Motor learning without movement.

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

Dissociable Codes in Motor Working Memory.

Working memory has been comprehensively studied in sensory domains, like vision, but little attention has been paid to how motor information (e.g., kinematics of recent movements) is maintained and manipulated in working memory. "Motor working memory" (MWM) is important for short-term behavioral control and skill learning. Here, we employed tasks that required participants to encode and recall reaching movements over short timescales. We conducted three experiments (N = 65 undergraduates) to examine MWM under varying cognitive loads, delays, and degrees of interference. The results support a model of MWM that includes an abstract code that flexibly transfers across effectors, and an effector-specific code vulnerable to interfering movements, even when interfering movements are irrelevant to the task. Neither code was disrupted by increasing visuospatial working memory load. These results echo distinctions between representational formats in other domains, suggesting that MWM shares a basic computational structure with other working memory subsystems.

Metacognitive judgments during visuomotor learning reflect the integration of error history.

People form metacognitive representations of their own abilities across a range of tasks. How these representations are influenced by errors during learning is poorly understood. Here, we ask how metacognitive confidence judgments of performance during motor learning are shaped by the learner's recent history of errors. Across four motor learning experiments, our computational modeling approach demonstrated that people's confidence judgments are best explained by a recency-weighted averaging of visually observed errors. Moreover, in the formation of these confidence estimates, people appear to reweight observed motor errors according to a subjective cost function. Confidence judgments were adaptive, incorporating recent motor errors in a manner that was sensitive to the volatility of the learning environment, integrating a shallower history when the environment was more volatile. Finally, confidence tracked motor errors in the context of both implicit and explicit motor learning but only showed evidence of influencing behavior in the latter. Our study thus provides a novel descriptive model that successfully approximates the dynamics of metacognitive judgments during motor learning.NEW & NOTEWORTHY This study examined how, during visuomotor learning, people's confidence in their performance is shaped by their recent history of errors. Using computational modeling, we found that confidence incorporated recent error history, tracked subjective error costs, was sensitive to environmental volatility, and in some contexts may influence learning. Together, these results provide a novel model of metacognitive judgments during motor learning that could be applied to future computational and neural studies at the interface of higher-order cognition and motor control.

Continuous manipulation of mental representations is compromised in cerebellar degeneration.

We introduce a novel perspective on how the cerebellum might contribute to cognition, hypothesizing that this structure supports dynamic transformations of mental representations. In support of this hypothesis, we report a series of neuropsychological experiments comparing the performance of individuals with degenerative cerebellar disorders on tasks that either entail continuous, movement-like mental operations or more discrete mental operations. In the domain of visual cognition, the cerebellar disorders group exhibited an impaired rate of mental rotation, an operation hypothesized to require the continuous manipulation of a visual representation. In contrast, the cerebellar disorders group showed a normal processing rate when scanning items in visual working memory, an operation hypothesized to require the maintenance and retrieval of remembered items. In the domain of mathematical cognition, the cerebellar disorders group was impaired at single-digit addition, an operation hypothesized to primarily require iterative manipulations along a mental number-line; this group was not impaired on arithmetic tasks linked to memory retrieval (e.g. single-digit multiplication). These results, obtained in tasks from two disparate domains, point to a potential constraint on the contribution of the cerebellum to cognitive tasks. Paralleling its role in motor control, the cerebellum may be essential for coordinating dynamic, movement-like transformations in a mental workspace.

Revisiting the Role of the Medial Temporal Lobe in Motor Learning.

Classic taxonomies of memory distinguish explicit and implicit memory systems, placing motor skills squarely in the latter branch. This assertion is in part a consequence of foundational discoveries showing significant motor learning in amnesics. Those findings suggest that declarative memory processes in the medial temporal lobe (MTL) do not contribute to motor learning. Here, we revisit this issue, testing an individual (L. S. J.) with severe MTL damage on four motor learning tasks and comparing her performance to age-matched controls. Consistent with previous findings in amnesics, we observed that L. S. J. could improve motor performance despite having significantly impaired declarative memory. However, she tended to perform poorly relative to age-matched controls, with deficits apparently related to flexible action selection. Further supporting an action selection deficit, L. S. J. fully failed to learn a task that required the acquisition of arbitrary action-outcome associations. We thus propose a modest revision to the classic taxonomic model: Although MTL-dependent memory processes are not necessary for some motor learning to occur, they play a significant role in the acquisition, implementation, and retrieval of action selection strategies. These findings have implications for our understanding of the neural correlates of motor learning, the psychological mechanisms of skill, and the theory of multiple memory systems.

Distinct Neural Signatures of Outcome Monitoring After Selection and Execution Errors.

Losing a point in tennis could result from poor shot selection or faulty stroke execution. To explore how the brain responds to these different types of errors, we examined feedback-locked EEG activity while participants completed a modified version of a standard three-armed bandit probabilistic reward task. Our task framed unrewarded outcomes as the result of either errors of selection or errors of execution. We examined whether amplitude of a medial frontal negativity (the feedback-related negativity [FRN]) was sensitive to the different forms of error attribution. Consistent with previous reports, selection errors elicited a large FRN relative to rewards, and amplitude of this signal correlated with behavioral adjustment after these errors. A different pattern was observed in response to execution errors. These outcomes produced a larger FRN, a frontocentral attenuation in activity preceding this component, and a subsequent enhanced error positivity in parietal sites. Notably, the only correlations with behavioral adjustment were with the early frontocentral attenuation and amplitude of the parietal signal; FRN differences between execution errors and rewarded trials did not correlate with subsequent changes in behavior. Our findings highlight distinct neural correlates of selection and execution error processing, providing insight into how the brain responds to the different classes of error that determine future action.

Executive Function Assigns Value to Novel Goal-Congruent Outcomes.

People often learn from the outcomes of their actions, even when these outcomes do not involve material rewards or punishments. How does our brain provide this flexibility? We combined behavior, computational modeling, and functional neuroimaging to probe whether learning from abstract novel outcomes harnesses the same circuitry that supports learning from familiar secondary reinforcers. Behavior and neuroimaging revealed that novel images can act as a substitute for rewards during instrumental learning, producing reliable reward-like signals in dopaminergic circuits. Moreover, we found evidence that prefrontal correlates of executive control may play a role in shaping flexible responses in reward circuits. These results suggest that learning from novel outcomes is supported by an interplay between high-level representations in prefrontal cortex and low-level responses in subcortical reward circuits. This interaction may allow for human reinforcement learning over arbitrarily abstract reward functions.

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.

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.

Credit assignment in movement-dependent reinforcement learning.

When a person fails to obtain an expected reward from an object in the environment, they face a credit assignment problem: Did the absence of reward reflect an extrinsic property of the environment or an intrinsic error in motor execution? To explore this problem, we modified a popular decision-making task used in studies of reinforcement learning, the two-armed bandit task. We compared a version in which choices were indicated by key presses, the standard response in such tasks, to a version in which the choices were indicated by reaching movements, which affords execution failures. In the key press condition, participants exhibited a strong risk aversion bias; strikingly, this bias reversed in the reaching condition. This result can be explained by a reinforcement model wherein movement errors influence decision-making, either by gating reward prediction errors or by modifying an implicit representation of motor competence. Two further experiments support the gating hypothesis. First, we used a condition in which we provided visual cues indicative of movement errors but informed the participants that trial outcomes were independent of their actual movements. The main result was replicated, indicating that the gating process is independent of participants' explicit sense of control. Second, individuals with cerebellar degeneration failed to modulate their behavior between the key press and reach conditions, providing converging evidence of an implicit influence of movement error signals on reinforcement learning. These results provide a mechanistically tractable solution to the credit assignment problem.

Implications of plan-based generalization in sensorimotor adaptation.

Generalization is a fundamental aspect of behavior, allowing for the transfer of knowledge from one context to another. The details of this transfer are thought to reveal how the brain represents what it learns. Generalization has been a central focus in studies of sensorimotor adaptation, and its pattern has been well characterized: Learning of new dynamic and kinematic transformations in one region of space tapers off in a Gaussian-like fashion to neighboring untrained regions, echoing tuned population codes in the brain. In contrast to common allusions to generalization in cognitive science, generalization in visually guided reaching is usually framed as a passive consequence of neural tuning functions rather than a cognitive feature of learning. While previous research has presumed that maximum generalization occurs at the instructed task goal or the actual movement direction, recent work suggests that maximum generalization may occur at the location of an explicitly accessible movement plan. Here we provide further support for plan-based generalization, formalize this theory in an updated model of adaptation, and test several unexpected implications of the model. First, we employ a generalization paradigm to parameterize the generalization function and ascertain its maximum point. We then apply the derived generalization function to our model and successfully simulate and fit the time course of implicit adaptation across three behavioral experiments. We find that dynamics predicted by plan-based generalization are borne out in the data, are contrary to what traditional models predict, and lead to surprising implications for the behavioral, computational, and neural characteristics of sensorimotor adaptation.NEW & NOTEWORTHY The pattern of generalization is thought to reveal how the motor system represents learned actions. Recent work has made the intriguing suggestion that maximum generalization in sensorimotor adaptation tasks occurs at the location of the learned movement plan. Here we support this interpretation, develop a novel model of motor adaptation that incorporates plan-based generalization, and use the model to successfully predict surprising dynamics in the time course of adaptation across several conditions.

Explicit and Implicit Processes Constitute the Fast and Slow Processes of Sensorimotor Learning.

A popular model of human sensorimotor learning suggests that a fast process and a slow process work in parallel to produce the canonical learning curve (Smith et al., 2006). Recent evidence supports the subdivision of sensorimotor learning into explicit and implicit processes that simultaneously subserve task performance (Taylor et al., 2014). We set out to test whether these two accounts of learning processes are homologous. Using a recently developed method to assay explicit and implicit learning directly in a sensorimotor task, along with a computational modeling analysis, we show that the fast process closely resembles explicit learning and the slow process approximates implicit learning. In addition, we provide evidence for a subdivision of the slow/implicit process into distinct manifestations of motor memory. We conclude that the two-state model of motor learning is a close approximation of sensorimotor learning, but it is unable to describe adequately the various implicit learning operations that forge the learning curve. Our results suggest that a wider net be cast in the search for the putative psychological mechanisms and neural substrates underlying the multiplicity of processes involved in motor learning.

Independent influences of movement distance and visual distance on Fitts' law.

Fitts' Law is one among a small number of psychophysical laws. However, a fundamental variable in Fitts' Law-the movement distance, D-confounds two quantities: The physical distance the effector has to move to reach a goal, and the visually perceived distance to that goal. While these two quantities are functionally equivalent in everyday motor behavior, decoupling them might improve our understanding of the factors that shape speed-accuracy tradeoffs. Here, we leveraged the phenomenon of visuomotor gain adaptation to de-confound movement and visual distance during goal-directed reaching. We found that movement distance and visual distance can influence movement times, supporting a variant of Fitts' Law that considers both. The weighting of movement versus visual distance was modified by restricting movement range and degrading visual feedback. These results may reflect the role of sensory context in early stages of motor planning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Oblique warping: A general distortion of spatial perception.

There are many putatively distinct phenomena related to perception in the oblique regions of space. For instance, the classic oblique effect describes a deficit in visual acuity for oriented lines in the obliques, and classic "prototype effects" reflect a bias to misplace objects towards the oblique regions of space. Yet these effects are explained in very different terms: The oblique effect itself is often understood as arising from orientation-selective neurons, whereas prototype effects are described as arising from categorical biases. Here, we explore the possibility that these effects (and others) may stem from a single underlying spatial distortion. We show that there is a general distortion of (angular) space in the oblique regions that influences not only orientation judgments, but also location, extent, and size. We argue that these findings reflect oblique warping, a general distortion of spatial representations in the oblique regions which may be the root cause of many oblique effects.

A common format for representing spatial location in visual and motor working memory.

Does the mind rely on similar systems of spatial representation for both perception and action? Here, we assessed the format of location representations in two simple spatial localization tasks. In one task, participants simply remembered the location of an item based solely on visual input. In another, participants remembered the location of a point in space based solely on kinesthetic input. Participants' recall errors were more consistent with the use of polar coordinates than Cartesian coordinates in both tasks. Moreover, measures of spatial bias and performance were correlated across modalities. In a subsequent study, we tested the flexibility with which people use polar coordinates to represent space; we show that the format in which the information is presented to participants influences how that information is encoded and the errors that are made as a result. We suggest that polar coordinates may be a common means of representing location information across visual and motor modalities, but that these representations are also flexible in form.

"Don't [ruminate], be happy": A cognitive perspective linking depression and anhedonia.

Anhedonia, a lack of pleasure in things an individual once enjoyed, and rumination, the process of perseverative and repetitive attention to specific thoughts, are hallmark features of depression. Though these both contribute to the same debilitating disorder, they have often been studied independently and through different theoretical lenses (e.g., biological vs. cognitive). Cognitive theories and research on rumination have largely focused on understanding negative affect in depression with much less focus on the etiology and maintenance of anhedonia. In this paper, we argue that by examining the relation between cognitive constructs and deficits in positive affect, we may better understand anhedonia in depression thereby improving prevention and intervention efforts. We review the extant literature on cognitive deficits in depression and discuss how these dysfunctions may not only lead to sustained negative affect but, importantly, interfere with an ability to attend to social and environmental cues that could restore positive affect. Specifically, we discuss how rumination is associated to deficits in working memory and propose that these deficits in working memory may contribute to anhedonia in depression. We further argue that analytical approaches such as computational modeling are needed to study these questions and, finally, discuss implications for treatment.

Post-error Slowing During Instrumental Learning is Shaped by Working Memory-based Choice Strategies.

Post-error slowing (PES) - a relative increase in response time for a decision on trialtgiven an error on trialt - 1 - is a well-known effect in studies of human decision-making. Post-error processing is reflected in neural signatures such as reduced activity in sensorimotor regions and increased activity in medial prefrontal cortex. PES is thought to reflect the deployment of executive resources to get task performance back on track. This provides a general account of PES that cuts across perceptual decision-making, memory, and learning tasks. With respect to PES and learning, things are complicated by the fact that learning often reflectsmultiple qualitatively different processes with distinct neural correlates. It is unclear if multiple processes shape PES during learning, or if PES reflects a policy for reacting to errors generated by one particular process (e.g., cortico-striatal reinforcement learning). Here we provide behavioral and computational evidence that PES is influenced by the operation of multiple distinct processes. Human subjects learned a simple visuomotor skill (arbitrary visuomotor association learning) under low load conditionsmore amenable to simple working memory-based strategies, and high load conditions that were putatively more reliant on trial-by-trial reinforcement learning. PES decreased withload, even when the progress of learning (i.e., reinforcement history) was accounted for. This result suggested that PES during learning is influenced by the recruitment of working memory. Indeed, observed PES effects were approximated by a computational model with parallel working memory and reinforcement learning systems that are differentially recruited according to cognitive load.

Going beyond primary motor cortex to improve brain-computer interfaces.

Brain-computer interfaces (BCIs) for movement restoration typically decode the user's intent from neural activity in their primary motor cortex (M1) and use this information to enable 'mental control' of an external device. Here, we argue that activity in M1 has both too little and too much information for optimal decoding: too little, in that many regions beyond it contribute unique motor outputs and have movement-related information that is absent or otherwise difficult to resolve from M1 activity; and too much, in that motor commands are tangled up with nonmotor processes such as attention and feedback processing, potentially hindering decoding. Both challenges might be circumvented, we argue, by integrating additional information from multiple brain regions to develop BCIs that will better interpret the user's intent.