Journal of Neurophysiology

Precision reaching tasks often require corrective submovements for successful completion. Most studies of reaching have focused on single initial movements, and the cortical encoding model was implied to be the same for all submovements. However, corrective submovements may show different encoding patterns from the initial submovement with distinct patterns of activation across the population. Two rhesus macaques performed a precision center-out-task with small targets. Neural activity from single units in primary motor cortex and associated behavioral data were recorded to evaluate movement characteristics. Neural population data and individual neuronal firing rates identified with a peak finding algorithm to identify peaks in hand speed were examined for encoding differences between initial and corrective submovements. Individual neurons were fitted with a regression model that included the reach vector, position, and speed to predict firing rate. For both initial and corrective submovements, the largest effect remained movement direction. We observed a large subset changed their preferred direction greater than 45{degrees} between initial and corrective submovements. Neuronal depth of modulation also showed considerable variation when adjusted for movement speed. By utilizing principal component analysis, neural trajectories of initial and corrective submovements progressed through different neural subspaces. These findings all suggest that different neural encoding patterns exist for initial and corrective submovements within the cortex. We hypothesize that this variation in how neurons change to encode small, corrective submovements might allow for a larger portion of the neural space being used to encode a greater range of movements with varying amplitudes and levels of precision. New and NoteworthyNeuronal recordings matched with kinematic behavior were collected in a precision center-out task that often required corrective movements. We reveal large differences in preferred direction and depth of modulation between initial and corrective submovements across the neural population. We then present a model of the neural population describing how these shifts in tuning create different subspaces for signaling initial and corrective movements likely to improve motor precision.

Moving effectively is essential for any animal. Thus, many different kinds of brain processes likely contribute to learning and adapting movement. How these contributions are combined is unknown. Nevertheless, the field of motor adaptation has been working under the assumption that measures of explicit and implicit motor adaptation can simply be added in total adaptation. While this has been tested, we show that these tests were insufficient. We put this additivity assumption to the test in various ways, and find that measures of implicit and explicit adaptation are not additive. This means that future studies should measure both implicit and explicit adaptation directly. It also challenges us to disentangle how various motor adaptation processes do combine when producing movements, and may have implications for our understanding of other kinds of learning as well. (data and code: https://osf.io/dh86e)

When learning new movements some people make larger kinematic errors than others, interpreted as a reduction in motor-learning ability. Consider a learning task where error-cancelling strategies incur higher effort costs, specifically where subjects reach to targets in a force field. Concluding that those with greater error have learned less has a critical assumption: everyone uses the same error-canceling strategy. Alternatively, it could be that those with greater error may be choosing to sacrifice error reduction in favor of a lower effort movement. Here, we test this hypothesis in a dataset that includes both younger and older adults, where older adults exhibited greater kinematic errors. Utilizing the framework of optimal control theory, we infer subjective costs (i.e., strategies) and internal model accuracy (i.e., proportion of the novel dynamics learned) by fitting a model to each populations trajectory data. Our results demonstrate trajectories are defined by a combination of the amount learned and strategic differences represented by relative cost weights. Based on the model fits, younger adults could have learned between 65-90% of the novel dynamics. Critically, older adults could have learned between 60-85%. Each model fit produces trajectories that match the experimentally observed data, where a lower proportion learned in the model is compensated for by increasing costs on kinematic errors relative to effort. This suggests older and younger adults could be learning to the same extent, but older adults have a higher relative cost on effort compared to younger adults. These results call into question the proposition that older adults learn less than younger adults and provide a potential explanation for the equivocal findings in the literature. Importantly, our findings suggest that the metrics commonly used to probe motor learning paint an incomplete picture, and that to accurately quantify the learning process the subjective costs of movements should be considered. Author SummaryHere we show that how a person values effort versus error in their movements has an impact on their overall strategy for performing those movements and adapting to a novel environment. When error alone is considered as a measure of learning, it appears that certain populations such as older adults are significantly worse at learning new motor tasks. However, using an optimal control framework, we are able to parse out differences in how much a population or person has learned, as well as how they subjectively value factors such as effort and error. In the case of older adults, we show that they could be learning as much as younger adults but exhibit larger errors because they care more about expending extra effort to reduce them.

Human motor adaptation relies on both explicit conscious strategies and implicit unconscious updating of internal models to correct motor errors. Implicit adaptation is powerful, requiring less preparation time before executing adapted movements, but recent work suggests it is limited to some absolute magnitude regardless of the size of a visuomotor perturbation when the perturbation is introduced abruptly. It is commonly assumed that gradually introducing a perturbation should lead to improved implicit learning beyond this limit, but outcomes are conflicting. We tested whether introducing a perturbation in two distinct gradual methods can overcome the apparent limit and explain past conflicting findings. We found that gradually introducing a perturbation in a stepped manner, where participants were given time to adapt to each partial step before being introduced to a larger partial step, led to [~]80% higher implicit aftereffects of learning, but introducing it in a ramped manner, where participants adapted larger rotations on each subsequent reach, did not. Our results clearly show that gradual introduction of a perturbation can lead to substantially larger implicit adaptation, as well as identify the type of introduction that is necessary to do so.

Theories of human motor learning commonly assume that the degree to which movement plans are adjusted in response to movement errors scales with the precision of sensory feedback received regarding their success. However, support for such error-scaling models has mainly come from experiments that limit the amount of correction that can occur within an ongoing movement. In contrast, we have recently shown that when this restriction is relaxed, and both within-movement and between-movement corrections co-occur, movement plans undergo large and abrupt changes that are strongly correlated with the degree of sensory uncertainty present on the previous trial and are insensitive to the magnitude and direction of the experienced movement error. Here, we show that the presence of these abrupt and error-insensitive changes can only be reliably detected when different levels of sensory precision are interleaved pseudo randomly on a trial-by-trial basis. These results augment our earlier findings and suggest that the co-occurrence of within-movement and between-movement corrections is not the only important aspect of our earlier study that challenged the error-scaling models of motor learning under uncertainty. Author summaryA large body of literature shows that sensory uncertainty inversely scales the degree of error-driven corrections made to motor plans from one trial to the next. However, by limiting sensory feedback to the endpoint of movements, these studies prevent corrections from taking place during the movement. We have recently shown that when such corrections are promoted, sensory uncertainty punctuates between-trial movement corrections with abrupt changes that closely track the degree of sensory uncertainty but are insensitive to the magnitude and direction of movement error. Here, we show that this result requires different levels of sensory uncertainty to be mixed on a trial-by-trial basis. This carries important implications for how previous studies of motor learning under uncertainty are interpreted, and what future studies will likely constitute progress for the field.

The dynamics of muscle activation during fast visually guided reaching are suggestive of two neural control signals; an early signal that acts at "express" latencies in response to the visual stimulus, and a longer latency signal that executes a strategic reach plan. Here we developed a task designed to temporally isolate the express visuomotor response from the longer latency muscle response, and to characterize the time course of corticospinal excitability changes in the express response time window when the late voluntary response is delayed. We tested this by measuring electromyograms (EMG) and changes in Motor Evoked Potential (MEP) amplitudes following Transcranial Magnetic Stimulation (TMS) of the motor cortex, as participants reached either towards or away from visual targets. Crucially, the information about the task rule was provided by the luminance of the target itself, and so was unknown to the subject until the instant of target presentation. This feature delayed reaction times, likely because additional (presumably cortical) processing was required to interpret and apply the rule before formation of a goal directed reach plan. The earliest EMG responses to target presentation occurred with a 70-105 ms time window, and were oriented to bring the hand toward the location of the target. However, there was also a slightly later response that was also time-locked to target appearance in a 105-140 ms time window. This second response was "reciprocal" to the first, such that it was oriented to take the hand in the direction opposite from the target. In some participants, additional oscillating cycles were apparent after the first two target-related responses. These multiphasic express visuomotor responses were nearly identical in both pro- and anti-reach conditions. These muscle activity responses were generally reflected in the temporal pattern of corticospinal excitability modulations in experiment two. Indeed, the MEP and background EMG responses showed an alternating pattern similar to that in experiment one, although the effect was clearer in the anti-reach than the pro-reach condition. Overall, the data show that the express and voluntary responses are indeed distinct neural control signals, which supports the hypothesis that at least two separate neural pathways (one slow and one fast) contribute to the control of visually guided reaching. The properties of the fast pathway are consistent with a tecto-reticulospinal pathway, while those of the slow pathway are consistent with a transcortical loop.

The aim of this study was to improve our understanding of muscle contractions in the arm as a function of hand orientation for grasp. While there have been several reports on arm kinematics for reach and grasp movements, little has been done at the muscular level. To this end, we analyzed the modulation of shoulder, elbow and hand muscles for a reach and grasp task involving a target in either horizontal or vertical orientation. We hypothesized that unlike what has been observed for kinematics, at the muscular level we would see less correlation between the three muscle groups. A decoding approach with Machine Learning revealed adaptation patterns that were not visible using classical methods. Reach-and-grasp has traditionally been treated as being made of two components - the reach and the grasp components. Our dynamic decoding approach revealed a more complex picture with very different dynamics in the shoulder and elbow muscle groups during reach. All muscle groups showed peak capacity for predicting hand orientation before the start of grasp and followed the ubiquitous proximo-distal organization. The patterns of muscular modulation for hand orientation were strongly perturbed by the eyes closed and slow movement conditions, potentially decreasing the available degrees of freedom for adaptation.

Many contexts in motor learning require a learner to change from an existing movement solution to a novel movement solution to perform the same task. Recent evidence has pointed to motor variability prior to learning as a potential marker for predicting individual differences in motor learning. However, it is not known if this variability is predictive of the ability to adopt a new movement solution for the same task. Here, we examined this question in the context of a redundant precision task requiring control of motor variability. Fifty young adults learned a precision task that involved throwing a virtual puck toward a target using both hands. Because the speed of the puck depended on the sum of speeds of both hands, this task could be achieved using multiple solutions. Participants initially performed a baseline task where there was no constraint on the movement solution, and then performed a novel task where they were constrained to adopt a specific movement solution requiring asymmetric left and right hand speeds. Results showed that participants were able to learn the new solution, and this change was associated with changes in both the amount and structure of variability. However, individual differences in baseline motor variability were only weakly correlated with initial and final task performance when using the new solution, with greater variability being associated with higher errors. We also found a strong specificity component - initial variability when using the new solution was highly correlated with final task performance with the new solution, but once again, higher variability was associated with greater errors. These results suggest that motor variability is not necessarily indicative of flexibility and highlight the need to consider the task context in determining the relation between motor variability and learning.

Accumulating evidence indicates that the humans proprioception map appears subject-specific. However, whether the idiosyncratic pattern persists across time with good within-subject consistency has not been quantitatively examined. Here we measured the proprioception by a hand visual-matching task in multiple sessions over two days. We found that people improved their proprioception when tested repetitively without performance feedback. Importantly, despite the reduction of average error, the spatial pattern of proprioception errors remained idiosyncratic. Based on individuals proprioceptive performance, a standard convolutional neural network classifier could identify people with good accuracy. We also found that subjects baseline proprioceptive performance could not predict their motor performance in a visual trajectory-matching task even though both tasks require accurate mapping of hand position to visual targets in the same workspace. Using a separate experiment, we not only replicated these findings but also ruled out the possibility that performance feedback during a few familiarization trials caused the observed improvement in proprioception. We conclude that the conventional proprioception test itself, even without feedback, can improve proprioception but leave the idiosyncrasy of proprioception unchanged.

Responding to an external stimulus takes [~]200 ms, but this can be shortened to as little as [~]120 ms with the additional presentation of a startling acoustic stimulus. This phenomenon is hypothesized to arise from the involuntary release of a prepared movement (a StartReact effect). However, a startling acoustic stimulus also expedites rapid mid-flight, reactive adjustments to unpredictably displaced targets which could not have been prepared in advance. We surmise that for such rapid visuomotor transformations, intersensory facilitation may occur between auditory signals arising from the startling acoustic stimulus and visual signals relayed along a fast subcortical network. To explore this, we examined how a startling acoustic stimulus shortens reaction times in a task that produces express visuomotor responses, which are brief bursts of muscle activity that arise from a fast tectoreticulospinal network. We measured express visuomotor responses on upper limb muscles in humans as they reached either toward or away from a stimulus in blocks of trials where movements could either be fully prepared or not, occasionally pairing stimulus presentation with a startling acoustic stimulus. The startling acoustic stimulus reliably produced larger but fixed-latency express visuomotor responses in a target-selective manner, and also shortened reaction times, which were equally short for prepared and unprepared movements. Our results provide insights into how a startling acoustic stimulus shortens the latency of reactive movements without full motor preparation. We propose that the reticular formation is the likely node for intersensory convergence during the most rapid transformations of vision into targeted reaching actions. KEY POINTSO_LIA startling acoustic stimulus (SAS) shortens reaction times by releasing fully prepared motor programs (the StartReact effect), but can also hasten responses in reflexive tasks without any movement preparation C_LIO_LIHere we measure the effect of a SAS on reaction times and upper limb muscle recruitment in a reflexive reaching task, focusing on express visuomotor responses that are evoked by visual target presentation and demarcate activity along a subcortical tectoreticulospinal pathway C_LIO_LIA SAS robustly increased the magnitude of express visuomotor responses without changing their timing, and this increase was tightly related to the subsequent reaction time even in the absence of motor preparation C_LIO_LIOur results attest to intersensory facilitation within the tectoreticulospinal pathway, which provides the shortest pathway mediating visuomotor transformations for reaching C_LIO_LIThese results reconcile discrepant findings by emphasizing the importance of intersensory facilitation in SAS-induced hastening of reaction times in reflexive tasks C_LI

The occurrence of an error when performing a motor sequence causes an immediate reduction in speed on subsequent trials, which is referred to as post-error slowing. However, understanding how post-error slowing changes with practice has been difficult because it requires extended practice on a novel sequence task. To address this issue, we examined post-error slowing in a novel glove-based typing task that participants performed for 15 consecutive days. Speed and accuracy improved from the early to middle stages of practice, but did not show any further improvements between middle and late stage of practice. However, when we analyzed the response to errors, we found that participants decreased both the magnitude and duration of post-error slowing with practice, even after there were no detectable improvements in overall task performance. These results indicate that learning not only improves overall task performance but also modifies the ability to respond to errors.

People usually reach for objects to place them in some position and orientation, but the placement component of this sequence is often ignored. For example, reaches are influenced by gaze position, visual feedback, and memory delays, but their influence on object placement is unclear. Here, we tested these factors in a task where participants placed and oriented a trapezoidal block against 2D visual templates displayed on a frontally located computer screen. In Experiment 1, participants matched the block to three possible orientations: 0{degrees} (horizontal), +45{degrees} and -45{degrees}, with gaze fixated 10{degrees} to the left/right. The hand and template either remained illuminated (closed-loop), or visual feedback was removed (open-loop). In Experiment 2, a memory delay was added, and participants sometimes performed saccades (centripetal, centrifugal, or opposite side). In Experiment 1, the hand consistently overshot the template relative to gaze (similar to reaching), especially in the open-loop task. After a memory delay, location errors were influenced by both template orientation and gaze position. Based on previous reach experiments, we expected these errors to be independent of the previous eye position, but placement overshoot also depended on previous saccade metrics. Hand orientation over-rotated relative to template orientation (all conditions). Orientation was influenced by gaze position in Experiment 1, but this vanished in the presence of a memory delay. These results demonstrate interactions between gaze, location, and orientation signals in the planning and execution of hand placement and suggest different neural mechanisms for closed-loop, open-loop, and memory delay placement. NEW & NOTEWORTHYEye-hand coordination studies usually focus on object acquisition, but placement is equally important. Here, we investigated how gaze position influences object placement toward a 2D template, with different levels of visual feedback. Like reach, placement overestimated goal location relative to gaze, but was also influenced by previous saccade metrics. Gaze also modulated hand orientation, which generally overestimated template orientation. Gaze influence was feedback-dependent, with location errors increasing but orientation errors decreasing after a memory delay.

How do precision of movement and proprioception influence motor control and adaptation? Several theories--such as the exploration-exploitation hypothesis--propose that variability plays a key role in motor performance and learning. However, empirical measures of motor and proprioceptive precision are often limited by small sample sizes, and proprioceptive estimates, especially those relying on efferent signals, are difficult to isolate and quantify. In this study, we leveraged a large dataset of 270 participants--including a subsample of older adults (ages 54- 84)--to assess the precision of hand movements and proprioceptive estimates, and to examine whether these factors predict individual differences in motor learning and adaptation. We found that baseline reach variance did not predict learning or changes in hand localization. Although active hand localization (which includes efferent contributions) was slightly more precise--showing an 8.6% reduction in variance--this suggests that unseen hand estimates rely primarily on proprioception. Neither motor nor sensory precision varied with age. However, reach aftereffects were modestly associated with proprioceptive precision before training and proprioceptive recalibration after training. No other measure of learning or variance was reliably associated. These findings suggest that reach aftereffects may partly reflect changes in hand proprioception, but overall, we identified no predictors of adaptation to a rotated visual cursor.

Behavioral studies have shown that humans account for inertial acceleration in their decisions of hand choice when reaching during body motion. Physiologically, it is unclear at what stage of movement preparation information about body motion is integrated in the process of hand selection. Here, we addressed this question by applying transcranial magnetic stimulation over motor cortex (M1) of human participants who performed a preferential reach task while they were sinusoidally translated on a linear motion platform. If M1 only represents a read-out of the final hand choice, we expect the body motion not to affect the MEP amplitude. If body motion biases the hand selection process prior to target onset, we expect corticospinal excitability to modulate with the phase of the motion, with larger MEP amplitudes for phases that show a bias to using the right hand. Behavioral results replicate our earlier findings of a sinusoidal modulation of hand choice bias with motion phase. MEP amplitudes also show a sinusoidal modulation with motion phase, suggesting that body motion influences corticospinal excitability which may ultimately reflect changes of hand preference. The modulation being present prior to target onset suggests that competition between hands is represented throughout the corticospinal tract. Its phase relationship with the motion profile suggests that other processes after target onset take up time until the hand selection process has been completely resolved, and the reach is initiated. We conclude that the corticospinal correlates of hand preference are modulated by body motion.

Muscle vibration alters both perceived limb position and velocity by increasing muscle spindle afferent firing rates. In particular, the type Ia afferents are affected, which mainly encode muscle stretch velocity. Predictive frameworks of sensorimotor control, such as Active Inference and Optimal Feedback Control, suggest that velocity signals should inform position estimates. Such a function would predict that errors in perceived limb position and velocity should be correlated, but this prediction remains empirically underexplored. We hypothesised that an online evaluation of the integral of sensed velocity influences the perceived arm position during active movements. Using a virtual reality-based reaching task, we investigated how vibration-biased proprioceptive feedback influences voluntary movement control and inference of arm position and movement. Our results suggest that muscle vibration biases perceived movement velocity, with downstream effects on perceived limb position and reflexive corrections of movement speed. We found that (i) antagonist vibration during active movement caused participants to both overestimate their movement speed while also slowing down, (ii) movement speed and endpoint errors were correlated, with muscle vibration affecting both in congruent directions, and (iii) adjustments in movement speed to muscle vibration are sufficiently fast to be reflexive. Together, these findings support the hypothesis that proprioceptive velocity signals are integrated to augment inference of position, consistent with predictive frameworks of sensorimotor control. Key pointsO_LIDuring movement without visual feedback, the central nervous system (CNS) has access to both position- and velocity-based proprioceptive signals, which are used to estimate limb state. C_LIO_LIMuscle vibration biases the perception of limb position, as seen in the classically observed pattern of biased endpoint errors, through the stimulation of primary (type Ia) muscle spindles, primarily a velocity sensor. C_LIO_LIWe investigated how proprioceptive velocity signals affect position estimation during movement by applying muscle vibration while measuring perceived movement speed, actual movement speed, and endpoint errors in a virtual reality (VR) based reaching task. C_LIO_LIWe show that errors in perceived limb position and velocity are correlated during active movements, consistent with predictive frameworks of sensorimotor control. C_LIO_LIThese findings support the idea that the CNS maintains a self-consistent estimate of limb state across both position and velocity domains. C_LI

During visually guided reaching, proximal limb muscles can be activated within 80 ms of target appearance. Such "express" visuomotor responses are temporally aligned with target appearance rather than movement onset, and invariably tuned towards the direction of the visual target regardless of the instructed reach direction. These features prompt the hypothesis that express visuomotor responses are driven by a subcortical pathway. We tested this by measuring the changes in Motor Evoked Potential (MEP) size following Transcranial Magnetic Stimulation (TMS) or Transcranial Electrical Stimulation (TES) of the motor cortex, as participants reached either towards or away from visual targets. We found that 70-80 ms after target presentation, MEPs in a primary shoulder flexor muscle (pectoralis major) were oriented towards the target direction regardless of whether the participant subsequently reached towards or away from the target. Similar target-oriented MEP modulations occurred in posterior deltoid and biceps brachii muscles, whereas MEPs in a finger muscle were affected neither by target nor reach direction. Critically, there were no significant differences in modulation of responses to TMS versus TES across all reaching conditions, which suggests that the target-oriented modulation occurs downstream of the motor cortex output neurons. Combined, our results are consistent with a subcortical rather than cortical origin for the earliest changes in corticospinal excitability following visual target onset. A prime candidate for such subcortical modulation includes the superior colliculus and reticular formation.

The extent to which a sequence of movements is prepared before initiating the first movement is a longstanding question in motor neuroscience. The observation that reaction time (RT) increases for longer sequences has been used as evidence of sequence preparation - reflecting the additional demands of preparing multiple movements before initiating a sequential action. However, many processes contribute to RT, making it unclear whether the observed RT increases specifically reflect sequence preparation. For example, with longer sequences, participants face greater ambiguity in selecting their first movement in the sequence. Here, we test how much of the observed RT increases can be explained by the first-target ambiguity when reaching toward spatial targets. In our paradigm, we independently manipulate: (i) the number of future targets displayed, (ii) the number of targets to be acquired, and (iii) the spatial arrangement of the targets. This approach allows us to vary the demands of sequence preparation and first-target ambiguity, thereby enabling a direct assessment of their respective influence on RT. We report that RT increases with additional sequence elements but that this effect is fully explained by the ambiguity in selecting the first reach target. That is, sequence preparation causes no RT increase. In fact, when first-target ambiguity is eliminated, RT is constant across the number of displayed targets even though kinematic analysis reveals that participants have prepared a sequence. Together, these results indicate that preparing multiple reaches to spatial targets does not impose additional temporal costs relative to preparing a single reach.

Humans can flexibly acquire entirely new sensorimotor mappings, a process known as de novo motor learning. A central challenge in de novo motor learning is that the learner must discover a viable solution from scratch within a highly redundant control space, without predefined task constraints. Understanding what types of sensorimotor information contribute to the formation of accurate motor behavior in such situations is therefore critical for explaining how novel sensorimotor skills are acquired. While previous studies have suggested that novel visuomotor mappings can be formed based on movement direction and target position, it remains unclear how these two types of information contribute to the learning process. To address this question, we trained 25 human participants to learn arbitrary joystick-to-cursor mapping. We then employed a generalization paradigm to selectively restrict learning experience to either movement direction or target position. Three distinct target conditions were designed: one emphasized target position (P), another emphasized movement direction (D), and a third (P&D) encouraged learning of both components separately. As a result, direction experience improved movement initiation, whereas position experience enhanced movement termination. However, in the P&D condition, combining these experiences did not yield additive generalization. Instead, endpoint accuracy was positively correlated with the degree of alignment between direction- and position-based joystick outputs within the control space. These results suggest that accurate formation of a novel sensorimotor map depends on the coordinated use of directional and positional experiences. Significant StatementHow do humans build entirely new sensorimotor relationships from scratch? This study examined how distinct sensorimotor experiences (movement direction and target position) contribute to the acquisition of a novel joystick-to-cursor mapping. By isolating these experiences, we found that direction experience improved movement initiation, while position experience enhanced movement termination. However, combining these experiences did not lead to more accurate movements as a whole. Instead, the accuracy was related to how well directional and positional joystick outputs were aligned in a control space. These findings suggest that de novo motor learning requires the coordinated use of directional and positional information.

Psychological pressure is thought to relate to performance in an inverted-U pattern, yet evidence is mixed, possibly because manipulations rarely produce high pressure. We induced scalable pressure using a streak goal that resets after a failure in a force-control task. Participants pursued ten consecutive successes (streak goal) or 100 successes irrespective of sequence (total goal). Under the streak goal, heart rate, pupil size, and perceived pressure rose as participants approached their maximum streak; under the total goal, heart rate and pupil size showed little modulation. Performance followed an inverted-U under the streak goal--improving then declining at the maximum streak--whereas the total goal showed no late-stage drop. This dissociation suggests the late-stage decline reflects pressure, not the streak itself. Despite this clear performance decrement, analyses of movement vigor, feedforward/feedback kinematics, and individual differences in pressure responses revealed no consistent systematic signatures at the group level. Fragile streak goals thus provide a multimodal pressure manipulation and a platform for testing mechanisms underlying choking in human motor control.

Motor learning involves plasticity in a network of brain areas across the cortex and cerebellum. Such traces of learning have the potential to affect subsequent learning of other tasks. In some cases, prior learning can interfere with subsequent learning, but it may be possible to potentiate learning of one task with a prior task if they are sufficiently different. Because prism adaptation involves extensive neuroplasticity, we reasoned that the elevated excitability of neurons could increase their readiness to undergo structural changes, and in turn, create an optimal state for learning a subsequent task. We tested this idea, selecting two different forms of learning tasks, asking whether exposure to a sensorimotor adaptation task can improve subsequent de novo motor skill learning. Participants first learned a new visuomotor mapping induced by prism glasses in which prism strength varied trial-to-trial. Immediately after and the next day, we tested participants on a mirror tracing task, a form of de novo skill learning. Prism-trained and control participants both learned the mirror tracing task, with similar reductions in error and increases in distance traced. Both groups also showed evidence of offline performance gains between the end of day 1 and the start of day 2. However, we did not detect differences between groups. Overall, our results do not support the idea that prism adaptation learning can potentiate subsequent de novo learning. We discuss factors that may have contributed to this result.