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Modeling Brain Responses to Natural Conversation via LLM-Derived Informational and Relational Metrics
The project uses large language models (LLMs) and information-theory measures to quantify how people think in real conversations. The goal is to create a pipeline that quantifies informational content (the purpose of the utterance is to provide/inquire information) and relational content (the purpose is to build a stronger relational bond with the other speaker) in conversation utterances. Then, we will test whether these conversation features can predict which parts of the brain become active while people talk and listen in natural, everyday conversations. This work will help Princeton and the University of Toronto develop better, more realistic models of human communication and brain function, and will produce tools and analyses that can support future research in neuroscience and AI.
Faculty Supervisor:
Michael Guerzhoy
Student:
Partner:
Princeton University
Discipline:
Computer science
Sector:
Artificial Intelligence; Health and Related Sciences and Technology
University:
University of Toronto
Program:
Globalink Research Award
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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.