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Brain network differences in second language learning depend on individual competencies.

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Paper

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The authors' code

Markdown · 80 lines · 2.8 KB · no license

  1. # EEG Dataset and Reproducibility Materials
  3. This repository contains the data-processing and analysis materials associated with the manuscript:
  5. > *Brain Network Differences in Second Language Learning Depend on Individual Competencies*
  7. The manuscript has been accepted for publication in *Imaging Neuroscience*.
  9. ## Preprocessed EEG dataset
  11. The preprocessed EEG dataset is publicly archived on Zenodo:
  13. **DOI:** [10.5281/zenodo.16538184](https://doi.org/10.5281/zenodo.16538184)
  15. The Zenodo dataset contains:
  17. - Preprocessed EEGLAB datasets (`.set` and `.fdt`) for each participant
  18. - Separate `pre/` and `post/` session folders
  19. - The RAGU-compatible TANOVA input file `ragu_exp.mat`
  21. The Zenodo record contains preprocessed EEG data only. Raw BrainVision EEG files are not included on Zenodo or OSF.
  23. The preprocessing scripts in this OSF repository document the pipeline applied to the raw EEG data. Analyses beginning with the preprocessed EEG datasets can be reproduced using the files available on Zenodo.
  25. ## Participant identifiers
  27. Pseudonymous participant IDs are consistent across the following files:
  29. - `vocabulary_test_data.csv`
  30. - `demographic.xlsx`
  31. - Preprocessed EEG filenames
  32. - Cartool segmentation outputs, including the `Group1`–`Group4` `ColumnsAsFactors.csv` files
  34. For the segmentation outputs, please refer to the current files in:
  36. `04_Translation_Recognition_Task_EEG/Segmentation_analysis/`
  38. ## OSF repository contents
  40. This OSF repository contains:
  42. - Behavioral and EEG analysis scripts written in MATLAB and R
  43. - EEG preprocessing scripts
  44. - Vocabulary-test data and analysis materials
  45. - Recognition-task behavioral analyses
  46. - ERP and N400 analyses
  47. - TANOVA and microstate-segmentation analyses
  48. - Figure files and statistical outputs included in the respective analysis folders
  49. - Task and stimulus materials
  50. - Control analyses
  52. Software dependencies and required directory structures are documented within the corresponding scripts.
  54. ## Software
  56. The analyses use software including:
  58. - MATLAB
  59. - EEGLAB 2024.1
  60. - ERPLAB
  61. - R
  62. - Cartool
  63. - RAGU
  65. Additional R-package dependencies are specified at the beginning of the corresponding analysis scripts.
  67. ## Citation
  69. When using the preprocessed EEG dataset, please cite the Zenodo record:
  71. > Skieresz, N. (2025). *Preprocessed EEG dataset for Brain Network Differences in Second Language Learning Depend on Individual Competencies*. Zenodo. https://doi.org/10.5281/zenodo.16538184
  73. When using the analysis materials, scripts, behavioral data, or task materials, please cite the associated article in *Imaging Neuroscience* and this OSF repository.
  75. ## Contact
  77. For questions concerning the dataset or analysis materials, please contact:
  79. Nicole Skieresz
  80. [email hidden]

README.md, no license · at the source

Overview

Authors: Nicole H Skieresz1,2,3, Sandy C Marca1,4, Micah M Murray2,3,5, Thomas P Reber1,6, Nicolas Rothen1
ORCID iDs: Nicole H Skieresz, Sandy C Marca, Micah M Murray, Thomas P Reber, Nicolas Rothen
  1. Faculty of Psychology, UniDistance Suisse, Brig, Switzerland
  2. The LINE (Laboratory for Integrative Neurosciences), Radiology Department, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
  3. The Sense Innovation and Research Center, Lausanne, Switzerland
  4. Faculty of Psychology, University of Geneva, Geneva, Switzerland
  5. Department of Hearing and Speech Sciences, Vanderbilt University, Nashville, TN, United States
  6. Department of Epileptology, University of Bonn Medical Center, Bonn, Germany
Institutions: UniDistance Suisse (Switzerland); The Sense Innovation and Research Center (Switzerland); University of Lausanne (Switzerland); University of Geneva (Switzerland); Vanderbilt University (United States); University of Bonn (Germany)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1379
Dates: received 16 September 2025; accepted 25 August 2026; published online 30 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1379 · PMID 42824898 · PMCID PMC13628499 · OpenAlex W7212083964
Open access: diamond, a free copy (OpenAlex)
Status: empty repository (how the code is found and verified)
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Preprocessing, Evoked potentials, Physiology & signal measures
Keywords: N400, ERP template maps, GFP, GMD, vocabulary acquisition, learning strategies
MeSH: Brain*, Learning*, Multilingualism*, Adult, Brain Mapping, Electroencephalography, Evoked Potentials, Female, Humans, Male, Semantics, Vocabulary, Young Adult (* major topic)
Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 73 references in the paper

Abstract

Integrating new words into an existing semantic network is a core challenge of second language (L2) acquisition. We investigated how evidence-based learning strategies and individual performance relate to the neurocognitive dynamics of vocabulary learning. Eighty-three adults proficient in German or French learned 48 Finnish (L2) nouns over 14 days using a mobile app that systematically varied retrieval practice, corrective feedback, multisensory learning, and distributed learning. Before and after training, EEG was recorded during a translation recognition task designed to elicit the N400, an index of semantic integration. Vocabulary accuracy increased from 0.41% pre-learning to 75.5% post-learning (dz = 3.96), and the N400 incongruity effect increased significantly, F(1, 75) = 99.52, p < .001, η²g = .32, reflecting successful integration of new L2 words into the mental lexicon. High performers showed larger N400 responses, consistent with more differentiated semantic processing. In addition, performance group differences emerged in event-related potential (ERP) template-map preponderance (i.e., the proportion of epoch time points assigned to a given template map), indicating differences in processing dynamics and network configurations. Despite systematic manipulation of learning strategies, no single approach yielded consistent behavioral or neural advantages, suggesting that overall exposure and cumulative practice, rather than any specific strategy, may have been primary contributors to the observed learning gains. ERP template-map analyses further revealed that learning not only increased neural responses but also shifted map preponderance within the N400 window as a function of Session and Congruency, indicating a qualitative reorganization of semantic processing. These findings suggest that vocabulary learning success is more closely related to the degree of semantic integration than to any single instructional strategy.

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

Repository

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OSF bj6pt

License: none: the authors keep all their rights
State: the link answers, verified on 3 October 2026
Evidence: files inventoried
Size: 2 files, 0 scripts
Software Heritage: not checked
Found in: “Data and Code Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 3 October 2026: the link answers (HTTP 200)
  • 3 October 2026: the link answers (HTTP 200)
1 file
At the source: osf.io/bj6pt/

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

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Data

Datasets cited

Data and Code Availability

Data and analysis scripts supporting the findings of this study are publicly available on the Open Science Framework at https://osf.io/bj6pt/. The preprocessed EEG dataset is publicly archived separately on Zenodo (DOI: https://doi.org/10.5281/zenodo.16538184).

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

Versions

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Version 1, 3 October 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 13 MeSH terms, 67 references.

Cite

This paper

Skieresz, N. H., Marca, S. C., Murray, M. M., Reber, T. P., & Rothen, N. (2026). Brain network differences in second language learning depend on individual competencies. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1379. https://doi.org/10.1162/imag.a.1379

BibTeX

@article{skieresz2026brain,
author = {Skieresz, Nicole H and Marca, Sandy C and Murray, Micah M and Reber, Thomas P and Rothen, Nicolas},
title = {{Brain network differences in second language learning depend on individual competencies}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = sep,
volume = {4},
pages = {IMAG.a.1379},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1379},
url = {https://doi.org/10.1162/imag.a.1379},
pmid = {42824898},
pmcid = {PMC13628499}
}

RIS

TY - JOUR
AU - Skieresz, Nicole H
AU - Marca, Sandy C
AU - Murray, Micah M
AU - Reber, Thomas P
AU - Rothen, Nicolas
TI - Brain network differences in second language learning depend on individual competencies
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/09/30
VL - 4
SP - IMAG.a.1379
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1379
UR - https://doi.org/10.1162/imag.a.1379
LA - en
ER -

CSL-JSON

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