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Shared Neural Computations for Syntactic and Morphological Structures: Evidence From Mandarin Chinese.

Code ↔ Paper

3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 3 matches
  1. [1] § Methods › EEG data analysis › Topographical similarity analysis ↔ script/analysis_toposim.m, lines 216–265 · score 0.58 · temporal adjacency, fdr_bh, clusters
  2. [2] § Methods › EEG data analysis ↔ script/analysis_ERP.m, lines 98–164 · score 0.53 · compound nouns, simplex nouns, inseparable verbs, EEGLAB, 100 uV, ERPs
  3. [3] § Results › ERP analysis: The LAN time window (275:400 ms) ↔ script/analysis_ERP.m, lines 98–164 · score 0.50 · compound nouns, simplex nouns, Grand, inseparable verbs, ROI, ERP

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 239 lines · 8.9 KB · no license · 2 matches

  1. %% Script for ERP analyses and plotting
  3. % Author: Xinchi Yu
  4. % last checked on Sep. 20, 2024.
  6. clear all;
  8. sub_list = 1:24;
  10. %% 1 - Get ERP data within the 275-400 ms time window acorss 5 ROIs
  12. eeglab;
  14. file_path = '../data/EEG/';
  16. ROIs = {...
  17. {'F3','F7','FC5','FC7','FT9'},...
  18. {'F4','F8','FC2','FC6','FT10'},...
  19. {'T7','CP5','P7','P3','TP9'},...
  20. {'CP6','T8','P4','P8','TP10'},...
  21. {'CP1','CP2','Pz','C3','C4'}...
  22. };
  24. conditions = {'separable ','non_separable ','train ','coffee'};
  26. tw = [275 400]; % time window, cf. Wei et al., 2023
  28. all_ERP_ROI = [];
  30. % "artifacts" records the number of kept trials for each condition and subject after artefact rejection.
  31. clear artifacts;
  33. for iROI=1:1:size(ROIs,2)
  35. artifact_coffee = [];artifact_train = [];artifact_sep = [];artifact_nonsep = [];
  37. for i=1:1:length(sub_list)
  39. this_ERP = pop_loaderp('filename', ['MTSHA' num2str(sub_list(i)) '_avref.erp'], 'filepath', file_path);
  41. times = this_ERP.times;
  43. ROI = ROIs{iROI};
  44. ROI_idx = find(ismember({this_ERP.chanlocs.labels},ROI));
  45. this_ERP_ROI = squeeze(mean(this_ERP.bindata(ROI_idx,:,:),1));
  46. % this_ERP_ROI shape: time points x conditions [i.e., bins]
  48. all_ERP_ROI(iROI,i,:,:) = this_ERP_ROI;
  49. % shape: ROI x subject x time points x conditions
  52. % 1-coffee; 2-train; 3-separable; 4-inseparable
  53. artifact_coffee = [artifact_coffee this_ERP.ntrials.accepted(1)];
  54. artifact_train = [artifact_train this_ERP.ntrials.accepted(2)];
  55. artifact_sep = [artifact_sep this_ERP.ntrials.accepted(3)];
  56. artifact_nonsep = [artifact_nonsep this_ERP.ntrials.accepted(4)];
  58. end
  60. artifacts{iROI} = [artifact_coffee;artifact_train;artifact_sep;artifact_nonsep]';
  62. end
  65. conditions_idx = [];
  66. for i=1:1:length(conditions)
  67. conditions_idx(i) = find(ismember(this_ERP.bindescr,conditions{i}));
  68. end
  69. % here 1,2,3,4 is assigned according to the order in conditions = {'separable ','non_separable ','train ','coffee'};
  70. coffee_ERP_ROI = all_ERP_ROI(:,:,:,conditions_idx(4));
  71. coffee_ERP_ROI = permute(coffee_ERP_ROI, [1 3 2]);
  72. train_ERP_ROI = all_ERP_ROI(:,:,:,conditions_idx(3));
  73. train_ERP_ROI = permute(train_ERP_ROI, [1 3 2]);
  74. sep_ERP_ROI = all_ERP_ROI(:,:,:,conditions_idx(1));
  75. sep_ERP_ROI = permute(sep_ERP_ROI, [1 3 2]);
  76. nonsep_ERP_ROI = all_ERP_ROI(:,:,:,conditions_idx(2));
  77. nonsep_ERP_ROI = permute(nonsep_ERP_ROI, [1 3 2]);
  79. % time window analysis
  80. tw_idx = find((times>=tw(1) & times<=tw(2))==1);
  82. coffee_ERP_ROI_tw = squeeze(mean(coffee_ERP_ROI(:,tw_idx,:),2));
  83. train_ERP_ROI_tw = squeeze(mean(train_ERP_ROI(:,tw_idx,:),2));
  84. sep_ERP_ROI_tw = squeeze(mean(sep_ERP_ROI(:,tw_idx,:),2));
  85. nonsep_ERP_ROI_tw = squeeze(mean(nonsep_ERP_ROI(:,tw_idx,:),2));
  88. all_ROI_tw = [train_ERP_ROI_tw', coffee_ERP_ROI_tw', sep_ERP_ROI_tw', nonsep_ERP_ROI_tw'];
  89. all_ROI_tw = array2table(all_ROI_tw, 'VariableNames', {'compR1','compR2','compR3','compR4','compR5',...
  90. 'simpR1','simpR2','simpR3','simpR4','simpR5',...
  91. 'sepR1','sepR2','sepR3','sepR4','sepR5',...
  92. 'nonsepR1','nonsepR2','nonsepR3','nonsepR4','nonsepR5',...
  93. });
  94. all_ROI_tw.sub = sub_list';
  96. writetable(all_ROI_tw,['../output/all_ROI_tw_' num2str(tw(1)) '_' num2str(tw(2)) '.csv']);
  98. %% 2 - Plot grand ERPs
  100. eeglab;
  102. figure;
  104. file_path = '../data/EEG/';
  106. ROIs = {{'F3','F7','FC5','FC7','FT9'}, {'F3','F7','FC5','FC7','FT9'},...
  107. {'CP1','CP2','Pz','C3','C4'}, {'CP1','CP2','Pz','C3','C4'}};
  108. conditionss = {{'separable ','non_separable '},{'train ','coffee'},...
  109. {'separable ','non_separable '},{'train ','coffee'}};
  110. captions = {{"separable verb","inseparable verb"},{"compound noun","simplex noun"},...
  111. {"separable verb","inseparable verb"},{"compound noun","simplex noun"}};
  112. ylims = {[-1.5,2.5],[-1.5,2.5],[-2.4,1.6],[-2.4,1.6]};
  114. for k=1:1:4
  116. ROI = ROIs{k}; conditions = conditionss{k}; caption = captions{k}; ylim_ = ylims{k};
  118. all_ERP_ROI = [];
  120. for i=1:1:length(sub_list)
  122. this_ERP = pop_loaderp('filename', ['MTSHA' num2str(sub_list(i)) '_avref.erp'], 'filepath', file_path);
  124. times = this_ERP.times;
  126. ROI_idx = find(ismember({this_ERP.chanlocs.labels},ROI));
  127. this_ERP_ROI = squeeze(mean(this_ERP.bindata(ROI_idx,:,:),1));
  128. % this_ERP_ROI shape: time points x conditions [i.e., bins]
  130. all_ERP_ROI(i,:,:) = this_ERP_ROI;
  132. end
  134. conditions_idx = [];
  135. for i=1:1:length(conditions)
  136. conditions_idx(i) = find(ismember(this_ERP.bindescr,conditions{i}));
  137. end
  138. condition1_ERP_ROI = all_ERP_ROI(:,:,conditions_idx(1));
  139. condition1_ERP_ROI = permute(condition1_ERP_ROI, [2 1]);
  140. condition2_ERP_ROI = all_ERP_ROI(:,:,conditions_idx(2));
  141. condition2_ERP_ROI = permute(condition2_ERP_ROI, [2 1]);
  143. subplot(2,2,k);
  144. % this is the shade behind:
  145. rectangle('Position', [275 -3 400-275 6], 'FaceColor', [204,205,198]/255, 'EdgeColor', [204,205,198]/255); hold on;
  146. % end
  147. plot(times, squeeze(mean(condition1_ERP_ROI,2)), '-', 'LineWidth',4); hold on;
  148. plot(times, squeeze(mean(condition2_ERP_ROI,2)), '-', 'LineWidth',4); hold on;
  149. legend(caption);
  151. ylabel('amplitude (μV)');
  152. xlabel('time (ms)');
  153. set(gca,'TickDir','out');
  154. set(gca, 'FontSize', 18); % 14
  155. ylim(ylim_);
  156. %ylim([0,3]);
  157. xlim([-201,801]);
  158. %set(gca,'xtick',-200:100:1800);
  159. set(gca, 'YDir','reverse');
  160. legend('boxoff');
  161. box off;
  162. colormap('parula');
  164. end
  166. %% 3 - Plot averaged response amplitude within time windows
  168. eeglab;
  170. figure;
  171. file_path = '../data/EEG/';
  173. ROIs = {{'F3','F7','FC5','FC7','FT9'},{'CP1','CP2','Pz','C3','C4'}};
  174. tws = {[275 400],[275 400]};
  175. ylims = {[-0.5,1.5],[-2.5,-0.5]};
  177. conditions = {'separable ','non_separable ','train ','coffee'};
  179. for k=1:1:2
  181. ROI = ROIs{k}; tw = tws{k}; ylim_ = ylims{k};
  183. all_ERP_ROI = [];
  185. for i=1:1:length(sub_list)
  187. this_ERP = pop_loaderp('filename', ['MTSHA' num2str(sub_list(i)) '_avref.erp'], 'filepath', file_path);
  189. times = this_ERP.times;
  191. ROI_idx = find(ismember({this_ERP.chanlocs.labels},ROI));
  192. this_ERP_ROI = squeeze(mean(this_ERP.bindata(ROI_idx,:,:),1));
  193. % this_ERP_ROI shape: time points x conditions [i.e., bins]
  195. all_ERP_ROI(i,:,:) = this_ERP_ROI;
  197. end
  199. conditions_idx = [];
  200. for i=1:1:length(conditions)
  201. conditions_idx(i) = find(ismember(this_ERP.bindescr,conditions{i}));
  202. end
  203. condition1_ERP_ROI = all_ERP_ROI(:,:,conditions_idx(1));
  204. condition1_ERP_ROI = permute(condition1_ERP_ROI, [2 1]);
  205. condition2_ERP_ROI = all_ERP_ROI(:,:,conditions_idx(2));
  206. condition2_ERP_ROI = permute(condition2_ERP_ROI, [2 1]);
  207. condition3_ERP_ROI = all_ERP_ROI(:,:,conditions_idx(3));
  208. condition3_ERP_ROI = permute(condition3_ERP_ROI, [2 1]);
  209. condition4_ERP_ROI = all_ERP_ROI(:,:,conditions_idx(4));
  210. condition4_ERP_ROI = permute(condition4_ERP_ROI, [2 1]);
  212. tw_idx = find((times>=tw(1) & times<=tw(2))==1);
  214. condition1_ERP_ROI_tw = mean(condition1_ERP_ROI(tw_idx,:),1);
  215. condition2_ERP_ROI_tw = mean(condition2_ERP_ROI(tw_idx,:),1);
  216. condition3_ERP_ROI_tw = mean(condition3_ERP_ROI(tw_idx,:),1);
  217. condition4_ERP_ROI_tw = mean(condition4_ERP_ROI(tw_idx,:),1);
  218. conds_ERP_ROI_tw = [condition1_ERP_ROI_tw', condition2_ERP_ROI_tw', condition3_ERP_ROI_tw', condition4_ERP_ROI_tw'];
  220. subplot(2,1,k);
  222. plot([1-0.1,2-0.1], [mean(condition1_ERP_ROI_tw);mean(condition2_ERP_ROI_tw)], '-','Color',[0,0,0],'linewidth', 2); hold on;
  223. plot([1+0.1,2+0.1], [mean(condition3_ERP_ROI_tw);mean(condition4_ERP_ROI_tw)], '-','Color',[0,0,0],'linewidth', 2); hold on;
  224. errorbar([1-0.1], [mean(condition1_ERP_ROI_tw)], [std(condition1_ERP_ROI_tw)/sqrt(length(sub_list))], [std(condition1_ERP_ROI_tw)/sqrt(length(sub_list))], '^','Color',[0,0,0],'linewidth', 2, 'MarkerEdgeColor', [10,108,176]/256, 'MarkerFaceColor', [10,108,176]/256, 'MarkerSize',10); hold on;
  225. errorbar([2-0.1], [mean(condition2_ERP_ROI_tw)], [std(condition2_ERP_ROI_tw)/sqrt(length(sub_list))], [std(condition2_ERP_ROI_tw)/sqrt(length(sub_list))], '^','Color',[0,0,0],'linewidth', 2, 'MarkerEdgeColor', [207,83,28]/256, 'MarkerFaceColor', [207,83,28]/256, 'MarkerSize',10); hold on;
  226. errorbar([1+0.1], [mean(condition3_ERP_ROI_tw)], [std(condition3_ERP_ROI_tw)/sqrt(length(sub_list))], [std(condition3_ERP_ROI_tw)/sqrt(length(sub_list))], '^','Color',[0,0,0],'linewidth', 2, 'MarkerEdgeColor', [10,108,176]/256, 'MarkerFaceColor', [10,108,176]/256, 'MarkerSize',10); hold on;
  227. errorbar([2+0.1], [mean(condition4_ERP_ROI_tw)], [std(condition4_ERP_ROI_tw)/sqrt(length(sub_list))], [std(condition4_ERP_ROI_tw)/sqrt(length(sub_list))], '^','Color',[0,0,0],'linewidth', 2, 'MarkerEdgeColor', [207,83,28]/256, 'MarkerFaceColor', [207,83,28]/256, 'MarkerSize',10); hold on;
  228. ylabel('ERP amplitude (μV)');
  229. set(gca,'TickDir','out');
  230. set(gca, 'FontSize', 14);
  231. set(gca, 'FontName', 'Verdana');
  232. colormap('parula');
  233. ylim(ylim_);
  234. xlim([0.5,2.5]);
  235. set(gca,'xtick',[1,2]);
  236. xticklabels({'more complex', 'less complex'});
  237. box off;
  238. end

analysis_ERP.m, no license · at the source

Overview

Authors: Xinchi Yu1,2, Sebastián Mancha2, Xing Tian3,4,5, Ellen Lau1,2
ORCID iDs: Xinchi Yu, Sebastián Mancha, Xing Tian, Ellen Lau
  1. Program in Neuroscience and Cognitive Science University of Maryland
  2. Department of Linguistics University of Maryland
  3. Division of Arts and Sciences New York University Shanghai
  4. Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science East China Normal University
  5. NYU‐ECNU Institute of Brain and Cognitive Science at NYU Shanghai
Institutions: University of Maryland, College Park (United States); New York University Shanghai (China); New York University (United States); East China Normal University (China)
Journal: Cognitive science, volume 50, issue 5, article e70220
Dates: received 9 October 2024; accepted 24 March 2026; published online 12 May 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/cogs.70220 · PMID 42118826 · PMCID PMC13167000 · OpenAlex W7160890420
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Evoked potentials, fMRI & imaging, Physiology & signal measures, Machine learning
Keywords: Syntactic structure, Morphological structure, Non‐lexicalism, Lexicalism, Language comprehension
MeSH: Brain*, Comprehension*, Language*, Brain Mapping, Electroencephalography, Humans, Linguistics (* major topic)
Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NSF Graduate Research Fellowship
Citations: not cited yet (Europe PMC); 145 references in the paper

Abstract

Although psycho‐/neuro‐linguistics has assumed a distinction between morphological and syntactic structure building as in traditional theoretical linguistics, this distinction has been increasingly challenged by theoretical linguists in recent years. Opposing a sharp, lexicalist distinction between morphology and syntax, non‐lexicalist theories propose common morpho‐syntactic structure‐building operations that cut across the realms of “morphology” and “syntax,” which are considered distinct territories in lexicalist theories. Taking advantage of two pairs of contrasts in Mandarin Chinese with desirable linguistic properties, namely, compound versus simplex nouns (the “morphology” contrast, differing in morphological structure complexity per lexicalist theories) and separable versus inseparable verbs (the “syntax” contrast, differing in syntactic structure complexity per lexicalist theories), we report one of the first pieces of evidence for shared neural responses for morphological and syntactic structure complexity in language comprehension, supporting a non‐lexicalist view where shared neural computations are employed across morpho‐syntactic structure building. Specifically, we observed that the two contrasts both modulated neural responses in left anterior and centro‐parietal electrodes in an a priori 275:400 ms time window, corroborated by topographical similarity analyses. These results serve as preliminary yet prima facie evidence toward shared neural computations across morphological and syntactic structure building in language comprehension.

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

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OSF qt6x8

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (7)
Size: 87 files, 7 scripts
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (2 files), ERPLAB (2 files), fdr_bh (Benjamini-Hochberg FDR) (1 file), Image Processing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
7 files
At the source: osf.io/qt6x8/

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

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  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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

Data Availability Statement

The stimuli, data, and data processing scripts are publicly shared on OSF: https://osf.io/qt6x8/.

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 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 7 MeSH terms, 1 funder, 68 references.

Cite

This paper

Yu, X., Mancha, S., Tian, X., & Lau, E. (2026). Shared Neural Computations for Syntactic and Morphological Structures: Evidence From Mandarin Chinese. Cognitive science, 50(5), e70220. https://doi.org/10.1111/cogs.70220

BibTeX

@article{yu2026shared,
author = {Yu, Xinchi and Mancha, Sebastián and Tian, Xing and Lau, Ellen},
title = {{Shared Neural Computations for Syntactic and Morphological Structures: Evidence From Mandarin Chinese}},
journal = {Cognitive science},
year = {2026},
month = may,
volume = {50},
number = {5},
pages = {e70220},
publisher = {Wiley},
issn = {0364-0213},
doi = {10.1111/cogs.70220},
url = {https://doi.org/10.1111/cogs.70220},
pmid = {42118826},
pmcid = {PMC13167000}
}

RIS

TY - JOUR
AU - Yu, Xinchi
AU - Mancha, Sebastián
AU - Tian, Xing
AU - Lau, Ellen
TI - Shared Neural Computations for Syntactic and Morphological Structures: Evidence From Mandarin Chinese
T2 - Cognitive science
J2 - Cogn Sci
PY - 2026
DA - 2026/05/01
VL - 50
IS - 5
SP - e70220
SN - 0364-0213
PB - Wiley
DO - 10.1111/cogs.70220
UR - https://doi.org/10.1111/cogs.70220
LA - en
ER -

CSL-JSON

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"title": "Shared Neural Computations for Syntactic and Morphological Structures: Evidence From Mandarin Chinese",
"container-title": "Cognitive science",
"author": [
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"family": "Yu",
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"given": "Xing"
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"given": "Ellen"
}
],
"container-title-short": "Cogn Sci",
"volume": "50",
"issue": "5",
"page": "e70220",
"DOI": "10.1111/cogs.70220",
"PMID": "42118826",
"PMCID": "PMC13167000",
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"URL": "https://doi.org/10.1111/cogs.70220",
"language": "en",
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