OSCR

Learned statistical regularity modulates anticipatory micro-saccades toward suppressed distractor locations.

Code ↔ Paper

8 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 8 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Micro-saccade detection ↔ Codes/Analysis Codes/Micro-saccade detection/microsacc.R, the whole file · a weak match · score 0.79 · velocity threshold, minimum duration, peak velocity, Engbert, Micro saccades, amplitude
  2. [2] § Methods › Classification analysis ↔ Codes/Analysis Codes/Fig.2g.m, lines 157–236 · score 0.66 · Support Vector, SVM ECOC, fitcecoc, accuracies, training, model
  3. [3] § Methods › Time-frequency analysis ↔ Codes/Analysis Codes/Fig.2b.m, lines 16–96 · score 0.66 · 8–14 Hz, 2500 ms, alpha, epoch, power, electrodes
  4. [4] § Methods › Micro-saccade-locked activity ↔ Codes/Analysis Codes/Fig2.d_e_f.m, the whole file · a weak match · score 0.56 · 500–1000 ms, pre, activity, 500 ms, EEG, electrodes
  5. [5] § Methods › Micro-saccade detection ↔ Codes/Analysis Codes/Micro-saccade detection/binsacc.R, the whole file · a weak match · score 0.54 · peak velocity, Engbert, Micro saccades, amplitude, vertical, Horizontal
  6. [6] § Results › Micro-saccade-locked activity ↔ Codes/Analysis Codes/Fig2.d_e_f.m, the whole file · a weak match · score 0.53 · 500–1000 ms, topography, activity, window, 500 ms
  7. [7] § Methods › Inverted encoding model ↔ Codes/Analysis Codes/Fig.2b.m, lines 198–266 · score 0.52 · weight matrix, channel response, model
  8. [8] § Methods › Inverted encoding model ↔ Codes/Analysis Codes/Fig.2b.m, lines 16–96 · score 0.51 · circularly shifted, band, alpha, channel, power, electrode

Paper

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

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The authors' code

MATLAB · 267 lines · 11 KB · no license · 3 matches

  1. %dapated from Foster et al., 2016
  2. clear, close all, warning('off','all'),clc,clear all
  3. readdir = 'path to preprocessed EEG data';
  4. cd(readdir)
  5. sublist=dir(readdir);
  6. sublist={sublist.name};
  7. sublist = sublist(3:end);
  8. for subno =1:length(sublist)
  10. clear EEG
  11. dname = sublist{subno};
  13. fprintf('Loading subject %s for analysis ...\n',dname);
  14. load([readdir filesep dname]);
  16. %% Select chans of interes
  17. chan={'O1','O2','Oz','PO3','PO4','PO7','PO8','POz','P1','P2','P3','P4','P5','P6','P7','P8','Pz'};
  19. chan2plot=[];
  20. for ch=1:length(chan)
  21. chan2plot(ch) = find(strcmpi({EEG.chanlocs.labels},chan(ch)));
  22. end
  23. EEG = pop_select(EEG,'channel',chan);
  24. fprintf('Loading subject %s for analysis ...\n',dname);
  26. % parameters to set
  27. em.nChans = 6; % # of channels
  28. em.nBins = em.nChans; % # of stimulus bins
  29. em.nIter = 10; % # of iterations
  30. em.nBlocks = 3; % # of blocks for cross-validation
  31. em.frequencies = [8 14]; % frequency bands to analyze
  32. em.bands = {'Alpha'};
  33. em.time = -2500:2:1000; % time points of interest
  34. em.window = 2;
  35. em.Fs = EEG.srate;
  36. em.nElectrodes = length(chan);
  37. em.sampRate = 2; % downsampled sample rate (in ms)
  38. em.time = -2500:2:1000; % specificy time window of interest
  39. em.dtime = -2500:em.sampRate:1000; % downsampled time points
  40. em.stepSize = em.sampRate/(1000/em.Fs); % number of samples the classifer jumps with each shift
  41. em.nSamps = length(em.time); % # of samples
  42. em.dSamps = length(em.dtime); % # of samples at downsampled rate
  43. cnt = 0;
  44. clear epoch_markers distance_markers
  45. posBin=[];
  46. epoch_markers = [];
  47. for i = 1:EEG.trials
  48. marker = cell2mat(EEG.epoch(i).eventtype);
  49. lantancy = cell2mat(EEG.epoch(i).eventlatency);
  50. if length(EEG.epoch(i).eventlatency)>3
  51. RT(i) = lantancy(4);
  52. else
  53. RT(i) = 2001;
  54. end
  55. lantanct_index = find(lantancy==0);
  56. epoch_marker = marker(lantanct_index);
  57. response_marker = marker(lantanct_index+1);
  58. epoch_markers(i) = epoch_marker;
  59. posBin(i) = epoch_marker;
  60. end
  61. EEG.epoch_markers = epoch_markers;
  63. eegs = EEG.data;
  64. clear RT
  66. nChans = em.nChans;
  67. nBins = em.nBins;
  68. nIter = em.nIter;
  69. nBlocks = em.nBlocks;
  70. freqs = em.frequencies;
  71. times = em.time;
  72. nFreqs = size(em.frequencies,1); %%
  73. nElectrodes = em.nElectrodes;
  74. disp(nElectrodes);
  75. disp(chan);
  76. nSamps = length(em.time);
  77. Fs = em.Fs;
  78. dSamps = em.dSamps;
  79. sampRate = em.sampRate;
  80. stepSize = em.stepSize;
  81. window = em.window;
  82. % Specify basis set
  83. em.sinPower = 7;%sin power at 25th in Sutterer&Foster_2019_Plos Biology
  84. em.x = linspace(0, 2*pi-2*pi/nBins, nBins);
  85. em.cCenters = linspace(0, 2*pi-2*pi/nChans, nChans);
  86. em.cCenters = rad2deg(em.cCenters);
  87. pred = sin(0.5*em.x).^em.sinPower; % hypothetical channel responses
  88. pred = wshift('1D',pred,3); % shift the initial basis function
  89. basisSet = nan(nChans,nBins);
  90. for c = 1:nChans
  91. basisSet(c,:) = wshift('1D',pred,-c+1); % generate circularly shifted basis functions
  92. end
  93. em.basisSet = basisSet;
  94. eegs = EEG.data;%(:,:,EEG.index);
  95. posBin = posBin';
  96. em.posBin = posBin;
  99. % Grab data------------------------------------------------------------
  101. % Shift So that highprob loctions are catogorized into bin1
  102. em.nTrials = length(posBin); nTrials = em.nTrials; % # of good trials
  103. nTimes = length(times);
  105. %----------------------------------------------------------------------
  107. % Preallocate Matrices
  108. tf_evoked = nan(nFreqs,nIter,nSamps,nBlocks,nChans); tf_total = tf_evoked;
  109. C2_evoked = nan(nFreqs,nIter,nSamps,nBlocks,nBins,nChans); C2_total = C2_evoked;
  110. em.blocks = nan(nTrials,nIter);
  113. eegs = permute(eegs,[3 1 2]);%
  114. nPoint = length(eegs);
  115. epoch_index = dsearchn(EEG.times',times');
  116. % Loop through each frequency
  117. for f = 1:nFreqs
  118. tic % start timing frequency loop
  119. fprintf('Frequency %d out of %d\n', f, nFreqs)
  121. % Filter Data
  122. fdata_evoked = nan(nTrials,nElectrodes,nPoint);
  123. fdata_total = nan(nTrials,nElectrodes,nPoint);
  124. tic
  125. parfor c = 1:nElectrodes
  126. %fdata_total(:,c,:) = (eegfilt(squeeze((eegs(:,c,:))),Fs,freqs(f,1),freqs(f,2))')';
  127. disp(c)
  128. fdata_evoked(:,c,:) = hilbert(eegfilt(squeeze((eegs(:,c,:))),Fs,freqs(f,1),freqs(f,2))')';
  129. fdata_total(:,c,:) = abs(hilbert(eegfilt(squeeze((eegs(:,c,:))),Fs,freqs(f,1),freqs(f,2))')').^2;
  130. % fdata_total(:,c,:) = eegfilt(squeeze(eegs(:,c,:)),Fs,freqs(1,1),freqs(1,2));% instantaneous power calculated here for induced activity.
  131. end
  132. toc
  134. fdata_total = fdata_total(:,:,epoch_index);
  135. fdata_evoked = fdata_evoked(:,:,epoch_index);
  139. for iter = 1:nIter
  142. blocks = nan(size(posBin));
  143. shuffBlocks = nan(size(posBin));
  145. % count number of trials within each position bin
  146. clear binCnt %bincnt = bin count
  147. for bin = 1:nBins
  148. binCnt(bin) = sum(posBin == bin);%%
  149. end
  151. minCnt = min(binCnt); %
  152. nPerBin = floor(minCnt/nBlocks);
  153. % max # of trials such that the # of trials for each bin can be equated within each block
  157. shuffInd = randperm(nTrials)'; % create shuffle index
  158. shuffBin = posBin(shuffInd); % shuffle trial order
  161. for bin = 1:nBins
  162. idx = find(shuffBin == bin); % get index for trials belonging to the current bin%
  163. idx = idx(1:nPerBin*nBlocks); % drop excess trials
  164. x = repmat(1:nBlocks',nPerBin,1); shuffBlocks(idx) = x; % assign randomly order trials to blocks
  165. end
  167. % unshuffle block assignment ÕâÀïΪʲôҪunshuffle?
  168. blocks(shuffInd) = shuffBlocks;
  170. % save block assignment
  171. em.blocks(:,iter) = blocks; % block assignment
  172. em.nTrialsPerBlock = length(blocks(blocks == 1)); % ÌáÈ¡³öÿ¸öblockÖÐÓжàÉÙ¸ötrial
  174. %-------------------------------------------------------------------------
  176. % Average data for each position bin across blocks
  177. posBins = 1:nBins;
  178. blockDat_evoked = nan(nBins*nBlocks,nElectrodes,nSamps); % averaged evoked data
  179. blockDat_total = nan(nBins*nBlocks,nElectrodes,nSamps); % averaged total data
  180. labels = nan(nBins*nBlocks,1); % bin labels for averaged data
  181. blockNum = nan(nBins*nBlocks,1); % block numbers for averaged data
  182. c = nan(nBins*nBlocks,nChans); % predicted channel responses for averaged data
  183. bCnt = 1;
  184. for ii = 1:nBins
  185. for iii = 1:nBlocks
  186. blockDat_evoked(bCnt,:,:) = abs(squeeze(mean(fdata_evoked(posBin==posBins(ii) & blocks==iii,:,:),1))).^2;
  187. blockDat_total(bCnt,:,:) = squeeze(mean(fdata_total(posBin==posBins(ii) & blocks==iii,:,:),1));
  188. % blockDat_total(bCnt,:,:); = squeeze(mean(fdata_total;(posBin==posBins(1) & blocks==1,:,:),1));
  189. labels(bCnt) = ii;
  190. blockNum(bCnt) = iii;
  191. c(bCnt,:) = basisSet(ii,:);
  192. bCnt = bCnt+1;
  193. end
  194. end
  196. parfor t = 1:nSamps
  198. % grab data for timepoint t
  199. toi = ismember(times,times(t)-em.window/2:times(t)+em.window/2); % time window of interest
  200. de = squeeze(mean(blockDat_evoked(:,:,toi),3)); % evoked data
  201. dt = squeeze(mean(blockDat_total(:,:,toi),3)); % total data
  203. % Do forward model
  205. for i=1:nBlocks % loop through blocks, holding each out as the test set
  207. trnl = labels(blockNum~=i); % training labels
  208. tstl = labels(blockNum==i); % test labels
  210. %-----------------------------------------------------%
  211. % Analysis on Evoked Power %
  212. %-----------------------------------------------------%
  213. B1 = de(blockNum~=i,:); % training data
  214. B2 = de(blockNum==i,:); % test data
  215. C1 = c(blockNum~=i,:); % predicted channel outputs for training data
  216. W = C1\B1;% estimate weight matrix
  217. C2 = (W'\B2')'; % estimate channel responses
  219. C2_evoked(f,iter,t,i,:,:) = C2; % save the unshifted channel responses
  221. % shift eegs to common center
  222. n2shift = ceil(size(C2,2)/2);
  223. for ii=1:size(C2,1)
  224. [~, shiftInd] = min(abs(posBins-tstl(ii)));
  225. C2(ii,:) = wshift('1D', C2(ii,:), shiftInd-n2shift-1);
  226. end
  228. tf_evoked(f,iter,t,i,:,:) = mean(C2,1); % average shifted channel responses
  230. %-----------------------------------------------------%
  231. % Analysis on Total Power %
  232. %-----------------------------------------------------%
  233. B1 = dt(blockNum~=i,:); % training data
  234. B2 = dt(blockNum==i,:); % test data
  235. C1 = c(blockNum~=i,:); % predicted channel outputs for training data
  236. W = C1\B1; % estimate weight matrix
  237. C2 = (W'\B2')'; % estimate channel responses
  239. C2_total(f,iter,t,i,:,:) = C2;
  241. % shift eegs to common center
  242. n2shift = ceil(size(C2,2)/2);
  243. for ii=1:size(C2,1)
  244. [~, shiftInd] = min(abs(posBins-tstl(ii)));
  245. C2(ii,:) = wshift('1D', C2(ii,:), shiftInd-n2shift-1);
  246. end
  248. tf_total(f,iter,t,i,:) = mean(C2,1);
  249. C2_total_shifted(f,iter,t,i,:,:) = C2;
  250. % average shifted channel responses
  251. %-----------------------------------------------------%
  253. end
  254. end
  255. end
  256. toc % stop timing the frequency loop
  257. end
  259. %tf_total_mean = squeeze(mean(mean(tf_total,2),4));
  260. fName = ['save path\' filesep sublist{subno}(1:end)];
  261. em.C2_total_shifted = C2_total_shifted;
  262. em.tfs.evoked = tf_evoked;
  263. em.tfs.total = tf_total;
  264. em.nBlocks = nBlocks;
  265. save(fName,'em','-v7.3');
  266. end

Fig.2b.m, no license · at the source

Overview

Authors: Sirui Chen1,2, Xin Zhang3, Xinyu Li3, Ole Jensen4, Jan Theeuwes5, Benchi Wang1,2
ORCID iDs: Xinyu Li, Ole Jensen, Jan Theeuwes, Benchi Wang
  1. Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangdong-Hong Kong Joint Laboratory for Psychiatric Disorders, Guangdong Province Key Laboratory of Psychiatric Disorders, Guangdong Basic Research Center of Excellence for Integrated Traditional and Western Medicine for Qingzhi Diseases, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University,Guangzhou, China
  2. Institute for Brain Research and Rehabilitation, South China Normal University,Guangzhou, China
  3. Department of Psychology, Zhejiang Normal University,Jinhua, China
  4. Department of Psychiatry and Experimental Psychology, University of Oxford,Oxford, United Kingdom
  5. Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam,Amsterdam, the Netherlands
Institutions: Southern Medical University (China); South China Normal University (China); Zhejiang Normal University (China); University of Oxford (United Kingdom); Vrije Universiteit Amsterdam (Netherlands)
Journal: Nature communications, volume 17, issue 1, article 7091
Dates: received 2 February 2025; accepted 22 May 2026; published online 2 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-73916-1 · PMID 42230612 · PMCID PMC13392092 · OpenAlex W7163176395
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Physiology & signal measures, Connectivity
Keywords: Human behaviour, Saccades, Attention
MeSH: Attention*, Learning*, Saccades*, Adult, Electroencephalography, Female, Humans, Male, Photic Stimulation, Reaction Time, Young Adult (* major topic)
Topic: Generative Adversarial Networks and Image Synthesis (Computer Vision and Pattern Recognition, Computer Science), according to OpenAlex
Funding: Wellcome Trust (227420)
Citations: not cited yet (Europe PMC); 65 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 8 matches between paragraphs and lines of code.

OSF dfrpq

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (7), R (5), Python (1)
Size: 30 files, 13 scripts
Software Heritage: not checked
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (3 files), Statistics and Machine Learning Toolbox (2 files), Image Processing Toolbox (1 file), Signal Processing Toolbox (1 file), shadedErrorBar (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
14 files
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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 3 keywords, 11 MeSH terms, 1 funder, 65 references.

Cite

This paper

Chen, S., Zhang, X., Li, X., Jensen, O., Theeuwes, J., & Wang, B. (2026). Learned statistical regularity modulates anticipatory micro-saccades toward suppressed distractor locations. Nature communications, 17(1), 7091. https://doi.org/10.1038/s41467-026-73916-1

BibTeX

@article{chen2026learned,
author = {Chen, Sirui and Zhang, Xin and Li, Xinyu and Jensen, Ole and Theeuwes, Jan and Wang, Benchi},
title = {{Learned statistical regularity modulates anticipatory micro-saccades toward suppressed distractor locations}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7091},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73916-1},
url = {https://doi.org/10.1038/s41467-026-73916-1},
pmid = {42230612},
pmcid = {PMC13392092}
}

RIS

TY - JOUR
AU - Chen, Sirui
AU - Zhang, Xin
AU - Li, Xinyu
AU - Jensen, Ole
AU - Theeuwes, Jan
AU - Wang, Benchi
TI - Learned statistical regularity modulates anticipatory micro-saccades toward suppressed distractor locations
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/02
VL - 17
IS - 1
SP - 7091
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73916-1
UR - https://doi.org/10.1038/s41467-026-73916-1
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-73916-1",
"type": "article-journal",
"title": "Learned statistical regularity modulates anticipatory micro-saccades toward suppressed distractor locations",
"container-title": "Nature communications",
"author": [
{
"family": "Chen",
"given": "Sirui"
},
{
"family": "Zhang",
"given": "Xin"
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{
"family": "Li",
"given": "Xinyu"
},
{
"family": "Jensen",
"given": "Ole"
},
{
"family": "Theeuwes",
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}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "7091",
"DOI": "10.1038/s41467-026-73916-1",
"PMID": "42230612",
"PMCID": "PMC13392092",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-73916-1",
"language": "en",
"issued": {
"date-parts": [
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2026,
6,
2
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]
}
}

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