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...

A wide range of motor function declines with aging. Motor adaptation, which occurs when participants learn to reach accurately to a target despite a perturbation, does not deviate from this rule. There are currently three major hypotheses that have been put forward to explain this age-related decline in adaptation: deterioration of internal model recalibration due to age-related cerebellar degeneration, impairment of the cognitive component of motor adaptation, and deficit in the retention of the learned movement. In the present study, we systematically investigated these three hypotheses in a large sample of older women and men. We demonstrate that age-related deficits in motor adaptation are not due to impaired internal model recalibration or impaired retention of motor memory. Rather, we found that the cognitive component was reduced in older people. Therefore, our study suggests the interesting possibility that cerebellar-based mechanisms do not deteriorate with age despite cerebellar degeneration. In contrast, internal model recalibration appears to compensate for deficits in the cognitive component of this type of learning.

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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