Improving Memory via Automated Targeted Memory Reactivation during Sleep

Abstract A widely accepted view in memory research is that previously acquired information can be reactivated during sleep, leading to persistent memory storage. Recently, Targeted Memory Reactivation (TMR) has been developed as a technique whereby specific memories can be reactivated during sleep using a sensory stimulus linked to prior learning. TMR can improve various types of memory, raising the possibility that it may be useful for cognitive enhancement and clinical therapy. A major challenge for the expanded use of TMR is that experimenters must manually control stimulation timing and intensity, which is impractical in most settings. To address this limitation, we developed the SleepStim system for automated TMR in the home environment. SleepStim includes a smartwatch to collect movement and heart-rate data, plus a smartphone to emit auditory cues. A machine-learning model identifies periods of deep non-REM sleep and triggers TMR sounds within these periods. We tested whether this system could replicate the spatial-memory benefit of in-lab TMR. Participants learned the locations of objects on a grid, and then half of the object locations were reactivated during sleep over three nights. In an experiment with 61 participants, the TMR effect was nonsignificant but varied systematically with stimulus intensity; low-intensity but not high-intensity stimuli produced memory benefits. In a second experiment with 24 participants, we limited stimulus intensity and found that TMR reliably improved spatial memory, consistent with effects observed in laboratory studies. We conclude that SleepStim can effectively accomplish automated TMR and that avoiding sleep disruption is critical for TMR benefits.