Lexical Representations of the Native and Second Languages During L2 Word Reading in Chinese-English Bilinguals.

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Paper

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The authors' code

MATLAB · 39 lines · 1.6 KB · no license

function Step01_Preprocess(substart,subend)
% EEGLAB history file generated on the 12-Nov-2024
% ------------------------------------------------
subs = substart:subend;
channelLocationFile = 'D:\\eeglab2020_0\\eeglab2020_0\\plugins\\dipfit\\standard_BESA\\standard-10-5-cap385.elp';
RawData = 'D:\WordSemEEG\EEGdata\rawdata';
ResultsData = 'D:\WordSemEEG\EEGdata\rawdata';
% load EEGlab
[ALLEEG EEG CURRENTSET ALLCOM] = eeglab('nogui');
for s = subs
% load & out sub's file
Filename = sprintf('%s\\sub%03d.cnt',RawData,s);
% import eegdata
EEG = pop_loadcnt(Filename, 'dataformat', 'auto', 'memmapfile', '');
Newname = sprintf('sub%03d',s);
[ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, 0,'setname',Newname,'gui','off');
% channel location
EEG=pop_chanedit(EEG, 'lookup',channelLocationFile);
% delete channels
EEG = pop_select( EEG, 'nochannel',{'CB1','CB2','HEO','VEO'});
[ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, 1,'setname',Newname,'overwrite','on','gui','off');
% filter 0.1Hz & 30Hz
EEG = pop_eegfiltnew(EEG, 'locutoff',0.1,'plotfreqz',1);
[ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, 1,'setname',Newname,'overwrite','on','gui','off');
EEG = pop_eegfiltnew(EEG, 'hicutoff',30,'plotfreqz',1);
[ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, 1,'setname',Newname,'overwrite','on','gui','off');
EEG = pop_eegfiltnew(EEG, 'locutoff',48,'hicutoff',52,'revfilt',1,'plotfreqz',1);
[ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, 1,'setname',Newname,'overwrite','on','gui','off');
% save data
OutputFilename = sprintf('sub%03d_S1_pre.set',s);
EEG = pop_saveset(EEG, 'filename',OutputFilename,'filepath',ResultsData);
end

Step01_Preprocess.m, no license · at the source

Overview

Authors: Xiaoyu Liu^1,2, Lala Gu^1,3,4, Xiaoxue Feng^1,3,4, Yuan Feng^1,3,4, Xingying Lin^1,3,4, Nannan Gu^1,3,4, Leilei Mei^1,3,4

ORCID iDs: Lala Gu, Leilei Mei

Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents, South China Normal University; Ministry of Education, Guangzhou, China
School of Psychology, Zhejiang Normal University, Jinhua, China
Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
School of Psychology, South China Normal University, Guangzhou, China

Institutions: Zhejiang Normal University (China); South China Normal University (China)

Journal: Neurobiology of language (Cambridge, Mass.), volume 7, article NOL.a.255

Dates: received 19 August 2025; accepted 23 March 2026; published online 16 June 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1162/nol.a.255 · PMID 42367745 · PMCID PMC13293728 · OpenAlex W7148456316

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: EEG (modality), fMRI (modality), cognitive (subfield)

Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Connectivity, fMRI & imaging, Machine learning

Keywords: bilingualism, EEG, fMRI, lexical access, representational similarity analysis

Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: National Natural Science Foundation of China (32271098, 32571226); Basic and Applied Basic Research Foundation of Guangdong Province (2024A1515011023)

Citations: not cited yet (Europe PMC); 111 references in the paper

Abstract

The non-selective processing hypothesis for bilinguals posits that during the processing of the target language, the lexical information of the non-target language is concurrently activated. Nonetheless, the lexical representation of bilinguals in both languages and the temporal dynamics of lexical information representation remain unclear. Here, we utilized fMRI in Experiment 1 and EEG in Experiment 2, combined with representational similarity analysis (RSA), to explore the neural representation of lexical access in both the target and non-target languages during L2 word reading and the modulatory effects of processing demands. Results of two Experiments jointly revealed that the lexical information of L2 was represented widely and earlier during L2 lexical processing, whereas the lexical information of L1 was represented widely and earlier during L2 semantic processing. These findings provide spatiotemporally integrated evidence for the bilingual non-selective access hypothesis, indicating that non-selective processing occurs only under conditions of high semantic processing demands.

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 uxhqp

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Languages: MATLAB (37)

Size: 170 files, 37 scripts

Software Heritage: not checked

Found in: “DATA AND CODE AVAILABILITY STATEMENTS”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: EEGLAB (11 files), FieldTrip (6 files), Statistics and Machine Learning Toolbox (5 files), Image Processing Toolbox (3 files), ERPLAB (2 files), SPM (2 files), ICLabel (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

37 files

code_lala/Step01_Preprocess.m, MATLAB, 39 lines
code_lala/Step03_RunICA.m, MATLAB, 41 lines
code_lala/Step04_rerefer.m, MATLAB, 27 lines
code_lala/Step05_epoch.m, MATLAB, 34 lines
code_lala/Step06_task.m, MATLAB, 22 lines
code_lala/Step07_NeuralDSM.m, MATLAB, 56 lines
code_lala/Step08_MeanSub.m, MATLAB, 23 lines
code_lala/Step08_partialcomputeRSA.m, MATLAB, 38 lines
code_lala/Step09_computeRSA.m, MATLAB, 36 lines
code_lala/Step10_MakingPic.m, MATLAB, 31 lines
code_lala/Step10_Making_partialPic.m, MATLAB, 32 lines
code_lala/Step11_computeCluster.m, MATLAB, 69 lines
code_lala/Step18_computeRSA.m, MATLAB, 39 lines
code_lala/Step19_subRSA.m, MATLAB, 60 lines
code_lala/cluster_based_permutation.m, MATLAB, 239 lines
code_lala/clusterstat.m, MATLAB, 559 lines
code_lala/findcluster.m, MATLAB, 142 lines
code_lala/just_plot.m, MATLAB, 129 lines
code_xy/Step016_MeanSub.m, MATLAB, 39 lines
code_xy/Step017_NeuralDSM.m, MATLAB, 55 lines
code_xy/Step018_computeRSA.m, MATLAB, 34 lines
code_xy/Step019_MakingPic.m, MATLAB, 31 lines
code_xy/Step01_Preprocess.m, MATLAB, 51 lines
code_xy/Step02_RejectBadChannel.m, MATLAB, 49 lines
code_xy/Step03_ExtractEpoch.m, MATLAB, 33 lines
code_xy/Step04_RunICA.m, MATLAB, 41 lines
code_xy/Step05_ExtractNeuralData.m, MATLAB, 39 lines
code_xy/Step06_NeuralDSM.m, MATLAB, 56 lines
code_xy/Step07_MeanSub.m, MATLAB, 40 lines
code_xy/Step08_computeRSA.m, MATLAB, 35 lines
code_xy/Step09_MakingPic.m, MATLAB, 31 lines
code_xy/Step10_computeCluster.m, MATLAB, 69 lines
code_xy/avesub_corr_plot.m, MATLAB, 129 lines
code_xy/clusterstat.m, MATLAB, 559 lines
code_xy/corr_avesub_plot.m, MATLAB, 129 lines
code_xy/findcluster.m, MATLAB, 142 lines
code_xy/pipeline.m, MATLAB, 53 lines

At the source: osf.io/uxhqp/

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;
37 scripts, each with its path and the digest of its content;
no match between paragraphs and code yet;
neither the text of the paper nor the code itself.

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Data

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

Data and code availability statements

The materials, statistical code, and data are available at OSF (https://osf.io/uxhqp/).

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

Funding: added National Natural Science Foundation of China: 32271098, 32571226; Basic and Applied Basic Research Foundation of Guangdong Province: 2024A1515011023

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 7 authors, 5 keywords, 108 references.

Cite

This paper

Liu, X., Gu, L., Feng, X., Feng, Y., Lin, X., Gu, N., & Mei, L. (2026). Lexical Representations of the Native and Second Languages During L2 Word Reading in Chinese-English Bilinguals. Neurobiology of language (Cambridge, Mass.), 7, NOL.a.255. https://doi.org/10.1162/nol.a.255

BibTeX

@article{liu2026lexical, author = {Liu, Xiaoyu and Gu, Lala and Feng, Xiaoxue and Feng, Yuan and Lin, Xingying and Gu, Nannan and Mei, Leilei}, title = {{Lexical Representations of the Native and Second Languages During L2 Word Reading in Chinese-English Bilinguals}}, journal = {Neurobiology of language (Cambridge, Mass.)}, year = {2026}, month = jun, volume = {7}, pages = {NOL.a.255}, publisher = {MIT Press}, issn = {2641-4368}, doi = {10.1162/nol.a.255}, url = {https://doi.org/10.1162/nol.a.255}, pmid = {42367745}, pmcid = {PMC13293728} }

RIS

TY - JOUR AU - Liu, Xiaoyu AU - Gu, Lala AU - Feng, Xiaoxue AU - Feng, Yuan AU - Lin, Xingying AU - Gu, Nannan AU - Mei, Leilei TI - Lexical Representations of the Native and Second Languages During L2 Word Reading in Chinese-English Bilinguals T2 - Neurobiology of language (Cambridge, Mass.) J2 - Neurobiol Lang (Camb) PY - 2026 DA - 2026/06/16 VL - 7 SP - NOL.a.255 SN - 2641-4368 PB - MIT Press DO - 10.1162/nol.a.255 UR - https://doi.org/10.1162/nol.a.255 LA - en ER -

CSL-JSON

{ "id": "10.1162/nol.a.255", "type": "article-journal", "title": "Lexical Representations of the Native and Second Languages During L2 Word Reading in Chinese-English Bilinguals", "container-title": "Neurobiology of language (Cambridge, Mass.)", "author": [ { "family": "Liu", "given": "Xiaoyu" }, { "family": "Gu", "given": "Lala" }, { "family": "Feng", "given": "Xiaoxue" }, { "family": "Feng", "given": "Yuan" }, { "family": "Lin", "given": "Xingying" }, { "family": "Gu", "given": "Nannan" }, { "family": "Mei", "given": "Leilei" } ], "container-title-short": "Neurobiol Lang (Camb)", "volume": "7", "page": "NOL.a.255", "DOI": "10.1162/nol.a.255", "PMID": "42367745", "PMCID": "PMC13293728", "ISSN": "2641-4368", "publisher": "MIT Press", "URL": "https://doi.org/10.1162/nol.a.255", "language": "en", "issued": { "date-parts": [ [ 2026, 6, 16 ] ] } }

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