A Unified Neural Time Course for Words, Phrases, and Sentences: MEG Evidence from Parallel Presentation.
Overview
- Department of Linguistics, New York University, New York, NY, USA
- Department of Psychology, New York University, New York, NY, USA
Abstract
Recent behavioral and neural research on reading shows that humans can extract syntactic structure from short sentences within a fraction of a second—faster than many estimates for recognizing the meaning of a single word. This challenges a core assumption of many language processing models—that combinatory operations depend on prior lexical access. Furthermore, studies using parallel presentation of full sentences have revealed electrophysiological responses remarkably similar to those well established for single words. This raises the question of whether words, phrases, and sentences all move through the same processing stages, regardless of syntactic complexity. Using magnetoencephalography, we examined how single words, phrases, and sentences are processed when all visual information is available at once. Across all three levels, we observed highly similar waveform dynamics, with early responses reflecting bottom-up detection of form, followed by activity in the left anterior and posterior temporal cortices and ventromedial prefrontal cortex consistent with combinatory processing. Of these regions, the left anterior temporal lobe showed effects of bigram frequency suggestive of serial left-to-right dynamics. Together, these results support a Global-to-Sequential Assembly model in which the brain first detects the global form of the stimulus in a snapshot-like manner and then probes its combinatory properties through partially serial processes.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
The paper links to its data, not to its authors' code: see the Data section.
The paper's code and data availability statement is in the Data section.
Tracing map
A tracing map links a paper to the code its authors published: this paper has none, so it has no map.
Data
Datasets cited
Data Availability Statement
The raw data and analysis scripts supporting the findings of this study are publicly available on the Open Science Framework (OSF) repository: https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.
Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 2 authors, 5 keywords, 1 funder, 74 references.
Cite
This paper
Flower, N., & Pylkkänen, L. (2026). A Unified Neural Time Course for Words, Phrases, and Sentences: MEG Evidence from Parallel Presentation. Neurobiology of language (Cambridge, Mass.), 7, NOL.a.254. https://
BibTeX
@article{flower2026unifi
author = {Flower, Nigel and Pylkkänen, Liina},
title = {{A Unified Neural Time Course for Words, Phrases, and Sentences: MEG Evidence from Parallel Presentation}},
journal = {Neurobiology of language (Cambridge, Mass.)},
year = {2026},
month = jun,
volume = {7},
pages = {NOL.a.254},
publisher = {MIT Press},
issn = {2641-4368},
doi = {10.1162/
url = {https://
pmid = {42367746},
pmcid = {PMC13293727}
}
RIS
TY - JOUR
AU - Flower, Nigel
AU - Pylkkänen, Liina
TI - A Unified Neural Time Course for Words, Phrases, and Sentences: MEG Evidence from Parallel Presentation
T2 - Neurobiology of language (Cambridge, Mass.)
J2 - Neurobiol Lang (Camb)
PY - 2026
DA - 2026/
VL - 7
SP - NOL.a.254
SN - 2641-4368
PB - MIT Press
DO - 10.1162/
UR - https://
LA - en
ER -
CSL-JSON
{
"id": "10.1162/
"type": "article-journal",
"title": "A Unified Neural Time Course for Words, Phrases, and Sentences: MEG Evidence from Parallel Presentation",
"container-title": "Neurobiology of language (Cambridge, Mass.)",
"author": [
{
"family": "Flower",
"given": "Nigel"
},
{
"family": "Pylkkänen",
"given": "Liina"
}
],
"container-title-short":
"volume": "7",
"page": "NOL.a.254",
"DOI": "10.1162/
"PMID": "42367746",
"PMCID": "PMC13293727",
"ISSN": "2641-4368",
"publisher": "MIT Press",
"URL": "https://
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
16
]
]
}
}
Similar papers
The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.
- [1] doi:10.1126/sciadv.aec0518 [code]
- Hybrid spatial organization and evidence for magnitude-independent neural coding of linguistic information during sentence production.Journal: Science advancesIn common: 12 references
- [2] doi:10.1162/nol.a.264 [code]
- The Temporal Dynamics of the Labeling Algorithm During Natural Language Comprehension: Neural Evidence for Phrase Grammatical Type Generation.Journal: Neurobiology of language (Cambridge, Mass.)In common: 11 references
- [3] doi:10.1162/nol.a.247 [code]
- Same Sentences, Different Grammars, Different Brain Responses?
: An MEG Study on Case and Agreement Encoding in Hindi and Nepali Split-Ergative Structures. Journal: Neurobiology of language (Cambridge, Mass.)In common: MEG, 10 references - [4] doi:10.1111/cogs.70220 [code]
- Shared Neural Computations for Syntactic and Morphological Structures: Evidence From Mandarin Chinese.Journal: Cognitive scienceIn common: 7 references
- [5] doi:10.1038/s41586-026-10691-5 [code]
- Mapping the neuronal building blocks of human language with language models.Journal: NatureIn common: 7 references
- [6] doi:10.1038/s41467-026-75745-8 [code]
- A language network in the individualized functional connectomes of 1199 human brains doing arbitrary tasks.Journal: Nature communicationsIn common: 7 references
- [7] doi:10.1073/pnas.2422097122
- Hierarchical dynamic coding coordinates speech comprehension in the human brainJournal: n/aIn common: MEG, 6 references
- [8] doi:10.1038/s41467-026-76598-x
- Preserved topography, lateralization, selectivity, and functional connectivity of the language network in older brains.Journal: Nature communicationsIn common: 5 references
- [9] doi:10.1162/nol.a.271 [code]
- Compositional Complexity in Text and Images.Journal: Neurobiology of language (Cambridge, Mass.)In common: 5 references
- [10] doi:10.7554/elife.110320 [code]
- Neural categorization of visual words of alphabetic and non-alphabetic languages.Journal: eLifeIn common: MEG, 4 references
Contribute
The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.
Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.
Claim this paper
Correct its record
Say what each link of this record is, remove the ones that are not the paper's, add the ones that are missing. The correction becomes a new version of the record, in its Versions section.
Request its removal
To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).
Discussion, reproductions, activity
Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.
Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.
Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.
