Incorporating motor preparation time transforms micro-offline gains into micro-offline losses

During explicit sequence learning (ESL), micro-offline gains (MOGS) occur during brief rest periods. MOGS are calculated as the difference in sequence speed between the first correct sequence of one trial and the last sequence of the preceding trial. To date, all studies evaluating MOGS have calculated sequence speed from the execution time that occurs between keypresses, but this approach ignores potential contributions from motor preparation that occur before the first keypress. Given that ESL relies on both premovement motor planning and subsequent motor execution, we hypothesized that ignoring motor preparation time neglects a critical component of skill acquisition, potentially misrepresenting the true magnitude of MOGS. To test this, we calculated MOGS with and without preparation time in 30 adults who performed an ESL task. The dataset used for this analysis was obtained as part of a larger study to evaluate the effects of pretrial temporal predictability on ESL performance and learning by controlling the predictability of trial onset. Our results show that including preparation time flipped MOGS from positive to negative and significantly increased the positive correlation between early learning and a gold-standard ESL metric: the number of correct sequences performed. Our results suggest that preparation time should be incorporated into MOGS calculations and that excluding it overestimates micro-offline learning.NEW & NOTEWORTHY Current standards quantify micro-offline gains in terms of execution speed, entirely ignoring motor preparation time. If these gains reflect true learning, they should persist when accounting for premotor planning. Our results demonstrate a striking reversal: integrating motor preparation time completely flips micro-offline gains to micro-offline losses. Furthermore, by correcting this methodological artifact, our study provides a behaviorally validated, necessary framework for future studies investigating micro-offline gains.