After extended practice, motor adaptation reaches a limit in which learning appears to stop, despite the fact that residual errors persist. What prevents the brain from eliminating the residual errors? Here we found that the adaptation limit was causally dependent on the second order statistics of the perturbation; when variance was high, learning was impaired and large residual errors persisted. However, when learning relied solely on explicit strategy, both the adaptation limit and its dependence on perturbation variability disappeared. In contrast, when learning depended entirely, or in part on implicit learning, residual errors developed. Residual errors in implicit performance were caused by variance-dependent modifications to error sensitivity, not forgetting. These observations are consisted with a model of learning in which the implicit system becomes more sensitive to error when errors are consistent, but forgets this memory of errors over time. Thus, residual errors in motor adaptation are a signature of the implicit learning system, caused by an error sensitivity that depends on the history of past errors. Introduction During motor adaptation, perturbations alter the sensory consequences of motor commands, yielding sensory prediction errors. In humans and other animals, the brain learns from these errors and adjusts its motor commands on subsequent attempts. Over many trials, the adjustments accumulate, but surprisingly, adaptation often remains incomplete; even after extended periods of practice, residual errors persist in many behaviors including reaching 1-4 , saccades 5,6 , and walking 7 . Why does learning appear to stop despite the fact that errors remain?Current models suggest that adaptation is supported by distinct learning systems: one implicit 8 , and the other explicit [9][10][11] . It is thought that the implicit system contributes little to modulation of asymptotic performance; when challenged with fixed errors, the implicit system appears to saturate at identical levels 12,13 . In contrast, explicit strategy provides greater flexibility; its asymptotic behavior is altered as people age [14][15][16] , under different types of feedback 17 , and with the time allotted for the preparation of a movement 18 . Therefore, current evidence suggests that the explicit system alone modifies the asymptotic state of learning.Here we tested this view using stochastic perturbations that affected reaching movements. We found that when perturbation variability was high, residual errors increased [19][20][21] . Furthermore, when perturbation variability was increased mid-experiment, the asymptotic performance decreased, causing participants to lose what they had already learned. Thus, the asymptote of adaptation was not a hard limit, but a dynamic variable that depended on the second order statistics of the perturbation. Which adaptive system was responsible for limiting the adaptation process?To answer this question we isolated implicit and explicit components of adaptation using several methodologies inc...

Anterograde interference refers to the negative impact of prior learning on the propensity for future learning. There is currently no consensus on whether this phenomenon is transient or long lasting, with studies pointing to an effect in the time scale of hours to days. These inconsistencies might be caused by the method employed to quantify performance, which often confounds changes in learning rate and retention. Here, we aimed to unveil the time course of anterograde interference by tracking its impact on visuomotor adaptation at different intervals throughout a 24-h period. Our empirical and model-based approaches allowed us to measure the capacity for new learning separately from the influence of a previous memory. In agreement with previous reports, we found that prior learning persistently impaired the initial level of performance upon revisiting the task. However, despite this strong initial bias, learning capacity was impaired only when conflicting information was learned up to 1 h apart, recovering thereafter with passage of time. These findings suggest that when adapting to conflicting perturbations, impairments in performance are driven by two distinct mechanisms: a long-lasting bias that acts as a prior and hinders initial performance and a short-lasting anterograde interference that originates from a reduction in error sensitivity.

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

Experience of a prediction error recruits multiple motor learning processes, some that learn strongly from error but have weak retention and some that learn weakly from error but exhibit strong retention. These processes are not generally observable but are inferred from their collective influence on behavior. Is there a robust way to uncover the hidden processes? A standard approach is to consider a state space model where the hidden states change following experience of error and then fit the model to the measured data by minimizing the squared error between measurement and model prediction. We found that this least-squares algorithm (LMSE) often yielded unrealistic predictions about the hidden states, possibly because of its neglect of the stochastic nature of error-based learning. We found that behavioral data during adaptation was better explained by a system in which both error-based learning and movement production were stochastic processes. To uncover the hidden states of learning, we developed a generalized expectation maximization (EM) algorithm. In simulation, we found that although LMSE tracked the measured data marginally better than EM, EM was far more accurate in unmasking the time courses and properties of the hidden states of learning. In a power analysis designed to measure the effect of an intervention on sensorimotor learning, EM significantly reduced the number of subjects that were required for effective hypothesis testing. In summary, we developed a new approach for analysis of data in sensorimotor experiments. The new algorithm improved the ability to uncover the multiple processes that contribute to learning from error. NEW & NOTEWORTHY Motor learning is supported by multiple adaptive processes, each with distinct error sensitivity and forgetting rates. We developed a generalized expectation maximization algorithm that uncovers these hidden processes in the context of modern sensorimotor learning experiments that include error-clamp trials and set breaks. The resulting toolbox may improve the ability to identify the properties of these hidden processes and reduce the number of subjects needed to test the effectiveness of interventions on sensorimotor learning.

When we experience an error during a movement, we update our motor commands to partially correct for this error on the next trial. How does experience of error produce the improvement in the subsequent motor commands? During the course of an erroneous reaching movement, proprioceptive and visual sensory pathways not only sense the error, but also engage feedback mechanisms, resulting in corrective motor responses that continue until the hand arrives at its goal. One possibility is that this feedback response is co-opted by the learning system and used as a template to improve performance on the next attempt. Here we used electromyography (EMG) to compare neural correlates of learning and feedback to test the hypothesis that the feedback response to error acts as a template for learning. We designed a task in which mixtures of error-clamp and force-field perturbation trials were used to deconstruct EMG time courses into error-feedback and learning components. We observed that the error-feedback response was composed of excitation of some muscles, and inhibition of others, producing a complex activation/deactivation pattern during the reach. Despite this complexity, across muscles the learning response was consistently a scaled version of the error-feedback response, but shifted 125 ms earlier in time. Across people, individuals who produced a greater feedback response to error, also learned more from error. This suggests that the feedback response to error serves as a teaching signal for the brain. Individuals who learn faster have a better teacher in their feedback control system.

22In many forms of motor adaptation, performance approaches a limit at which point learning stops, 23 despite the fact that errors remain. What causes this adaptation limit? Here we found that while reach 24 adaptation exhibited an asymptotic limit, this limit was not fixed: when the variance of the perturbation 25 decreased, the adaptation limit increased, and performance improved. Moreover, the limit could be 26 altered in real-time by changing perturbation variance. The same was true at low reaction times, 27indicating that implicit processes influenced the adaptation limit. Using a mathematical model that 28describes adaptation as a balance between learning and forgetting, we found that participants altered 29 their adaptation limit by changing their sensitivity to reaching errors. Changes in error sensitivity were 30 linked to the consistency of past errors. These observations suggest that during adaptation, time-varying 31 error sensitivities compete with constant forgetting, setting the boundary conditions that produce an 32 apparent limit in total adaptation. 33

Sensorimotor adaptation benefits from learning in two parallel systems: one that has access to explicit knowledge, and another that relies on implicit, unconscious correction. However, it is unclear how these systems interact: does enhancing one system's contributions, for example through instruction, impair the other, or do they learn independently? Here we illustrate that certain contexts can lead to competition between implicit and explicit learning. In some cases, each system is responsive to a task-related visual error. This shared error appears to create competition between these systems, such that when the explicit system increases its response, errors are siphoned away from the implicit system, thus reducing its learning. This model suggests that explicit strategy can mask changes in implicit error sensitivity related to savings and interference. Other contexts suggest that the implicit system can respond to multiple error sources. When these error sources conflict, a second type of competition occurs. Thus, the data show that during sensorimotor adaptation, behavior is shaped by competition between parallel learning systems.

Every movement ends in a period of stillness. Current models assume that commands that hold the limb at a target location do not depend on the commands that moved the limb to that location. Here, we report a surprising relationship between movement and posture in primates: on a within-trial basis, the commands that hold the arm and finger at a target location depend on the mathematical integration of the commands that moved the limb to that location. Following damage to the corticospinal tract, both the move and hold period commands become more variable. However, the hold period commands retain their dependence on the integral of the move period commands. Thus, our data suggest that the postural controller possesses a feedforward module that uses move commands to calculate a component of hold commands. This computation may arise within an unknown subcortical system that integrates cortical commands to stabilize limb posture.