NSF grant ~$2.06M: how does the brain decide what to say next? Decoding conversational speech with LLMs (USC)
A ~$2.06 million ($2,057,137) NSF grant to the University of Southern California. Subjects hold weekly conversations for a full year while their brain activity is recorded with fMRI; large language models are then mapped onto that activity to reveal how the brain drives a conversation forward. The resulting dataset will be shared widely as a community benchmark.
Grant overview (primary data)
- Award amount$2,057,137
- RecipientUniversity of Southern California (California)
- ProgramCross-Directorate Activities
- Period2026-07-01 〜 2031-06-30
- FunderU.S. National Science Foundation (NSF) / NSF
Key points
- Recipient: University of Southern California; ~$2.06M ($2,057,137); July 2026–June 2031 (5 years)
- A CRCNS (computational neuroscience) proposal targeting the understudied side of language: production, not comprehension
- Longitudinal fMRI dataset: the same pairs converse weekly for a year, capturing accumulated conversational memory
- LLM internals are mapped onto brain activity to identify the neural signatures that drive the next utterance
- The dataset will be shared widely — a benchmark asset for naturalistic-dialogue neuroscience
We use language to organize thought, externalize ideas, and explain reasoning — yet how the brain decides what to say next has remained a blank spot in neuroscience. Comprehension has been studied extensively; production has not, largely because natural conversation is hard to recreate in a laboratory.
This project's novelty is a longitudinal design that sidesteps the problem: the same pairs of participants converse weekly for a year, so the accumulating shared memory that real conversation rests on is captured along with the dialogue itself.
1Using AI as an instrument, not a subject
The second key move is using AI as an instrument rather than a subject. LLMs are the first generation of models that can turn natural-language input into meaningful responses, and their internal representations serve as concrete hypotheses for the computation of "composing a reply." Aligning those representations with fMRI signals gives researchers a ruler for locating where and how that computation unfolds in the brain.
AI is used to read the brain; what is learned feeds back into the design of future AI — a bootstrapping loop emblematic of where the field now stands.
2A public dataset as a deliverable
Just as important, the deliverable is not only papers but a public dataset. Naturalistic conversational fMRI data is extremely expensive to collect; shared, it lets researchers worldwide test hypotheses on common ground. It is a textbook case of an NSF award doubling as investment in field-wide research infrastructure.
Why it matters
Understanding speech production underpins more human-like conversational AI, assistive technology for speech disorders, and brain–machine interfaces. The two-way cycle — measuring the brain with AI, refining AI with brain science — plus the open-dataset infrastructure angle make this a useful reference point for the AI × neuroscience trend.
FAQ
Why use LLMs to study the brain?
What is new here?
Who can use the results?
Sources (primary)
Source: NSF Award Search (U.S. National Science Foundation, public domain). Amounts are the obligated amount. For privacy, we do not handle principal investigator names.
- NSF Award (original, official)
- NSF Award ID: 2605721