This project is a truly collaborative and interdisciplinary effort from psychologists and computer scientists from universities in Freiburg im Breisgau, Tübingen and Munich. It would not have been possible without support from the DFG, BrainLinks-BrainTools//IMBIT and the Hector Fellow Academy.

By facilitating the integration of separate memory representations, sleep enables the formation of a more coherent and accessible memory trace, improving implicit rule abstraction in an information-integration category learning task and the application of learned information to novel situations.

Our findings demonstrate that sleep plays a critical role in resolving competition between explicit and implicit memory systems, fostering a more cooperative relationship that enhances memory performance and generalization.

Additionally, sleep benefitted performance on a task that allowed the (C) cooperative use of explicit and implicit memory, and participants who slept showed (B) superior performance in generalizing their knowledge to unseen exemplars. (A) Sleep did not affect recognition memory performance.

This model showed that participants in the night-sleep group demonstrated better transfer of the acquired exemplar value representation from learning to testing compared to the day-wake group.

To better understand the mechanisms underlying this resolved competition between memory systems after sleep, we again made use of the RL model fitted on participants’ learning task performance to model choices in the memory tests.

However, this initially competitive relationship was selectively resolved after a 12 h-consolidation interval including sleep but not after a day of wakefulness.

Our main goal was to test how sleep modulates the relationship between explicit and implicit memory representations. Immediately after learning, the relationship between explicit and implicit aspects of participants' memory was competitive, as indicated by a significant negative relationship.

Memory tests that were designed to encourage the use of different explicit (exemplar-based) and implicit (information-integration learning) memory systems engaged different brain networks associated with (A) explicit recognition memory and (B) implicit information-integration learning, respectively.

(A) During task training, participants successfully learned about winning and losing exemplars. Moreover, validity of a (B) reinforcement learning (RL) model designed to predict participants’ behavior during later memory tests was confirmed by (C) good prediction of participants’ learning behavior.

Explicit and implicit learning systems can compete during learning, but whether they continue to interact during offline periods remains unclear. Here, we investigate for feedback-driven classification learning, whether sleep integrates explicit and implicit aspects of memory.

Sleep supports stabilization of explicit, declarative memory and benefits implicit, procedural memory. In addition, sleep may change the quality of memory representations.

Together with @philipppaulus.bsky.social, Hao Zhu, Florian Pargent, @mariejakob.bsky.social, Jana Werle, Michael Czisch, Joschka Boedecker, Steffen Gais, @mschoenauer.bsky.social