The cerebellum acts as the analog to the medial temporal lobe for sensorimotor memory.

The cerebellum is critical for sensorimotor learning. The specific contribution that it makes, however, remains unclear. Inspired by the classic finding that for declarative memories, medial temporal lobe (MTL) structures provide a gateway to the formation of long-term memory but are not required for short-term memory, we hypothesized that for sensorimotor memories, the cerebellum may play an analogous role. Here, we studied the sensorimotor learning of individuals with severe ataxia from cerebellar degeneration. We dissected the memories they formed during sensorimotor learning into a short-term temporally-volatile component, that decays rapidly with a time constant of just 15 to 20 s and thus cannot lead to long-term retention, and a longer-term temporally-persistent component that is stable for 60 s or more and leads to long-term retention. Remarkably, we find that these individuals display dramatically reduced levels of temporally-persistent sensorimotor memory, despite spared and even elevated levels of temporally-volatile sensorimotor memory. In particular, we find both impairment that systematically worsens with memory window duration over shorter memory windows (<12 s) and near-complete impairment of memory maintenance over longer memory windows (>25 s). This dissociation uncovers a unique role for the cerebellum as a gateway for the formation of long-term but not short-term sensorimotor memories, mirroring the role of the MTL for declarative memories. It thus reveals the existence of distinct neural substrates for short-term and long-term sensorimotor memory, and it explains both the trial-to-trial differences identified in this study and long-standing study-to-study differences in the effects of cerebellar damage on sensorimotor learning ability.

A double dissociation between savings and long-term memory in motor learning.

Memories are easier to relearn than learn from scratch. This advantage, known as savings, has been widely assumed to result from the reemergence of stable long-term memories. In fact, the presence of savings has often been used as a marker for whether a memory has been consolidated. However, recent findings have demonstrated that motor learning rates can be systematically controlled, providing a mechanistic alternative to the reemergence of a stable long-term memory. Moreover, recent work has reported conflicting results about whether implicit contributions to savings in motor learning are present, absent, or inverted, suggesting a limited understanding of the underlying mechanisms. To elucidate these mechanisms, we investigate the relationship between savings and long-term memory by experimentally dissecting the underlying memories based on short-term (60-s) temporal persistence. Components of motor memory that are temporally-persistent at 60 s might go on to contribute to stable, consolidated long-term memory, whereas temporally-volatile components that have already decayed away by 60 s cannot. Surprisingly, we find that temporally-volatile implicit learning leads to savings, whereas temporally-persistent learning does not, but that temporally-persistent learning leads to long-term memory at 24 h, whereas temporally-volatile learning does not. This double dissociation between the mechanisms for savings and long-term memory formation challenges widespread assumptions about the connection between savings and memory consolidation. Moreover, we find that temporally-persistent implicit learning not only fails to contribute to savings, but also that it produces an opposite, anti-savings effect, and that the interplay between this temporally-persistent anti-savings and temporally-volatile savings provides an explanation for several seemingly conflicting recent reports about whether implicit contributions to savings are present, absent, or inverted. Finally, the learning curves we observed for the acquisition of temporally-volatile and temporally-persistent implicit memories demonstrate the coexistence of implicit memories with distinct time courses, challenging the assertion that models of context-based learning and estimation should supplant models of adaptive processes with different learning rates. Together, these findings provide new insight into the mechanisms for savings and long-term memory formation.

The Role of Variability in Motor Learning.

Trial-to-trial variability in the execution of movements and motor skills is ubiquitous and widely considered to be the unwanted consequence of a noisy nervous system. However, recent studies have suggested that motor variability may also be a feature of how sensorimotor systems operate and learn. This view, rooted in reinforcement learning theory, equates motor variability with purposeful exploration of motor space that, when coupled with reinforcement, can drive motor learning. Here we review studies that explore the relationship between motor variability and motor learning in both humans and animal models. We discuss neural circuit mechanisms that underlie the generation and regulation of motor variability and consider the implications that this work has for our understanding of motor learning.

Dissociating effects of error size, training duration, and amount of adaptation on the ability to retain motor memories.

Extensive computational and neurobiological work has focused on how the training schedule, i.e., the duration and rate at which an environmental disturbance is presented, shapes the formation of motor memories. If long-lasting benefits are to be derived from motor training, however, retention of the performance improvements gained during practice is essential. Thus a better understanding of mechanisms that promote retention could lead to the design of more effective training procedures. The few studies that have investigated how retention depends on the training schedule have suggested that the gradual exposure of a perturbation leads to improved retention of motor memory compared with an abrupt exposure. However, several of these previous studies showed small effects, and although some controlled the training duration and others the level of learning, none have controlled both. In the present study we disambiguated both of these effects from exposure rate by systematically varying the duration of training, type of trained dynamics, and exposure rate for these dynamics in human force-field adaptation. After controlling for both training duration and the amount of learning, we found essentially identical retention when comparing gradual and abrupt training for two different types of force-field dynamics. By contrast, we found that retention was markedly higher for long-duration compared with short-duration training for both types of dynamics. These results demonstrate that the duration of training has a far greater effect on the retention of motor memory than the exposure rate during training. We show that a multirate learning model provides a computational mechanism for these findings.NEW & NOTEWORTHY Previous studies have suggested that a gradual, incremental introduction of a novel environment is helpful for improving retention. However, we used experimental and computational approaches to demonstrate that previously reported improvements in retention associated with gradual introductions fail to persist when other factors, including the duration of training and the degree of initial learning, are accounted for.

Error correction, sensory prediction, and adaptation in motor control.

Motor control is the study of how organisms make accurate goal-directed movements. Here we consider two problems that the motor system must solve in order to achieve such control. The first problem is that sensory feedback is noisy and delayed, which can make movements inaccurate and unstable. The second problem is that the relationship between a motor command and the movement it produces is variable, as the body and the environment can both change. A solution is to build adaptive internal models of the body and the world. The predictions of these internal models, called forward models because they transform motor commands into sensory consequences, can be used to both produce a lifetime of calibrated movements, and to improve the ability of the sensory system to estimate the state of the body and the world around it. Forward models are only useful if they produce unbiased predictions. Evidence shows that forward models remain calibrated through motor adaptation: learning driven by sensory prediction errors.

Temporal specificity of the initial adaptive response in motor adaptation.

Repeated exposure to a novel physical environment eventually leads to a mature adaptive response whereby feedforward changes in motor output mirror both the amplitude and temporal structure of the environmental perturbations. However, adaptive responses at the earliest stages of learning have been found to be not only smaller, but systematically less specific in their temporal structure compared to later stages of learning. This observation has spawned a lively debate as to whether the temporal structure of the initial adaptive response is, in fact, stereotyped and non-specific. To settle this debate, we directly measured the adaptive responses to velocity-dependent and position-dependent force-field perturbations (vFFs and pFFs) at the earliest possible stage of motor learning in humans-after just a single-movement exposure. In line with previous work, we found these earliest stage adaptive responses to be more similar than the perturbations that induced them. However, the single-trial adaptive responses for vFF and pFF perturbations were clearly distinct, and the disparity between them reflected the difference between the temporal structure of the perturbations that drove them. Critically, we observed these differences between single-trial adaptive responses when vFF and pFF perturbations were randomly intermingled from one trial to the next within the same block, indicating perturbation response specificity at the single trial level. These findings demonstrate that the initial adaptive responses to physical perturbations are not stereotyped. Instead, the neural plasticity in sensorimotor areas is sensitive to the temporal structure of a movement perturbation even at the earliest stage in learning. This insight has direct implications for the development of computational models of early-stage motor adaptation and the evolution of this adaptive response with continued training.

Flexible Control of Safety Margins for Action Based on Environmental Variability.

To reduce the risk of slip, grip force (GF) control includes a safety margin above the force level ordinarily sufficient for the expected load force (LF) dynamics. The current view is that this safety margin is based on the expected LF dynamics, amounting to a static safety factor like that often used in engineering design. More efficient control could be achieved, however, if the motor system reduces the safety margin when LF variability is low and increases it when this variability is high. Here we show that this is indeed the case by demonstrating that the human motor system sizes the GF safety margin in proportion to an internal estimate of LF variability to maintain a fixed statistical confidence against slip. In contrast to current models of GF control that neglect the variability of LF dynamics, we demonstrate that GF is threefold more sensitive to the SD than the expected value of LF dynamics, in line with the maintenance of a 3-sigma confidence level. We then show that a computational model of GF control that includes a variability-driven safety margin predicts highly asymmetric GF adaptation between increases versus decreases in load. We find clear experimental evidence for this asymmetry and show that it explains previously reported differences in how rapidly GFs and manipulatory forces adapt. This model further predicts bizarre nonmonotonic shapes for GF learning curves, which are faithfully borne out in our experimental data. Our findings establish a new role for environmental variability in the control of action.

The Decay of Motor Memories Is Independent of Context Change Detection.

When the error signals that guide human motor learning are withheld following training, recently-learned motor memories systematically regress toward untrained performance. It has previously been hypothesized that this regression results from an intrinsic volatility in these memories, resulting in an inevitable decay in the absence of ongoing error signals. However, a recently-proposed alternative posits that even recently-acquired motor memories are intrinsically stable, decaying only if a change in context is detected. This new theory, the context-dependent decay hypothesis, makes two key predictions: (1) after error signals are withheld, decay onset should be systematically delayed until the context change is detected; and (2) manipulations that impair detection by masking context changes should result in prolonged delays in decay onset and reduced decay amplitude at any given time. Here we examine the decay of motor adaptation following the learning of novel environmental dynamics in order to carefully evaluate this hypothesis. To account for potential issues in previous work that supported the context-dependent decay hypothesis, we measured decay using a balanced and baseline-referenced experimental design that allowed for direct comparisons between analogous masked and unmasked context changes. Using both an unbiased variant of the previous decay onset analysis and a novel highly-powered group-level version of this analysis, we found no evidence for systematically delayed decay onset nor for the masked context change affecting decay amplitude or its onset time. We further show how previous estimates of decay onset latency can be substantially biased in the presence of noise, and even more so with correlated noise, explaining the discrepancy between the previous results and our findings. Our results suggest that the decay of motor memories is an intrinsic feature of error-based learning that does not depend on context change detection.

The generalization of visuomotor learning to untrained movements and movement sequences based on movement vector and goal location remapping.

The planning of goal-directed movements is highly adaptable; however, the basic mechanisms underlying this adaptability are not well understood. Even the features of movement that drive adaptation are hotly debated, with some studies suggesting remapping of goal locations and others suggesting remapping of the movement vectors leading to goal locations. However, several previous motor learning studies and the multiplicity of the neural coding underlying visually guided reaching movements stand in contrast to this either/or debate on the modes of motor planning and adaptation. Here we hypothesize that, during visuomotor learning, the target location and movement vector of trained movements are separately remapped, and we propose a novel computational model for how motor plans based on these remappings are combined during the control of visually guided reaching in humans. To test this hypothesis, we designed a set of experimental manipulations that effectively dissociated the effects of remapping goal location and movement vector by examining the transfer of visuomotor adaptation to untrained movements and movement sequences throughout the workspace. The results reveal that (1) motor adaptation differentially remaps goal locations and movement vectors, and (2) separate motor plans based on these features are effectively averaged during motor execution. We then show that, without any free parameters, the computational model we developed for combining movement-vector-based and goal-location-based planning predicts nearly 90% of the variance in novel movement sequences, even when multiple attributes are simultaneously adapted, demonstrating for the first time the ability to predict how motor adaptation affects movement sequence planning.

The cerebellum acts as the analog to the medial temporal lobe for sensorimotor memory.

The cerebellum is critical for sensorimotor learning. The specific contribution that it makes, however, remains unclear. Inspired by the classic finding that, for declarative memories, medial temporal lobe structures provide a gateway to the formation of long-term memory but are not required for short-term memory, we hypothesized that, for sensorimotor memories, the cerebellum may play an analogous role. Here we studied the sensorimotor learning of individuals with severe ataxia from cerebellar degeneration. We dissected the memories they formed during sensorimotor learning into a short-term temporally-volatile component, that decays rapidly with a time constant of just 15-20sec and thus cannot lead to long-term retention, and a longer-term temporally-persistent component that is stable for 60 sec or more and leads to long-term retention. Remarkably, we find that these individuals display dramatically reduced levels of temporally-persistent sensorimotor memory, despite spared and even elevated levels of temporally-volatile sensorimotor memory. In particular, we find both impairment that systematically increases with memory window duration over shorter memory windows (<12 sec) and near-complete impairment of memory maintenance over longer memory windows (>25 sec). This dissociation uncovers a new role for the cerebellum as a gateway for the formation of long-term but not short-term sensorimotor memories, mirroring the role of the medial temporal lobe for declarative memories. It thus reveals the existence of distinct neural substrates for short-term and long-term sensorimotor memory, and it explains both newly-identified trial-to-trial differences and long-standing study-to-study differences in the effects of cerebellar damage on sensorimotor learning ability. Significance Statement: A key discovery about the neural underpinnings of memory, made more than half a century ago, is that long-term, but not short-term, memory formation depends on neural structures in the brain's medial temporal lobe (MTL). However, this dichotomy holds only for declarative memories - memories for explicit facts such as names and dates - as long-term procedural memories - memories for implicit knowledge such as sensorimotor skills - are largely unaffected even with substantial MTL damage. Here we demonstrate that the formation of long-term, but not short-term, sensorimotor memory depends on a neural structure known as the cerebellum, and we show that this finding explains the variability previously reported in the extent to which cerebellar damage affects sensorimotor learning.

Shifts in neural tuning systematically alter sensorimotor learning ability.

Sensorimotor learning can change the tuning of neurons in motor-related brain areas and rotate their preferred directions (PDs). These PD rotations are commonly interpreted as reflecting motor command changes; however, cortical neurons that display PD rotations also contribute to sensorimotor learning. Sensorimotor learning should, therefore, alter not only motor commands but also the tuning of neurons responsible for this learning, and thus impact subsequent learning ability. Here, we investigate this possibility with computational modeling and by directly measuring adaptive responses during sensorimotor learning in humans. Modeling shows that the PD rotations induced by sensorimotor learning, predict specific anisotropic changes in PD distributions that in turn predict a specific spatial pattern of changes in learning ability. Remarkably, experiments in humans then reveal large, systematic changes in learning ability in a spatial pattern that precisely reflects these model-predicted changes. We find that this pattern defies conventional wisdom and implements Newton's method, a learning rule where the step size is inversely proportional rather than proportional to the learning gradient's amplitude, limiting overshooting in the adaptive response. Our findings indicate that PD rotation provides a mechanism whereby the motor system can simultaneously learn how to move and learn how to learn.

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.

Motor memory is encoded as a gain-field combination of intrinsic and extrinsic action representations.

Actions can be planned in either an intrinsic (body-based) reference frame or an extrinsic (world-based) frame, and understanding how the internal representations associated with these frames contribute to the learning of motor actions is a key issue in motor control. We studied the internal representation of this learning in human subjects by analyzing generalization patterns across an array of different movement directions and workspaces after training a visuomotor rotation in a single movement direction in one workspace. This provided a dense sampling of the generalization function across intrinsic and extrinsic reference frames, which allowed us to dissociate intrinsic and extrinsic representations and determine the manner in which they contributed to the motor memory for a trained action. A first experiment showed that the generalization pattern reflected a memory that was intermediate between intrinsic and extrinsic representations. A second experiment showed that this intermediate representation could not arise from separate intrinsic and extrinsic learning. Instead, we find that the representation of learning is based on a gain-field combination of local representations in intrinsic and extrinsic coordinates. This gain-field representation generalizes between actions by effectively computing similarity based on the (Mahalanobis) distance across intrinsic and extrinsic coordinates and is in line with neural recordings showing mixed intrinsic-extrinsic representations in motor and parietal cortices.

Limb motion dictates how motor learning arises from arbitrary environmental dynamics.

A key idea in motor learning is that internal models of environmental dynamics are internally represented as functions of spatial variables including position, velocity, and acceleration of body motion. We refer to such a representation as motion dependent. The evidence for a motion-dependent representation is, however, primarily based on examination of the adaptation to motion-dependent dynamic environments. To more rigorously test this idea, we examined the adaptive response to perturbations that cannot be well approximated by motion-state: force-impulses--brief, high-amplitude pulses of force. The induced adaptation characterizes the impulse response of the system--a widely used technique for probing system dynamics in engineering systems identification. Here we examined the adaptive responses to two different force-impulse perturbations during human voluntary reaching movements. We found that although neither could be well approximated by motion-state (R(2) < 0.18 in both cases), both perturbations induced single-trial adaptive responses that were (R(2) > 0.87). Moreover, these responses were similar in shape to those induced by low-fidelity motion-based approximations of the force-impulses (r > 0.88). Remarkably, we found that the motion dependence of the adaptive responses to force-impulses persisted, even after prolonged exposure (R(2) > 0.95). During a 300-trial training period, trial-to-trial fluctuations in the position, velocity, and acceleration of motion accurately predicted trial-to-trial fluctuations in the adaptive response, and the adaptation gradually became more specific to the perturbation, but only via reorganization of the structure of the motion-dependent representation. These results indicate that internal models of environmental dynamics represent these dynamics in a motion-dependent manner, regardless of the nature of the dynamics encountered.

The training schedule affects the stability, not the magnitude, of the interlimb transfer of learned dynamics.

The way that a motor adaptation is trained, for example, the manner in which it is introduced or the duration of the training period, can influence its internal representation. However, recent studies examining the gradual versus abrupt introduction of a novel environment have produced conflicting results. Here we examined how these effects determine the effector specificity of motor adaptation during visually guided reaching. After adaptation to velocity-dependent dynamics in the right arm, we estimated the amount of adaptation transferred to the left arm, using error-clamp measurement trials to directly measure changes in learned dynamics. We found that a small but significant amount of generalization to the untrained arm occurs under three different training schedules: a short-duration (15 trials) abrupt presentation, a long-duration (160 trials) abrupt presentation, and a long-duration gradual presentation of the novel dynamic environment. Remarkably, we found essentially no difference between the amount of interlimb generalization when comparing these schedules, with 9-12% transfer of the trained adaptation for all three. However, the duration of training had a pronounced effect on the stability of the interlimb transfer: The transfer elicited from short-duration training decayed rapidly, whereas the transfer from both long-duration training schedules was considerably more persistent (<50% vs. >90% retention over the first 20 trials). These results indicate that the amount of interlimb transfer is similar for gradual versus abrupt training and that interlimb transfer of learned dynamics can occur after even a brief training period but longer training is required for an enduring effect.

Valve surgery for infective endocarditis is associated with high hospital charges.

BACKGROUND AND AIM OF THE STUDY: Half of all patients with infective endocarditis (IE) will require early surgical intervention, and another 40% will eventually undergo surgical treatment for their disease. Although the surgical management of IE is effective, the financial impact of the disease has never been assessed. METHODS: All patients who underwent valve surgery for native valve IE at the present authors' institution over a 10-year period (1996-2006) were reviewed retrospectively. Hospital charges were identified and adjusted to reflect US$ in 2006. A logistic regression analysis was performed to identify factors affecting charges and the patients' length of stay (LOS). RESULTS: A total of 369 patients (252 males, 117 females; mean age 53 +/- 15 years) underwent surgery for IE. Of these patients, 121 (33%) had preoperative renal failure and 70 (20%) were intravenous drug users. In addition, 159 patients (43%) had aortic IE, 112 (30%) had mitral IE, and 45 (12%) had both aortic and mitral valve IE. Right- and left-sided IE was identified in 42 patients (11%), and 11 (3%) had isolated right-sided IE. The median hospital charges were US$ 60,072 (interquartile range (IQR) US$ 39,386-103,960), with a median LOS of 15 days (IQR 9-29 days). Both, hospital charges and LOS were higher for patients undergoing emergent operations, or those with active IE (p < 0.001). The 30-day mortality was 2.7%. Regression analyses showed preoperative renal failure (p = 0.007), intraoperative transfusion (p = 0.028) and postoperative gastrointestinal complications (p < 0.001), renal failure (p = 0.012), heart block (p < 0.001), in-hospital mortality (p < 0.001), and patients undergoing emergent procedures (p < 0.001), or with active infection (p < 0.001) to be associated with significantly increased hospital charges. Factors that significantly affected LOS were other non-white race (p = 0.039), postoperative gastrointestinal complications (p = 0.001), stroke (p = 0.014), heart block (p < 0.001), and patients undergoing emergent procedures (p < 0.001) or with active infection (p < 0.001). CONCLUSION: The present series was among the largest to include patients with IE, and the first in which risk factors were assessed for increased hospital charges and resource utilization following surgery for endocarditis. Operations for IE are associated with a significant financial burden to the healthcare system, despite a relatively low percentage of complications. Patients with significant preoperative comorbidities, those with postoperative complications, and those who underwent emergent procedures or who had active IE, were associated with a prolonged LOS and increased hospital charges.

The binding of learning to action in motor adaptation.

In motor tasks, errors between planned and actual movements generally result in adaptive changes which reduce the occurrence of similar errors in the future. It has commonly been assumed that the motor adaptation arising from an error occurring on a particular movement is specifically associated with the motion that was planned. Here we show that this is not the case. Instead, we demonstrate the binding of the adaptation arising from an error on a particular trial to the motion experienced on that same trial. The formation of this association means that future movements planned to resemble the motion experienced on a given trial benefit maximally from the adaptation arising from it. This reflects the idea that actual rather than planned motions are assigned 'credit' for motor errors because, in a computational sense, the maximal adaptive response would be associated with the condition credited with the error. We studied this process by examining the patterns of generalization associated with motor adaptation to novel dynamic environments during reaching arm movements in humans. We found that these patterns consistently matched those predicted by adaptation associated with the actual rather than the planned motion, with maximal generalization observed where actual motions were clustered. We followed up these findings by showing that a novel training procedure designed to leverage this newfound understanding of the binding of learning to action, can improve adaptation rates by greater than 50%. Our results provide a mechanistic framework for understanding the effects of partial assistance and error augmentation during neurologic rehabilitation, and they suggest ways to optimize their use.

Linear hypergeneralization of learned dynamics across movement speeds reveals anisotropic, gain-encoding primitives for motor adaptation.

The ability to generalize learned motor actions to new contexts is a key feature of the motor system. For example, the ability to ride a bicycle or swing a racket is often first developed at lower speeds and later applied to faster velocities. A number of previous studies have examined the generalization of motor adaptation across movement directions and found that the learned adaptation decays in a pattern consistent with the existence of motor primitives that display narrow Gaussian tuning. However, few studies have examined the generalization of motor adaptation across movement speeds. Following adaptation to linear velocity-dependent dynamics during point-to-point reaching arm movements at one speed, we tested the ability of subjects to transfer this adaptation to short-duration higher-speed movements aimed at the same target. We found near-perfect linear extrapolation of the trained adaptation with respect to both the magnitude and the time course of the velocity profiles associated with the high-speed movements: a 69% increase in movement speed corresponded to a 74% extrapolation of the trained adaptation. The close match between the increase in movement speed and the corresponding increase in adaptation beyond what was trained indicates linear hypergeneralization. Computational modeling shows that this pattern of linear hypergeneralization across movement speeds is not compatible with previous models of adaptation in which motor primitives display isotropic Gaussian tuning of motor output around their preferred velocities. Instead, we show that this generalization pattern indicates that the primitives involved in the adaptation to viscous dynamics display anisotropic tuning in velocity space and encode the gain between motor output and motion state rather than motor output itself.

Reduction in learning rates associated with anterograde interference results from interactions between different timescales in motor adaptation.

Prior experiences can influence future actions. These experiences can not only drive adaptive changes in motor output, but they can also modulate the rate at which these adaptive changes occur. Here we studied anterograde interference in motor adaptation--the ability of a previously learned motor task (Task A) to reduce the rate of subsequently learning a different (and usually opposite) motor task (Task B). We examined the formation of the motor system's capacity for anterograde interference in the adaptive control of human reaching-arm movements by determining the amount of interference after varying durations of exposure to Task A (13, 41, 112, 230, and 369 trials). We found that the amount of anterograde interference observed in the learning of Task B increased with the duration of Task A. However, this increase did not continue indefinitely; instead, the interference reached asymptote after 15-40 trials of Task A. Interestingly, we found that a recently proposed multi-rate model of motor adaptation, composed of two distinct but interacting adaptive processes, predicts several key features of the interference patterns we observed. Specifically, this computational model (without any free parameters) predicts the initial growth and leveling off of anterograde interference that we describe, as well as the asymptotic amount of interference that we observe experimentally (R(2) = 0.91). Understanding the mechanisms underlying anterograde interference in motor adaptation may enable the development of improved training and rehabilitation paradigms that mitigate unwanted interference.

Motor planning under uncertainty.

Actions often require the selection of a specific goal amongst a range of possibilities, like when a softball player must precisely position her glove to field a fast-approaching ground ball. Previous studies have suggested that during goal uncertainty the brain prepares for all potential goals in parallel and averages the corresponding motor plans to command an intermediate movement that is progressively refined as additional information becomes available. Although intermediate movements are widely observed, they could instead reflect a neural decision about the single best action choice given the uncertainty present. Here we systematically dissociate these possibilities using novel experimental manipulations and find that when confronted with uncertainty, humans generate a motor plan that optimizes task performance rather than averaging potential motor plans. In addition to accurate predictions of population-averaged changes in motor output, a novel computational model based on this performance-optimization theory accounted for a majority of the variance in individual differences between participants. Our findings resolve a long-standing question about how the brain selects an action to execute during goal uncertainty, providing fundamental insight into motor planning in the nervous system.