Reactivating Memories from a Mathematical Task over a Period of Sleep

Abstract

Sleep, especially slow-wave sleep (SWS), has been found to facilitate memory consolidation for many types of learning. Mathematical learning, however, has seldom been examined in this context. Solving multiplication problems involves multiple steps before problems can be mastered or answers memorized, and thus it can depend on both skill learning and fact learning. Here we aimed to test the hypothesis that memory reactivation during sleep contributes to multiplication learning. To do so, we used a technique known as targeted memory reactivation (TMR), or the pairing of newly learned information with specific stimuli that are later presented during sleep. With TMR, specific memories can be reactivated over a period of sleep without disrupting ongoing sleep. We applied TMR during an afternoon nap to reactivate half of the multiplication problems that had previously been practiced. Results showed no effect of TMR on response time or accuracy of multiplication problem solving. Because these results were unexpected, we also used a variation of this paradigm to examine results in subjects who remained awake. Comparisons between the wake and sleep groups showed no difference in response time or accuracy in either the initial test or the final test. Although neither TMR nor sleep differentially influenced multiplication performance, correlational analysis provided some clues about mathematical problem solving and sleep. On the basis of these findings, even though they did not provide convincing support for our hypotheses, we suggest future experiments that could help produce a better understanding of the relevance of sleep and memory reactivation for this type of learning.