Whole-body procedural learning benefits from targeted memory reactivation in REM sleep and task-related dreaming.
Claudia Picard-Deland, Tomy Aumont, Arnaud Samson-Richer, Tyna Paquette, Tore Nielsen
Neurobiology of Learning and Memory September 1, 2021 DOI: 10.1016/j.nlm.2021.107460 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Randomized controlled trial Peer reviewed |
|---|---|
| Population | Healthy participants |
| Duration | Morning nap or rest period |
| Topics | Dreaming |
| Keywords | Kinesthetic imagery Procedural learning Targeted memory reactivation Virtual reality |
| Key findings | Learning benefits most from TMR when applied in REM sleep compared to a Control-sleep group, and REM dreams reactivating kinesthetic elements of the VR task are associated with higher improvement than dreams reactivating visual elements or no reactivations. |
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.