OSCR

Neural tracking of prosodic and statistical rhythms jointly supports artificial language learning.

Code ↔ Paper

21 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 21 matches
  1. [1] § STAR★Methods › Quantification and statistical analysis › Time-domain analyses ↔ S05_01_01_compute_erf.m, lines 156–197 · score 0.89 · full width half, 170–250 ms, 300–500 ms, peak latencies, 170 ms, FWHM
  2. [2] § STAR★Methods › Quantification and statistical analysis › Data preprocessing ↔ S02_01_02_preprocessing.m, lines 147–214 · score 0.86 · SQUID jumps, 2–15 Hz, artifact detection, cutoff, ICA, EOG
  3. [3] § STAR★Methods › Method details › Stimuli ↔ S00_03_01_lexicons.py, lines 8–24 · score 0.85 · native German speakers, phonological feature, German corpus, lowest, phonemic, bigram
  4. [4] § STAR★Methods › Method details › Stimuli ↔ S00_02_01_prosody.py, lines 34–38 · score 0.84 · Bo kennt Sue, die sehr stinkt, prosodically marked, weak, sentence, prosody
  5. [5] § STAR★Methods › Quantification and statistical analysis › Source analyses ↔ S07_01_01_sourceanalysis.m, lines 183–255 · score 0.80 · covariance matrix, 150–1000 ms, common filter, recognition phase, 150 ms, part words
  6. [6] § STAR★Methods › Method details › Procedure ↔ S01_00_00_neuraltrack.m, lines 78–170 · score 0.76 · 1.5–2 s, 1–1.5 s, mini block, jittered, breaks, phase
  7. [7] § STAR★Methods › Method details › Stimuli ↔ S00_03_01_lexicons.py, lines 8–24 · score 0.75 · Levenshtein distance, phonotactic constraint, artificial lexicons, phonemic, part word, transitioned
  8. [8] § STAR★Methods › Method details › Stimuli ↔ S00_01_01_syllables.py, lines 8–17 · score 0.70 · binary matrix, phonological feature, German corpus, consonant, vowel, phoneme
  9. [9] § STAR★Methods › Method details › Stimuli ↔ S00_01_01_syllables.py, lines 8–17 · score 0.67 · consonant vowel, long vowels, German corpus, phonological, phoneme, uniformize
  10. [10] § STAR★Methods › Quantification and statistical analysis › Data preprocessing ↔ S02_01_02_preprocessing.m, lines 54–100 · score 0.66 · 0.1–30 Hz, detrended, demeaned, FieldTrip, MaxFilterTM, preprocessing
  11. [11] § STAR★Methods › Quantification and statistical analysis › Data preprocessing ↔ S02_01_03_cleaning.m, lines 227–270 · score 0.66 · independent component, rejected trials, std, ICA, artifacts, preprocessing
  12. [12] § Results › M200 and M400 fields are sensitive to violations of PR or TP patterns ↔ S05_01_01_compute_erf.m, lines 156–197 · score 0.64 · 170–250 ms, 300–500 ms, 170 ms, FWHM, M200, M400
  13. [13] § STAR★Methods › Method details › Procedure ↔ S00_05_01_stimset.m, lines 118–162 · score 0.62 · balanced Latin square, counterbalanced, mini, prosody, blocks, lexicon
  14. [14] § STAR★Methods › Quantification and statistical analysis › Frequency-domain analyses ↔ S03_01_02_analyze_pow.m, lines 34–87 · score 0.59 · baseline power, neighboring frequencies, ratio, channels, block
  15. [15] § Results ↔ S07_01_01_sourceanalysis.m, lines 183–255 · score 0.58 · inter trial phase, recognition phase, coherence, ITPC, ERF, pseudowords
  16. [16] § Results ↔ S05_01_01_compute_erf.m, lines 1–53 · score 0.56 · event related field, recognition phase, ERF, M200, M400, MEG
  17. [17] § Results › PR and TP patterns are both needed for learning pseudowords ↔ S08_01_02_lme_behavior.R, lines 144–210 · score 0.54 · behavioral accuracy, history, PR, TP
  18. [18] § STAR★Methods › Quantification and statistical analysis › Frequency-domain analyses ↔ S04_01_01_compute_itc.m, lines 42–128 · score 0.54 · artifact free trials, wavelet, Fourier, channels, chunk
  19. [19] § STAR★Methods › Quantification and statistical analysis › Source analyses ↔ S05_01_01_compute_erf.m, lines 55–130 · score 0.52 · 150–1000 ms, 150 ms, pre, part words, ERF, latencies
  20. [20] § STAR★Methods › Quantification and statistical analysis › Behavioral analyses ↔ S00_05_01_stimset.m, lines 118–162 · score 0.52 · balanced Latin square, stimuli, PR, TP
  21. [21] § Results › PR and TP patterns modulate neural tracking of syllables and chunks ↔ S03_01_02_analyze_pow.m, lines 34–87 · score 0.51 · baseline power, neighboring frequencies, ratio

Paper

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

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The authors' code

MATLAB · 234 lines · 8.8 KB · no license · 4 matches

  1. %% Setup
  3. % Clear the workspace
  4. clearvars
  5. close all
  6. clc
  8. % Set paths
  9. project_dir = '/data/pt_02740/neuraltrack';
  10. expdata_dir = [project_dir filesep 'data'];
  11. results_dir = [project_dir filesep 'results'];
  12. figures_dir = [project_dir filesep 'figures' filesep 'evoked'];
  13. ft_defaults
  15. % Set parameters
  16. testID = {'Main effect PR';
  17. 'Main effect TP'}; % Labels of effects of interest
  18. condID = {'PR- TP-';
  19. 'PR- TP+';
  20. 'PR+ TP-';
  21. 'PR+ TP+'}; % Labels of conditions in the exposure phase
  22. typeID = {'Pseudowords';
  23. 'Part-words'}; % Labels of "word" types in the recognition phase
  24. ERFsID = {'M200';
  25. 'M400'}; % Labels of event-related fields (ERFs) of interest
  26. timeID = linspace(-150, 1000, 1151); % Time array for analyses of ERFs
  27. nStart = 40; % Start number of participants
  28. nSubjs = 32; % Total number of participants
  29. nBlock = 8; % Total number of blocks
  30. fSampl = 1000; % MEG sampling frequency
  31. nConds = length(condID); % Number of conditions
  32. nTypes = length(typeID); % Number of word type
  33. nPeaks = length(ERFsID); % Number of ERFs
  34. nTimes = length(timeID); % Epoch length
  36. % Set filenames
  37. subject_num = cellstr(string((1:nSubjs) + nStart));
  38. subject_lst = strcat('nt', subject_num, 'a');
  39. trldata_lst = strcat(repmat(subject_lst, nBlock, 1)', ...
  40. repmat(cellstr(string(1:nBlock)), nSubjs, 1), ...
  41. repmat({'_trl.mat'}, [nSubjs nBlock]));
  42. predata_lst = strcat(repmat(subject_lst, nBlock, 1)', ...
  43. repmat(cellstr(string(1:nBlock)), nSubjs, 1), ...
  44. repmat({'_dat.mat'}, [nSubjs nBlock]));
  45. erfdata_lst = strcat(repmat(subject_lst, nBlock, 1)', ...
  46. repmat(cellstr(string(1:nBlock)), nSubjs, 1), ...
  47. repmat({'_erf.mat'}, [nSubjs nBlock]));
  48. avgdata_lst = strcat(repmat(subject_lst, nBlock, 1)', ...
  49. repmat(cellstr(string(1:nBlock)), nSubjs, 1), ...
  50. repmat({'_avg.mat'}, [nSubjs nBlock]));
  51. rawdata_lst = strcat(repmat(subject_lst, nBlock, 1)', ...
  52. repmat(cellstr(string(1:nBlock)), nSubjs, 1), ...
  53. repmat({'_raw.mat'}, [nSubjs nBlock]));
  55. %% Compute timelock analysis
  57. % Initialize
  58. AVG = cell(nSubjs, nConds, nTypes);
  60. % Loop through subjects
  61. for i_subj = 1:nSubjs
  63. % Define directories
  64. expdata_sub = [expdata_dir filesep subject_lst{i_subj}];
  65. expdata_pre = [expdata_sub filesep '02_preproc'];
  66. expdata_erf = [expdata_sub filesep '05_evoked'];
  67. if ~isfolder(expdata_erf)
  68. mkdir(expdata_erf)
  69. end
  71. % Loop through blocks
  72. for i_blck = 2:2:nBlock
  74. % Load data
  75. expdata_art = fullfile(expdata_pre, artdata_lst{i_subj, i_blck});
  76. expdata_dat = fullfile(expdata_pre, predata_lst{i_subj, i_blck});
  77. load(expdata_art, 'art')
  78. load(expdata_dat, 'dat')
  80. % Reject trials with visual artifacts
  81. cfg = art.xxx.cfg;
  82. data = ft_rejectartifact(cfg, dat);
  84. % Split trials by condition
  85. i_trig = data.trialinfo(:, 1);
  86. i_cond = floor(i_trig(1)/20);
  87. idxs_A = i_trig - i_cond * 20 < 5; % pseudowords
  88. idxs_B = i_trig - i_cond * 20 > 5; % part-words
  89. trlAll = 1:length(data.trial);
  90. trls_A = trlAll(idxs_A);
  91. trls_B = trlAll(idxs_B);
  92. trlCnd = {trls_A, trls_B};
  94. % Filtering and baseline correction
  95. cfg = [];
  96. cfg.baselinewindow = [-0.15 0];
  97. cfg.demean = 'yes';
  98. cfg.hpfreq = 1;
  99. cfg.hpfilter = 'yes';
  100. cfg.hpfilttype = 'firws';
  101. data = ft_preprocessing(cfg, data);
  102. erf = data;
  104. % Compute average over trials (AVG)
  105. cfg = [];
  106. cfg.trials = trlCnd{1};
  107. cfg.latency = [-0.15 1];
  108. avg{1} = ft_timelockanalysis(cfg, data);
  109. cfg.trials = trlCnd{2};
  110. avg{2} = ft_timelockanalysis(cfg, data);
  112. % Compute RAW difference of AVG over trials
  113. cfg = [];
  114. cfg.parameter = 'avg';
  115. cfg.operation = 'x2-x1';
  116. raw = ft_math(cfg, avg{1}, avg{2});
  118. % Store ERF avg by condition
  119. AVG{i_subj, i_cond, 1} = avg{1};
  120. AVG{i_subj, i_cond, 2} = avg{2};
  122. % Save data
  123. erfdata_erf = fullfile(expdata_erf, erfdata_lst{i_subj, i_blck});
  124. erfdata_avg = fullfile(expdata_erf, avgdata_lst{i_subj, i_blck});
  125. erfdata_raw = fullfile(expdata_erf, rawdata_lst{i_subj, i_blck});
  126. save(erfdata_erf, 'erf');
  127. save(erfdata_avg, 'avg');
  128. save(erfdata_raw, 'raw');
  129. end
  130. end
  132. %% Compute grand averages
  134. % Compute grand average ERF by condition
  135. Grand_Avg_ERF = cell(nConds, nTypes);
  136. for i_type = 1:nTypes
  137. for i_cond = 1:nConds
  138. Grand_Avg_ERF{i_cond, i_type} = ft_timelockgrandaverage([], AVG{:, i_cond, i_type});
  139. end
  140. end
  142. % Compute grand average ERF and RMS across channels
  143. Grand_AVG_ERF_across_channels = zeros(nConds, nTypes, nTimes);
  144. Grand_AVG_RMS_across_channels = zeros(nConds, nTypes, nTimes);
  145. for i_type = 1:nTypes
  146. for i_cond = 1:nConds
  147. Grand_AVG_ERF_across_channels(i_cond, i_type, :) = mean(Grand_Avg_ERF{i_cond, i_type}.avg);
  148. Grand_AVG_RMS_across_channels(i_cond, i_type, :) = rms(Grand_Avg_ERF{i_cond, i_type}.avg);
  149. end
  150. end
  152. % Compute grand average ERF and RMS across participants and word types
  153. GRAND_AVG_ERF = squeeze(mean(Grand_AVG_ERF_across_channels, [1 2]));
  154. GRAND_AVG_RMS = squeeze(mean(Grand_AVG_RMS_across_channels, [1 2]));
  156. %% Extract peak latencies
  158. % Windows of interest
  159. M200 = [0.170 0.250];
  160. M400 = [0.300 0.500];
  161. ERFs = [M200; M400];
  162. TOIs = zeros(nPeaks, 2);
  163. Peak = zeros(size(ERFs, 1), 1);
  165. % Loop through ERFs
  166. for i_peak = 1:nPeaks
  168. % Extract time boundaries for the current component
  169. l_bound = find(timeID == ERFs(i_peak, 1) * fSampl);
  170. r_bound = find(timeID == ERFs(i_peak, 2) * fSampl);
  172. % Restrict timecourse to time of interest
  173. rms_erf = GRAND_AVG_RMS(l_bound:r_bound);
  175. % Find maximum amplitude in the time window
  176. [m, ix] = max(rms_erf);
  177. peakLat = ix + l_bound;
  179. % Compute full width half max (FWHM) around the peak
  180. l_flank = fliplr(GRAND_AVG_RMS(1 : peakLat - 1)');
  181. r_flank = GRAND_AVG_RMS(peakLat + 1: end);
  182. l_nadir = find(diff(l_flank) > 0, 1);
  183. r_nadir = find(diff(r_flank) > 0, 1);
  184. bs_mean = mean([l_flank(l_nadir) r_flank(r_nadir)]);
  185. halfMax = m/2 + bs_mean;
  186. l_bound = find(l_flank < halfMax, 1);
  187. r_bound = find(r_flank < halfMax, 1);
  188. fwhm_ix = ((peakLat - l_bound : peakLat + r_bound) - 150) / fSampl;
  190. % Extrcat latency around the peak based on FWHM
  191. TOIs(i_peak, :) = [fwhm_ix(1) fwhm_ix(end)];
  192. Peak(i_peak) = (peakLat - 150) / 1000;
  193. end
  195. % Summarize
  196. RMS_latency = {TOIs, Peak, ERFs, ERFsID};
  197. RMS_average = GRAND_AVG_RMS;
  199. %% Save results
  201. % Save results
  202. save(fullfile(results_dir, 'RMS_latency.mat'), 'RMS_latency')
  203. save(fullfile(results_dir, 'RMS_average.mat'), 'RMS_average')
  205. %% Plot grand average rms
  207. % Open figure
  208. figure;
  210. % Plot grand average rms across all trials
  211. toi = TOIs(1, :) * 1000 - 150;
  212. lat = find(timeID >= toi(1) & timeID <= toi(2));
  213. l_bound = lat(1);
  214. r_bound = lat(end);
  215. rectangle('Position', [l_bound, 0, r_bound - l_bound, 50], ...
  216. 'FaceColor', [.9 .9 .9], 'EdgeColor', [.5 .5 .5], ...
  217. 'HandleVisibility', 'off'); hold on
  218. toi = TOIs(2, :) * 1000 - 150;
  219. lat = find(timeID >= toi(1) & timeID <= toi(2));
  220. l_bound = lat(1);
  221. r_bound = lat(end);
  222. rectangle('Position', [l_bound, 0, r_bound - l_bound, 50], ...
  223. 'FaceColor', [.9 .9 .9], 'EdgeColor', [.5 .5 .5], ...
  224. 'HandleVisibility', 'off');
  225. plot(timeID, GRAND_AVG_RMS * 1e15, 'black-', 'LineWidth', 2);
  226. title('Grand Average RMS', 'FontSize', 12, 'FontName', 'Arial', 'FontWeight', 'bold'); ylim([0 50])
  227. xlabel('Time (ms)', 'FontSize', 10, 'FontName', 'Arial', 'FontWeight', 'normal');
  228. ylabel('Magnetic field strength (fT)', 'FontSize', 10, 'FontName', 'Arial', 'FontWeight', 'normal');
  230. % Save figure
  231. fname = fullfile(figures_dir, 'grand_avg_rms.eps');
  232. exportgraphics(gcf, fname, 'ContentType', 'vector')
  233. fname = fullfile(figures_dir, 'grand_avg_rms.tiff');
  234. saveas(gcf, fname)

S05_01_01_compute_erf.m, no license · at the source

Overview

Authors: Lorenzo Titone1, Burkhard Maess2, Lars Meyer1,3,4
ORCID iDs: Lorenzo Titone
  1. Max Planck Research Group Language Cycles, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
  2. Methods and Development Group Brain Networks, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
  3. Department of English and Linguistics, Johannes Gutenberg University Mainz, Mainz, Germany
  4. Clinic for Phoniatrics and Pedaudiology, University Hospital Münster, Münster, Germany
Institutions: Max Planck Institute for Human Cognitive and Brain Sciences (Germany); Johannes Gutenberg University Mainz (Germany); University Hospital Münster (Germany)
Journal: iScience, volume 29, issue 7, article 116458
Dates: received 4 November 2025; accepted 2 June 2026; published online 24 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.116458 · PMID 42389590 · PMCID PMC13320337 · OpenAlex W7165801358
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, fMRI & imaging, Physiology & signal measures
Keywords: medical imaging, cognitive neuroscience, biocomputational method, high-performance computing in bioinformatics
Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Max Planck Society; MPRG Language Cycles
Citations: not cited yet (Europe PMC); 85 references in the paper

Abstract

Neural tracking of prosodic and statistical patterns supports speech segmentation and word learning. Yet, acoustic and abstract patterns have proven difficult to dissociate empirically. Our study employs an artificial language learning paradigm generated with a method that orthogonalizes acoustic and statistical patterns in auditory stimuli. MEG recordings from 32 human adults who had been exposed to continuous syllable streams reveal distinct brain sources tracking periodic acoustic, prosodic, and statistical patterns. After encoding, M200 and M400 field responses to isolated trisyllabic chunks show that participants retained both prosodic and statistical patterns for learning pseudowords from the syllable streams. Behavioral data extend our findings, suggesting not only that integrating both acoustic and abstract patterns is critical to learning, but also that prior knowledge of the pseudowords is sufficient for recognition. The findings advance our understanding of the neural sources that encode and integrate acoustic and statistical patterns during and after language learning.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 21 matches between paragraphs and lines of code.

OSF qtrz9

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (23), Python (5), R (4)
Size: 35 files, 32 scripts
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (18 files), Statistics and Machine Learning Toolbox (5 files), emmeans (2 files), ggplot2 (2 files), lme4 (2 files), lmerTest (2 files), mgcv (2 files), NumPy (2 files), SciPy (2 files), tidyverse (2 files), Matplotlib (1 file), Psychtoolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
32 files
At the source: doi.org/10.17605/OSF.IO/QTRZ9

milosen/alparc

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 8b83adc0848439bff86897eb412c1b8a693e37c5, 29 April 2026
Languages: Python (20), Jupyter (4)
Size: 51 files, 24 scripts
Software Heritage: not archived
Found in: the text, “Additional resources”
Holds: README, license file, environment (pyproject.toml, uv.lock), tests, 4 notebooks
Not found: CITATION.cff, continuous integration, documentation
Tools: NumPy (14 files), SciPy (4 files), pandas (3 files), Matplotlib (2 files), Pingouin (2 files), seaborn (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
26 files
At the source: github.com/milosen/alparc

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 56 scripts, each with its path and the digest of its content;
  • 21 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data and code availability

• The original data reported in this study cannot be deposited in a public repository because of ethical restrictions. The aggregated data and summary statistics have been deposited at Zenodo and are publicly available as of the date of publication at: https://doi.org/10.5281/zenodo.17990155. • Stimuli and analysis code have been deposited at OSF and is publicly available as of the date of publication at https://doi.org/10.17605/OSF.IO/QTRZ9. • Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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

  • Authors: added Lorenzo Titone (0009-0004-6442-6980); removed Lorenzo Titone

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 4 keywords, 2 funders, 77 references.

Cite

This paper

Titone, L., Maess, B., & Meyer, L. (2026). Neural tracking of prosodic and statistical rhythms jointly supports artificial language learning. iScience, 29(7), 116458. https://doi.org/10.1016/j.isci.2026.116458

BibTeX

@article{titone2026neural,
author = {Titone, Lorenzo and Maess, Burkhard and Meyer, Lars},
title = {{Neural tracking of prosodic and statistical rhythms jointly supports artificial language learning}},
journal = {iScience},
year = {2026},
month = jun,
volume = {29},
number = {7},
pages = {116458},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116458},
url = {https://doi.org/10.1016/j.isci.2026.116458},
pmid = {42389590},
pmcid = {PMC13320337}
}

RIS

TY - JOUR
AU - Titone, Lorenzo
AU - Maess, Burkhard
AU - Meyer, Lars
TI - Neural tracking of prosodic and statistical rhythms jointly supports artificial language learning
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/06/24
VL - 29
IS - 7
SP - 116458
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116458
UR - https://doi.org/10.1016/j.isci.2026.116458
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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Circuit response to neuromodulation characterized with simultaneous deep brain stimulation and precision neuroimaging in humans.
Journal: Nature neuroscience
In common: Pingouin, emmeans, lmerTest, 8 other tools, 1 reference
[8] doi:10.1162/imag.a.1245 [code]
Towards precision EEG connectomics: Evaluating the benefits of dense sampling.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: Pingouin, FieldTrip, lmerTest, 9 other tools
[9] doi:10.1038/s41593-026-02345-6 [code]
Human hippocampal ripples tune cortical responses based on predicted uncertainty.
Journal: Nature neuroscience
In common: FieldTrip, emmeans, lme4, 2 other tools, cognitive, 5 references
[10] doi:10.1038/s41597-026-07350-9 [code]
An open multi-center MEG-EEG dataset for studying conscious visual perception.
Journal: Scientific data
In common: Pingouin, emmeans, lmerTest, 8 other tools, 1 reference

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