Decoding the Brain in Conversation with NLP Models

Jing Cai and collaborators uncover the dynamic neural choreography behind human conversation by integrating intracranial recordings with state-of-the-art natural language processing (NLP) models. Studying real-time dialogues, the team identified distinct yet overlapping brain activity patterns associated with both speech production and comprehension, distributed across frontotemporal regions and multiple frequency bands. Crucially, these patterns were sensitive not only to specific words and sentences but also to their contextual order, revealing fine-grained linguistic encoding in the brain. Moreover, the transition between speaker and listener roles was marked by precise, time-aligned shifts in neural dynamics. This work exemplifies how NLP models can illuminate the complex and fluid neural mechanisms underpinning natural language exchange, pushing the frontier of brain-language research.