Implicit adaptation compensates for erratic explicit strategy in human motor learning

Sports are replete with strategies, yet coaching lore often emphasizes ‘quieting the mind’, ‘trusting the body’ and ‘avoiding overthinking’ in referring to the importance of relying less on high-level explicit strategies in favor of low-level implicit motor learning. We investigated the interactions between explicit strategy and implicit motor adaptation by designing a sensorimotor learning paradigm that drives adaptive changes in some dimensions but not others. We find that strategy and implicit adaptation synergize in driven dimensions, but effectively cancel each other in undriven dimensions. Independent analyses—based on time lags, the correlational structure in the data and computational modeling—demonstrate that this cancellation occurs because implicit adaptation effectively compensates for noise in explicit strategy rather than the converse, acting to clean up the motor noise resulting from low-fidelity explicit strategy during motor learning. These results provide new insight into why implicit learning increasingly takes over from explicit strategy as skill learning proceeds. Implicit learning increases the fidelity of performance during motor learning by acting to adaptively clean up the noise resulting from a low-fidelity explicit strategy.