ArticleNature communications2025
Natural language processing models reveal neural dynamics of human conversation.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Review
- Brain-AI convergence: Generative world models and hierarchical attention for human intelligence.Patterns (New York, N.Y.) · 2026Review
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- Aligning brains into a shared space improves their alignment with large language models.Nature computational science · 2026Article
- Brain activity and the production of written text during mediumistic trance: A controlled EEG study.PloS one · 2026Article
- The evolution of phonemic verbal fluency test: bridging tradition with contemporary insights.Frontiers in psychology · 2026Review
- Learning semantic similarity from sentence pairs using hybrid features centric approach and explainable siamese neural networks.PloS one · 2026Article
- Linguistic coupling between neural systems for speech production and comprehension during real-time dyadic conversations.bioRxiv : the preprint server for biology · 2025Article
- Whole-brain dynamics of articulatory, acoustic and semantic speech representations.Communications biology · 2025Article
- Decoding semantics from natural speech using human intracranial EEG.bioRxiv : the preprint server for biology · 2025Article
- Unsupervised Transformer Learning for Rapid and High-Quality MRI Data Acquisition.Health data science · 2025Article
- What does my network learn? Assessing interpretability of deep learning for EEG.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- A shared model-based linguistic space for transmitting our thoughts from brain to brain in natural conversations.Neuron · 2024Article
- A shared linguistic space for transmitting our thoughts from brain to brain in natural conversations.bioRxiv : the preprint server for biology · 2023Article
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10 authors.
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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.
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