Electrophysiological Signatures of Distinct Working Memory Subprocesses That Predict Long-term Memory Success

Electrophysiological signatures track distinct subprocesses of working memory, including the number of items and the spatial locations of those items. By identifying how these subprocesses predict long-term memory success in healthy young adults, this project should lead to an intricate understanding of the relationship between working memory and long-term memory. This study will investigate when and how long-term memory failures arise, by using sophisticated machine learning analyses of neural data. Moreover, this study will test the extent to which the investigators can track working memory processes in real time and how the investigators can leverage that information to improve long-term memory success. This will inform basic theories of the relationship between working memory and long-term memory and motivate future applications.