Adaptation paths to novel motor tasks are shaped by prior structure learning

Savings in sensorimotor adaptation without an explicit strategy

The hallmark of long-term retention of sensorimotor adaptation is a faster relearning when similar perturbations are encountered again. However, what processes underlie this saving effect is in debate. Though motor adaptation is traditionally viewed as a type of procedural learning, its savings has been recently shown to be solely based on a quick recall of explicit adaptation strategy. Here, we showed that adaptation to a novel error-invariant perturbation without an explicit strategy could enable subsequent savings. We further showed that adaptation to gradual perturbations could enable savings, which was supported by enhanced implicit learning. Our study provides supporting evidence that long-term retention of motor adaptation is possible without forming or recalling a cognitive strategy, and the interplay between implicit and explicit learning critically depends on the specifics of learning protocol and available sensory feedback.

NEW & NOTEWORTHY Savings in motor learning sometimes refers to faster learning when one encounters the same perturbation again. Previous studies assert that forming a cognitive strategy for countering perturbations is necessary for savings. We used novel experimental techniques to prevent the formation of a cognitive strategy during initial adaptation and found that savings still existed during relearning. Our findings suggest that savings in sensorimotor adaptation do not exclusively depend on forming and recalling an explicit strategy.

Rapid visuomotor feedback gains are tuned to the task dynamics

Here, we test whether rapid visuomotor feedback responses are selectively tuned to the task dynamics. The responses do not exhibit gain scaling, but they do vary with the level and stability of task dynamics. Moreover, these feedback gains are independently tuned to perturbations to the left and right, depending on these dynamics. Our results demonstrate that the sensorimotor control system regulates the feedback gain as part of the adaptation process, tuning them appropriately to the environment.

Adaptation to novel dynamics requires learning a motor memory, or a new pattern of predictive feedforward motor commands. Recently, we demonstrated the upregulation of rapid visuomotor feedback gains early in curl force field learning, which decrease once a predictive motor memory is learned. However, even after learning is complete, these feedback gains are higher than those observed in the null field trials. Interestingly, these upregulated feedback gains in the curl field were not observed in a constant force field. Therefore, we suggest that adaptation also involves selectively tuning the feedback sensitivity of the sensorimotor control system to the environment. Here, we test this hypothesis by measuring the rapid visuomotor feedback gains after subjects adapt to a variety of novel dynamics generated by a robotic manipulandum in three experiments. To probe the feedback gains, we measured the magnitude of the motor response to rapid shifts in the visual location of the hand during reaching. While the feedback gain magnitude remained similar over a larger than a fourfold increase in constant background load, the feedback gains scaled with increasing lateral resistance and increasing instability. The third experiment demonstrated that the feedback gains could also be independently tuned to perturbations to the left and right, depending on the lateral resistance, demonstrating the fractionation of feedback gains to environmental dynamics. Our results show that the sensorimotor control system regulates the gain of the feedback system as part of the adaptation process to novel dynamics, appropriately tuning them to the environment.

NEW & NOTEWORTHY Here, we test whether rapid visuomotor feedback responses are selectively tuned to the task dynamics. The responses do not exhibit gain scaling, but they do vary with the level and stability of task dynamics. Moreover, these feedback gains are independently tuned to perturbations to the left and right, depending on these dynamics. Our results demonstrate that the sensorimotor control system regulates the feedback gain as part of the adaptation process, tuning them appropriately to the environment.

Visuomotor Map Determines How Visually Guided Reaching Movements are Corrected Within and Across Trials

When a visually guided reaching movement is unexpectedly perturbed, it is implicitly corrected in two ways: immediately after the perturbation by feedback control (online correction) and in the next movement by adjusting feedforward motor commands (offline correction or motor adaptation). Although recent studies have revealed a close relationship between feedback and feedforward controls, the nature of this relationship is not yet fully understood. Here, we show that both implicit online and offline movement corrections utilize the same visuomotor map for feedforward movement control that transforms the spatial location of visual objects into appropriate motor commands. First, we artificially distorted the visuomotor map by applying opposite visual rotations to the cursor representing the hand position while human participants reached for two different targets. This procedure implicitly altered the visuomotor map so that changes in the movement direction to the target location were more insensitive or more sensitive. Then, we examined how such visuomotor map distortion influenced online movement correction by suddenly changing the target location. The magnitude of online movement correction was altered according to the shape of the visuomotor map. We also examined offline movement correction; the aftereffect induced by visual rotation in the previous trial was modulated according to the shape of the visuomotor map. These results highlighted the importance of the visuomotor map as a foundation for implicit motor control mechanisms and the intimate relationship between feedforward control, feedback control, and motor adaptation.

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

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

Nonspeech Oral Movements and Oral Motor Disorders: A Narrative Review

PURPOSE

Speech and other oral functions such as swallowing have been compared and contrasted with oral behaviors variously labeled quasispeech, paraspeech, speechlike, and nonspeech, all of which overlap to some degree in neural control, muscles deployed, and movements performed. Efforts to understand the relationships among these behaviors are hindered by the lack of explicit and widely accepted definitions. This review article offers definitions and taxonomies for nonspeech oral movements and for diverse speaking tasks, both overt and covert.

METHOD

Review of the literature included searches of Medline, Google Scholar, HighWire Press, and various online sources. Search terms pertained to speech, quasispeech, paraspeech, speechlike, and nonspeech oral movements. Searches also were carried out for associated terms in oral biology, craniofacial physiology, and motor control.

RESULTS AND CONCLUSIONS

Nonspeech movements have a broad spectrum of clinical applications, including developmental speech and language disorders, motor speech disorders, feeding and swallowing difficulties, obstructive sleep apnea syndrome, trismus, and tardive stereotypies. The role and benefit of nonspeech oral movements are controversial in many oral motor disorders. It is argued that the clinical value of these movements can be elucidated through careful definitions and task descriptions such as those proposed in this review article.

Structure Learning in Bayesian Sensorimotor Integration

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

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

Formation of a long-term memory for visuomotor adaptation following only a few trials of practice

The term savings refers to faster motor adaptation upon reexposure to a previously experienced perturbation, a phenomenon thought to reflect the existence of a long-term motor memory. It is commonly assumed that sustained practice during the first perturbation exposure is necessary to create this memory. Here we sought to test this assumption by determining the minimum amount of experience necessary during initial adaptation to a visuomotor rotation to bring about savings the following day. Four groups of human subjects experienced 2, 5, 10, or 40 trials of a counterclockwise 30° cursor rotation during reaching movements on one day and were retested the following day to assay for savings. Groups that experienced five trials or more of adaptation on day 1 showed clear savings on day 2. Subjects in all groups learned significantly more from the first rotation trial on day 2 than on day 1, but this learning rate advantage was maintained only in groups that had reached asymptote during the initial exposure. Additional experiments revealed that savings occurred when the magnitude, but not the direction, of the rotation differed across exposures, and when a 5-min break, rather than an overnight one, separated the first and second exposure. The overall pattern of savings we observe across conditions can be explained as rapid retrieval of the state of learning attained during the first exposure rather than as modulation of sensitivity to error. We conclude that a long-term memory for compensating for a perturbation can be rapidly acquired and rapidly retrieved.

Prospective errors determine motor learning

Diverse features of motor learning have been reported by numerous studies, but no single theoretical framework concurrently accounts for these features. Here, we propose a model for motor learning to explain these features in a unified way by extending a motor primitive framework. The model assumes that the recruitment pattern of motor primitives is determined by the predicted movement error of an upcoming movement (prospective error). To validate this idea, we perform a behavioural experiment to examine the model’s novel prediction: after experiencing an environment in which the movement error is more easily predictable, subsequent motor learning should become faster. The experimental results support our prediction, suggesting that the prospective error might be encoded in the motor primitives. Furthermore, we demonstrate that this model has a strong explanatory power to reproduce a wide variety of motor-learning-related phenomena that have been separately explained by different computational models.

Motor learning is characterized by diverse cognitive processes, which lack a unified theoretical framework. Here, Takiyama et al. present a model demonstrating that motor learning is determined by prospective errors, which they test in a specially designed visuomotor adaptation task.

Minimally invasive surgery training using multiple port sites to improve performance

BACKGROUND

Structural learning theory suggests that experiencing motor task variation enables the central nervous system to extract general rules regarding tasks with a similar structure - rules that can subsequently be applied to novel situations. Complex minimally invasive surgery (MIS) requires different port sites, but switching ports alters the limb movements required to produce the same endpoint control of the surgical instrument. The purpose of the present study was to determine if structural learning theory can be applied to MIS to inform training methods.

METHODS

A tablet laptop running bespoke software was placed within a laparoscopic box trainer and connected to a monitor situated at eye level. Participants (right-handed, non-surgeons, mean age = 23.2 years) used a standard laparoscopic grasper to move between locations on the screen. There were two training groups: the M group (n = 10) who trained using multiple port sites, and the S group (n = 10) who trained using a single port site. A novel port site was used as a test of generalization. Performance metrics were a composite of speed and accuracy (SACF) and normalized jerk (NJ; a measure of movement 'smoothness').

RESULTS

The M group showed a statistically significant performance advantage over the S group at test, as indexed by improved SACF (p < 0.05) and NJ (p < 0.05).

CONCLUSIONS

This study has demonstrated the potential benefits of incorporating a structural learning approach within MIS training. This may have practical applications when training junior surgeons and developing surgical simulation devices.

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

Prior learning of a motor skill creates motor memories that can facilitate or interfere with learning of new, but related, motor skills. One hypothesis of motor learning posits that for a sensorimotor task with redundant degrees of freedom, the nervous system learns the geometric structure of the task and improves performance by selectively operating within that task space. We tested this hypothesis by examining if transfer of learning between two tasks depends on shared dimensionality between their respective task spaces. Human participants wore a data glove and learned to manipulate a computer cursor by moving their fingers. Separate groups of participants learned two tasks: a prior task that was unique to each group and a criterion task that was common to all groups. We manipulated the mapping between finger motions and cursor positions in the prior task to define task spaces that either shared or did not share the task space dimensions (x-y axes) of the criterion task. We found that if the prior task shared task dimensions with the criterion task, there was an initial facilitation in criterion task performance. However, if the prior task did not share task dimensions with the criterion task, there was prolonged interference in learning the criterion task due to participants finding inefficient task solutions. These results show that the nervous system learns the task space through practice, and that the degree of shared task space dimensionality influences the extent to which prior experience transfers to subsequent learning of related motor skills.

Rapid feedback responses correlate with reach adaptation and properties of novel upper limb loads

A hallmark of voluntary motor control is the ability to adjust motor patterns for novel mechanical or visuomotor contexts. Recent work has also highlighted the importance of feedback for voluntary control, leading to the hypothesis that feedback responses should adapt when we learn new motor skills. We tested this prediction with a novel paradigm requiring that human subjects adapt to a viscous elbow load while reaching to three targets. Target 1 required combined shoulder and elbow motion, target 2 required only elbow motion, and target 3 (probe target) required shoulder but no elbow motion. This simple approach controlled muscle activity at the probe target before, during, and after the application of novel elbow loads. Our paradigm allowed us to perturb the elbow during reaching movements to the probe target and identify several key properties of adapted stretch responses. Adapted long-latency responses expressed (de-) adaptation similar to reaching errors observed when we introduced (removed) the elbow load. Moreover, reaching errors during learning correlated with changes in the long-latency response, showing subjects who adapted more to the elbow load displayed greater modulation of their stretch responses. These adapted responses were sensitive to the size and direction of the viscous training load. Our results highlight an important link between the adaptation of feedforward and feedback control and suggest a key part of motor adaptation is to adjust feedback responses to the requirements of novel motor skills.

Structural learning in feedforward and feedback control

For smooth and efficient motor control, the brain needs to make fast corrections during the movement to resist possible perturbations. It also needs to adapt subsequent movements to improve future performance. It is important that both feedback corrections and feedforward adaptation need to be made based on noisy and often ambiguous sensory data. Therefore, the initial response of the motor system, both for online corrections and adaptive responses, is guided by prior assumptions about the likely structure of perturbations. In the context of correcting and adapting movements perturbed by a force field, we asked whether these priors are hard wired or whether they can be modified through repeated exposure to differently shaped force fields. We found that both feedback corrections to unexpected perturbations and feedforward adaptation to a new force field changed, such that they were appropriate to counteract the type of force field that participants had experienced previously. We then investigated whether these changes were driven by a common mechanism or by two separate mechanisms. Participants experienced force fields that were either temporally consistent, causing sustained adaptation, or temporally inconsistent, causing little overall adaptation. We found that the consistent force fields modified both feedback and feedforward responses. In contrast, the inconsistent force field modified the temporal shape of feedback corrections but not of the feedforward adaptive response. These results indicate that responses to force perturbations can be modified in a structural manner and that these modifications are at least partly dissociable for feedback and feedforward control.