OSCR

Alpha-band phase modulates perceptual sensitivity by changing internal noise and sensory tuning.

Code ↔ Paper

1 match 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 1 match
  1. [1] § Methods › Phase–behavior coupling analysis ↔ PIP_masterscript.m, lines 20–139 · score 0.54 · cluster correction, permutation threshold, iteratively, mapping, phase

Paper

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

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 408 lines · 14 KB · no license · 1 match

  2. clear all; close all
  4. % script requires functions from:
  5. %CircStats and the "helper functions" folder
  7. load('peakalphafreq.mat');
  8. load('hilbert_output.mat');
  9. load('analysis_perms.mat');
  11. %define list of subjects
  12. subs = {'01' ,'02','03', '04', '05','06'};
  14. %analysis window
  15. awin = dsearchn(times',[-700 0]');
  16. tp = dsearchn(times',[-450,0]');
  17. sig_ind = tp(1):tp(2);
  20. %% cluster correction
  22. %take out the needed real and permutated data for each participant
  23. for s=1:size(dp_veclength_perm,3)
  24. %dp
  25. best_dp(:,s) = movmean(squeeze(mean(dp_veclength(best_alphachans(:,s),awin(1):awin(2),s),1)),8);
  26. dp_perms(:,s,:) = movmean(mean(dp_veclength_perm(best_alphachans(:,s),awin(1):awin(2),s,:),1),8);
  27. %crit
  28. best_crit(:,s)= movmean(squeeze(mean(crit_veclength(best_alphachans(:,s),awin(1):awin(2),s),1)),8);
  29. crit_perms(:,s,:) = movmean(mean(crit_veclength_perm(best_alphachans(:,s),awin(1):awin(2),s,:),1),8);
  30. end
  31. dp_group_sigthresh = prctile(squeeze(mean(dp_perms,2)),95,2);
  32. crit_group_sigthresh = prctile(squeeze(mean(crit_perms,2)),95,2);
  34. %dp
  35. for i = 1:size(dp_veclength_perm,4)
  36. iperm = mean(dp_perms(:,:,i),2); %this iteration's perm
  37. perm_thresh = prctile(squeeze(mean(dp_perms(:,:,1:end~=i),2)),95,2);
  38. cc_sig_ind = logical(iperm > perm_thresh); %locations where the perm cross sig
  39. if sum(cc_sig_ind) == 0
  40. clust=0;
  41. else
  42. [clust, ~] = clustsize(cc_sig_ind);
  43. end
  44. perm_clust(i) = max(clust);
  45. end
  46. clust_thresh = prctile(perm_clust,95);
  47. perm_thresh = dp_group_sigthresh;
  48. cc_sig_ind = logical(mean(best_dp,2) > perm_thresh); %real data
  49. if sum(cc_sig_ind) == 0
  50. clust=0; sig_map = zeros(1,length(times(awin(1):awin(2))));
  51. else
  52. [clust, sig_map] = clustsize(cc_sig_ind);
  53. end
  54. sig_clust = find(clust > clust_thresh); %find all the cluster numbers (that will be in sig_map) that surpass the clust_thresh
  55. groupdp_corr_sigind = ismember(sig_map, sig_clust);
  57. %crit
  58. for i = 1:size(dp_veclength_perm,4)
  59. iperm = mean(crit_perms(:,:,i),2);
  60. perm_thresh = prctile(squeeze(mean(crit_perms(:,:,1:end~=i),2)),95,2);
  61. cc_sig_ind = logical(iperm > perm_thresh);
  62. if sum(cc_sig_ind) == 0
  63. clust=0;
  64. else
  65. [clust, ~] = clustsize(cc_sig_ind);
  66. end
  67. perm_clust(i) = max(clust);
  68. end
  69. clust_thresh = prctile(perm_clust,95);
  70. perm_thresh = crit_group_sigthresh;
  71. cc_sig_ind = logical(mean(best_crit,2) > perm_thresh);
  72. if sum(cc_sig_ind) == 0
  73. clust=0; sig_map = zeros(1,length(times(awin(1):awin(2))));
  74. else
  75. [clust, sig_map] = clustsize(cc_sig_ind);
  76. end
  77. sig_clust = find(clust > clust_thresh);
  78. groupcrit_corr_sigind(:) = ismember(sig_map, sig_clust);
  81. %shift vars
  82. for s = 1:size(hrphase,4)
  83. for ch = 1:3
  84. for t= 1:length(sig_ind)
  85. [~, ind] = max(squeeze(dpphase(best_alphachans(ch,s),sig_ind(t),:,s)));
  86. shift_amount = length(binc) - ind;
  87. shifted_dp(ch,t,:,s) = circshift(squeeze(dpphase(best_alphachans(ch,s),sig_ind(t),:,s)), shift_amount);
  88. shifted_crit(ch,t,:,s) = circshift(squeeze(critphase(best_alphachans(ch,s),sig_ind(t),:,s)), shift_amount);
  89. shifted_hr(ch,t,:,s) = circshift(squeeze(hrphase(best_alphachans(ch,s),sig_ind(t),:,s)), shift_amount);
  90. shifted_far(ch,t,:,s) = circshift(squeeze(farphase(best_alphachans(ch,s),sig_ind(t),:,s)), shift_amount);
  91. shifted_conf(ch,t,:,s) = circshift(squeeze(confphase(best_alphachans(ch,s),sig_ind(t),:,s)), shift_amount);
  92. shifted_imag(ch,t,:,s) = circshift(squeeze(imagphase(best_alphachans(ch,s),sig_ind(t),:,s)), shift_amount);
  93. end
  94. end
  95. end
  97. %response consistency
  98. binw = 2*pi/2;
  99. for s = 1:length(subs)
  101. load(['fulldat_' subs{s}])
  103. for ch = 1:size(best_alphachans,1)
  104. for t = 1:length(sig_ind)
  105. best_binc = dp_vecangle(best_alphachans(ch,s),sig_ind(t),s);
  106. absdist = squeeze(abs(circ_dist(dat.phase_angles(best_alphachans(ch,s),sig_ind(t),:), best_binc)));
  107. bestbinind = absdist>=0 & absdist<=binw/2; %1 = good, 0 = bad
  109. [bb_con, notbb_con] = deal([]);
  110. [bb_c, notbb_c] = deal(1);
  111. for i = 1:size(dat.seedpairs,2)
  112. %if the phase of the pair is the same
  113. if bestbinind(dat.seedpairs(1,i)) == bestbinind(dat.seedpairs(2,i))
  114. %check to see whether it's best
  115. if bestbinind(dat.seedpairs(1,i)) == 1
  116. bb_con(bb_c) = dat.resp(dat.seedpairs(1,i)) == dat.resp(dat.seedpairs(2,i));
  117. bb_c = bb_c+1;
  118. else
  119. notbb_con(notbb_c) = dat.resp(dat.seedpairs(1,i)) == dat.resp(dat.seedpairs(2,i));
  120. notbb_c = notbb_c+1;
  121. end
  122. else
  123. notbb_con(notbb_c)= dat.resp(dat.seedpairs(1,i)) == dat.resp(dat.seedpairs(2,i));
  124. notbb_c = notbb_c+1;
  125. end
  126. end
  127. bb_consist(ch,t,s) = mean(bb_con);
  128. notbb_consist(ch,t,s) = mean(notbb_con);
  129. end
  130. end
  131. end
  133. front_angle = squeeze(dp_vecangle(34,sig_ind,:)); %AFz
  134. occip_angle = squeeze(dp_vecangle(16,sig_ind,:)); %Oz
  135. for t = 1:size(front_angle,1)
  136. fo_diff(t,:)= circ_dist(front_angle(t,:), occip_angle(t,:)); %front-occip diff
  137. end
  139. angle_diff = circ_mean(fo_diff);
  142. %% RevCorr/CIs
  143. %https://github.com/antoniofs23/reverse-correlation-demo/blob/main/RC_DEMO_AF.ipynb
  145. %num of bootstraps
  146. nboot = 10000;
  148. %mirror across ori?
  149. mirr = 1;
  151. %load, trim, and rescale
  152. load CI_betas.mat
  153. goodphase = betavals.goodphase;
  154. badphase = betavals.badphase;
  156. %define x and y
  157. ori = linspace(-80, 80,19);
  158. sf = linspace(0.5, 4,15);
  160. %meshgrid for surface
  161. [xx yy] = meshgrid(ori, sf);
  163. %define 2d gauss
  164. gauss2 = @(par,xy) par(1)*exp(-((xy(:,1)-par(2)).^2/2/par(3)^2 + (xy(:,2)-par(4)).^2/2/par(5)^2)) + par(6);
  166. %starting point for search
  167. startparams = [100 0 20 2 1 0]; %amp, meanX, sdX, meanY, sdY, offset
  168. lb = [0 -20 2 0.5 0.1 -50];%lower bound for each param
  169. ub = [200 20 60 3 4 50];%upper bound for each param
  171. for b = 1:nboot
  172. %generate bootstramp sample indicies
  173. bsamp = randsample(6,6,'true');
  175. %get CIs from this bootstrap sample and average over sample
  176. gci = mean(goodphase(:,:,bsamp),3);
  177. bci = mean(badphase(:,:,bsamp),3);
  179. if mirr
  180. gci = (gci + fliplr(gci)) ./2;
  181. bci = (bci + fliplr(bci)) ./2;
  182. end
  184. diffmap(:,:,b) = gci-bci;
  185. goodphaseb(:,:,b) = gci;
  186. badphaseb(:,:,b) = bci;
  188. %fit data
  189. fitparamsg(:,b) = lsqcurvefit(gauss2, startparams, [xx(:) yy(:)], gci(:), lb, ub);
  191. gauss2fix = @(par,xy) (par(1)* fitparamsg(1,b))* exp(-((xy(:,1)-(par(2)+fitparamsg(2,b))).^2/2/(par(3)*fitparamsg(3,b))^2 + (xy(:,2)-(par(4)*fitparamsg(4,b))).^2/2/(par(3)*fitparamsg(5,b))^2)) + (par(5)*fitparamsg(6,b));
  193. startparamsfix = [1 0 1 1 1]; %gain, x, sd, y, offset
  194. lbfix = [-10 -20 -10 -5 -10];%lower bound for each param
  195. ubfix = [10 20 10 5 10];%upper bound for each param
  197. fitparamsb(:,b) = lsqcurvefit(gauss2fix, startparamsfix, [xx(:) yy(:)], bci(:), lbfix, ubfix);
  199. %evaluate fit params at actual data points
  200. predg = gauss2(fitparamsg(:,b), [xx(:), yy(:)]);
  201. predb = gauss2fix(fitparamsb(:,b), [xx(:), yy(:)]);
  203. %compute r2
  204. rrg(b) = corr(predg, gci(:)).^2;
  205. rrb(b) = corr(predb, bci(:)).^2;
  207. %for plotting
  208. predmatg(:,:,b) = reshape(predg,size(gci));
  209. predmatb(:,:,b) = reshape(predb,size(bci));
  211. disp(b)
  212. end
  215. %% stats
  217. %frontal and occipital comparison
  218. [p, v] = circ_vtest(angle_diff,pi)
  219. [p, v] = circ_vtest(angle_diff,0)
  221. %response consitency
  222. [h, p, ci, stats] = ttest(squeeze(mean(mean(bb_consist,1),2)), squeeze(mean(mean(notbb_consist,1),2)))
  224. %angle of FAR and HR
  225. hr_far_diff = circ_dist(hr_vecangle, far_vecangle);
  226. for s = 1:length(subs)
  227. hr_far_anglediff(s) = circ_mean(circ_mean(hr_far_diff(best_alphachans(:,s),sig_ind,s),[],1),[],2);
  228. end
  229. [p, v] = circ_vtest(hr_far_anglediff,pi)
  230. [p, v] = circ_vtest(hr_far_anglediff,0)
  232. %bootstrapped CI parameters
  233. pvals = min([2*(sum(fitparamsb<1,2)./nboot) 2*(sum(fitparamsb>1,2)./nboot)],[],2);
  236. %% Supplementary confidence and imagination
  239. %take out the needed real and permutated data for each participant
  240. for s=1:size(dp_veclength_perm,3)
  241. %conf
  242. best_conf(:,s)= movmean(squeeze(mean(conf_veclength(best_alphachans(:,s),awin(1):awin(2),s),1)),8);
  243. conf_perms(:,s,:) = movmean(mean(conf_veclength_perm(best_alphachans(:,s),awin(1):awin(2),s,:),1),8);
  244. %imag
  245. best_imag(:,s)= movmean(squeeze(mean(imag_veclength(best_alphachans(:,s),awin(1):awin(2),s),1)),8);
  246. imag_perms(:,s,:) = movmean(mean(imag_veclength_perm(best_alphachans(:,s),awin(1):awin(2),s,:),1),8);
  247. end
  248. conf_group_sigthresh = prctile(squeeze(mean(conf_perms,2)),95,2);
  249. imag_group_sigthresh = prctile(squeeze(mean(imag_perms,2)),95,2);
  251. %conf
  252. for i = 1:size(dp_veclength_perm,4)
  253. iperm = mean(conf_perms(:,:,i),2);
  254. perm_thresh = prctile(squeeze(mean(conf_perms(:,:,1:end~=i),2)),95,2);
  255. cc_sig_ind = logical(iperm > perm_thresh);
  256. if sum(cc_sig_ind) == 0
  257. clust=0;
  258. else
  259. [clust, ~] = clustsize(cc_sig_ind);
  260. end
  261. perm_clust(i) = max(clust);
  262. end
  263. clust_thresh = prctile(perm_clust,95);
  264. perm_thresh = conf_group_sigthresh;
  265. cc_sig_ind = logical(mean(best_conf,2) > perm_thresh);
  266. if sum(cc_sig_ind) == 0
  267. clust=0; sig_map = zeros(1,length(times(awin(1):awin(2))));
  268. else
  269. [clust, sig_map] = clustsize(cc_sig_ind);
  270. end
  271. sig_clust = find(clust > clust_thresh);
  272. groupconf_corr_sigind(:) = ismember(sig_map, sig_clust);
  274. %imag
  275. for i = 1:size(dp_veclength_perm,4)
  276. iperm = mean(imag_perms(:,:,i),2);
  277. perm_thresh = prctile(squeeze(mean(imag_perms(:,:,1:end~=i),2)),95,2);
  278. cc_sig_ind = logical(iperm > perm_thresh);
  279. if sum(cc_sig_ind) == 0
  280. clust=0;
  281. else
  282. [clust, ~] = clustsize(cc_sig_ind);
  283. end
  284. perm_clust(i) = max(clust);
  285. end
  286. clust_thresh = prctile(perm_clust,95);
  287. perm_thresh = imag_group_sigthresh;
  288. cc_sig_ind = logical(mean(best_imag,2) > perm_thresh);
  289. if sum(cc_sig_ind) == 0
  290. clust=0; sig_map = zeros(1,length(times(awin(1):awin(2))));
  291. else
  292. [clust, sig_map] = clustsize(cc_sig_ind);
  293. end
  294. sig_clust = find(clust > clust_thresh);
  295. groupimag_corr_sigind(:) = ismember(sig_map, sig_clust);
  298. %% Supplementary power
  300. load('pow_vars.mat')
  302. %average across channels
  303. dp_line = squeeze(mean(pow_ondp,1));
  304. crit_line = squeeze(mean(pow_oncrit,1));
  305. conf_line = squeeze(mean(pow_onconf,1));
  306. imag_line = squeeze(mean(pow_onimag,1));
  307. perm_pow_ondp = squeeze(mean(perm_pow_ondp,1));
  308. perm_pow_oncrit = squeeze(mean(perm_pow_oncrit,1));
  309. perm_pow_onconf = squeeze(mean(perm_pow_onconf,1));
  310. perm_pow_onimag = squeeze(mean(perm_pow_onimag,1));
  312. %average across participants and get sig threshold
  313. dp_group_sigthresh = prctile(squeeze(mean(perm_pow_ondp,2)),2.5,2);
  314. crit_group_sigthresh = prctile(squeeze(mean(perm_pow_oncrit,2)),2.5,2);
  315. conf_group_sigthresh = prctile(squeeze(mean(perm_pow_onconf,2)),2.5,2);
  316. imag_group_sigthresh = prctile(squeeze(mean(perm_pow_onimag,2)),2.5,2);
  318. %dp
  319. for i = 1:size(perm_pow_ondp,3)
  320. iperm = mean(perm_pow_ondp(:,:,i),2);
  321. perm_thresh = prctile(squeeze(mean(perm_pow_ondp(:,:,1:end~=i),2)),2.5,2);
  322. cc_sig_ind = logical(iperm < perm_thresh);
  323. if sum(cc_sig_ind) == 0
  324. clust=0;
  325. else
  326. [clust, ~] = clustsize(cc_sig_ind);
  327. end
  328. perm_clust(i) = max(clust);
  329. end
  330. clust_thresh = prctile(perm_clust,95);
  331. perm_thresh = dp_group_sigthresh;
  332. cc_sig_ind = logical(mean(dp_line,2) < perm_thresh);
  333. if sum(cc_sig_ind) == 0
  334. clust=0; sig_map = zeros(size(times));
  335. else
  336. [clust, sig_map] = clustsize(cc_sig_ind);
  337. end
  338. sig_clust = find(clust > clust_thresh);
  339. groupdp_corr_sigind = ismember(sig_map, sig_clust);
  341. %crit
  342. for i = 1:size(perm_pow_oncrit,3)
  343. iperm = mean(perm_pow_oncrit(:,:,i),2);
  344. perm_thresh = prctile(squeeze(mean(perm_pow_oncrit(:,:,1:end~=i),2)),2.5,2);
  345. cc_sig_ind = logical(iperm < perm_thresh);
  346. if sum(cc_sig_ind) == 0
  347. clust=0;
  348. else
  349. [clust, ~] = clustsize(cc_sig_ind);
  350. end
  351. perm_clust(i) = max(clust);
  352. end
  353. clust_thresh = prctile(perm_clust,95);
  354. perm_thresh = crit_group_sigthresh;
  355. cc_sig_ind = logical(mean(crit_line,2) < perm_thresh);
  356. if sum(cc_sig_ind) == 0
  357. clust=0; sig_map = zeros(size(times));
  358. else
  359. [clust, sig_map] = clustsize(cc_sig_ind);
  360. end
  361. sig_clust = find(clust > clust_thresh);
  362. groupcrit_corr_sigind = ismember(sig_map, sig_clust);
  364. %conf
  365. for i = 1:size(perm_pow_onconf,3)
  366. iperm = mean(perm_pow_onconf(:,:,i),2);
  367. perm_thresh = prctile(squeeze(mean(perm_pow_onconf(:,:,1:end~=i),2)),2.5,2);
  368. cc_sig_ind = logical(iperm < perm_thresh);
  369. if sum(cc_sig_ind) == 0
  370. clust=0;
  371. else
  372. [clust, ~] = clustsize(cc_sig_ind);
  373. end
  374. perm_clust(i) = max(clust);
  375. end
  376. clust_thresh = prctile(perm_clust,95);
  377. perm_thresh = conf_group_sigthresh;
  378. cc_sig_ind = logical(mean(conf_line,2) < perm_thresh);
  379. if sum(cc_sig_ind) == 0
  380. clust=0; sig_map = zeros(size(times));
  381. else
  382. [clust, sig_map] = clustsize(cc_sig_ind);
  383. end
  384. sig_clust = find(clust > clust_thresh);
  385. groupconf_corr_sigind = ismember(sig_map, sig_clust);
  387. %imag
  388. for i = 1:size(perm_pow_onimag,3)
  389. iperm = mean(perm_pow_onimag(:,:,i),2);
  390. perm_thresh = prctile(squeeze(mean(perm_pow_onimag(:,:,1:end~=i),2)),2.5,2);
  391. cc_sig_ind = logical(iperm < perm_thresh);
  392. if sum(cc_sig_ind) == 0
  393. clust=0;
  394. else
  395. [clust, ~] = clustsize(cc_sig_ind);
  396. end
  397. perm_clust(i) = max(clust);
  398. end
  399. clust_thresh = prctile(perm_clust,95);
  400. perm_thresh = imag_group_sigthresh;
  401. cc_sig_ind = logical(mean(imag_line,2) < perm_thresh);
  402. if sum(cc_sig_ind) == 0
  403. clust=0; sig_map = zeros(size(times));
  404. else
  405. [clust, sig_map] = clustsize(cc_sig_ind);
  406. end
  407. sig_clust = find(clust > clust_thresh);
  408. groupimag_corr_sigind = ismember(sig_map, sig_clust);

PIP_masterscript.m, no license · at the source

Overview

Authors: April Pilipenko1, Alexandra McGowan1, Jason Samaha1
ORCID iDs: April Pilipenko, Jason Samaha
  1. Department of Psychology, University of California, Santa Cruz, Santa Cruz, United States
Institutions: University of California, Santa Cruz (United States)
Journal: eLife, volume 15, article RP110000
Dates: published online 20 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.110000 · PMID 42008355 · PMCID PMC13095207 · OpenAlex W7128173374
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism)
Methods: Connectivity, Statistics, Spectral & time-frequency, Preprocessing, Physiology & signal measures
Keywords: Human
MeSH: Alpha Rhythm*, Visual Perception*, Electroencephalography, Humans, Photic Stimulation (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: cited by 1 paper (Europe PMC); 47 references in the paper

Abstract

Alpha-band neural oscillations (8–13 Hz) are theorized to phasically inhibit visual processing based, in part, on results showing that pre-stimulus alpha phase predicts detection (i.e., hit rates). However, recent failures to replicate and a lack of a mechanistic understanding regarding how alpha impacts detection have called this theory into question. We recorded EEG while six observers (6020 trials each) detected near-threshold Gabor targets embedded in noise. Using signal detection theory (SDT) and reverse correlation, we observed an effect of occipital and frontal pre-stimulus alpha phase on sensitivity (d'), not criterion. Hit and false alarm rates were counterphased, consistent with a reduction in internal noise during optimal alpha phases. Perceptual reports were also more consistent when two identical stimuli were presented during the optimal phase, suggesting a decrease in internal noise rather than signal amplification. Classification images revealed sharper spatial frequency and orientation tuning during the optimal alpha phase, implying that alpha phase shapes sensitivity by modulating sensory tuning towards relevant stimulus features.

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 1 match between paragraphs and lines of code.

antoniofs23/reverse-correlation-demo

License: GPL-3.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: dbb38c87f8a07100c514c25fe5959c1bff8f70e6, 18 December 2023
Languages: Jupyter (1)
Size: 3 files, 1 script
Software Heritage: archived
Found in: the text, “Reverse correlation”
Holds: README, license file, 1 notebook
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
3 files
At the source: github.com/antoniofs23/reverse-correlation-demo

OSF eup3s

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: MATLAB (3)
Size: 14 files, 3 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (2 files), Statistics and Machine Learning Toolbox (2 files), CircStat (1 file), Optimization Toolbox (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
3 files
At the source: osf.io/eup3s

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;
  • 4 scripts, each with its path and the digest of its content;
  • 1 match 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.

Data availability

All electrophysiological and behavioral datasets, as well as the code for analysis, are freely available in Alpha-Band Phase Modulates Perceptual Sensitivity by Changing Internal Noise and Sensory Tuning at https://osf.io/eup3s.

The following dataset was generated:

Pilipenko A, McGowan A, Samaha J. 2025. Alpha-Band Phase Modulates Perceptual Sensitivity by Changing Internal Noise and Sensory Tuning. Open Science Framework. eup3s

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 1 keyword, 5 MeSH terms, 43 references.

Cite

This paper

Pilipenko, A., McGowan, A., & Samaha, J. (2026). Alpha-band phase modulates perceptual sensitivity by changing internal noise and sensory tuning. eLife, 15, RP110000. https://doi.org/10.7554/elife.110000

BibTeX

@article{pilipenko2026alpha,
author = {Pilipenko, April and McGowan, Alexandra and Samaha, Jason},
title = {{Alpha-band phase modulates perceptual sensitivity by changing internal noise and sensory tuning}},
journal = {eLife},
year = {2026},
month = apr,
volume = {15},
pages = {RP110000},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.110000},
url = {https://doi.org/10.7554/elife.110000},
pmid = {42008355},
pmcid = {PMC13095207}
}

RIS

TY - JOUR
AU - Pilipenko, April
AU - McGowan, Alexandra
AU - Samaha, Jason
TI - Alpha-band phase modulates perceptual sensitivity by changing internal noise and sensory tuning
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/04/20
VL - 15
SP - RP110000
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.110000
UR - https://doi.org/10.7554/elife.110000
LA - en
ER -

CSL-JSON

{
"id": "10.7554/elife.110000",
"type": "article-journal",
"title": "Alpha-band phase modulates perceptual sensitivity by changing internal noise and sensory tuning",
"container-title": "eLife",
"author": [
{
"family": "Pilipenko",
"given": "April"
},
{
"family": "McGowan",
"given": "Alexandra"
},
{
"family": "Samaha",
"given": "Jason"
}
],
"container-title-short": "eLife",
"volume": "15",
"page": "RP110000",
"DOI": "10.7554/elife.110000",
"PMID": "42008355",
"PMCID": "PMC13095207",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.110000",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
20
]
]
}
}

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

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1162/imag.a.1263
Individual alpha frequency predicts the sensitivity of time perception.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: 4 references, author Jason Samaha
[2] doi:10.1093/cercor/bhag117 [code]
Spontaneous changes in the prestimulus alpha power predict both objective and subjective aspects of perception-evidence from 4 paradigms and 471 experimental sessions.
Journal: Cerebral cortex (New York, N.Y. : 1991)
In common: EEG, 6 references
[3] doi:10.1523/jneurosci.0154-26.2026 [code]
Faster but less precise: expectation enhances response speed while reducing sensory fidelity.
Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience
In common: CircStat, Optimization Toolbox, Image Processing Toolbox, 1 other tool, EEG, 1 reference
[4] doi:10.1523/eneuro.0346-25.2026 [code]
Effects of TMS on the Decoding and Electrophysiology of Priority in Working Memory.
Journal: eNeuro
In common: CircStat, Statistics and Machine Learning Toolbox, EEG, 3 references
[5] doi:10.1038/s41467-026-73106-z [code]
Respiratory pauses highlight sleep architecture in mice.
Journal: Nature communications
In common: CircStat, Optimization Toolbox, Image Processing Toolbox, 1 other tool, EEG
[6] doi:10.1186/s12915-026-02630-7 [code]
Phasic modulation of attentional rhythmic sampling according to task demands.
Journal: BMC biology
In common: CircStat, Statistics and Machine Learning Toolbox, EEG, 2 references
[7] doi:10.1371/journal.pbio.3003818 [code]
Human neuronal firing varies with the frequency of local field potential oscillations.
Journal: PLoS biology
In common: CircStat, Optimization Toolbox, Image Processing Toolbox, 1 other tool
[8] doi:10.1038/s41562-026-02533-1 [code]
Fluctuations in arousal reflect latent state transitions that facilitate behavioural optimization.
Journal: Nature human behaviour
In common: CircStat, Optimization Toolbox, Statistics and Machine Learning Toolbox, EEG, 1 reference
[9] doi:10.7554/elife.108408 [code]
Frequency and laminar profile of feature-specific visual activity revealed by interleaved EEG-fMRI.
Journal: eLife
In common: Image Processing Toolbox, Statistics and Machine Learning Toolbox, EEG, 2 references
[10] doi:10.1126/sciadv.aeh7220 [code]
Central complex representations of self-movement are sufficient to compute wind direction in flight.
Journal: Science advances
In common: CircStat, Optimization Toolbox, Image Processing Toolbox, 1 other tool

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.