Long-term memory gated encoding in working memory

Humans have the remarkable ability to hold onto information - preserving it in working memory (WM) where it is ready for ongoing thought and action. Although WM is undisputedly limited to storing only small amounts of information, it is clear that we can extend this capacity by capitalizing on existing knowledge in long-term memory (LTM). But how do we connect prior knowledge in LTM to benefit WM? Imagine you need to hold a phone number in mind: you recognize that it consists of a familiar chunk such as your birth year, and it immediately becomes easier to remember. Instead of encoding each digit of the phone number in WM, one can strategically encode only the novel parts in WM and recruit LTM for the known parts. Although this is a well-established behavioral finding, theories diverge in the mechanism of connection between LTM and WM, and neural theories have never been tested empirically. I propose a novel, neurally plausible mechanism in which encoding in WM is guided by LTM to proactively decrease the cognitive load on the system: "LTM-gated encoding". Objective 1 will elucidate the bounds of this mechanism in a novel behavioral task, Objective 2 will delineate the neural interactions underlying this mechanism with magnetoencephalography (MEG), and
Objective 3 will describe the mechanism with a computational model. I will receive training in state-of-the-art neurophysiological and machine-learning methods which will prepare me for a career in computational neuroscience. The project will integrate the supervisor's technical and computational skills with my empirical domain knowledge, to build and test this new quantitative framework which contrasts with previous work in the field.