RESEARCH

NLP

From Thought to Action: How a Hierarchy of Neural Dynamics Supports Language Production

February 06, 2025

Abstract

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.

Download the Paper

AUTHORS

Written by

Mingfang (Lucy) Zhang

F.-Xavier Alario

Pierre Bourdillon

Svetlana Pinet

Jarod Levy

Jean Remi King

Jérémy Rapin

Stéphane d'Ascoli

Publisher

NA

Related Publications

June 05, 2026

CONVERSATIONAL AI

RANKING AND RECOMMENDATIONS

Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrieval

Anshumali Shrivastava, Jason Chen, Qi Ma, Zeyu Yang

June 05, 2026

May 26, 2026

HUMAN & MACHINE INTELLIGENCE

THEORY

Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images

Valentin Wyart, Huy V. Vo, Jean Remi King, Josephine Raugel, Jérémy Rapin, Marc Szafraniec, Max Seitzer, Patrick Labatut, Piotr Bojanowski

May 26, 2026

May 20, 2026

HUMAN & MACHINE INTELLIGENCE

RESEARCH

EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data

Alvin W. M. Tan, Nicolas Hamilakis, Manel Khentout, Sho Tsuji, Balázs Kégl, Michael C. Frank, Angel Villar Corrales, Charles-Eric Saint-James, Dongyan Lin, Emmanuel Dupoux, Jiayi Shen, Juan Pino, Mahi Luthra, Martin Gleize, Phillip Rust, Rashel Moritz, Sheila Krogh-Jespersen, Surya Parimi, Tom Fizycki, Vanessa Stark, Yosuke Higuchi, Youssef Benchekroun

May 20, 2026

May 18, 2026

CONVERSATIONAL AI

RESEARCH

GIM: Evaluating models via tasks that integrate multiple cognitive domains

Alexandre Rezende, Rohit Patel, Steven McClain

May 18, 2026

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.