Date/Time: Wednesday April 12, 12-1pm

Location: MIT 46-6011

Zoom link: https://bit.ly/jongauthierdefense

Title: Multi-level models of language comprehension in the mind and brain

Abstract: What are the mental and neural representations that drive language comprehension? Large language models (LLMs) offer an exciting opportunity to address these questions more ambitiously than ever before in cognitive neuroscience, by finding mappings between the way humans and LLMs represent linguistic inputs. However, I argue in this thesis that many brain mapping methods relying on LLMs are limited in the types of claims about representational content they can safely support. I then present two case studies of a path forward. The first designs controlled interventions on LLMs’ internal representational contents, and tests the consequences of these interventions in a brain mapping evaluation. We apply this method in an fMRI brain decoding study, which reveals findings about the time-course of human syntactic representations. The second study integrates an LLM into a structured model of auditory word recognition, which is designed from the start for model interpretability. I apply this model to explain EEG data recorded as subjects listened to naturalistic English speech. The model enables us to discover distinct neural traces of how humans recognize and integrate the meanings of words in real time. I conclude by discussing the implications of these findings for the mental computations that drive online language comprehension.