Natural language processing models reveal neural dynamics of human conversation

Jing Cai (), Alex E. Hadjinicolaou, Angelique C. Paulk, Daniel J. Soper, Tian Xia, Alexander F. Wang, John D. Rolston, R. Mark Richardson, Ziv M. Williams () and Sydney S. Cash ()
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Jing Cai: Harvard Medical School
Alex E. Hadjinicolaou: Harvard Medical School
Angelique C. Paulk: Harvard Medical School
Daniel J. Soper: Harvard Medical School
Tian Xia: Harvard Medical School
Alexander F. Wang: Harvard Medical School
John D. Rolston: Harvard Medical School
R. Mark Richardson: Harvard Medical School
Ziv M. Williams: Harvard Medical School
Sydney S. Cash: Harvard Medical School

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Through conversation, humans engage in a complex process of alternating speech production and comprehension to communicate. The neural mechanisms that underlie these complementary processes through which information is precisely conveyed by language, however, remain poorly understood. Here, we used pre-trained deep learning natural language processing models in combination with intracranial neuronal recordings to discover neural signals that reliably reflected speech production, comprehension, and their transitions during natural conversation between individuals. Our findings indicate that the neural activities that reflected speech production and comprehension were broadly distributed throughout frontotemporal areas across multiple frequency bands. We also find that these activities were specific to the words and sentences being conveyed and that they were dependent on the word’s specific context and order. Finally, we demonstrate that these neural patterns partially overlapped during language production and comprehension and that listener-speaker transitions were associated with specific, time-aligned changes in neural activity. Collectively, our findings reveal a dynamical organization of neural activities that subserve language production and comprehension during natural conversation and harness the use of deep learning models in understanding the neural mechanisms underlying human language.

Date: 2025
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DOI: 10.1038/s41467-025-58620-w

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