UC San Diego

Two hypotheses have been advanced for when motor sequence learning occurs: offline between bouts of practice or online concurrently with practice. A third possibility is that learning occurs both online and offline. A complication for differentiating between those hypotheses is a process known as reactive inhibition, whereby performance worsens over consecutively executed sequences, but dissipates during breaks. We advance a new quantitative modeling framework that incorporates reactive inhibition and in which the three learning accounts can be implemented. Our results show that reactive inhibition plays a far larger role in performance than is appreciated in the literature. Across four groups of participants in which break times and correct sequences per trial were varied, the best overall fits were provided by a hybrid model. The version of the offline model that does not account for reactive inhibition, which is widely assumed in the literature, had the worst fits. We discuss implications for extant hypotheses and directions for future research.