Abstract
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.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
We have added new data to our work. These new experiments use a variety of experimental conditions to resolve how implicit and explicit components of memory contribute to the saturation of motor performance. Our new results (Fig. 2) demonstrate that saturation is an inherent property of implicit adaptation, but explicit adaptation has no upper limit on performance. We show that the saturation point of implicit learning can be flexibly altered through error consistency. Furthermore, we demonstrate that perturbation variability alters only the properties of implicit learning, not explicit learning. In Fig. 4 we track changes in implicit error sensitivity over time. Lastly, we have extended our mathematical model (Fig. 5) to demonstrate that error sensitivity is increased by error consistency, but decreased due to the passage of trials. We show that this trial-based decay in error sensitivity accounts for other data in the literature such as the saturation of learning under fixed error conditions, and the dissolution of savings over time.
