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Natl. Acad. Sci. U.S.A."],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Speech comprehension involves transforming an acoustic waveform into meaning. To do so, the human brain generates a hierarchy of features that converts the sensory input into increasingly abstract language properties. However, little is known about how rapid incoming sequences of hierarchical features are continuously coordinated. Here, we propose that each language feature is supported by a dynamic neural code, which represents the sequence history of hierarchical features in parallel. To test this \u201chierarchical dynamic coding\u201d (HDC) hypothesis, we use time-resolved decoding of brain activity to track the construction, maintenance, and update of a comprehensive hierarchy of language features spanning phonetic, word form, lexical\u2013syntactic, syntactic, and semantic representations. For this, we recorded 21 native English participants with magnetoencephalography (MEG), while they listened to two hours of short stories in English. Our analyses reveal three main findings. First, the brain represents and simultaneously maintains a sequence of hierarchical features. Second, the duration of these representations depends on their level in the language hierarchy. Third, each representation is maintained by a dynamic neural code, which evolves at a speed commensurate with its corresponding linguistic level. This HDC preserves the maintenance of information over time while limiting destructive interference between successive features. Overall, HDC reveals how the human brain maintains and updates the continuously unfolding language hierarchy during natural speech comprehension, thereby anchoring linguistic theories to their biological implementations.<\/jats:p>","DOI":"10.1073\/pnas.2422097122","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T17:47:41Z","timestamp":1760723261000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":34,"title":["Hierarchical dynamic coding coordinates speech comprehension in the human brain"],"prefix":"10.1073","volume":"122","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9213-588X","authenticated-orcid":false,"given":"Laura","family":"Gwilliams","sequence":"first","affiliation":[{"name":"Department of Psychology, Stanford University","place":["Stanford"]},{"name":"Wu Tsai Neurosciences Institute, Stanford University","place":["Stanford"]},{"name":"Stanford Data Science, Stanford University","place":["Stanford"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4805-2910","authenticated-orcid":false,"given":"Alec","family":"Marantz","sequence":"additional","affiliation":[{"name":"Department of Psychology, New York University","place":["New York"]},{"name":"Department of Linguistics, New York University","place":["New York"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0184-163X","authenticated-orcid":false,"given":"David","family":"Poeppel","sequence":"additional","affiliation":[{"name":"Department of Psychology, New York University","place":["New York"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2121-170X","authenticated-orcid":false,"given":"Jean-R\u00e9mi","family":"King","sequence":"additional","affiliation":[{"name":"Ecole Normale Superieure, Paris Sciences et Lettres (PSL), CNRS","place":["Paris, France"]},{"name":"Meta AI","place":["Paris, France"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"341","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"e_1_3_4_1_2","first-page":"111245","article-title":"Computational architecture of speech comprehension in the human brain","volume":"11","author":"Gwilliams L.","year":"2024","unstructured":"L. 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