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...

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.

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.

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