Doctoral dissertation research: The encoding, maintenance and retrieval of complex linguistic representations in working memory

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

Human language comprehension relies on the support from working memory (WM), but it is known that WM capacity is limited. To combat this limitation, comprehenders form structured representations (chunks) comprising multiple words at various levels of complexity. How the WM encoding, maintenance and retrieval processes support structures of varying complexity remains poorly understood.

This dissertation project examines how linguistic representations are formed and stored in WM by identifying the costs and benefits associated with supporting complex representations in WM. While there has been previous work on complexity, this project aims to provide a more nuanced perspective by examining different aspects of linguistic complexity.

By contributing to the body of work on the interplay between WM and language comprehension, the project also has potential clinical implications for language-related disorders such as specific language impairment and various forms of aphasia.

The project investigates the relatively unexplored interaction between representational complexity and the three working memory processes (encoding, maintenance, and retrieval). The first set of studies investigates whether forming complex representations with more features can facilitate or hinder one or more of the three core WM processes. The second set of studies further examines how the facilitation or interference effects are modulated by the similarity between elements within a chunked representation.

Finally, the third set of studies examines how the formation of a hierarchically chunked structure affects the retrieval of its individual components. To provide a multidimensional view of the interaction between WM processes and linguistic complexity, the project employs both behavioral (self-paced reading) and electrophysiological (EEG) methodologies.

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.