Sleep resolves competition between explicit exemplar memory and implicit memory generalization

Sleep supports stabilization of explicit, declarative memory and benefits implicit statistical learning. In addition, sleep may change the quality of memory representations. Explicit and implicit learning systems, usually linked to hippocampus and striatum, 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 exemplar memory and implicit information-integration. The negative relationship between implicit and explicit memory components was resolved over sleep, but not wakefulness. Additionally, sleep benefitted performance on a categorization task that allows the use of both explicit and implicit strategies, and participants who slept showed superior performance in generalizing their knowledge to unseen exemplars. A reinforcement learning model relates this to better transfer of the learned exemplar value representation after sleep. Together, this suggests that sleep allows flexible access to information learned by different routes to respond optimally to everyday life contingencies.