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The Temporal Dynamics of the Labeling Algorithm During Natural Language Comprehension: Neural Evidence for Phrase Grammatical Type Generation.

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Paper

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The authors' code

R · 89 lines · 2.8 KB · no license

  1. library(lme4)
  2. library(lmerTest)
  3. library(emmeans)
  5. ######de1/de2 vs. baseline_condition
  6. rm(list=ls())
  7. dat = read.csv(file.choose(), header = T )
  8. str(dat)
  10. dat$Condition <- factor(dat$Condition, levels=c('baseline_condition', 'de1', 'de2'))
  11. hm <- cbind(c(-0.5,0.5,0),
  12. c(-0.5,0,0.5))
  14. cor(hm)
  15. contrasts(dat$Condition) <- t(MASS::ginv(hm))
  18. dat$Hemisphere <- factor(dat$Hem, levels=c('left', 'right'))
  19. contrasts(dat$Hemisphere)<-c(0.5,-0.5)
  20. cor(contrasts(dat$Hemisphere))
  23. dat$Area <- factor(dat$Area, levels=c('anterior', 'posterior'))
  24. contrasts(dat$Area)<-c(0.5,-0.5)
  25. contrasts(dat$Area)
  27. dat$sub <- as.factor(dat$Sub)
  28. dat$Item <- as.factor(dat$Item)
  29. dat$channel <- as.factor(dat$channel)
  31. ####Critical word T420_700
  33. T420_700.model <- lmer(T420_700 ~ Condition * Hemisphere * Area + Baseline_window + (1 + Hemisphere |sub) + (1 | Item) + (1 | channel), data = dat)
  34. summary(T420_700.model)
  36. ##Simple effects
  37. ##Condition1:Area1 anterior
  39. dat$de1vsbaseline_condition <- ifelse(dat$Condition=='de1',0.5,-0.5)
  41. T420_700.model<- lmer(T420_700 ~ de1vsbaseline_condition + Baseline_window + (1 | sub) + (1 + de1vsbaseline_condition | Item) + (1 | channel), data = subset(dat, Condition %in% c("de1", "baseline_condition") & Area == "anterior"))
  42. summary(T420_700.model)
  44. ##Condition1:Area1 posterior
  45. dat$de1vsbaseline_condition <- ifelse(dat$Condition=='de',0.5,-0.5)
  47. T420_700.model<- lmer(T420_700 ~ de1vsbaseline_condition + Baseline_window + (1 | sub) + (1 + de1vsbaseline_condition | Item) + (1 | channel), data = subset(dat, Condition %in% c("de1", "baseline_condition") & Area == "posterior"))
  48. summary(T420_700.model)
  50. ####Word1_300_500
  51. W1_300_500.model<- lmer(W1_300_500 ~ Condition * Hemisphere * Area + (1 + Hemisphere | sub) + (1 + Hemisphere | Item) + (1 | channel), data = dat)
  52. summary(W1_300_500.model)
  58. ######de2 vs.de1
  59. rm(list=ls())
  60. dat = read.csv(file.choose(), header = T )
  61. str(dat)
  63. dat$Condition <- factor(dat$Condition, levels=c('de2', 'de1'))
  64. contrasts(dat$Condition) <-c(0.5,-0.5)
  67. dat$Hemisphere <- factor(dat$Hem, levels=c('left', 'right'))
  68. contrasts(dat$Hemisphere)<-c(0.5,-0.5)
  69. cor(contrasts(dat$Hemisphere))
  72. dat$Area <- factor(dat$Area, levels=c('anterior', 'posterior'))
  73. contrasts(dat$Area)<-c(0.5,-0.5)
  74. contrasts(dat$Area)
  76. dat$sub <- as.factor(dat$Sub)
  77. dat$Item <- as.factor(dat$Item)
  78. dat$channel <- as.factor(dat$channel)
  80. ####Critical word T420_700
  82. T420_700.model <- lmer(T420_700 ~ Condition * Hemisphere * Area + Baseline_window + (1 | sub) + (1 + Condition | Item) + (1 | channel), data = dat)
  83. summary(T420_700.model)
  85. ####Word1_300_500
  87. W1_300_500.model<- lmer(W1_300_500 ~ Condition * Hemisphere * Area + (1 + Area | sub) + (1 | Item) + (1 | channel), data = dat)
  88. summary(W1_300_500.model)

Best_Model.R, no license · at the source

Overview

Authors: Zhenghui Sun1,2, Feipeng Chen1, Shaodong Gui3, Yajiao Shi4, Yiming Yang1,5,6
ORCID iDs: Zhenghui Sun, Yiming Yang
  1. School of Linguistic Sciences and Arts, Jiangsu Normal University, Xuzhou, China
  2. College of Biomedical Engineering and Instrument Sciences, Zhejiang University, Hangzhou, China
  3. Suzhou Xingze Experimental School, Suzhou, China
  4. College of Humanities and Communication, Zhejiang University of Finance & Economics, Hangzhou, China
  5. Linguistic Science Laboratory of Jiangsu Normal University, Laboratory of Philosophy and Social Sciences at Universities in Jiangsu Province, Xuzhou, China
  6. Collaborative Innovation Center for Language Ability, Jiangsu Normal University, Xuzhou, China
Institutions: Jiangsu Normal University (China); Zhejiang University (China); Zhejiang University of Finance and Economics (China)
Journal: Neurobiology of language (Cambridge, Mass.), volume 7, article NOL.a.264
Dates: received 8 October 2025; accepted 14 April 2026; published online 1 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/nol.a.264 · PMID 42491983 · PMCID PMC13379301 · OpenAlex W7155559672
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Physiology & signal measures, Machine learning
Keywords: EEG, grammatical type, labeling algorithm, representational similarity analysis (RSA), syntax-semantics interface
Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Major Program of the Social Science Foundation of China (17ZDA301); National Natural Science Foundation of China (31271196); Major Entrusted Program of the National Social Science Foundation of China
Citations: not cited yet (Europe PMC); 100 references in the paper

Abstract

Language, as a uniquely human faculty, combines words into hierarchical phrases with diverse syntactic-semantic relations (e.g., subject–predicate and modifier–head phrases). These phrase types are theoretically generated through the labeling algorithm at the syntax-semantics interface. Nevertheless, previous research has focused predominantly on the syntactic system, whereas the neural basis and temporal dynamics of labeling remain poorly understood. To address this gap, we developed the Head-Anchored Labeling Manipulation approach. This method manipulates labeling by embedding the same Mandarin noun–verb dual-category words as heads in tightly controlled modifier–head constructions, where the head alone determines the phrase’s grammatical type. The representational similarity analysis of EEG data revealed that labeling representations emerged during both the early and middle stages: 192–227, 290–318, 330–360, and 385–416 ms following the dual-category word. Labeling emerged as early as ~200 ms, concurrent with syntactic Merge, and continued into the N400 window. These results demonstrate that labeling dynamically links syntax to conceptual–intentional system and generates phrase types by determining the constituent head. Its early engagement challenges syntax-first models and supports parallel interactive accounts. Moreover, combinability representations (520–596 and 604–632 ms), together with a late event-related brain potential negativity (420–700 ms) elicited by the dual-category word in the baseline condition, reflect increased difficulty in reconciling semantic associations with atypical syntactic configurations. Together, these findings provide clear electrophysiological evidence for the temporal dynamics of labeling and elucidate the processes at the syntax-semantics interface.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

OSF 9qvtx

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 8 files
Software Heritage: not checked
Found in: “DATA AND CODE AVAILABILITY STATEMENT”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: emmeans (1 file), lme4 (1 file), lmerTest (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
1 file
At the source: osf.io/9qvtx/?view_only=fd625500841b459d9f72433ce04ab0f7

The paper's code and data availability statement is in the Data section.

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data and code availability statement

The data and analysis scripts related to the reported results are available on OSF: https://osf.io/9qvtx/?view_only=fd625500841b459d9f72433ce04ab0f7.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 3 funders, 94 references.

Cite

This paper

Sun, Z., Chen, F., Gui, S., Shi, Y., & Yang, Y. (2026). The Temporal Dynamics of the Labeling Algorithm During Natural Language Comprehension: Neural Evidence for Phrase Grammatical Type Generation. Neurobiology of language (Cambridge, Mass.), 7, NOL.a.264. https://doi.org/10.1162/nol.a.264

BibTeX

@article{sun2026temporal,
author = {Sun, Zhenghui and Chen, Feipeng and Gui, Shaodong and Shi, Yajiao and Yang, Yiming},
title = {{The Temporal Dynamics of the Labeling Algorithm During Natural Language Comprehension: Neural Evidence for Phrase Grammatical Type Generation}},
journal = {Neurobiology of language (Cambridge, Mass.)},
year = {2026},
month = jul,
volume = {7},
pages = {NOL.a.264},
publisher = {MIT Press},
issn = {2641-4368},
doi = {10.1162/nol.a.264},
url = {https://doi.org/10.1162/nol.a.264},
pmid = {42491983},
pmcid = {PMC13379301}
}

RIS

TY - JOUR
AU - Sun, Zhenghui
AU - Chen, Feipeng
AU - Gui, Shaodong
AU - Shi, Yajiao
AU - Yang, Yiming
TI - The Temporal Dynamics of the Labeling Algorithm During Natural Language Comprehension: Neural Evidence for Phrase Grammatical Type Generation
T2 - Neurobiology of language (Cambridge, Mass.)
J2 - Neurobiol Lang (Camb)
PY - 2026
DA - 2026/07/01
VL - 7
SP - NOL.a.264
SN - 2641-4368
PB - MIT Press
DO - 10.1162/nol.a.264
UR - https://doi.org/10.1162/nol.a.264
LA - en
ER -

CSL-JSON

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"PMCID": "PMC13379301",
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