Action initiation and action inhibition follow the same time course when compared under matched experimental conditions

The ability to initiate an action quickly when needed and the ability to cancel an impending action are both fundamental to action control. It is often presumed that they are qualitatively distinct processes, yet they have largely been studied in isolation and little is known about how they relate to one another. Comparing previous experimental results shows a similar time course for response initiation and response inhibition. However, the exact time course varies widely depending on experimental conditions, including the frequency of different trial types and the urgency to respond. For example, in the stop-signal task, where both action initiation and action inhibition are involved and could be compared, action inhibition is typically found to be much faster. However, this apparent difference is likely due to there being much greater urgency to inhibit an action than to initiate one in order to avoid failing at the task. This asymmetry in the urgency between action initiation and action inhibition makes it impossible to compare their relative time courses in a single task. Here, we demonstrate that when action initiation and action inhibition are measured separately under conditions that are matched as closely as possible, their speeds are not distinguishable and are positively correlated across participants. Our results raise the possibility that action initiation and action inhibition may not necessarily be qualitatively distinct processes but may instead reflect complementary outcomes of a single decision process determining whether or not to act.NEW & NOTEWORTHY The time courses of initiating an action and canceling an action have largely been studied in isolation, and little is known about their relationship. Here, we show that when measured under comparable conditions the speeds of action initiation and action inhibition are the same. This finding raises the possibility that these two functions may be more closely related than previously assumed, with potentially important implications for their underlying neural basis.

Evidence for a common mechanism supporting invigoration of action selection and action execution

The speed, or vigor, of our movements can vary depending on circumstances. For instance, the promise of a reward leads to faster movements. Reward also leads us to move with a lower reaction time, suggesting that the process of action selection can also be invigorated by reward. It has been proposed that invigoration of action selection and of action execution might occur through a common mechanism, and thus these aspects of behavior might be coupled. To test this hypothesis, we asked participants to make reaching movements to "shoot" through a target at varying speeds to assess whether moving more quickly was also associated with more rapid action selection. We found that, when participants were required to move with a lower velocity, the speed of their action selection was also significantly slowed. This finding was recapitulated in a further dataset in which participants determined their own movement speed, but had to move slowly to stop their movement inside the target. By reanalyzing a previous dataset, we also found evidence for the converse relationship between action execution and action selection; when pressured to select actions more rapidly, people also executed movements with higher velocity. Our results establish that invigoration of action selection and action execution vary in tandem with one another, supporting the hypothesis of a common underlying mechanism.NEW & NOTEWORTHY We show that voluntary increases in the vigor of action execution lead action selection to also occur more rapidly. Conversely, hastening action selection by imposing a deadline to act also leads to increases in movement speed. These findings provide evidence that these two distinct aspects of behavior are modulated by a common underlying mechanism.

Kinematic coordinations capture learning during human-exoskeleton interaction

Human-exoskeleton interactions have the potential to bring about changes in human behavior for physical rehabilitation or skill augmentation. Despite significant advances in the design and control of these robots, their application to human training remains limited. The key obstacles to the design of such training paradigms are the prediction of human-exoskeleton interaction effects and the selection of interaction control to affect human behavior. In this article, we present a method to elucidate behavioral changes in the human-exoskeleton system and identify expert behaviors correlated with a task goal. Specifically, we observe the joint coordinations of the robot, also referred to as kinematic coordination behaviors, that emerge from human-exoskeleton interaction during learning. We demonstrate the use of kinematic coordination behaviors with two task domains through a set of three human-subject studies. We find that participants (1) learn novel tasks within the exoskeleton environment, (2) demonstrate similarity of coordination during successful movements within participants, (3) learn to leverage these coordination behaviors to maximize success within participants, and (4) tend to converge to similar coordinations for a given task strategy across participants. At a high level, we identify task-specific joint coordinations that are used by different experts for a given task goal. These coordinations can be quantified by observing experts and the similarity to these coordinations can act as a measure of learning over the course of training for novices. The observed expert coordinations may further be used in the design of adaptive robot interactions aimed at teaching a participant the expert behaviors.

Control becomes habitual early on when learning a novel motor skill

When people perform the same task repeatedly, their behavior becomes habitual, or inflexible to changes in the goals or structure of a task. While habits have been hypothesized to be a key aspect of motor skill acquisition, there has been little empirical work investigating the relationship between skills and habits. To better understand this relationship, we examined whether and when people's behavior would become habitual as they learned a challenging new motor skill: maneuvering an on-screen cursor using a non-intuitive bimanual mapping from hand to cursor position. After participants practiced using this mapping for up to ten days, we altered the mapping between the hands and the cursor to assess whether participants could flexibly adjust their behavior or would habitually persist in performing the task the way they had originally learned. We found that participants' behavior became habitual within two days of practice, at which point they were still relatively unskilled. Further practice led to improved skill but did not alter the strength of habitual behavior. These data demonstrate that motor skills become habitual after relatively little training, but can nevertheless further improve with practice. We suggest that building habits early in learning may be a crucial step in acquiring new motor skills.

De novo motor learning of a bimanual control task over multiple days of practice

Although much research on motor learning has focused on how we adapt our movements to maintain performance in the face of imposed perturbations, in many cases we must learn new skills from scratch, or de novo. In comparison to adaptation, relatively little is known about de novo learning. In part, this is because learning a new skill can involve many challenges, including learning to recognize new patterns of sensory input and generate new patterns of motor output. However, even with familiar sensory cues and well-practiced movements, the problem of quickly selecting the appropriate actions in response to the current state is challenging. Here, we devised a bimanual hand-to-cursor mapping which isolates this control problem. We find that participants initially struggled to control the cursor under this bimanual mapping, despite explicit knowledge of the mapping. Performance improved steadily over multiple days of practice, however. Participants exhibited no aftereffects when reverting to a veridical cursor, confirming that participants learned the new task de novo, rather than through adaptation. Corrective responses to mid-movement perturbations of the target were initially weak, but with practice, participants gradually became able to respond rapidly and robustly to these perturbations. After four days of practice, participants' behavior under the bimanual mapping almost matched performance with a veridically mapped cursor. However, there remained a small but persistent difference in performance level. Our findings illustrate the dynamics and limitations of learning a novel controller and introduce a promising paradigm for tractably investigating this aspect of motor skill learning.

Age-related increases in reaction time result from slower preparation, not delayed initiation

Recent work indicates that healthy younger adults can prepare accurate responses faster than their voluntary reaction times would suggest, leaving a seemingly unnecessary delay of 80-100 ms before responding. Here, we examined how the preparation of movements, initiation of movements, and the delay between them are affected by aging. Participants made planar reaching movements in two conditions. The "free reaction time" condition assessed the voluntary reaction times with which participants responded to the appearance of a stimulus. The "forced reaction time" condition assessed the minimum time actually needed to prepare accurate movements by controlling the time allowed for movement preparation. The time taken to both initiate movements in the free reaction time and to prepare movements in the forced response condition increased with age. Notably, the time required to prepare accurate movements was significantly shorter than participants' self-selected initiation times; however, the delay between movement preparation and initiation remained consistent across the lifespan (∼90 ms). These results indicate that the slower reaction times of healthy older adults are not due to an increased hesitancy to respond, but can instead be attributed to changes in their ability to process stimuli and prepare movements accordingly, consistent with age-related changes in brain structure and function.NEW & NOTEWORTHY Previous research argues that older adults have slower response times because they hesitate to react, favoring accuracy over speed. The present results challenge this proposal. We found the delay between the minimum time required to prepare movements and the self-selected time at which they initiated remained consistent at ∼90 ms from ages 21 to 80. We therefore suggest older adults' slower response times can be attributed to changes in their ability to process stimuli and prepare movements.

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

Implicit motor recalibration allows us to flexibly move in novel and changing environments. Conventionally, implicit recalibration is thought to be driven by errors in predicting the sensory outcome of movement (i.e., sensory prediction errors). However, recent studies have shown that implicit recalibration is also influenced by errors in achieving the movement goal (i.e., task errors). Exactly how sensory prediction errors and task errors interact to drive implicit recalibration and, in particular, whether task errors alone might be sufficient to drive implicit recalibration remain unknown. To test this, we induced task errors in the absence of sensory prediction errors by displacing the target mid-movement. We found that task errors alone failed to induce implicit recalibration. In additional experiments, we simultaneously varied the size of sensory prediction errors and task errors. We found that implicit recalibration driven by sensory prediction errors could be continuously modulated by task errors, revealing an unappreciated dependency between these two sources of error. Moreover, implicit recalibration was attenuated when the target was simply flickered in its original location, even though this manipulation did not affect task error - an effect likely attributed to attention being directed away from the feedback cursor. Taken as a whole, the results were accounted for by a computational model in which sensory prediction errors and task errors, modulated by attention, interact to determine the extent of implicit recalibration.

Competition between parallel sensorimotor learning systems

Sensorimotor learning is supported by at least two parallel systems: a strategic process that benefits from explicit knowledge and an implicit process that adapts subconsciously. How do these systems interact? Does one system's contributions suppress the other, or do they operate independently? Here, we illustrate that during reaching, implicit and explicit systems both learn from visual target errors. This shared error leads to competition such that an increase in the explicit system's response siphons away resources that are needed for implicit adaptation, thus reducing its learning. As a result, steady-state implicit learning can vary across experimental conditions, due to changes in strategy. Furthermore, strategies can mask changes in implicit learning properties, such as its error sensitivity. These ideas, however, become more complex in conditions where subjects adapt using multiple visual landmarks, a situation which introduces learning from sensory prediction errors in addition to target errors. These two types of implicit errors can oppose each other, leading to another type of competition. Thus, during sensorimotor adaptation, implicit and explicit learning systems compete for a common resource: error.

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

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

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

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

Did We Get Sensorimotor Adaptation Wrong? Implicit Adaptation as Direct Policy Updating Rather than Forward-Model-Based Learning

The human motor system can rapidly adapt its motor output in response to errors. The prevailing theory of this process posits that the motor system adapts an internal forward model that predicts the consequences of outgoing motor commands and uses this forward model to plan future movements. However, despite clear evidence that adaptive forward models exist and are used to help track the state of the body, there is no definitive evidence that such models are used in movement planning. An alternative to the forward-model-based theory of adaptation is that movements are generated based on a learned policy that is adjusted over time by movement errors directly ("direct policy learning"). This learning mechanism could act in parallel with, but independent of, any updates to a predictive forward model. Forward-model-based learning and direct policy learning generate very similar predictions about behavior in conventional adaptation paradigms. However, across three experiments with human participants (N = 47, 26 female), we show that these mechanisms can be dissociated based on the properties of implicit adaptation under mirror-reversed visual feedback. Although mirror reversal is an extreme perturbation, it still elicits implicit adaptation; however, this adaptation acts to amplify rather than to reduce errors. We show that the pattern of this adaptation over time and across targets is consistent with direct policy learning but not forward-model-based learning. Our findings suggest that the forward-model-based theory of adaptation needs to be re-examined and that direct policy learning provides a more plausible explanation of implicit adaptation.SIGNIFICANCE STATEMENT The ability of our brain to adapt movements in response to error is one of the most widely studied phenomena in motor learning. Yet, we still do not know the process by which errors eventually result in adaptation. It is known that the brain maintains and updates an internal forward model, which predicts the consequences of motor commands, and the prevailing theory of motor adaptation posits that this updated forward model is responsible for trial-by-trial adaptive changes. Here, we question this view and show instead that adaptation is better explained by a simpler process whereby motor output is directly adjusted by task errors. Our findings cast doubt on long-held beliefs about adaptation.

Movement Repetition Facilitates Response Preparation

Our sensorimotor system appears to be influenced by the recent history of our movements. Repeating movements toward a particular direction is known to have a dramatic effect on involuntary movements elicited by cortical stimulation-a phenomenon that has been termed use-dependent plasticity. However, analogous effects of repetition on behavior have proven elusive. Here, we show that movement repetition enhances the generation of similar movements in the future by reducing the time required to select and prepare the repeated movement. We further show that this reaction time advantage for repeated movements is attributable to more rapid, but still flexible, preparation of the repeated movement rather than anticipation and covert advance preparation of the previously repeated movement. Our findings demonstrate a powerful and beneficial effect of movement repetition on response preparation, which may represent a behavioral counterpart to use-dependent plasticity effects in primary motor cortex.

Practice induces a qualitative change in the memory representation for visuomotor learning

Adaptation of our movements to changes in the environment is known to be supported by multiple learning processes that operate in parallel. One is an implicit recalibration process driven by sensory-prediction errors; the other process counters the perturbation through more deliberate compensation. Prior experience is known to enable adaptation to occur more rapidly, a phenomenon known as “savings,” but exactly how experience alters each underlying learning process remains unclear. We measured the relative contributions of implicit recalibration and deliberate compensation to savings across 2 days of practice adapting to a visuomotor rotation. The rate of implicit recalibration showed no improvement with repeated practice. Instead, practice led to deliberate compensation being expressed even when preparation time was very limited. This qualitative change is consistent with the proposal that practice establishes a cached association linking target locations to appropriate motor output, facilitating a transition from deliberate to automatic action selection.

NEW & NOTEWORTHY Recent research has shown that savings for visuomotor adaptation is attributable to retrieval of intentional, strategic compensation. This does not seem consistent with the implicit nature of memory for motor skills and calls into question the validity of visuomotor adaptation of reaching movements as a model for motor skill learning. Our findings suggest a solution: that additional practice adapting to a visuomotor perturbation leads to the caching of the initially explicit strategy for countering it.

The multiple effects of practice: skill, habit and reduced cognitive load

When learning a new skill, even if we have been instructed exactly what to do, it is often necessary to practice for hours or even weeks before we achieve proficient and fluid performance. Practice has a multitude of effects on behavior, including increasing the speed of performance, rendering the practiced behavior habitual and reducing the cognitive load required to perform the task. These effects are often collectively referred to as automaticity. Here, we argue that these effects can be explained as multiple consequences of a single principle: caching of the outcome of frequently occuring computations. We further argue that, in the context of more complex task representations, caching different intermediate computations can give rise to more nuanced behavioral signatures, including dissociation between skill, habit and cognitive load.

Reaction times can reflect habits rather than computations

Reaction times (RTs) are assumed to reflect the underlying computations required for making decisions and preparing actions. Recent work, however, has shown that movements can be initiated earlier than typically expressed without affecting performance; hence, the RT may be modulated by factors other than computation time. Consistent with that view, we demonstrated that RTs are influenced by prior experience: when a previously performed task required a specific RT to support task success, this biased the RTs in future tasks. This effect is similar to the use-dependent biases observed for other movement parameters such as speed or direction. Moreover, kinematic analyses revealed that these RT biases could occur without changing the underlying computations used to perform the action. Thus the RT is not solely determined by computational requirements but is an independent parameter that can be habitually set by prior experience.

Motor planning flexibly optimizes performance under uncertainty about task goals

In an environment full of potential goals, how does the brain determine which movement to execute? Existing theories posit that the motor system prepares for all potential goals by generating several motor plans in parallel. One major line of evidence for such theories is that presenting two competing goals often results in a movement intermediate between them. These intermediate movements are thought to reflect an unintentional averaging of the competing plans. However, normative theories suggest instead that intermediate movements might actually be deliberate, generated because they improve task performance over a random guessing strategy. To test this hypothesis, we vary the benefit of making an intermediate movement by changing movement speed. We find that participants generate intermediate movements only at (slower) speeds where they measurably improve performance. Our findings support the normative view that the motor system selects only a single, flexible motor plan, optimized for uncertain goals.

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.

Explicit knowledge enhances motor vigor and performance: motivation versus practice in sequence tasks

Motor skill learning involves a practice-induced improvement in the speed and/or accuracy of a discrete movement. It is often thought that paradigms involving repetitive practice of discrete movements performed in a fixed sequence result in a further enhancement of skill beyond practice of the individual movements in a random order. Sequence-specific performance improvements could, however, arise without practice as a result of knowledge of the sequence order; knowledge could operate by either enabling advanced motor planning of the known sequence elements or by increasing overall motivation. Here, we examined how knowledge and practice contribute to performance of a sequence of movements. We found that explicit knowledge provided through instruction produced practice-independent improvements in reaction time and execution quality. These performance improvements occurred even for random elements within a partially known sequence, indicative of a general motivational effect rather than a sequence-specific effect of advanced planning. This motivational effect suggests that knowledge influences performance in a manner analogous to reward. Additionally, practice led to similar improvements in execution quality for both known and random sequences. The lack of interaction between knowledge and practice suggests that any skill acquisition occurring during discrete sequence tasks arises solely from practice of the individual movement elements, independent of their order. We conclude that performance improvements in discrete sequence tasks arise from the combination of knowledge-based motivation and sequence-independent practice; investigating this interplay between cognition and movement may facilitate a greater understanding of the acquisition of skilled behavior.

Motor Planning

Motor planning colloquially refers to any process related to the preparation of a movement that occurs during the reaction time prior to movement onset. However, this broad definition encompasses processes that are not strictly motor-related, such as decision-making about the identity of task-relevant stimuli in the environment. Furthermore, the assumption that all motor-planning processes require processing time, and can therefore be studied behaviorally by measuring changes in the reaction time, needs to be reexamined. In this review, we take a critical look at the processes leading from perception to action and suggest a definition of motor planning that encompasses only those processes necessary for a movement to be executed-that is, processes that are strictly movement related. These processes resolve the ambiguity inherent in an abstract goal by defining a specific movement to achieve it. We propose that the majority of processes that meet this definition can be completed nearly instantaneously, which means that motor planning itself in fact consumes only a small fraction of the reaction time.

Unlearning versus savings in visuomotor adaptation: comparing effects of washout, passage of time, and removal of errors on motor memory

Humans are able to rapidly adapt their movements when a visuomotor or other systematic perturbation is imposed. However, the adaptation is forgotten or unlearned equally rapidly once the perturbation is removed. The ultimate cause of this unlearning remains poorly understood. Unlearning is often considered to be a passive process due to inability to retain an internal model. However, we have recently suggested that it may instead be a process of reversion to habit, without necessarily any forgetting per se. We compared the timecourse and nature of unlearning across a variety of protocols where unlearning is known to occur: error-clamp trials, removal of visual feedback, removal of the perturbation, or simply a period of inactivity. We found that, in agreement with mathematical models, there was no significant difference in the rate of decay between subject who experienced zero-error clamp trials, and subjects who made movements with no visual feedback. Time alone did lead to partial unlearning (over the duration we tested), but the amount of unlearning was inconsistent across subjects. Upon re-exposure to the same perturbation, subjects who unlearned through time or by reverting to veridical feedback exhibited savings. By contrast, no savings was observed in subjects who unlearned by having visual feedback removed or by being placed in a series of error-clamp trials. Thus although these various forms of unlearning can all revert subjects back to baseline behavior, they have markedly different effects on whether long-term memory for the adaptation is spared or is also unlearned. On the basis of these and previous findings, we suggest that unlearning is not due to passive forgetting of an internal model, but is instead an active process whereby adapted behavior gradually reverts to baseline habits.

Sensitivity to prediction error in reach adaptation

It has been proposed that the brain predicts the sensory consequences of a movement and compares it to the actual sensory feedback. When the two differ, an error signal is formed, driving adaptation. How does an error in one trial alter performance in the subsequent trial? Here we show that the sensitivity to error is not constant but declines as a function of error magnitude. That is, one learns relatively less from large errors compared with small errors. We performed an experiment in which humans made reaching movements and randomly experienced an error in both their visual and proprioceptive feedback. Proprioceptive errors were created with force fields, and visual errors were formed by perturbing the cursor trajectory to create a visual error that was smaller, the same size, or larger than the proprioceptive error. We measured single-trial adaptation and calculated sensitivity to error, i.e., the ratio of the trial-to-trial change in motor commands to error size. We found that for both sensory modalities sensitivity decreased with increasing error size. A reanalysis of a number of previously published psychophysical results also exhibited this feature. Finally, we asked how the brain might encode sensitivity to error. We reanalyzed previously published probabilities of cerebellar complex spikes (CSs) and found that this probability declined with increasing error size. From this we posit that a CS may be representative of the sensitivity to error, and not error itself, a hypothesis that may explain conflicting reports about CSs and their relationship to error.