Research Spotlight: Using artificial intelligence to reveal the neural dynamics of human conversation

(Press-News.org) What were you investigating?

We investigated how our brains process language during real-life conversations. Specifically, we wanted to understand which brain regions become active when we're speaking and listening, and how these patterns relate to the specific words and context of the conversation.

What methods did you use?

We employed artificial intelligence (AI) to take a closer look at how our brains handle the back-and-forth of real conversations. We combined advanced AI, specifically language models like those behind ChatGPT, with neural recordings using electrodes placed within the brain. This allowed us to simultaneously track the linguistic features of conversations and the corresponding neural activity in different brain regions.

By analyzing these synchronized data streams, we could map how specific aspects of language–like the words being spoken and the conversational context–were represented in the dynamic patterns of brain activity during conversation.

What did you find?

We found that both speaking and listening during a conversation engage a widespread network of brain areas in the frontal and temporal lobes. What's interesting is that these brain activity patterns are highly specific, changing depending on the exact words being used, the context and order of those words.

We also observed that some brain regions are active during both speaking and listening, suggesting a partially shared neural basis for these processes. Finally, we identified specific shifts in brain activity that occur when people switch from listening to speaking during a conversation.

Overall, our findings illuminate the dynamic way our brains organize themselves to produce and understand language during a conversation.

What are the implications?

Our findings offer significant insights into how the brain pulls off the seemingly effortless feat of conversation. It highlights just how distributed and dynamic the neural machinery for language is–it's not just one spot lighting up, but a network across different brain regions. The fact that these patterns are so finely tuned to the specifics of words and context shows the brain's remarkable ability to process the nuances of language as it unfolds.

The partial overlap we saw between the brain regions involved in speaking and listening hints at an efficient neural system, potentially a shared mechanism that gets repurposed depending on whether we're sending or receiving information. This could tell us a lot about how we efficiently switch roles during a conversation.

What are the next steps?

The next step involves semantic decoding. This means moving beyond simply identifying which brain regions are active during conversation and decoding the meaning of the words and concepts being processed.

Ultimately, this level of decoding could provide profound insights into the neural representation of language. This work could contribute to the development of brain-integrated communication technologies that can help individuals whose speech is affected by neurodegenerative conditions like amyotrophic lateral sclerosis (ALS).

 

Authorship: In addition to Jing Cai, additional Mass General Brigham authors include Alex Hadjinicolaou, Angelique Paulk, Daniel Soper, Tian Xia, Alexander Wang, John Rolston, R. Mark Richardson, and senior authors Ziv Williams and Sydney Cash.

Paper cited: Cai, J et al. “Natural language processing models reveal neural dynamics of human conversation” Nature Communications DOI: 10.1038/s41467-025-58620-w

Funding: Jing Cai is supported by the Mussallem Transformative Award and American Association of University Women. Ziv Williams is supported by NIH R01DC019653 and NIH U01NS123130. Sydney Cash, Alex Hadjinicolaou, Angelique Paulk and Daniel Soper are supported by NIH U01NS098968.

Disclosures: None

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