OSCR

Tracking Visual Statistical Learning with Steady-State Visual Evoked Potentials: Effects of Exemplar and Category Information.

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] § METHOD › Data Processing ↔ EEG_data/MATLAB_scripts/JB_Analysis_ITC_SNR_PSD.m, lines 185–252 · score 0.77 · power spectral density, Fourier Transformation, EEG epoch, FFT, electrodes, MATLAB
  2. [2] § RESULTS › Post-Test Trials › RT. ↔ data_analysis/R_scripts/JungleBunch_Analysis_Stats_ACC_RT.R, lines 105–143 · score 0.54 · pairwise comparisons, Holm corrected, Image Position, RT, ANOVA, SD
  3. [3] § METHOD › Data Processing ↔ EEG_data/MATLAB_scripts/JB_Analysis_ITC_SNR_PSD.m, lines 185–252 · score 0.53 · Fourier transform, EEG epoch, phase, ITC, SNR

Paper

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The authors' code

MATLAB · 268 lines · 11 KB · no license · 2 matches

  1. function [dataOut, dataOutStats, ITC_ga, SNR_ga, PSD_ga] = JB_Analysis_ITC_SNR_PSD(eegEpochDur, ...
  2. nFqBinNeighbours, nFqBinSkip, eegClusterName)
  3. % Function to calculate InterTrialCoherence (ITC), SignalToNoiseRatio
  4. % (SNR), and power spectral density (PSD) per participant, and across all
  5. % participants for each condition (grand average)
  6. %
  7. % Natasa Ganea - Mar 2024 - [email hidden]
  8. % Dominik Garber - Mar 2023 - [email hidden]
  9. %
  10. % Copyright © 2024 Natasa Ganea & Dominik Garber. All Rights Reserved.
  11. %
  12. % NG -- 2025-06-20 -- renamed fnc; old name: JB_Analysis_InterTrialCoherence
  13. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  15. % defaults
  16. if nargin < 1 || isempty(eegEpochDur)
  17. eegEpochDur = 27; % 27s epoch duration
  18. % eegEpochDur = 9;
  19. end
  21. if nargin < 2 || isempty(nFqBinNeighbours)
  22. nFqBinNeighbours = 11; % eeg epoch length is set to 27 because the trial was 0.9s FadeIn + 27s Exp + 0.9s FadeOut
  23. % nFqBinNeighbours = 4;
  24. end
  26. if nargin < 3 || isempty(nFqBinSkip)
  27. nFqBinSkip = 1; % number of freq bins adjacent Target Freq to use when calculating SNR
  28. end
  30. if nargin < 4 || isempty(eegClusterName)
  31. eegClusterName = 'Occ';
  32. end
  34. % EEG recording info
  35. eegSampleRate = 500; % 500 Hz EGI sampling rate
  36. condNum = 3; % number of experimental conditions
  37. maxFreq = eegEpochDur*10; % maximum number of freq to display in grand average plots (2 freq bins per freq)
  39. % frequency bins for ODDBALL Fq & BASE Fq
  40. if eegEpochDur == 27
  41. b = 30; % EEG epoch = 27s; fq bin size = 1s/27s = 0.037 Hz
  42. elseif eegEpochDur == 9
  43. b = 10; % EEG epoch = 9s; fq bin size = 1s/9s = 0.111 Hz
  44. end
  45. binNrFq1 = b + 1; % fq1 = 1.111; oddballFq
  46. binNrFq2 = 3*b + 1; % fq2 = 3.333; baseFq
  48. % data files
  49. p = fullfile(pwd, '4_roi'); % path directory
  50. DD = dir(p); % read directory
  51. fileNum = 0; % count files in the directory that meet the criterion
  52. fileName = strings(length(DD),1); % store the name of the files that meet the criterion
  53. for i = 1:length(DD)
  54. if ~strcmp(DD(i).name(1),'.') && strcmp(DD(i).name(end-2:end), 'mat')
  55. fileNum = fileNum + 1;
  56. fileName(fileNum,1) = DD(i).name;
  57. end
  58. end
  59. fileName = fileName(1:fileNum); % keep only the cells that have values in them
  61. % initialize variables
  62. ps = fileNum/condNum; % number of participants; 3 groups; 17 participants per gr
  63. if mod(ps,1) ~= 0
  64. ps = 17; % for the H1 vs H2 analysis, I had to exclude N0 JB_40 and this resulted in 16 participants for the N0 gr
  65. end
  66. ITC_ps = nan(ps,eegSampleRate/2*eegEpochDur+1); % store ITC per participant
  67. SNR_ps = nan(ps,eegSampleRate/2*eegEpochDur+1); % store SNR per participant
  68. PSD_ps = nan(ps,eegSampleRate/2*eegEpochDur+1); % store PSD (power spectral density) per participant
  69. fileName_ps = strings(ps,1); % store participant filename
  70. ITC_ga = nan(condNum,eegSampleRate/2*eegEpochDur+1); % ITC grand average per experimental condition
  71. SNR_ga = nan(condNum,eegSampleRate/2*eegEpochDur+1); % SNR grand average per experimental condition
  72. PSD_ga = nan(condNum,eegSampleRate/2*eegEpochDur+1); % PSD grand average per experimental condition
  73. dataOut = struct('condName', strings(condNum,1), 'filename', strings(ps,1),...
  74. 'ITC_ps', zeros(ps,eegSampleRate/2*eegEpochDur+1), 'SNR_ps', zeros(ps,eegSampleRate/2*eegEpochDur+1),...
  75. 'PSD_ps', zeros(ps,eegSampleRate/2*eegEpochDur+1));
  77. % for each experimental condition
  78. for cond = 1:condNum
  79. switch cond
  80. case 1
  81. condName = 'A1'; % grA1
  82. case 2
  83. condName = 'A2'; % grA2
  84. case 3
  85. condName = 'N0'; % grA1
  86. end
  88. % reset the file count at the beginning of each experimental condition
  89. ss = 0;
  91. % for each participant
  92. for s = 1:length(fileName)
  94. % if filename contains name of experimental condition, analyze it
  95. if contains(fileName(s),condName) == 1
  97. ss = ss + 1; % count filesound
  98. fileName_ps(ss,1) = fileName(s); % store filename
  100. % load data
  101. file = fullfile(pwd, '4_roi', fileName(s)); % full path to file
  102. load(file,'data'); % load data
  104. % calculate ITC, SNR, PSD for that dataset
  105. eegEpochs = size(data,1); % varying number of epoch across participants
  106. eegCh = size(data,2); % Occ cluster is the last electrode
  107. if ~contains(eegClusterName,'Occ')
  108. eegCh = eegCh-1; % Fro cluster is the penultimate eletrode
  109. end
  110. [ITC, PSD_db, freq, SNR] = coherence_power(data, eegSampleRate, eegSampleRate*eegEpochDur, eegEpochs,...
  111. eegCh, nFqBinNeighbours, nFqBinSkip);
  113. % % plot ITC, SNR, PSD per participant
  114. % figure(1); plot(freq(1:maxFreq), ITC(1:maxFreq));
  115. % figure(2); plot(freq(1:maxFreq), SNR(1:maxFreq));
  116. % figure(3); plot(freq(1:maxFreq), PSD_db(1:maxFreq));
  118. % store individual ITC, SNR, PSD in a master variable
  119. ITC_ps(ss,:) = ITC; % one row = one participant; one column = one freq bin (= 0.5 Hz, up to 250 Hz)
  120. SNR_ps(ss,:) = SNR;
  121. PSD_ps(ss,:) = PSD_db;
  122. end
  124. end
  126. % for each condition, calculate the grand average
  127. ITC_ga(cond,:) = mean(ITC_ps,1,'omitnan'); % average across participants (rows); one column = one freq bin
  128. SNR_ga(cond,:) = mean(SNR_ps,1,'omitnan');
  129. PSD_ga(cond,:) = mean(PSD_ps,1,'omitnan');
  131. % store data per condition in a structure
  132. dataOut(cond).condName = condName;
  133. dataOut(cond).filename = fileName_ps;
  134. dataOut(cond).ITC_ps = ITC_ps;
  135. dataOut(cond).SNR_ps = SNR_ps;
  136. dataOut(cond).PSD_ps = PSD_ps;
  138. % reset these variables at the end of each cond
  139. fileName_ps = strings(ps,1); % store participant filename
  140. ITC_ps = nan(ps,eegSampleRate/2*eegEpochDur+1); % store ITC per participant
  141. SNR_ps = nan(ps,eegSampleRate/2*eegEpochDur+1); % store SNR per participant
  142. PSD_ps = nan(ps,eegSampleRate/2*eegEpochDur+1); % store PSD (power spectral density) per participant
  144. end
  146. dataOutStats = struct(...
  147. 'eegClusterName', eegClusterName,... % cluster
  148. 'filename', fileName,... % filename
  149. 'ITC_ps', [dataOut(1).ITC_ps(:,binNrFq1), dataOut(1).ITC_ps(:,binNrFq2); ... % grA1 Inter Trial Coherence 1.11 Hz & 3.33 Hz
  150. dataOut(2).ITC_ps(:,binNrFq1), dataOut(2).ITC_ps(:,binNrFq2); ... % grA2
  151. dataOut(3).ITC_ps(:,binNrFq1), dataOut(3).ITC_ps(:,binNrFq2)],... % grN0
  152. 'SNR_ps', [dataOut(1).SNR_ps(:,binNrFq1), dataOut(1).SNR_ps(:,binNrFq2); ... % grA1 Signal Noise Ratio 1.11 Hz & 3.33 Hz
  153. dataOut(2).SNR_ps(:,binNrFq1), dataOut(2).SNR_ps(:,binNrFq2); ... % grA2
  154. dataOut(3).SNR_ps(:,binNrFq1), dataOut(3).SNR_ps(:,binNrFq2)],... % grN0
  155. 'PSD_ps', [dataOut(1).PSD_ps(:,binNrFq1), dataOut(1).PSD_ps(:,binNrFq2);... % grA1 Power Spectrum Density 1.11 Hz & 3.33 Hz
  156. dataOut(2).PSD_ps(:,binNrFq1), dataOut(2).PSD_ps(:,binNrFq2);... % grA2
  157. dataOut(3).PSD_ps(:,binNrFq1), dataOut(3).PSD_ps(:,binNrFq2)]); % grN0
  159. %% Fig 1 & 2
  160. % Display values for 6Hz flicker freq test block
  162. % ITC
  163. figure(1);
  164. plot(freq(1:maxFreq+1), ITC_ga(1,1:maxFreq+1), 'b'); % grA1
  165. ylim([0 1]); % ITC value can be betweem 0 and 1
  166. hold on;
  167. plot(freq(1:maxFreq+1), ITC_ga(2,1:maxFreq+1), 'r'); % grA2
  168. hold on;
  169. plot(freq(1:maxFreq+1), ITC_ga(3,1:maxFreq+1), 'g'); % grN0
  170. hold off;
  172. % SNR
  173. figure(2);
  174. plot(freq(1:maxFreq+1), SNR_ga(1,1:maxFreq+1), 'b'); % grA1
  175. ylim([0 25]); %[0 inf]
  176. hold on;
  177. plot(freq(1:maxFreq+1), SNR_ga(2,1:maxFreq+1), 'r'); % grA2
  178. hold on;
  179. plot(freq(1:maxFreq+1), SNR_ga(3,1:maxFreq+1), 'g'); % grN0
  180. hold off;
  183. end
  185. function [ITC, PSD_db, freq, SNR] = coherence_power(data, fs, numsmp, numTrials, ch, n_neighbors, n_skip) %takes in a datamatrix nTrials x nTimepoints
  187. if nargin < 2 || isempty(fs)
  188. fs = 256; %sample rate
  189. end
  191. if nargin < 3 || isempty(numsmp)
  192. numsmp = 1796; %number of samples (should be the same as the second dimension of data)
  193. end
  195. if nargin < 4 || isempty(numTrials)
  196. numTrials = 17; % number of trials (should be the same as the first dimension of data)
  197. end
  199. if nargin < 5 || isempty(ch)
  200. ch = []; % select specific electrode?
  201. end
  203. if nargin < 6 || isempty(n_neighbors)
  204. n_neighbors = 11; % number of adjacent fq bin used for SNR
  205. end
  207. if nargin < 7 || isempty(n_skip)
  208. n_skip = 1; % number of adjacent fq bin to skip when calculating the SNR
  209. end
  211. freq = 0:fs/numsmp:fs/2; %create an array of frequencies spanning 0 to the nyquist freq
  213. % pre-initialize
  214. itc_t = zeros(numTrials,numsmp/2);
  216. if ~isempty(ch)
  217. data = data(:,ch,:,:,:,:);
  218. end
  220. for j = 1:numTrials
  222. data_trial = data(j,:);
  224. % computes the discrete Fourier transform
  225. fft_t = fft(data_trial);
  227. % keep frequencies that are lower than the Nyquist frequency
  228. fft_t = fft_t(1:numsmp/2+1);
  230. % calculate phase for each EEG epoch
  231. itc_t(j,1:length(fft_t)) = fft_t ./ abs(fft_t);
  233. end
  235. % calculate ITC across the trials (using method 3 from Benjamin et al., 2021: Inter Trial Coherence)
  236. ITC = abs(mean(itc_t, 1, 'omitnan'));
  238. % now calculate power (using method 2 from Benjamin et al., 2021: FFT of the average of all trials)
  239. mean_tc = mean(data(1:numTrials,:), 1, 'omitnan'); % average the timecourse across trials
  240. fft_t = fft(mean_tc); % FFT of the average
  241. fft_t = fft_t(1:numsmp/2+1);
  242. psd = abs(fft_t).^2 .* ((1/numsmp)*(1/fs)); % get the PSD (note: may want to multiple this by 2: https://www.mathworks.com/help/signal/ug/power-spectral-density-estimates-using-fft.html)
  243. % psd(2:end-1) = 2*psd(2:end-1); % In order to conserve the total power, multiply all frequencies that occur in both sets — the positive and negative frequencies — by a factor of 2. Zero frequency (DC) and the Nyquist frequency do not occur twice
  245. % calculate SNR, then convert to dB
  246. SNR = snr(psd, n_neighbors, n_skip); % 2 adjacent neighboring frequencies, skip the first neighbor; here it's 0 because DG adds 1 in his SNR fnc calculation
  247. SNR = 10.*log10(SNR);
  249. % convert power to dB
  250. PSD_db = 10*log10(psd);
  252. end
  254. function [SNR] = snr(spectrum, n_neighbors, n_skip) % signal to noise ratio for a frequency spectrum
  255. % Creaded by Dominik Garber - e: [email hidden] - 2023-04-03
  257. padded_spectrum = [nan(1, n_neighbors) spectrum nan(1, n_neighbors)];
  258. SNR = nan(1, length(spectrum));
  260. for freq = (n_neighbors+1):(length(spectrum)+n_neighbors)
  262. neighbors = [padded_spectrum((freq-n_neighbors):(freq-1-n_skip)) padded_spectrum((freq+1+n_skip):(freq+n_neighbors))];
  263. noise = mean(neighbors,2,'omitnan');
  264. SNR(freq-n_neighbors) = spectrum(freq-n_neighbors)./noise;
  266. end
  268. end

JB_Analysis_ITC_SNR_PSD.m, no license · at the source

Overview

Authors: Natasa Ganea1, Dominik Garber2, Richard N Aslin1,3,4, David J Lewkowicz1,3
ORCID iDs: David J Lewkowicz
  1. Child Study Center, Yale School of Medicine, New Haven, CT, USA
  2. Central European University, Vienna, Austria
  3. Department of Psychology, Yale University, New Haven, CT, USA
  4. Wu Tsai Institute, Yale University, New Haven, CT, USA
Institutions: Yale University (United States); Central European University (Austria)
Journal: Open mind : discoveries in cognitive science, volume 10, pages 808-826
Dates: received 24 July 2025; accepted 24 April 2026; published online 17 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/opmi.a.358 · PMID 42396548 · PMCID PMC13327787 · OpenAlex W7166073287
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Connectivity, Preprocessing, fMRI & imaging, Physiology & signal measures
Keywords: statistical learning, EEG, SSVEP, neural entrainment
Topic: Face Recognition and Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 48 references in the paper

Abstract

This study examined visual statistical learning using EEG-based steady-state visual evoked potentials (SSVEP). Fifty-one adults were exposed to image sequences organized into triplets across three conditions (n = 17 per condition) in which the alignment of category-level and exemplar-level information was manipulated. Neural entrainment at the triplet frequency (1.11 Hz) differed significantly across conditions (ηp2 = .13), with stronger responses in the Single-Category and No-Category conditions than in the Mixed-Category condition. There were no differences at the image frequency (3.33 Hz; ηp2 = .05). Behavioral reaction times mirrored this pattern, showing faster responses to the last exemplar in the triplet in the Single-Category (ηp2 = .71) and No-Category (ηp2 = .22) conditions, but not in the Mixed-Category (ηp2 = .10) condition. Both signal-to-noise ratio (SNR) and inter-trial coherence (ITC) captured neural entrainment across fronto-central and parietal-occipital electrode clusters. These findings validate SSVEP as an online measure of visual statistical learning and demonstrate that category-exemplar mismatch interfered with statistical learning.

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 3 matches between paragraphs and lines of code.

OSF mhr6y

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (5), MATLAB (3)
Size: 38 files, 8 scripts
Software Heritage: not checked
Found in: “Pre-Registration and Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (5 files), tidyverse (5 files), afex (4 files), BayesFactor (4 files), emmeans (4 files), car (2 files), brms (1 file), easystats (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
8 files
At the source: osf.io/mhr6y/?view_only=8879b507ac9644c384623f1260dc90d8

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;
  • 8 scripts, each with its path and the digest of its content;
  • 3 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

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

Availability of Data and Materials

This experiment was not pre-registered. Stimuli, data, and custom R and MATLAB scripts are available on the Open Science Framework: https://osf.io/mhr6y/?view_only=8879b507ac9644c384623f1260dc90d8.

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

Pre-Registration and Data Availability

This experiment was not pre-registered. Stimuli, data, and custom R and MATLAB scripts are available on OSF: https://osf.io/mhr6y/?view_only=8879b507ac9644c384623f1260dc90d8.

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 4 keywords, 47 references.

Cite

This paper

Ganea, N., Garber, D., Aslin, R. N., & Lewkowicz, D. J. (2026). Tracking Visual Statistical Learning with Steady-State Visual Evoked Potentials: Effects of Exemplar and Category Information. Open mind : discoveries in cognitive science, 10, 808-826. https://doi.org/10.1162/opmi.a.358

BibTeX

@article{ganea2026tracking,
author = {Ganea, Natasa and Garber, Dominik and Aslin, Richard N and Lewkowicz, David J},
title = {{Tracking Visual Statistical Learning with Steady-State Visual Evoked Potentials: Effects of Exemplar and Category Information}},
journal = {Open mind : discoveries in cognitive science},
year = {2026},
month = jun,
volume = {10},
pages = {808--826},
publisher = {MIT Press},
issn = {2470-2986},
doi = {10.1162/opmi.a.358},
url = {https://doi.org/10.1162/opmi.a.358},
pmid = {42396548},
pmcid = {PMC13327787}
}

RIS

TY - JOUR
AU - Ganea, Natasa
AU - Garber, Dominik
AU - Aslin, Richard N
AU - Lewkowicz, David J
TI - Tracking Visual Statistical Learning with Steady-State Visual Evoked Potentials: Effects of Exemplar and Category Information
T2 - Open mind : discoveries in cognitive science
J2 - Open Mind (Camb)
PY - 2026
DA - 2026/06/17
VL - 10
SP - 808
EP - 826
SN - 2470-2986
PB - MIT Press
DO - 10.1162/opmi.a.358
UR - https://doi.org/10.1162/opmi.a.358
LA - en
ER -

CSL-JSON

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"title": "Tracking Visual Statistical Learning with Steady-State Visual Evoked Potentials: Effects of Exemplar and Category Information",
"container-title": "Open mind : discoveries in cognitive science",
"author": [
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"family": "Ganea",
"given": "Natasa"
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{
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"family": "Lewkowicz",
"given": "David J"
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"container-title-short": "Open Mind (Camb)",
"volume": "10",
"page": "808-826",
"DOI": "10.1162/opmi.a.358",
"PMID": "42396548",
"PMCID": "PMC13327787",
"ISSN": "2470-2986",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/opmi.a.358",
"language": "en",
"issued": {
"date-parts": [
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2026,
6,
17
]
]
}
}

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