to when the comparison of the sensory prediction and feedback generates the strongest error signal.

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.

Measuring motion-to-photon latency for sensorimotor experiments with virtual reality systems

Consumer virtual reality (VR) systems are increasingly being deployed in research to study sensorimotor behaviors, but properties of such systems require verification before being used as scientific tools. The 'motion-to-photon' latency (the lag between a user making a movement and the movement being displayed within the display) is a particularly important metric as temporal delays can degrade sensorimotor performance. Extant approaches to quantifying this measure have involved the use of bespoke software and hardware and produce a single measure of latency and ignore the effect of the motion prediction algorithms used in modern VR systems. This reduces confidence in the generalizability of the results. We developed a novel, system-independent, high-speed camera-based latency measurement technique to co-register real and virtual controller movements, allowing assessment of how latencies change through a movement. We applied this technique to measure the motion-to-photon latency of controller movements in the HTC Vive, Oculus Rift, Oculus Rift S, and Valve Index, using the Unity game engine and SteamVR. For the start of a sudden movement, all measured headsets had mean latencies between 21 and 42 ms. Once motion prediction could account for the inherent delays, the latency was functionally reduced to 2-13 ms, and our technique revealed that this reduction occurs within ~25-58 ms of movement onset. Our findings indicate that sudden accelerations (e.g., movement onset, impacts, and direction changes) will increase latencies and lower spatial accuracy. Our technique allows researchers to measure these factors and determine the impact on their experimental design before collecting sensorimotor data from VR systems.

Implicit reward-based motor learning

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

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.

Mechanisms of sensorimotor adaptation in a hierarchical state feedback control model of speech

Upon perceiving sensory errors during movements, the human sensorimotor system updates future movements to compensate for the errors, a phenomenon called sensorimotor adaptation. One component of this adaptation is thought to be driven by sensory prediction errors-discrepancies between predicted and actual sensory feedback. However, the mechanisms by which prediction errors drive adaptation remain unclear. Here, auditory prediction error-based mechanisms involved in speech auditory-motor adaptation were examined via the feedback aware control of tasks in speech (FACTS) model. Consistent with theoretical perspectives in both non-speech and speech motor control, the hierarchical architecture of FACTS relies on both the higher-level task (vocal tract constrictions) as well as lower-level articulatory state representations. Importantly, FACTS also computes sensory prediction errors as a part of its state feedback control mechanism, a well-established framework in the field of motor control. We explored potential adaptation mechanisms and found that adaptive behavior was present only when prediction errors updated the articulatory-to-task state transformation. In contrast, designs in which prediction errors updated forward sensory prediction models alone did not generate adaptation. Thus, FACTS demonstrated that 1) prediction errors can drive adaptation through task-level updates, and 2) adaptation is likely driven by updates to task-level control rather than (only) to forward predictive models. Additionally, simulating adaptation with FACTS generated a number of important hypotheses regarding previously reported phenomena such as identifying the source(s) of incomplete adaptation and driving factor(s) for changes in the second formant frequency during adaptation to the first formant perturbation. The proposed model design paves the way for a hierarchical state feedback control framework to be examined in the context of sensorimotor adaptation in both speech and non-speech effector systems.

Implicit reward-based motor learning

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

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.

Orientation control strategies and adaptation to a visuomotor perturbation in rotational hand movements

Computational approaches to biological motor control are used to discover the building blocks of human motor behaviour. Models explaining features of human hand movements have been studied thoroughly, yet only a few studies attempted to explain the control of the orientation of the hand; instead, they mainly focus on the control of hand translation, predominantly in a single plane. In this study, we present a new methodology to study the way humans control the orientation of their hands in three dimensions and demonstrate it in two sequential experiments. We developed a quaternion-based score that quantifies the geodicity of rotational hand movements and evaluated it experimentally. In the first experiment, participants performed a simple orientation-matching task with a robotic manipulator. We found that rotations are generally performed by following a geodesic in the quaternion hypersphere, which suggests that, similarly to translation, the orientation of the hand is centrally controlled, possibly by optimizing geometrical properties of the hand’s rotation. This result established a baseline for the study of human response to perturbed visual feedback of the orientation of the hand. In the second experiment, we developed a novel visuomotor rotation task in which the rotation is applied on the hand’s rotation, and studied the adaptation of participants to this rotation, and the transfer of the adaptation to a different initial orientation. We observed partial adaptation to the rotation. The patterns of the transfer of the adaptation to a different initial orientation were consistent with the representation of the orientation in extrinsic coordinates. The methodology that we developed allows for studying the control of a rigid body without reducing the dimensionality of the task. The results of the two experiments open questions for future studies regarding the mechanisms underlying the central control of hand orientation. These results can be of benefit for many applications that involve fine manipulation of rigid bodies, such as teleoperation and neurorehabilitation.

Calibrating Vision: Concepts and Questions

The idea that visual coding and perception are shaped by experience and adjust to changes in the environment or the observer is universally recognized as a cornerstone of visual processing, yet the functions and processes mediating these calibrations remain in many ways poorly understood. In this article we review a number of facets and issues surrounding the general notion of calibration, with a focus on plasticity within the encoding and representational stages of visual processing. These include how many types of calibrations there are - and how we decide; how plasticity for encoding is intertwined with other principles of sensory coding; how it is instantiated at the level of the dynamic networks mediating vision; how it varies with development or between individuals; and the factors that may limit the form or degree of the adjustments. Our goal is to give a small glimpse of an enormous and fundamental dimension of vision, and to point to some of the unresolved questions in our understanding of how and why ongoing calibrations are a pervasive and essential element of vision.

Reduced feedback barely slows down proprioceptive recalibration

Introducing altered visual feedback of the hand produces quick adaptation of reaching movements. Our lab has shown that the associated shifts in estimates of the felt position of the hand saturate within a few training trials. The current study investigates whether the rapid changes in felt hand position that occur during classic visuomotor adaptation are diminished or slowed when training feedback is reduced. We reduced feedback by either providing visual feedback only at the end of the reach (terminal feedback) or constraining hand movements to reduce motor adaptation-related error signals such as sensory prediction errors and task errors (exposure). We measured changes as participants completed reaches with a 30° rotation, a -30° rotation, and clamped visual feedback, with these two "impoverished" training conditions, along with classic visuomotor adaptation training, while continuously estimating their felt hand position. Training with terminal feedback slightly reduced the initial rate of change in overall adaptation. However, the rate of change in hand localization, as well as the asymptote of hand localization shifts in both the terminal feedback group and the exposure training group was not noticeably different from those in the classic training group. Taken together, shifts in felt hand position are rapid and robust responses to sensory mismatches and are at best slightly modulated when feedback is reduced. This suggests that given the speed and invariance to the quality of feedback of proprioceptive recalibration, it could immediately contribute to all kinds of reach adaptation.NEW & NOTEWORTHY Reaching to targets with altered visual feedback about hand position leads to adaptation of movements as well as shifts in estimates of felt hand position. Felt hand position can shift in as little as one trial, and here we show that there is no noticeable reduction in speed when the feedback about movements is impoverished, indicating the robustness of the process of recalibrating felt hand position.

Implicit Adaptation Processes Promoted by Immediate Offline Visual and Numeric Feedback

In adaptation learning, visual feedback impacts how adaptation proceeds. With concurrent feedback, a more implicit/feedforward process is thought to be engaged, compared to feedback after movement, which promotes more explicit processes. Due to discrepancies across studies, related to timing and type of visual feedback, we isolated these conditions here. Four groups (N = 52) practiced aiming under rotated feedback conditions; feedback was provided concurrently, immediately after movement (visually or numerically), or visually after a 3 s delay. All groups adapted and only delayed feedback attenuated implicit adaptation as evidenced by post-practice after-effects. Contrary to some suggestions, immediately presented offline and numeric feedback resulted in implicit after-effects, potentially due to comparisons between feedforward information and seen or imagined feedback.

Additional cognitive load decreases performance but not adaptation to a visuomotor transformation

Dual-task paradigms are procedures for investigating interference with two tasks performed simultaneously. Studies that previously addressed dual-task paradigms within a visuomotor reaching task yielded mixed results. While some of the studies found evidence of cognitive interference, called dual-task costs, other studies did not. We assume that dual-task costs only manifest themselves within the explicit component of adaptation, as it involves cognitive resources for processing. We suspect the divergent findings to be due to the lack of differentiation between the explicit and implicit component. In this study, we aimed to investigate how a cognitive secondary task affects visuomotor adaptation overall and its different components, both during and after adaptation. In a series of posttests, we examined the explicit and implicit components separately. Eighty participants performed a center-outward reaching movement with a 30° cursor perturbation. Participants were either assigned to a single task group (ST) or a dual-task group (DT) with an additional auditory 1-back task. To further enhance our predicted effect of dual-task interference on the explicit component, we added a visual feedback delay condition to both groups (ST/DT_DEL). In the other condition, participants received visual feedback immediately after movement termination (ST/DT_NoDEL). While there were clear dual-task costs during the practice phase, there were no dual-task effects on any of the posttest measures. On one hand, our findings suggest that dual-task costs in visuomotor adaptation tasks can occur with sufficient cognitive demand, and on the other hand, that cognitive constraints may affect motor performance but not necessarily motor adaptation.

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.

Prolonged Feedback Duration Does Not Affect Implicit Recalibration in a Visuomotor Rotation Task

Visuomotor rotations are frequently used to study cognitive processes underlying motor adaptation. Explicit aiming strategies and implicit recalibration are two of these processes. A large body of literature indicates that both processes are in fact dissociable and mainly independent components that can be measured using different manipulations in visuomotor rotation tasks. Visual feedback is a crucial element in these tasks, and it therefore plays an important role when assessing explicit re-aiming and implicit recalibration. For instance, researchers have found timing of visual feedback to affect the contribution of implicit recalibration to learning: if feedback is shown only at the end of the movement (instead of continuously), implicit recalibration decreases. Similarly, participants show lower levels of implicit recalibration if visual feedback is presented with a delay (instead of immediately). We thus hypothesized that the duration of feedback availability might also play a role. The goal of this study was thus to investigate the effect of longer versus shorter feedback durations on implicit recalibration in human participants. To this end, we compared three feedback durations in a between-subject design: 200, 600, and 1200 ms. Using a large sample size, we found differences between groups to be quite small, to the point where most differences indicated statistical equivalence between group means. We therefore hypothesize that feedback duration, when only endpoint feedback is presented, has a negligible effect on implicit recalibration. We propose that future research investigate the effect of feedback duration on other parameters of adaptation, so as proprioceptive recalibration and explicit re-aiming.

Separation of multiple motor memories through implicit and explicit processes

Acquisition of multiple motor skills without interference is a remarkable ability in daily life. During adaptation to opposing perturbations, a common paradigm to study this ability, each perturbation can be successfully learned when a contextual follow-through movement is associated with the direction of the perturbation. It is still unclear, however, to what extent this learning engages the cognitive explicit process and the implicit process. Here, we untangled the individual contributions of the explicit and implicit components while participants learned opposing visuomotor perturbations, with a second unperturbed follow-through movement. In Exp. 1 we replicated previous adaptation results and showed that follow-through movements also allow learning for opposing visuomotor rotations. For one group of participants in Exp. 2 we isolated strategic explicit learning, while for another group we isolated the implicit component. Our data showed that opposing perturbations could be fully learned by explicit strategies; but when strategy was restricted, distinct implicit processes contributed to learning. In Exp.3, we examined whether learning is influenced by the disparity between the follow-through contexts. We found that the location of follow-through targets had little effect on total learning, yet it led to more instances in which participants failed to learn the task. In Exp. 4, we explored the generalization capability to untrained targets. Participants showed near-flat generalization of the implicit and explicit processes. Overall, our results indicate that follow-through contextual cues might activate, in part, top-down cognitive factors that influence not only the dynamics of the explicit learning, but also the implicit process.

A Revised Framework for the Investigation of Expectation Update Versus Maintenance in the Context of Expectation Violations: The ViolEx 2.0 Model

Expectations are probabilistic beliefs about the future that shape and influence our perception, affect, cognition, and behavior in many contexts. This makes expectations a highly relevant concept across basic and applied psychological disciplines. When expectations are confirmed or violated, individuals can respond by either updating or maintaining their prior expectations in light of the new evidence. Moreover, proactive and reactive behavior can change the probability with which individuals encounter expectation confirmations or violations. The investigation of predictors and mechanisms underlying expectation update and maintenance has been approached from many research perspectives. However, in many instances there has been little exchange between different research fields. To further advance research on expectations and expectation violations, collaborative efforts across different disciplines in psychology, cognitive (neuro)science, and other life sciences are warranted. For fostering and facilitating such efforts, we introduce the ViolEx 2.0 model, a revised framework for interdisciplinary research on cognitive and behavioral mechanisms of expectation update and maintenance in the context of expectation violations. To support different goals and stages in interdisciplinary exchange, the ViolEx 2.0 model features three model levels with varying degrees of specificity in order to address questions about the research synopsis, central concepts, or functional processes and relationships, respectively. The framework can be applied to different research fields and has high potential for guiding collaborative research efforts in expectation research.

Implicit adaptation to mirror reversal is in the correct coordinate system but the wrong direction

Learning in visuomotor adaptation tasks is the result of both explicit and implicit processes. Explicit processes, operationalized as reaiming an intended movement to a new goal, account for a significant proportion of learning. However, implicit processes, operationalized as error-dependent learning that gives rise to aftereffects, appear to be highly constrained. The limitations of implicit learning are highlighted in the mirror-reversal task, where implicit corrections act in opposition to performance. This is surprising given the mirror-reversal task has been viewed as emblematic of implicit learning. One potential issue not being considered in these studies is that both explicit and implicit processes were allowed to operate concurrently, which may interact, potentially in opposition. Therefore, we sought to further characterize implicit learning in a mirror-reversal task with a clamp design to isolate implicit learning from explicit strategies. We confirmed that implicit adaptation is in the wrong direction for mirror reversal and operates as if the perturbation were a rotation and only showed a moderate attenuation after 3 days of training. This result raised the question of whether implicit adaptation blindly operates as though perturbations were a rotation. In a separate experiment, which directly compared a mirror reversal and a rotation, we found that implicit adaptation operates in a proper coordinate system for different perturbations: adaptation to a mirror reversal and rotational perturbation is more consistent with Cartesian and polar coordinate systems, respectively. It remains an open question why implicit process would be flexible to the coordinate system of a perturbation but continue to be directed inappropriately.NEW & NOTEWORTHY Recent studies have found that implicit learning may operate inappropriately in some motor tasks, requiring explicit strategies to improve performance. However, this inappropriate adaptation could be attributable to competitive interactions between explicit and implicit processes. After isolating implicit processes, we found that implicit adaptation remained in the wrong direction for a mirror reversal, acting as if it were a rotation. Interestingly, however, the implicit system is sensitive to a particular coordinate system, treating mirror reversal and rotation differently.

Brain oscillatory correlates of visuomotor adaptive learning

Sensorimotor adaptation involves the recalibration of the mapping between motor command and sensory feedback in response to movement errors. Although adaptation operates within individual movements on a trial-to-trial basis, it can also undergo learning when adaptive responses improve over the course of many trials. Brain oscillatory activities related to these "adaptation" and "learning" processes remain unclear. The main reason for this is that previous studies principally focused on the beta band, which confined the outcome message to trial-to-trial adaptation. To provide a wider understanding of adaptive learning, we decoded visuomotor tasks with constant, random or no perturbation from EEG recordings in different bandwidths and brain regions using a multiple kernel learning approach. These different experimental tasks were intended to separate trial-to-trial adaptation from the formation of the new visuomotor mapping across trials. We found changes in EEG power in the post-movement period during the course of the visuomotor-constant rotation task, in particular an increased (i) theta power in prefrontal region, (ii) beta power in supplementary motor area, and (iii) gamma power in motor regions. Classifying the visuomotor task with constant rotation versus those with random or no rotation, we were able to relate power changes in beta band mainly to trial-to-trial adaptation to error while changes in theta band would relate rather to the learning of the new mapping. Altogether, this suggested that there is a tight relationship between modulation of the synchronization of low (theta) and higher (essentially beta) frequency oscillations in prefrontal and sensorimotor regions, respectively, and adaptive learning.

Does proprioceptive acuity influence the extent of implicit sensorimotor adaptation in young and older adults?

The ability to adjust movements to changes in the environment declines with aging. This age-related decline is caused by the decline of explicit adjustments. However, implicit adaptation remains intact and might even be increased with aging. Since proprioceptive information has been linked to implicit adaptation, it might well be that an age-related decline in proprioceptive acuity might be linked to the performance of older adults in implicit adaptation tasks. Indeed, age-related proprioceptive deficits could lead to altered sensory integration with an increased weighting of the visual sensory-prediction error. Another possibility is that reduced proprioceptive acuity results in an increased reliance on predicted sensory consequences of the movement. Both these explanations led to our preregistered hypothesis: we expected a relation between the decline of proprioception and the amount of implicit adaptation across ages. However, we failed to support this hypothesis. Our results question the existence of reliability-based integration of visual and proprioceptive signals during motor adaptation.

Different generalization of fast and slow visuomotor adaptation across locomotion and pointing tasks

Sensorimotor adaptation can involve multiple learning processes with different time courses, and these processes may have different patterns of transfer. In this study, we tested how slow learning and fast learning transfer across tasks, and the specificity of transfer. We tested two natural goal-directed tasks: pointing and walking toward a visible target. We also tested a novel "hand locomotion" task in which subjects used pointing movements to cause simulated self-motion in virtual reality. The hand locomotion task used the same physical movement as pointing, but performed the same function as stepping. During an experimental block, subjects performed alternating training trials with perturbed visual feedback and test trials with no feedback. The test trials were either the same task to measure adaptation, or a different task to measure transfer. Perturbations on adaptation trials varied over time as a sum of sinusoids with different frequencies. Fast learning would be expected to produce equal responses to fast and slow perturbations, while slower learning would dampen responses to higher frequency perturbations. Subjects were generally not aware of the smoothly varying perturbations, but showed detectable adaptation for all three tasks. Only pointing produced significantly different responses to high- and low-frequency perturbations, consistent with slow learning. Adaptation of pointing produced more transfer to the hand locomotion task, which shared the same effector and motor actions, than to the stepping task. The other tasks showed fast learning but little or no slow learning, and equal transfer to tasks with different effector or function. Our results suggest that the slower components of sensorimotor adaptation are more movement specific, while faster learning is more generalizable.

An implicit memory of errors limits human sensorimotor adaptation

During extended motor adaptation, learning appears to saturate despite persistence of residual errors. This adaptation limit is not fixed but varies with perturbation variance; when variance is high, residual errors become larger. These changes in total adaptation could relate to either implicit or explicit learning systems. Here, we found that when adaptation relied solely on the explicit system, residual errors disappeared and learning was unaltered by perturbation variability. In contrast, when learning depended entirely, or in part, on implicit learning, residual errors reappeared. Total implicit adaptation decreased in the high-variance environment due to changes in error sensitivity, not in forgetting. These observations suggest a model in which the implicit system becomes more sensitive to errors when they occur in a consistent direction. Thus, residual errors in motor adaptation are at least in part caused by an implicit learning system that modulates its error sensitivity in response to the consistency of past errors.

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

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

Acting in Temporal Contexts: On the Behavioral and Neurophysiological Consequences of Feedback Delays

Feedback on success or failure is critical to increase rewards through behavioral adaptation or learning of dependencies from trial and error. Learning from reward feedback is thereby treated as embedded in a reinforcement learning framework. Due to temporal discounting of reward, learning in this framework is suspected to be vulnerable to feedback delay. Together, investigations of reinforcement learning in learned decision making tasks show that performance and learning impairments due to feedback delay vary as a function of task type. Performance in tasks that require implicit processing is affected by the delayed availability of feedback compared to tasks that can be accomplished with explicit processing. At the same time, the feedback related negativity, an event related potential component in the electroencephalogram that is associated with feedback processing, is affected by feedback delay similarly independent of task type. With the idea of fully implicit or explicit processing as opposite endpoints of a continuum of reciprocal shares of the implicit and explicit processing systems with feedback delay as the determinant of where on this continuum processing can be located, a common explanatory approach of both, behavioral and electrophysiological findings, is suggested.

Taking aim at the perceptual side of motor learning: exploring how explicit and implicit learning encode perceptual error information through depth vision

Motor learning in visuomotor adaptation tasks results from both explicit and implicit processes, each responding differently to an error signal. Although the motor output side of these processes has been extensively studied, the visual input side is relatively unknown. We investigated if and how depth perception affects the computation of error information by explicit and implicit motor learning. Two groups of participants made reaching movements to bring a virtual cursor to a target in the frontoparallel plane. The Delayed group was allowed to reaim and their feedback was delayed to emphasize explicit learning, whereas the camped group received task-irrelevant clamped cursor feedback and continued to aim straight at the target to emphasize implicit adaptation. Both groups played this game in a highly detailed virtual environment (depth condition), leveraging a cover task of playing darts in a virtual tavern, and in an empty environment (no-depth condition). The delayed group showed an increase in error sensitivity under depth relative to no-depth. In contrast, the clamped group adapted to the same degree under both conditions. The movement kinematics of the delayed participants also changed under the depth condition, consistent with the target appearing more distant, unlike the Clamped group. A comparison of the delayed behavioral data with a perceptual task from the same individuals showed that the greater reaiming in the depth condition was consistent with an increase in the scaling of the error distance and size. These findings suggest that explicit and implicit learning processes may rely on different sources of perceptual information.NEW & NOTEWORTHY We leveraged a classic sensorimotor adaptation task to perform a first systematic assessment of the role of perceptual cues in the estimation of an error signal in the 3-D space during motor learning. We crossed two conditions presenting different amounts of depth information, with two manipulations emphasizing explicit and implicit learning processes. Explicit learning responded to the visual conditions, consistent with perceptual reports, whereas implicit learning appeared to be independent of them.

Speech auditory-motor adaptation to formant-shifted feedback lacks an explicit component: Reduced adaptation in adults who stutter reflects limitations in implicit sensorimotor learning

The neural mechanisms underlying stuttering remain poorly understood. A large body of work has focused on sensorimotor integration difficulties in individuals who stutter, including recently the capacity for sensorimotor learning. Typically, sensorimotor learning is assessed with adaptation paradigms in which one or more sensory feedback modalities are experimentally perturbed in real time. Our own previous work on speech with perturbed auditory feedback revealed substantial auditory-motor learning limitations in both children and adults who stutter (AWS). It remains unknown, however, which subprocesses of sensorimotor learning are impaired. Indeed, new insights from research on upper limb motor control indicate that sensorimotor learning involves at least two distinct components: (a) an explicit component that includes intentional strategy use and presumably is driven by target error and (b) an implicit component that updates an internal model without awareness of the learner and presumably is driven by sensory prediction error. Here, we attempted to dissociate these components for speech auditory-motor learning in AWS versus adults who do not stutter (AWNS). Our formant-shift auditory-motor adaptation results replicated previous findings that such sensorimotor learning is limited in AWS. Novel findings are that neither control nor stuttering participants reported any awareness of changing their productions in response to the auditory perturbation and that neither group showed systematic drift in auditory target judgments made throughout the adaptation task. These results indicate that speech auditory-motor adaptation to formant-shifted feedback relies exclusively on implicit learning processes. Thus, limited adaptation in AWS reflects poor implicit sensorimotor learning. Speech auditory-motor adaptation to formant-shifted feedback lacks an explicit component: Reduced adaptation in adults who stutter reflects limitations in implicit sensorimotor learning.

Motor learning by selection in visual working memory

Motor adaptation maintains movement accuracy over the lifetime. Saccadic eye movements have been used successfully to study the mechanisms and neural basis of adaptation. Using behaviorally irrelevant targets, it has been shown that saccade adaptation is driven by errors only in a brief temporal interval after movement completion. However, under natural conditions, eye movements are used to extract information from behaviorally relevant objects and to guide actions manipulating these objects. In this case, the action outcome often becomes apparent only long after movement completion, outside the supposed temporal window of error evaluation. Here, we show that saccade adaptation can be driven by error signals long after the movement when using behaviorally relevant targets. Adaptation occurred when a task-relevant target appeared two seconds after the saccade, or when a retro-cue indicated which of two targets, stored in visual working memory, was task-relevant. Our results emphasize the important role of visual working memory for optimal movement control.

Prolonged response time helps eliminate residual errors in visuomotor adaptation

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

Dissociated Development of Speech and Limb Sensorimotor Learning in Stuttering: Speech Auditory-motor Learning is Impaired in Both Children and Adults Who Stutter

Stuttering is a neurodevelopmental disorder of speech fluency. Various experimental paradigms have demonstrated that affected individuals show limitations in sensorimotor control and learning. However, controversy exists regarding two core aspects of this perspective. First, it has been claimed that sensorimotor learning limitations are detectable only in adults who stutter (after years of coping with the disorder) but not during childhood close to the onset of stuttering. Second, it remains unclear whether stuttering individuals' sensorimotor learning limitations affect only speech movements or also unrelated effector systems involved in nonspeech movements. We report data from separate experiments investigating speech auditory-motor learning (N = 60) and limb visuomotor learning (N = 84) in both children and adults who stutter versus matched nonstuttering individuals. Both children and adults who stutter showed statistically significant limitations in speech auditory-motor adaptation with formant-shifted feedback. This limitation was more profound in children than in adults and in younger children versus older children. Between-group differences in the adaptation of reach movements performed with rotated visual feedback were subtle but statistically significant for adults. In children, even the nonstuttering groups showed limited visuomotor adaptation just like their stuttering peers. We conclude that sensorimotor learning is impaired in individuals who stutter, and that the ability for speech auditory-motor learning-which was already adult-like in 3-6 year-old typically developing children-is severely compromised in young children near the onset of stuttering. Thus, motor learning limitations may play an important role in the fundamental mechanisms contributing to the onset of this speech disorder.

Design choices: Empirical recommendations for designing two-dimensional finger-tracking experiments

The continuous tracking of mouse or finger movements has become an increasingly popular research method for investigating cognitive and motivational processes such as decision-making, action-planning, and executive functions. In the present paper, we evaluate and discuss how apparently trivial design choices of researchers may impact participants' behavior and, consequently, a study's results. We first provide a thorough comparison of mouse- and finger-tracking setups on the basis of a Simon task. We then vary a comprehensive set of design factors, including spatial layout, movement extent, time of stimulus onset, size of the target areas, and hit detection in a finger-tracking variant of this task. We explore the impact of these variations on a broad spectrum of movement parameters that are typically used to describe movement trajectories. Based on our findings, we suggest several recommendations for best practice that avoid some of the pitfalls of the methodology. Keeping these recommendations in mind will allow for informed decisions when planning and conducting future tracking experiments.

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

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

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

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

Assessing explicit strategies in force field adaptation

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

How different effectors and action effects modulate the formation of separate motor memories

Humans can operate a variety of modern tools, which are often associated with different visuomotor transformations. Studies investigating this ability have shown that separate motor memories can be acquired implicitly when different sensorimotor transformations are associated with distinct (intended) postures or explicitly when abstract contextual cues are leveraged by aiming strategies. It still remains unclear how different transformations are remembered implicitly when postures are similar. We investigated whether features of planning to manipulate a visual tool, such as its visual identity or the environmental effect intended by its use (i.e. action effect) would enable implicit learning of opposing visuomotor rotations. Results show that neither contextual cue led to distinct implicit motor memories, but that cues only affected implicit adaptation indirectly through generalization around explicit strategies. In contrast, a control experiment where participants practiced opposing transformations with different hands did result in contextualized aftereffects differing between hands across generalization targets. It appears that different (intended) body states are necessary for separate aftereffects to emerge, suggesting that the role of sensory prediction error-based adaptation may be limited to the recalibration of a body model, whereas establishing separate tool models may proceed along a different route.

Environmental consistency modulation of error sensitivity during motor adaptation is explicitly controlled

Motor adaptation, the adjustment of a motor output in face of changes in the environment, may operate at different rates. When human participants encounter repeated or consistent perturbations, their corrections for the experienced errors are larger compared with when the perturbations are new or inconsistent. Such modulations of error sensitivity were traditionally considered to be an implicit process that does not require attentional resources. In recent years, the implicit view of motor adaptation has been challenged by evidence showing a contribution of explicit strategies to learning. These findings raise a fundamental question regarding the nature of the error sensitivity modulation processes. We tested the effect of explicit control on error sensitivity in a series of experiments, in which participants controlled a screen cursor to virtual targets. We manipulated environmental consistency by presenting rotations in random (low consistency) or random walk (high consistency) sequences and illustrated that perturbation consistency affects the rate of adaptation, corroborating previous studies. When participants were instructed to ignore the cursor and move directly to the target, thus eliminating the contribution of explicit strategies, consistency-driven error sensitivity modulation was not detected. In addition, delaying the visual feedback, a manipulation that affects implicit learning, did not influence error sensitivity under consistent perturbations. These results suggest that increases of learning rate in consistent environments are attributable to an explicit rather than implicit process in sensorimotor adaptation.NEW & NOTEWORTHY The consistency of an external perturbation modulates error sensitivity and the motor response. The roles of explicit and implicit processes in this modulation are unknown. We show that when humans are asked to ignore the perturbation, they do not show increased error sensitivity in consistent environments. When the implicit system is manipulated by delaying feedback, sensitivity to a consistent perturbation does not change. Overall, our results suggest that consistency affects adaptation mainly through explicit control.

Lack of generalization between explicit and implicit visuomotor learning

Visuomotor adaptation has been thought to occur implicitly, although recent findings suggest that it involves both explicit and implicit processes. Here, we investigated generalization between an explicit condition, in which subjects reached toward imaginary targets under a veridical visuomotor condition, and an implicit condition, in which subjects reached toward visual targets under a 30-degree counterclockwise rotation condition. In experiment 1, two groups of healthy young adults first experienced either the explicit or the implicit condition, then the other condition. The third group experienced the explicit, then the implicit condition with an instruction that the same cognitive strategy could be used in both conditions. Results showed that initial explicit learning did not facilitate subsequent implicit learning, or vice versa, in the first two groups. Subjects in the third group performed better at the beginning of the implicit condition, but still had to adapt to the rotation gradually. In experiment 2, three additional subject groups were tested. One group experienced the explicit, then an implicit condition in which the rotation direction was opposite (30-degree clockwise rotation). Generalization between the conditions was still minimal in that group. Two other groups experienced either the explicit or implicit condition, then performed reaching movements without visual feedback. Those who experienced the explicit condition did not demonstrate aftereffects, while those who experienced the implicit condition did. Collectively, these findings suggest that visuomotor adaptation primarily involves implicit processes, and that explicit processes can add up in a complementary fashion as individuals become increasingly aware of the perturbation.

Minimizing Precision-Weighted Sensory Prediction Errors via Memory Formation and Switching in Motor Adaptation

Humans predict the sensory consequences of motor commands by learning internal models of the body and of environment perturbations. When facing a sensory prediction error, should we attribute this error to a change in our body, and update the body internal model, or to a change in the environment? In the latter case, should we update an existing perturbation model or create a new model? Here, we propose that a decision-making process compares the models' prediction errors, weighted by their precisions, to select and update either the body model or an existing perturbation model. When no model can predict a perturbation, a new perturbation model is created and selected. When a model is selected, both the prediction's mean estimate and uncertainty are updated to minimize future prediction errors and to increase the precision of the predictions. Results from computer simulations, which we verified in an arm visuomotor adaptation experiment with subjects of both sexes, account for short aftereffects and large savings after adaptation to large, but not small, perturbations. Results also clarify previous data in the absence of errors (error-clamp): motor memories show an initial lack of decay after a large perturbation, but gradual decay after a small perturbation. Finally, qualitative individual differences in adaptation were explained by subjects selecting and updating either the body model or a perturbation model. Our results suggest that motor adaptation belongs to a general class of learning according to which memories are created when no existing memories can predict sensory data accurately and precisely.SIGNIFICANCE STATEMENT When movements are followed by unexpected outcomes, such as following the introduction of a visuomotor or a force field perturbation, or the sudden removal of such perturbations, it is unclear whether the CNS updates existing memories or creates new memories. Here, we propose a novel model of adaptation, and investigate, via computer simulations and behavioral experiments, how the amplitude and schedule of the perturbation, as well as the characteristics of the learner, lead to the selection and update of existing memories or the creation of new memories. Our results provide insights into a number of puzzling and contradictory motor adaptation data, as well as into qualitative individual differences in adaptation.

Generalization via superposition: combined effects of mixed reference frame representations for explicit and implicit learning in a visuomotor adaptation task

Studies on generalization of learned visuomotor perturbations have generally focused on whether learning is coded in extrinsic or intrinsic reference frames. This dichotomy, however, is challenged by recent findings showing that learning is represented in a mixed reference frame. Overlooked in this framework is how learning appears to consist of multiple processes, such as explicit reaiming and implicit motor adaptation. Therefore, the proposed mixed representation may simply reflect the superposition of explicit and implicit generalization functions, each represented in different reference frames. Here we characterized the individual generalization functions of explicit and implicit learning in relative isolation to determine whether their combination could predict the overall generalization function when both processes are in operation. We modified the form of feedback in a visuomotor rotation task in an attempt to isolate explicit and implicit learning and tested generalization across new limb postures to dissociate the extrinsic/intrinsic representations. We found that the amplitude of explicit generalization was reduced with postural change and was only marginally shifted, resembling an extrinsic representation. In contrast, implicit generalization maintained its amplitude but was significantly shifted, resembling a mixed representation. A linear combination of individual explicit and implicit generalization functions accounted for nearly 85% of the variance associated with the generalization function in a typical visuomotor rotation task, where both processes are in operation. This suggests that each form of learning results from a mixed representation with distinct extrinsic and intrinsic contributions and the combination of these features shapes the generalization pattern observed at novel limb postures. NEW & NOTEWORTHY Generalization following learning in visuomotor adaptation tasks can reflect how the brain represents what it learns. In this study, we isolated explicit and implicit forms of learning and showed that they are derived from a mixed reference frame representation with distinct extrinsic and intrinsic contributions. Furthermore, we showed that the overall generalization pattern at novel workspaces is due to the superposition of independent generalization effects developed by explicit and implicit learning processes.

Can patients with cerebellar disease switch learning mechanisms to reduce their adaptation deficits?

Systematic perturbations in motor adaptation tasks are primarily countered by learning from sensory-prediction errors, with secondary contributions from other learning processes. Despite the availability of these additional processes, particularly the use of explicit re-aiming to counteract observed target errors, patients with cerebellar degeneration are surprisingly unable to compensate for their sensory-prediction error deficits by spontaneously switching to another learning mechanism. We hypothesized that if the nature of the task was changed-by allowing vision of the hand, which eliminates sensory-prediction errors-patients could be induced to preferentially adopt aiming strategies to solve visuomotor rotations. To test this, we first developed a novel visuomotor rotation paradigm that provides participants with vision of their hand in addition to the cursor, effectively setting the sensory-prediction error signal to zero. We demonstrated in younger healthy control subjects that this promotes a switch to strategic re-aiming based on target errors. We then showed that with vision of the hand, patients with cerebellar degeneration could also switch to an aiming strategy in response to visuomotor rotations, performing similarly to age-matched participants (older controls). Moreover, patients could retrieve their learned aiming solution after vision of the hand was removed (although they could not improve beyond what they retrieved), and retain it for at least 1 year. Both patients and older controls, however, exhibited impaired overall adaptation performance compared to younger healthy controls (age 18-33 years), likely due to age-related reductions in spatial and working memory. Patients also failed to generalize, i.e. they were unable to adopt analogous aiming strategies in response to novel rotations. Hence, there appears to be an inescapable obligatory dependence on sensory-prediction error-based learning-even when this system is impaired in patients with cerebellar disease. The persistence of sensory-prediction error-based learning effectively suppresses a switch to target error-based learning, which perhaps explains the unexpectedly poor performance by patients with cerebellar degeneration in visuomotor adaptation tasks.

Neural signatures of reward and sensory error feedback processing in motor learning

At least two distinct processes have been identified by which motor commands are adapted according to movement-related feedback: reward-based learning and sensory error-based learning. In sensory error-based learning, mappings between sensory targets and motor commands are recalibrated according to sensory error feedback. In reward-based learning, motor commands are associated with subjective value, such that successful actions are reinforced. We designed two tasks to isolate reward- and sensory error-based motor adaptation, and we used electroencephalography in humans to identify and dissociate the neural correlates of reward and sensory error feedback processing. We designed a visuomotor rotation task to isolate sensory error-based learning that was induced by altered visual feedback of hand position. In a reward learning task, we isolated reward-based learning induced by binary reward feedback that was decoupled from the visual target. A fronto-central event-related potential called the feedback-related negativity (FRN) was elicited specifically by binary reward feedback but not sensory error feedback. A more posterior component called the P300 was evoked by feedback in both tasks. In the visuomotor rotation task, P300 amplitude was increased by sensory error induced by perturbed visual feedback and was correlated with learning rate. In the reward learning task, P300 amplitude was increased by reward relative to nonreward and by surprise regardless of feedback valence. We propose that during motor adaptation the FRN specifically reflects a reward-based learning signal whereas the P300 reflects feedback processing that is related to adaptation more generally. NEW & NOTEWORTHY We studied the event-related potentials evoked by feedback stimuli during motor adaptation tasks that isolate reward- and sensory error-based learning mechanisms. We found that the feedback-related negativity was specifically elicited by binary reward feedback, whereas the P300 was observed in both tasks. These results reveal neural processes associated with different learning mechanisms and elucidate which classes of errors, from a computational standpoint, elicit the feedback-related negativity and P300.

Dissociable cognitive strategies for sensorimotor learning

Computations underlying cognitive strategies in human motor learning are poorly understood. Here we investigate such strategies in a common sensorimotor transformation task. We show that strategies assume two forms, likely reflecting distinct working memory representations: discrete caching of stimulus-response contingencies, and time-consuming parametric computations. Reaction times and errors suggest that both strategies are employed during learning, and trade off based on task complexity. Experiments using pressured preparation time further support dissociable strategies: In response caching, time pressure elicits multi-modal distributions of movements; during parametric computations, time pressure elicits a shifting distribution of movements between visual targets and distal goals, consistent with analog re-computing of a movement plan. A generalization experiment reveals that discrete and parametric strategies produce, respectively, more localized or more global transfer effects. These results describe how qualitatively distinct cognitive representations are leveraged for motor learning and produce downstream consequences for behavioral flexibility.

Parallel learning processes of a visuomotor adaptation task in a changing environment

During the control of reaching movements, a key contribution of the visual system is the localization of relevant environmental targets. In motor adaptation processes, the visual evaluation of effector motor behavior enables learning from errors, which demands continuous visual attentional focus. However, most current adaptation paradigms include static targets; therefore, when a learning situation develops in a highly variable environment and there is a double demand for visual resources (environment and motor performance), the evolution of learning processes is unknown. In order to understand how learning processes evolve in a variable environment, a video game task was designed in which subjects were asked to manage a 60° counterclockwise-rotated cursor to capture descending targets with initially unpredictable trajectories. During the task, the cursor and eye movements were recorded to dissect visuomotor coordination. We observed that the pursuit of the targets conditioned a predominant and continuous visual inspection of the environment instead of the rotated cursor. As learning progressed, subjects exhibited a linear reduction in directional error and selected a motor strategy based on the degree of reward, which improved the performance. These results suggest that when the environment demands high visual attention, error-based and reinforced motor learning processes are implemented simultaneously, thus enabling efficient predictive behavior.

Sensory integration of movements and their visual effects is not enhanced by spatial proximity

Spatial proximity enhances the sensory integration of exafferent position information, likely because it indicates whether the information comes from a single physical source. Does spatial proximity also affect the integration of position information regarding an action (here a hand movement) with that of its visual effect (here a cursor motion), that is, when the sensory information comes from physically distinct objects? In this study, participants made out-and-back hand movements whereby the outward movements were accompanied by corresponding cursor motions on a monitor. Their subsequent judgments of hand or cursor movement endpoints are typically biased toward each other, consistent with an underlying optimal integration mechanism. To study the effect of spatial proximity, we presented the hand and cursor either in orthogonal planes (horizontal and frontal, respectively) or we aligned them in the horizontal plane. We did not find the expected enhanced integration strength in the latter spatial condition. As a secondary question we asked whether spatial transformations required for the position judgments (i.e., horizontal to frontal or vice versa) could be the origin of previously observed suboptimal variances of the integrated hand and cursor position judgments. We found, however, that the suboptimality persisted when spatial transformations were omitted (i.e., with the hand and cursor in the same plane). Our findings thus clearly show that the integration of actions with their visual effects is, at least for cursor control, independent of spatial proximity.

Accuracy of Motor Error Predictions for Different Sensory Signals

Detecting and evaluating errors in action execution is essential for learning. Through complex interactions of the inverse and the forward model, the human motor system can predict and subsequently adjust ongoing or subsequent actions. Inputs to such a prediction are efferent and afferent signals from various sources. The aim of the current study was to examine the impact of visual as well as a combination of efferent and proprioceptive input signals to error prediction in a complex motor task. Predicting motor errors has been shown to be correlated with a neural signal known as the error-related negativity (Ne/ERN). Here, we tested how the Ne/ERN amplitude was modulated by the availability of different sensory signals in a semi-virtual throwing task where the action outcome (hit or miss of the target) was temporally delayed relative to movement execution allowing participants to form predictions about the outcome prior to the availability of knowledge of results. 19 participants practiced the task and electroencephalogram was recorded in two test conditions. In the Visual condition, participants received only visual input by passively observing the throwing movement. In the EffProp condition, participants actively executed the task while visual information about the real and the virtual effector was occluded. Hence, only efferent and proprioceptive signals were available. Results show a significant modulation of the Ne/ERN in the Visual condition while no effect could be observed in the EffProp condition. In addition, amplitudes of the feedback-related negativity in response to the actual outcome feedback were found to be inversely related to the Ne/ERN amplitudes. Our findings indicate that error prediction is modulated by the availability of input signals to the forward model. The observed amplitudes were found to be attenuated in comparison to previous studies, in which all efferent and sensory inputs were present. Furthermore, we assume that visual signals are weighted higher than proprioceptive signals, at least in goal-oriented tasks with visual targets.

Credit Assignment in a Motor Decision Making Task Is Influenced by Agency and Not Sensory Prediction Errors

Failures to obtain reward can occur from errors in action selection or action execution. Recently, we observed marked differences in choice behavior when the failure to obtain a reward was attributed to errors in action execution compared with errors in action selection (McDougle et al., 2016). Specifically, participants appeared to solve this credit assignment problem by discounting outcomes in which the absence of reward was attributed to errors in action execution. Building on recent evidence indicating relatively direct communication between the cerebellum and basal ganglia, we hypothesized that cerebellar-dependent sensory prediction errors (SPEs), a signal indicating execution failure, could attenuate value updating within a basal ganglia-dependent reinforcement learning system. Here we compared the SPE hypothesis to an alternative, "top-down" hypothesis in which changes in choice behavior reflect participants' sense of agency. In two experiments with male and female human participants, we manipulated the strength of SPEs, along with the participants' sense of agency in the second experiment. The results showed that, whereas the strength of SPE had no effect on choice behavior, participants were much more likely to discount the absence of rewards under conditions in which they believed the reward outcome depended on their ability to produce accurate movements. These results provide strong evidence that SPEs do not directly influence reinforcement learning. Instead, a participant's sense of agency appears to play a significant role in modulating choice behavior when unexpected outcomes can arise from errors in action execution.SIGNIFICANCE STATEMENT When learning from the outcome of actions, the brain faces a credit assignment problem: Failures of reward can be attributed to poor choice selection or poor action execution. Here, we test a specific hypothesis that execution errors are implicitly signaled by cerebellar-based sensory prediction errors. We evaluate this hypothesis and compare it with a more "top-down" hypothesis in which the modulation of choice behavior from execution errors reflects participants' sense of agency. We find that sensory prediction errors have no significant effect on reinforcement learning. Instead, instructions influencing participants' belief of causal outcomes appear to be the main factor influencing their choice behavior.