Top-down semantic predictions align phonetic neuronal dynamics in human superior temporal gyrus

Speech processing involves a hierarchy of cognitive layers from low-level phonetic to high-level semantic representations. However, interactions are not only feed-forward; feedback mechanisms are crucial for real-time, accurate speech recognition. Yet how distant levels interface during speech processing remains unclear.

Here, we analyzed intracortical recordings from 624 neurons across three human participants implanted with microelectrode arrays in the anterior superior temporal gyrus during an auditory semantic categorization task and natural speech perception. We identified distinct neural subspaces, or manifolds, for lexico-semantic and phonetic features, with a functional separation of the corresponding low-dimensional dynamics. We contrasted a bottom-up cumulative and a top-down semantic hypothesis on phonetic-semantic temporal alignment, and found phonetic alignment to word-level semantic representations, signaling top-down prediction.

These effects were consistent across participants at the spiking level, and remained undetected in adjacent ECoG recordings. These findings demonstrate the reorganization of neuronal population dynamics supporting phonetic representations during semantic prediction. We thank the participants for taking part in this study.

We also thank the clinical and research teams involved in patient care, electrode implantation, recordings, and data collection. This work was funded by Swiss National Science Foundation career grant 193542 and 225979 (T.P.), EU FET-BrainCom project (A.L.G.), NCCR Evolving Language, Swiss National Science Foundation Agreement #51NF40_180888 (A.L.G.), Swiss National Science Foundation project grant 163040 (A.L.G.), and the Pablo J. Salame Goldman Sachs endowed Associate Professorship of Computational Neuroscience at Brown University (W.T.).

Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland Department of Neuroscience, Brown University, Providence, RI, USA Carney Institute for Brain Science, Brown University, Providence, RI, USA Department of Neuroscience & Radiology, University of California San Diego, La Jolla, CA, USA Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Institut Pasteur, Université Paris Cité, Hearing Institute, Paris, France Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland The authors declare no competing interests. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

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