From Thought to Action: How a Hierarchy of Neural Dynamics Supports Language Production
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Abstract (arXiv)
Humans effortlessly communicate their thoughts through intricate sequences of motor actions. Yet, the neural processes that coordinate language production remain largely unknown, in part because speech artifacts limit the use of neuroimaging. To elucidate the unfolding of language production in the brain, we investigate with magnetoencephalography (MEG) and electroencephalography (EEG) the neurophysiological activity of 35 skilled typists, while they typed sentences on a keyboard. This approach confirms the hierarchical predictions of linguistic theories: the neural activity preceding the production of each word is marked by the sequential rise and fall of context-, word-, syllable-, and letter-level representations. Remarkably, each of these neural representations is maintained over long time periods within each level of the language hierarchy. This phenomenon results in a superposition of successive representations that is supported by a hierarchy of dynamic neural codes. Overall, these findings provide a precise computational breakdown of the neural dynamics that coordinate the production of language in the human brain.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Feb 2025.
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Citing papers per year: 2025: 4; 2026: 5.
9 papers.
- 2025Brain-to-Text Decoding: A Non-invasive Approach via Typingcited by 22
- 2025Temporal Sparse Autoencoders: Leveraging the Sequential Nature of Language for Interpretabilitycited by 18
- 2026NeuralBench: A Unifying Framework to Benchmark NeuroAI Modelscited by 16
- 2025Foundation Models for Cross-Domain EEG Analysis Application: A Surveycited by 4
- 2025Is the `Agent' Paradigm a Limiting Framework for Next-Generation Intelligent Systems?cited by 0
- 2026State Stream Transformer (SST) V2: Parallel Training of Nonlinear Recurrence for Latent Space Reasoningcited by 0
- 2026DANCE: Detect and Classify Events in EEGcited by 0
- 2026From Neurons to Conversation: Speech Brain-Computer Interfacescited by 0
- 2026Removing Timing Shortcuts Improves Non-Invasive Brain-to-Textcited by 0
- 2026Removing Timing Shortcuts Improves Non-Invasive Brain-to-Text30 Sep 2026
- 2026From Neurons to Conversation: Speech Brain-Computer Interfaces29 Sep 2026
- 2026DANCE: Detect and Classify Events in EEG11 May 2026
- 2026NeuralBench: A Unifying Framework to Benchmark NeuroAI Models8 May 2026
- 2026State Stream Transformer (SST) V2: Parallel Training of Nonlinear Recurrence for Latent Space Reasoning30 Apr 2026
- 2025Temporal Sparse Autoencoders: Leveraging the Sequential Nature of Language for Interpretability30 Oct 2025
- 2025Is the `Agent' Paradigm a Limiting Framework for Next-Generation Intelligent Systems?13 Sep 2025
- 2025Foundation Models for Cross-Domain EEG Analysis Application: A Survey21 Aug 2025
- 2025Brain-to-Text Decoding: A Non-invasive Approach via Typing18 Feb 2025
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- 2025The Emergence of Abstract Thought in Large Language Models Beyond Any Language