Whole-body procedural learning benefits from targeted memory reactivation in REM sleep and task-related dreaming.

Applying targeted memory reactivation (TMR) during REM sleep improves learning of a whole-body procedural task more than TMR applied during slow-wave sleep, wake, or no reactivation. Dreams that incorporate kinesthetic elements of the task, such as flying or accelerating, are associated with greater performance gains than dreams with visual elements or no task-related content. TMR does not directly alter dream content, but its performance benefits are larger when task-related dream reactivations also occur in REM sleep. These results suggest that dream imagery may reflect memory reactivation processes that support skill consolidation during sleep.

Study at a glance

Abstract

Sleep facilitates memory consolidation through offline reactivations of memory traces. Dreaming may play a role in memory improvement and may reflect these memory reactivations. To experimentally address this question, we used targeted memory reactivation (TMR), i.e., application, during sleep, of a stimulus that was previously associated with learning, to assess whether it influences task-related dream imagery (or task-dream reactivations). Specifically, we asked if TMR or task-dream reactivations in either slow-wave (SWS) or rapid eye movement (REM) sleep benefit whole-body procedural learning. Healthy participants completed a virtual reality (VR) flying task prior to and following a morning nap or rest period during which task-associated tones were readministered in either SWS, REM sleep, wake or not at all. Findings indicate that learning benefits most from TMR when applied in REM sleep compared to a Control-sleep group. REM dreams that reactivated kinesthetic elements of the VR task (e.g., flying, accelerating) were also associated with higher improvement on the task than were dreams that reactivated visual elements (e.g., landscapes) or that had no reactivations. TMR did not itself influence dream content but its effects on performance were greater when coexisting with task-dream reactivations in REM sleep. Findings may help explain the mechanistic relationships between dream and memory reactivations and may contribute to the development of sleep-based methods to optimize complex skill learning.