Postdoctoral Fellowship: SPRF: Integration of new information into existing knowledge with sleep

This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Drs. Anna Schapiro and Sharon Thompson-Schill at the University of Pennsylvania, this postdoctoral fellowship award supports an early career scientist investigating how sleep supports the way we integrate across related memories to build up new knowledge. In our daily lives we are constantly bombarded with new information. However, this information is typically related in some way to our previous knowledge of the world. For example, when you go to the zoo, you may learn about a new species of bird that you have never heard of before. Learning about this new bird requires not only learning the unique aspects of the particular species, but also relating this information to what you already know about other birds. How does the brain integrate new and old information? The literature has pointed to a possible role for sleep — a time when new and old information can be reactivated and synthesized — in this integration process, but this idea lacks direct tests. This project will combine electroencephalography (EEG), behavioral memory assessments, and computational modeling to uncover how sleep supports this integration process. Specifically, we will test the hypothesis that in order to integrate new memories with previous knowledge, the brain needs to replay both kinds of memory during sleep, in an interleaved fashion. We will first test this empirically by running a multi-day experiment. Over the course of multiple training sessions, participants will learn about novel objects from different categories. They will then return for a final session one to two weeks later, during which they will learn about new objects from the same categories and subsequently take a nap in the laboratory with EEG recording. During the nap, a procedure known as Targeted Memory Reactivation (TMR) will be used to cue either newly learned objects (the objects learned just prior to the nap) or both newly learned and existing knowledge (both the objects learned prior to the nap and the objects learned the during the previous sessions) during sleep. Upon waking, we will assess participants’ memory for each object as well as their integration of new and old objects. This approach will allow us to test the hypothesis that integration of new information relies on the interleaved activation of that new information with previously learned information. We will also simulate the task (including TMR during sleep) in a neural network model of offline hippocampal-cortical interactions, providing a mechanistic account of the impact of different forms of replay on memory change. Together, the findings and resulting theoretical framework will advance our understanding of how sleep influences memory integration. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.