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Song Familiarity Relies on Evidence Accumulation.

Code ↔ Paper

7 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 7 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Results › EEG Results › Parametric Regression ↔ analyze_songfamiliarity_06_statsandfigures.m, lines 319–385 · score 0.93 · 308–600 ms, 468–488 ms, 116 ms, 308 ms, 468 ms, 348 ms
  2. [2] § Method › Data Analysis › EEG Preprocessing ↔ private/dm_prep.m, the whole file · a weak match · score 0.88 · bandpass filter, Ocular components, EEGLAB, downsampling, iclabel, notch
  3. [3] § Method › Data Analysis › Event‐Related Analysis ↔ analyze_songfamiliarity_05_erps.m, lines 126–211 · score 0.84 · uf_designmat, uf_timeexpandDesignmat, rERPs, design matrix, rows, artifacts
  4. [4] § Method › Participants ↔ analyze_songfamiliarity_01_bidsify.m, lines 28–122 · score 0.76 · curly hair, MacEwan, age, ambidextrous, female, power
  5. [5] § Method › Data Analysis › Event‐Related Analysis ↔ analyze_songfamiliarity_05_erps.m, lines 126–211 · score 0.72 · cross validation, rERPs, pinv, lambda, matrix, Error
  6. [6] § Method › Statistics ↔ private/doPermTest2.m, lines 1–84 · score 0.67 · cluster mass, temporal cluster, permuted, waveforms, sum, permutation
  7. [7] § Method › Data Analysis › EEG Preprocessing ↔ analyze_songfamiliarity_01_eegprep.m, lines 1–79 · score 0.60 · 0.1–20 Hz, EEGLAB, ICA, filter, SD, Preprocessing

Paper

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

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The authors' code

MATLAB · 211 lines · 7.2 KB · MIT · 2 matches

  1. % Compute the the regression ERPs
  3. % Other m-files required:
  4. % EEGLAB toolbox: https://github.com/sccn/eeglab
  5. % Unfold toolbox: https://github.com/unfoldtoolbox/unfold
  7. % Author: Cameron Hassall, Department of Psychology, MacEwan University
  8. % email address: [email hidden]
  9. % Website: http://www.cameronhassall.com
  12. init_unfold();
  14. % Folders
  15. if ispc
  16. projectFolder = 'C:\Users\chass\OneDrive\Projects\2024_EEG_SongFamiliarity_Hassall\';
  17. dataFolder = 'F:\2024_EEG_SongFamiliarity_Hassall\bids';
  18. else
  19. projectFolder = '/Users/HassallC/Library/CloudStorage/OneDrive-Personal/Projects/2024_EEG_SongFamiliarity_Hassall';
  20. dataFolder = '/Volumes/T7 (Data)/2024_EEG_SongFamiliarity_Hassall/bids';
  21. end
  22. resultsFolder = fullfile(projectFolder,'analysis','results');
  24. % Set this flag to determine the analysis
  25. % whichAnalysis flag
  26. % 1: Note events for unfamiliar and familiar unrecognized. Response events.
  27. % 2: Note position as a parametric regressor
  28. % 3: Same as 2, but exclude songs that were recognized
  29. % 4: Same as 2, but split by ID'd and NonID'd
  30. % sufficient trial numbers)
  31. whichAnalysis = 2; % Analysis flag
  33. % Set this flag to determine whether we do cross validation (we do this
  34. % once to determine hyperparameters, which we use for all analyses)
  35. doCV = 0; % Cross-validation flag
  37. % Set this flag to determine whether we do regularization (should be set to
  38. % 1)
  39. doReg = 1; % Regularization flag
  41. % Excluded participants
  42. % sub-08: No EEG recorded
  43. % sub-18: No familiar songs
  45. % Included participants
  46. subjStrs = {'sub-01','sub-02','sub-03','sub-04','sub-05','sub-06' ,'sub-07','sub-09','sub-10','sub-11','sub-12','sub-13','sub-14','sub-15','sub-16','sub-17','sub-19','sub-20','sub-21','sub-22','sub-23','sub-24','sub-25','sub-26','sub-27','sub-28','sub-29','sub-30'};
  48. % Remove additional participants if they are unable to be analyzed
  49. if whichAnalysis == 3
  50. % sub-21: no "familiar unrecognized" notes
  51. % sub-23: no "familiar unrecognized" notes
  52. toRemove = {'sub-21','sub-23'};
  53. toRemoveI = find(contains(subjStrs,toRemove));
  54. subjStrs(toRemoveI) = [];
  55. end
  57. if whichAnalysis == 4
  58. % sub-20: no "recognized" notes
  59. % sub-21: no "familiar unrecognized" notes
  60. % sub-23: no "familiar unrecognized" notes
  61. toRemove = {'sub-20','sub-21','sub-23'};
  62. toRemoveI = find(contains(subjStrs,toRemove));
  63. subjStrs(toRemoveI) = [];
  64. end
  66. % Deal with cross-validation and regularization flags
  67. if doCV
  68. regPar = [];
  69. elseif doReg
  70. load(fullfile(resultsFolder,['regPar_1.mat']),'regPar');
  71. else
  72. regPar = zeros(size(subjStrs));
  73. end
  75. % Result variables
  76. allBeta = [];
  77. allERP = [];
  78. numEventTypes = [];
  79. allBadChannels = {};
  80. allArtifactProp = [];
  82. for i = 1:length(subjStrs)
  84. % Load EEG
  85. thisPrepFolder = fullfile(dataFolder,subjStrs{i},'derivatives','eegprep');
  86. thisPrepFile = [subjStrs{i} '_task-songfamiliarity_eegpreprelabel.mat'];
  87. load(fullfile(thisPrepFolder, thisPrepFile),'EEG');
  89. if doCV
  90. EEG = pop_resample(EEG,100); % For speed
  91. end
  93. % sub-01 recording started a bit late, so remove these events
  94. if strcmp(subjStrs{i},'sub-01')
  95. EEG.event(1:6) = [];
  96. end
  98. % Relabel notes
  99. % 'f' = familiar but not recognized
  100. % 'r' = familiar and recognized
  101. % 'u' = unfamiliar
  102. % '2' = response
  103. for j = 1:length(EEG.event)
  104. if contains(EEG.event(j).type,'u')
  105. EEG.event(j).type = 'u';
  106. elseif contains(EEG.event(j).type,'f')
  107. EEG.event(j).type = 'f';
  108. elseif contains(EEG.event(j).type,'r') && whichAnalysis ~= 3 && whichAnalysis ~= 4
  109. EEG.event(j).type = 'f'; % Count these as familiar
  110. elseif contains(EEG.event(j).type,'r') && whichAnalysis == 4
  111. EEG.event(j).type = 'r'; % Keep these as a seprate condition ('recognized familiar');
  112. elseif strcmp(EEG.event(j).type,'2') && whichAnalysis == 4 % Split responses by condition
  113. if contains(EEG.event(j-1).type,'f')
  114. EEG.event(j).type = '2f';
  115. elseif contains(EEG.event(j-1).type,'r')
  116. EEG.event(j).type = '2r';
  117. end
  118. end
  119. end
  121. % Remove "type" from chanlocs so we don't confuse uf_continuousArtifactDetect()
  122. for j = 1:31
  123. EEG.chanlocs(j).type = [];
  124. end
  126. % Construct design matrix
  127. switch whichAnalysis
  128. case 1
  129. EEG = uf_designmat(EEG,'eventtypes',{'u','f','2'},'formula',{'y ~ 1','y ~ 1','y ~ 1'});
  130. case {2,3}
  131. EEG = uf_designmat(EEG,'eventtypes',{'f','2'},'formula',{'y ~ 1+pos','y ~ 1'});
  132. case {4}
  133. EEG = uf_designmat(EEG,'eventtypes',{'f','r','2f','2r'},'formula',{'y ~ 1+pos','y ~ 1+pos','y ~ 1','y ~ 1'});
  134. end
  136. % Define time window
  137. ufTime = [-1.5 1.5];
  139. % Make design matrix
  140. EEG = uf_timeexpandDesignmat(EEG,'timelimits',ufTime);
  142. % Flag zero rows
  143. nonZero = any(EEG.unfold.Xdc,2);
  144. isZero = ~nonZero;
  146. % Artifact detection
  147. [winrej, chanrej, rejProp] = uf_continuousArtifactDetect(EEG,'amplitudeThreshold',75,'windowsize',1000,'stepsize',100,'combineSegments',[]);
  148. isArtifact = zeros(size(isZero));
  149. isArtifactByChannel = zeros(size(isZero,1),EEG.nbchan);
  150. toRemove = [];
  151. for j = 1:size(winrej,1)
  152. toRemove = [toRemove winrej(j,1):winrej(j,2)];
  153. isArtifactByChannel(winrej(j,1):winrej(j,2),chanrej(j,:)==1) = 1;
  154. end
  155. isArtifact(toRemove) = 1;
  157. % Reject by channel
  158. artifactPropByChannel = mean(isArtifactByChannel(~isZero,:),1);
  160. isBad = artifactPropByChannel > 0.10;
  161. badChannels = {EEG.chanlocs(isBad).labels};
  162. allBadChannels{i} = badChannels;
  164. % Number of artifact as a proportion of samples of interest
  165. allArtifactProp(i) = mean(isArtifact & ~isZero);
  167. % Remove artifacts and non-zero rows
  168. EEG.unfold.Xdc(isArtifact | isZero,:) = [];
  169. EEG.data(:,isArtifact | isZero) = [];
  170. EEG.pnts = size(EEG.data,2);
  172. % Determine where the breaks between rERPs are (Unfold smooshes
  173. % everything together)
  174. numPoints = (ufTime(2)-ufTime(1))*EEG.srate;
  175. switch whichAnalysis
  176. case 1
  177. breakpoints = int32([numPoints 2*numPoints]);
  178. case {2,3}
  179. breakpoints = int32([numPoints 2*numPoints]);
  180. case {4}
  181. breakpoints = int32([numPoints 2*numPoints 3*numPoints 4*numPoints 5*numPoints]);
  182. end
  184. % Do cross-validation
  185. if doCV
  186. regtype = 'onediff';
  187. lambdas = [0 1E1 1E2 1E3 1E4 1E5 1E6 1E7 1E8];
  188. k = 10;
  189. [allErrors,bestBeta] = doRegCV(EEG.data,EEG.unfold.Xdc,regtype,{1:size(EEG.unfold.Xdc,2)},{breakpoints},lambdas,k);
  190. figure();
  191. plot(allErrors);
  192. drawnow();
  193. [~,j] = min(allErrors);
  194. disp(lambdas(j));
  195. regPar(i) = lambdas(j);
  196. end
  198. % Solve GLM
  199. regtype = 'onediff';
  200. lambda = regPar(i);
  201. thisPDM = pinv_reg(EEG.unfold.Xdc,lambda,regtype,breakpoints);
  202. tempBeta = thisPDM * EEG.data';
  203. allBeta(i,:,:) = tempBeta';
  205. end
  207. if doCV
  208. save(fullfile(resultsFolder,['regPar_' num2str(whichAnalysis) '.mat']),'regPar');
  209. end
  211. save(fullfile(resultsFolder,['results_' num2str(whichAnalysis) '_' num2str(doReg) '.mat']));

analyze_songfamiliarity_05_erps.m at commit e98e9c8, under MIT · at the source

Overview

Authors: Jared R. Girard1, Aaron Bishop1, Cameron D. Hassall1
ORCID iDs: Jared R. Girard, Aaron Bishop, Cameron D. Hassall
  1. Department of Psychology MacEwan University Edmonton Alberta Canada
Institutions: MacEwan University (Canada)
Journal: Psychophysiology, volume 63, issue 8, article e70370
Dates: received 4 September 2025; accepted 21 July 2026; published online 31 July 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/psyp.70370 · PMID 42538772 · PMCID PMC13428188 · OpenAlex W4413842841
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.08.25.672171
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Evoked potentials
Keywords: central‐parietal positivity, EEG, ERP, evidence accumulation, familiarity, memory
MeSH: Auditory Perception*, Decision Making*, Evoked Potentials*, Music*, Recognition, Psychology*, Adult, Electroencephalography, Female, Humans, Male, Young Adult (* major topic)
Topic: Natural Language Processing Techniques (Artificial Intelligence, Computer Science), according to OpenAlex
Funding: Natural Sciences and Engineering Research Council of Canada (RGPIN 2024‐04848)
Citations: not cited yet (Europe PMC); 70 references in the paper

Abstract

Familiarity judgments are thought to involve evidence accumulation, a decision‐making process in which information is gathered over time until a threshold is reached. Previous work has identified a scalp‐recorded signature of evidence accumulation called the central‐parietal positivity (CPP). We built on this previous work and recorded electroencephalography while participants listened to several melodies, instructing participants to respond as soon as the song felt familiar. A prominent CPP was noted, time‐locked to decisions. We then used linear regression to unmix overlapping neural responses and observed a stimulus‐locked indicator of evidence accumulation that increased in amplitude just prior to a familiarity decision. This result suggests that song familiarity relies on evidence accumulation, with individual notes in a familiar song acting as “evidence”.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

chassall/songfamiliarity

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: e98e9c8e400430944ef925c00eed1468118f00ce, 25 August 2025
Languages: MATLAB (13)
Size: 21 files, 13 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (6 files), Statistics and Machine Learning Toolbox (2 files), ICLabel (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
15 files
At the source: github.com/chassall/songfamiliarity

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 13 scripts, each with its path and the digest of its content;
  • 7 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 Availability Statement

EEG dataset is available at https://doi.org/10.18112/openneuro.ds005876.v1.0.1. Analysis scripts are available at https://github.com/chassall/songfamiliarity.

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 3, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 6 keywords, 11 MeSH terms, 1 funder, 69 references.

Cite

This paper

Girard, J. R., Bishop, A., & Hassall, C. D. (2026). Song Familiarity Relies on Evidence Accumulation. Psychophysiology, 63(8), e70370. https://doi.org/10.1111/psyp.70370

BibTeX

@article{girard2026song,
author = {Girard, Jared R. and Bishop, Aaron and Hassall, Cameron D.},
title = {{Song Familiarity Relies on Evidence Accumulation}},
journal = {Psychophysiology},
year = {2026},
month = aug,
volume = {63},
number = {8},
pages = {e70370},
publisher = {Wiley},
issn = {0048-5772},
doi = {10.1111/psyp.70370},
url = {https://doi.org/10.1111/psyp.70370},
pmid = {42538772},
pmcid = {PMC13428188}
}

RIS

TY - JOUR
AU - Girard, Jared R.
AU - Bishop, Aaron
AU - Hassall, Cameron D.
TI - Song Familiarity Relies on Evidence Accumulation
T2 - Psychophysiology
J2 - Psychophysiology
PY - 2026
DA - 2026/08/01
VL - 63
IS - 8
SP - e70370
SN - 0048-5772
PB - Wiley
DO - 10.1111/psyp.70370
UR - https://doi.org/10.1111/psyp.70370
LA - en
ER -

CSL-JSON

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"issue": "8",
"page": "e70370",
"DOI": "10.1111/psyp.70370",
"PMID": "42538772",
"PMCID": "PMC13428188",
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