OSCR

Trial-by-trial covariation of pupil dilation, microsaccades, and visual evoked potentials during saliency processing.

Code ↔ Paper

5 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 5 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] § STAR★Methods › Method details › Data analysis ↔ code/Step_06_PlotFigure_02.m, lines 58–207 · score 0.71 · 50–400 ms, late rebound, Microsaccade rate, vertical, horizontal, position
  2. [2] § STAR★Methods › Method details › Data analysis ↔ code/Step_07_PlotFigure_03.m, lines 59–116 · score 0.68 · 90–110 ms, 130 ms, 160 ms, 205 ms, 180 ms, 90 ms
  3. [3] § Results › Stimulus contrast modulated visually evoked potentials ↔ code/Step_07_PlotFigure_03.m, lines 59–116 · score 0.65 · 90–110 ms, 130 ms, 160 ms, 205 ms, 180 ms, 90 ms
  4. [4] § STAR★Methods › Method details › Data analysis ↔ code/functions/microsacc.m, the whole file · a weak match · score 0.57 · velocity threshold, SDs, vertical, median, horizontal, position
  5. [5] § STAR★Methods › Method details › Data analysis ↔ code/Step_03_PRET_SubjectLevel.m, lines 13–74 · score 0.52 · 500–1500 ms, window, median, RT, modeled

Paper

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

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The authors' code

MATLAB · 246 lines · 6.5 KB · no license · 2 matches

  1. clearvars
  3. load('..\data\processed\\eeg_c.mat')
  4. load('..\data\processed\\eeg_pn.mat')
  5. load('..\data\processed\\eeg_t.mat')
  6. load("..\data\raw\\EEGchanloc.mat")
  7. load('..\data\processed\\all_data.mat')
  8. load("..\data\processed\\op.mat")
  9. acc = mean(all_data(:,:,4),2);
  10. ol_acc = acc < 0.6;
  11. all_data(ol_acc,:,:) = [];
  12. eeg_c (ol_acc,:,:) = [];
  13. eeg_pn (ol_acc,:,:) = [];
  14. eeg_t (:,:,ol_acc,:) = [];
  17. %%
  19. fs = 1000;
  20. num_sub = size(eeg_c,1);
  21. num_trial = size(eeg_c,2);
  22. num_point = size(eeg_c,3);
  23. num_cond = 2;
  24. bc_tw = [901:1000];
  27. data_eeg_c = nan(num_cond,num_sub,num_point);
  28. data_eeg_pn = nan(num_cond,num_sub,num_point);
  30. for sub = 1:num_sub
  31. sub_beh = squeeze(all_data(sub,:,:));
  32. sub_eeg_c = squeeze(eeg_c(sub,:,:));
  33. sub_eeg_pn = squeeze(eeg_pn(sub,:,:));
  35. sub_eeg_c = sub_eeg_c - mean(sub_eeg_c(:,bc_tw),2);
  36. sub_eeg_pn = sub_eeg_pn - mean(sub_eeg_pn(:,bc_tw),2);
  38. for cond = 1:num_cond
  39. idx = find(sub_beh(:,4) & sub_beh(:,2) == cond);
  41. ol = [];
  42. for t = 1:length(idx)
  43. t_eeg = sub_eeg_c(idx(t),:);
  44. if isnan(mean(t_eeg(:)))
  45. ol = [ol,t];
  46. end
  47. end
  48. idx(ol) = [];
  50. if size(sub_eeg_c(idx,:),1) >= 30
  51. data_eeg_c(cond,sub,:) = mean(sub_eeg_c(idx,:),'omitnan');
  52. data_eeg_pn(cond,sub,:) = mean(sub_eeg_pn(idx,:),'omitnan');
  53. else
  54. disp(sub)
  55. end
  56. end
  57. end
  59. %%
  60. figure('Renderer', 'painters', 'Position', [0 0 800 800],'Visible','on');
  61. set(gcf, 'Color', 'white');
  63. clc
  65. subplot(4,4,[1 2 5 6])
  66. hold on
  67. tar_tw = [90 110];
  68. plotPMT(data_eeg_c, tar_tw, 'Amplitude (\muv)', 14, true)
  69. Yl = get(gca,'YLim');
  70. text(90,Yl(2),'C1','HorizontalAlignment','left','VerticalAlignment','top')
  71. lilChange('A',[-10:1:10])
  72. xlim([-100 500])
  73. PlotLed({'High','Low'},{[.8 .1 0];[0 .5 .8]},'b')
  75. subplot(4,4,[3 4 7 8])
  76. temp = mean(data_eeg_c(:,:,tar_tw(1)+1001:tar_tw(2)+1001),3);
  77. plot_violin(temp, 'Amplitude (\muv)', {'Low','High','Diff'}, 14)
  78. rsl(1,:) = doStast(temp);
  79. lilChange('B',[-10:1:10])
  80. op(1,6,:) = temp(1,:);
  81. op(2,6,:) = temp(2,:);
  82. op(3,6,:) = temp(2,:) - temp(1,:);
  84. subplot(4,4,[9 10 13 14])
  85. hold on
  86. tar_tw = [130 160; 180 205];
  87. plotPMT(data_eeg_pn, tar_tw, 'Amplitude (\muv)', 14, false)
  88. Yl = get(gca,'YLim');
  89. text(130,Yl(2),'P1','HorizontalAlignment','left','VerticalAlignment','top')
  90. text(180,Yl(2),'N1','HorizontalAlignment','left','VerticalAlignment','top')
  91. lilChange('D',[-10:1:10])
  92. xlim([-100 500])
  93. PlotLed({'High','Low'},{[.8 .1 0];[0 .5 .8]},'b')
  95. subplot(4,4,[11 15])
  96. temp = mean(data_eeg_pn(:,:,tar_tw(1,1)+1001:tar_tw(1,2)+1001),3);
  97. plot_violin(temp, 'Amplitude (\muv)', {'Low','High','Diff'}, 14)
  98. rsl(2,:) = doStast(temp);
  99. lilChange('E',[-10:2:10])
  100. op(1,7,:) = temp(1,:);
  101. op(2,7,:) = temp(2,:);
  102. op(3,7,:) = temp(2,:) - temp(1,:);
  104. subplot(4,4,[12 16])
  105. temp = mean(data_eeg_pn(:,:,tar_tw(2,1)+1001:tar_tw(2,2)+1001),3);
  106. plot_violin(temp, 'Amplitude (\muv)', {'Low','High','Diff'}, 14)
  107. rsl(3,:) = doStast(temp);
  108. lilChange('F',[-10:2:10])
  109. op(1,8,:) = temp(1,:);
  110. op(2,8,:) = temp(2,:);
  111. op(3,8,:) = temp(2,:) - temp(1,:);
  113. print('..\figure\Figure_03_01.jpeg','-djpeg','-r500')
  114. savefig("..\figure\Figure_03_01.fig")
  115. save("..\data\processed\op.mat","op")
  116. close all
  118. %%
  119. figure('Renderer', 'painters', 'Position', [0 0 800/3 800],'Visible','on');
  120. set(gcf, 'Color', 'white');
  122. subplot(3,1,1)
  123. tar_tw = [90:110];
  124. temp = squeeze(mean(mean(eeg_t(:,:,:,tar_tw+1001),4),3));
  125. temp = temp(2,:) - temp(1,:);
  127. topoplot(temp,cl,'maplimits',[-.5 .5]);
  128. text(0,.6,'C1','FontSize',14,'HorizontalAlignment','center')
  129. title('C','FontSize',20);ax = gca;ax.TitleHorizontalAlignment = 'left';
  131. c = cbar;
  132. c.Position(1) = .88;
  133. c.YTick = -1:.5:1;
  134. yl = ylabel(c,'\muv','FontSize',12,'HorizontalAlignment','right','VerticalAlignment','bottom');
  135. yl.Position(1) = min(xlim(c));
  136. axis fill
  138. subplot(3,1,2)
  139. tar_tw = [130:160];
  140. temp = squeeze(mean(mean(eeg_t(:,:,:,tar_tw+1001),4),3));
  141. temp = temp(2,:) - temp(1,:);
  142. topoplot(temp,cl,'maplimits',[-.5 .5]);
  143. text(0,.6,'P1','FontSize',14,'HorizontalAlignment','center')
  144. title('G','FontSize',20);ax = gca;ax.TitleHorizontalAlignment = 'left';
  145. c = cbar;
  146. c.Position(1) = .88;
  147. c.YTick = -1:.5:1;
  148. yl = ylabel(c,'\muv','FontSize',12,'HorizontalAlignment','right','VerticalAlignment','bottom');
  149. yl.Position(1) = min(xlim(c));
  150. axis fill
  153. subplot(3,1,3)
  154. tar_tw = [180:205];
  155. temp = squeeze(mean(mean(eeg_t(:,:,:,tar_tw+1001),4),3));
  156. temp = temp(2,:) - temp(1,:);
  157. topoplot(temp,cl,'maplimits',[-.5 .5]);
  158. text(0,.6,'N1','FontSize',14,'HorizontalAlignment','center')
  159. title('H','FontSize',20);ax = gca;ax.TitleHorizontalAlignment = 'left';
  160. c = cbar;
  161. c.Position(1) = .88;
  162. c.YTick = -1:.5:1;
  163. yl = ylabel(c,'\muv','FontSize',12,'HorizontalAlignment','right','VerticalAlignment','bottom');
  164. yl.Position(1) = min(xlim(c));
  165. axis fill
  166. print('..\figure\Figure_03_02.jpeg','-djpeg','-r500')
  167. % close all
  168. %%
  170. im1 = imread('..\figure\Figure_03_01.jpeg');
  171. im2 = imread('..\figure\Figure_03_02.jpeg');
  173. im1_b = mean(im1,[1,3]);
  174. im1 = im1(:,270:3765,:);
  175. im2_b = mean(im2,[1,3]);
  176. im2 = im2(:,271:end,:);
  177. im = [im1,im2];
  178. imshow(im)
  179. imwrite(im,'..\figure\Figure_03.jpeg')
  181. close all
  188. %%
  189. function lilChange(ttl,ytck)
  190. title(ttl,'FontSize',20);ax = gca;ax.TitleHorizontalAlignment = 'left';
  191. if ~isempty(ytck)
  192. yticks(ytck)
  193. end
  194. end
  196. function plotPMT(data, tar_tw, ttl, fs, one_tar)
  197. col = {[0 .5 .8];[.8 .1 0];[0 .5 0]};
  198. plot_tw = [-100:500];
  199. x = plot_tw;
  200. data = data(:,:,x+1001);
  203. for cond = 1:size(data,1)
  204. cond_pup = squeeze(data(cond,:,:));
  205. err_pup = std(cond_pup)/sqrt(length(cond_pup(:,1))-1);
  206. y = mean(cond_pup);
  207. plot(x,y,'linewidth',1,'Color',col{cond});
  208. fill([x';flipud(x')],[y'-err_pup';flipud(y'+err_pup')], ...
  209. col{cond}, ...
  210. 'FaceAlpha',0.2, ...
  211. 'EdgeColor','none')
  212. end
  214. f = gca;
  215. Yl = f.YLim;
  216. if one_tar
  217. fill([tar_tw(1) tar_tw(1) tar_tw(end) tar_tw(end)], ...
  218. [Yl(1) Yl(2) Yl(2) Yl(1)], 'k', ...
  219. 'FaceAlpha',0.1, ...
  220. 'EdgeColor','none')
  221. else
  222. fill([tar_tw(1,1) tar_tw(1,1) tar_tw(1,2) tar_tw(1,2)], ...
  223. [Yl(1) Yl(2) Yl(2) Yl(1)], 'k', ...
  224. 'FaceAlpha',0.1, ...
  225. 'EdgeColor','none')
  227. fill([tar_tw(2,1) tar_tw(2,1) tar_tw(2,2) tar_tw(2,2)], ...
  228. [Yl(1) Yl(2) Yl(2) Yl(1)], 'k', ...
  229. 'FaceAlpha',0.1, ...
  230. 'EdgeColor','none')
  231. end
  233. yline(0)
  234. xline(0)
  235. axis square;
  236. ylim(Yl);
  237. xlim([plot_tw(1) plot_tw(end)])
  238. ylabel(ttl,'FontSize',fs)
  239. xlabel(['Time frome target onset (ms)'],'FontSize',fs)
  240. end

Step_07_PlotFigure_03.m, no license · at the source

Overview

Authors: Yi-Hsuan Chang1,2, Chen-Chung Kuo1,2, Cesar Barquero3, Chin-An Wang1,4,5,6,7
ORCID iDs: Chin-An Wang
  1. Eye-Tracking Laboratory, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
  2. Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
  3. Department of Physical Activity and Sport Science, Universidad Peruana de Ciencias Aplicadas, Lima, Peru
  4. Department of Education and Humanities in Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
  5. Department of Anesthesiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
  6. Department of Anesthesiology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
  7. Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
Institutions: National Central University (Taiwan); Taipei Medical University-Shuang Ho Hospital (Taiwan); Taipei Medical University (Taiwan); Peruvian University of Applied Sciences (Peru)
Journal: iScience, volume 29, issue 6, article 116043
Dates: received 30 October 2025; accepted 5 May 2026; published online 20 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.isci.2026.116043 · PMID 42211119 · PMCID PMC13214550 · OpenAlex W7161823926
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), clinical / translational (subfield)
Methods: Statistics, Preprocessing, Evoked potentials, Physiology & signal measures
Keywords: cognitive neuroscience, sensory neuroscience
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Science and Technology Council (113-2628-H-038-001, 113-2410-H-038-028, 114-2410-H-038-048-MY2)
Citations: not cited yet (Europe PMC); 117 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

OSF 9fkm7

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (18), R (7)
Size: 145 files, 25 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: easystats (7 files), ggplot2 (7 files), lme4 (7 files), rstatix (7 files), tidyverse (7 files), Statistics and Machine Learning Toolbox (5 files), EEGLAB (1 file), Image Processing Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
24 files
At the source: osf.io/9fkm7

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;
  • 24 scripts, each with its path and the digest of its content;
  • 5 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.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to the authors' code: OSF 9fkm7
  • it says that the data are available on request
  • it says that the code is available on request

Read it in the paper: doi.org/10.1016/j.isci.2026.116043.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 2 keywords, 1 funder, 108 references.

Cite

This paper

Chang, Y.-H., Kuo, C.-C., Barquero, C., & Wang, C.-A. (2026). Trial-by-trial covariation of pupil dilation, microsaccades, and visual evoked potentials during saliency processing. iScience, 29(6), 116043. https://doi.org/10.1016/j.isci.2026.116043

BibTeX

@article{chang2026trial,
author = {Chang, Yi-Hsuan and Kuo, Chen-Chung and Barquero, Cesar and Wang, Chin-An},
title = {{Trial-by-trial covariation of pupil dilation, microsaccades, and visual evoked potentials during saliency processing}},
journal = {iScience},
year = {2026},
month = may,
volume = {29},
number = {6},
pages = {116043},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116043},
url = {https://doi.org/10.1016/j.isci.2026.116043},
pmid = {42211119},
pmcid = {PMC13214550}
}

RIS

TY - JOUR
AU - Chang, Yi-Hsuan
AU - Kuo, Chen-Chung
AU - Barquero, Cesar
AU - Wang, Chin-An
TI - Trial-by-trial covariation of pupil dilation, microsaccades, and visual evoked potentials during saliency processing
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/05/20
VL - 29
IS - 6
SP - 116043
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116043
UR - https://doi.org/10.1016/j.isci.2026.116043
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.isci.2026.116043",
"type": "article-journal",
"title": "Trial-by-trial covariation of pupil dilation, microsaccades, and visual evoked potentials during saliency processing",
"container-title": "iScience",
"author": [
{
"family": "Chang",
"given": "Yi-Hsuan"
},
{
"family": "Kuo",
"given": "Chen-Chung"
},
{
"family": "Barquero",
"given": "Cesar"
},
{
"family": "Wang",
"given": "Chin-An"
}
],
"container-title-short": "iScience",
"volume": "29",
"issue": "6",
"page": "116043",
"DOI": "10.1016/j.isci.2026.116043",
"PMID": "42211119",
"PMCID": "PMC13214550",
"ISSN": "2589-0042",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.isci.2026.116043",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
20
]
]
}
}

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