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Dynamic competition between bottom-up saliency and top-down goals in early visual cortex.

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  1. [1] § Methods › Eye-tracking analysis ↔ mkj6s/Part6_GazeBias.m, lines 90–148 · score 0.54 · saccade trials, head, dva, block, gaze, eye

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The authors' code

MATLAB · 334 lines · 15 KB · no license · 1 match

  1. %% Dissociating External and Internal Attentional Selection | ANALYSIS
  2. % RIFT Analysis | PreCue | part 5 of 5
  3. %
  4. % This file
  5. % - imports and preprocesses gaze position data (blink_correction, saccade removal, baselining)
  6. %
  7. % Requirements
  8. % - list of participants (corresponding to directory names) at the start
  9. % - directory for eeg_matrix and saving in lines 24,
  10. %
  11. % Outputs (if saving is on, saved in saved/"participant")
  12. % -
  14. %% Settings
  16. close all; clear all; clc;
  17. addpath(genpath("C:\Users\wang0175\Desktop\Data analysis\Step3_Gazebias\eyetracking analysis")) % add data and helper functions
  19. % Collection info
  20. f_s = 500; % Hz
  21. trial_latency = [0, 2.5]; % sec, has to include 0 % 0 means placeholder onset
  23. % Screen info
  24. dist_from_screen = 72; screen_dim = [48 27.2]; % cm
  25. screen_pxs = [1920 1080];
  26. screen_dva = rad2deg(2*atan((0.5*screen_dim)/dist_from_screen));
  27. dva_per_px = screen_dva(1)/screen_pxs(1);
  28. dir='C:\Users\wang0175\Desktop\Data analysis\Step3_Gazebias\eyelinkdata\';
  29. participants = ["03","04","05","06","07","08","09","10","11","12","13","17","18","20","21","22","S1","S2","S3","S4","S5","S7","S8","S9"];
  31. %% Import eyetracking data
  32. disp("(1/7) Importing data from edf files");
  34. for p = 1:length(participants)
  35. disp("Participant "+char(participants(p))+" of "+num2str(length(participants)));
  37. % Import data
  38. filename =dir+participants(p)+'.edf';
  39. edf_data{p} = Edf2Mat(char(filename));
  40. end
  41. % save('original_eyetracking_data.mat', 'edf_data', '-v7.3');
  42. %% Convert into a big matrix
  43. disp("(2/7) Converting edf into a matrix");
  44. x_data_blinkcorrected = []; y_data_blinkcorrected = [];
  46. % Trial number info
  47. for p = 1:length(participants)
  48. triggers{p} = find(strncmpi(edf_data{1, p}.Events.Messages.info, "Trial", 5)); % ignore triggers during calibration
  49. num_trials(p) = length(triggers{p});
  51. for eye = 1:2
  52. x_data_blinkcorrected{1, p}(eye, :) = draft_blink_edge_correction(edf_data{1, p}.Samples.pupilSize(:, eye)', edf_data{1, p}.Samples.posX(:, eye)', 500);
  53. y_data_blinkcorrected{1, p}(eye, :) = draft_blink_edge_correction(edf_data{1, p}.Samples.pupilSize(:, eye)', edf_data{1, p}.Samples.posY(:, eye)', 500);
  54. end
  55. disp("Done blink correction for participant "+char(participants(p))+" of "+num2str(length(participants)));
  56. end
  58. % Are all trial numbers same?
  59. if nnz(diff(num_trials))
  60. disp("Warning: Unequal numbers of trials across participants");
  61. end
  62. %%
  63. x_pos_unmatched = zeros(num_trials(1), floor(sum(abs(trial_latency))*f_s), 2, length(participants)); % both eyes
  64. y_pos_unmatched = x_pos_unmatched; % trials x timepoints x eyes x participants
  66. % Extract position traces and store
  67. for p = 1:length(participants)
  68. for tr = 1:num_trials(1)
  69. trig = triggers{p}(tr);
  71. % Start time as per trigger
  72. [~, trig_time_idx(tr)] = min(abs(edf_data{1, p}.Samples.time - edf_data{1, p}.Events.Messages.time(1, trig)));
  74. % Start and end times
  75. start_t = trig_time_idx(tr) + floor(trial_latency(1)*f_s);
  76. end_t = start_t + floor(sum(abs(trial_latency))*f_s) - 1;
  78. % Extract position
  79. for eye = 1:2
  80. x_pos_unmatched(tr, :, eye, p) = x_data_blinkcorrected{p}(eye ,start_t:end_t, :);
  81. y_pos_unmatched(tr, :, eye, p) = y_data_blinkcorrected{p}(eye, start_t:end_t, :);
  82. end
  83. end
  84. disp("Extract position traces and store "+char(participants(p))+" of "+num2str(length(participants)));
  86. end
  88. save('x_pos_unmatched', 'x_pos_unmatched', '-v7.3');
  89. save('y_pos_unmatched', 'y_pos_unmatched', '-v7.3');
  90. %% Mark trials with saccades
  91. % Collection info
  92. clear all;clc;
  93. load('y_pos_unmatched.mat');
  94. load('x_pos_unmatched.mat');
  95. x_pos_unmatched(577:864, :, :, 16) = NaN;
  96. y_pos_unmatched(577:864, :, :, 16) = NaN;
  97. x_pos_unmatched(721:864, :, :, 17) = NaN;
  98. y_pos_unmatched(721:864, :, :, 17) = NaN;
  99. % Screen info
  100. dir='C:\Users\wang0175\Desktop\Data analysis\Step3_Gazebias\eyelinkdata\';
  101. participants = ["03","04","05","06","07","08","09","10","11","12","13","17","18","20","21","22","S1","S2","S3","S4","S5","S7","S8","S9"];
  102. disp("(3/7) Marking trials with saccades");
  103. f_s = 500; % Hz
  104. trial_latency = [0, 2.5]; % sec, has to include 0 % 0 means placeholder onset
  105. num_trials=size(x_pos_unmatched);
  106. % Screen info
  107. dist_from_screen = 72; screen_dim = [48 27.2]; % cm
  108. screen_pxs = [1920 1080];
  109. screen_dva = rad2deg(2*atan((0.5*screen_dim)/dist_from_screen));
  110. dva_per_px = screen_dva(1)/screen_pxs(1);
  111. % Parameter
  112. % s
  113. screen_pxs = [1920 1080];
  114. saccade_threshold = 3.5; %dva
  115. x_deviation = []; y_deviation = []; eccentricities_px = []; saccade_thresh_dur = 0.05;
  117. % Identify timepoints exceeding threshold
  118. for p = 1:size(x_pos_unmatched,4)
  120. for eye = 1:2
  121. x_deviation(:, :, eye, p) = x_pos_unmatched(:, [floor(1.2*f_s):floor(2.4*f_s)], eye, p) - mean(x_pos_unmatched(:, :, eye, p), "all", "omitnan");
  122. y_deviation(:, :, eye, p) = y_pos_unmatched(:, [floor(1.2*f_s):floor(2.4*f_s)], eye, p) - mean(y_pos_unmatched(:, :, eye, p), "all", "omitnan");
  123. eccentricities_px(:, :, eye, p) = sqrt(x_deviation(:, :, eye, p).^2 + y_deviation(:, :, eye, p).^2);
  124. end
  125. end
  126. points_beyond_threshold = eccentricities_px > (saccade_threshold/dva_per_px);
  129. % Average eyes
  130. eyes_averaged = mean(points_beyond_threshold, 3);
  131. saccade_trials = zeros(num_trials(1), length(participants));
  133. for p = 1:length(participants)
  134. for tr = 1:num_trials(1)
  135. saccade_trials(tr, p) = (nnz(eyes_averaged(tr, :, p)) > saccade_thresh_dur*f_s);
  136. end
  137. disp("--- Participant "+char(participants(p))+" made saccades in "+num2str(nnz(saccade_trials(:, p)/num_trials(1)))+" trials out of "+num_trials(1))
  138. end
  139. saccade_trials(577:864, 16)=1;
  140. saccade_trials(721:864, 17)=1;
  141. xxx=sum(saccade_trials);
  142. save('C:\Users\wang0175\Desktop\Data analysis\Step2 coherence without saccade\saccade_trials1.mat', 'saccade_trials');
  144. % 单独分析第五和第六和block 对于第16个被试
  145. % 若是第16个被试，将第577-864个试次设为 NaN (fifth and sixth move head)
  147. % xxx= squeeze(mean(eccentricities_px,3));
  148. % xxx=squeeze(mean(xxx,2))*dva_per_px;
  149. %%
  150. load('y_pos_unmatched.mat');
  151. load('x_pos_unmatched.mat');
  152. % % 设置参数
  153. bad_trials_16 = 577:864;
  154. time_window = floor(1.2*f_s):floor(2.4*f_s);
  155. %
  156. % % 初始化
  157. % x_deviation_bad = NaN(length(bad_trials_16), length(time_window), 2); % trial × time × eye
  158. % y_deviation_bad = NaN(length(bad_trials_16), length(time_window), 2);
  159. % eccentricities_bad_px = NaN(length(bad_trials_16), length(time_window), 2);
  160. %
  161. % 被试编号
  162. p = 16;
  164. for eye = 1:2
  165. % 取出原始位置
  166. x_data = squeeze(x_pos_unmatched(bad_trials_16, time_window, eye, p));
  167. y_data = squeeze(y_pos_unmatched(bad_trials_16, time_window, eye, p));
  169. % 计算去均值后的偏移量
  170. x_dev = x_data - mean(x_data, 'all', 'omitnan');
  171. y_dev = y_data - mean(y_data, 'all', 'omitnan');
  173. % 保存
  174. x_deviation_bad(:, :, eye) = x_dev;
  175. y_deviation_bad(:, :, eye) = y_dev;
  176. eccentricities_bad_px(:, :, eye) = sqrt(x_dev.^2 + y_dev.^2);
  177. end
  178. % 参数
  179. bad_trials_16 = 577:864;
  180. p = 16; % 第16个被试
  181. threshold_px = saccade_threshold / dva_per_px;
  183. % 只针对这部分试次计算是否超过阈值
  184. points_beyond_threshold = eccentricities_px(bad_trials_16, :, :, p) > threshold_px;
  186. % 对双眼取平均
  187. eyes_averaged = mean(points_beyond_threshold, 3); % size: [288, time]
  189. % 初始化结果
  190. saccade_trials_16 = zeros(length(bad_trials_16), 1);
  192. % 计算每个试次是否为扫视试次
  193. for i = 1:length(bad_trials_16)
  194. saccade_trials_16(i) = nnz(eyes_averaged(i, :)) > saccade_thresh_dur * f_s;
  195. end
  197. % 输出统计
  198. disp("Participant 16 made saccades in " + num2str(nnz(saccade_trials_16)) + ...
  199. " trials out of " + num2str(length(bad_trials_16)) + " (trials 577-864)")
  201. %
  202. % % %% Mark trials with NaNs/blinks
  203. % % load ('x_pos_unmatched.mat');
  204. % % load('y_pos_unmatched.mat');
  205. % % f_s = 500; % Hz
  206. % % trial_latency = [0, 2.5]; % sec, has to include 0 % 0 means placeholder onset
  207. % % x_pos_uncorrected=[];
  208. % % y_pos_uncorrected=[];
  209. % % % Screen info
  210. % % dist_from_screen = 72; screen_dim = [48 27.2]; % cm
  211. % screen_pxs = [1920 1080];
  212. % participants = ["01","03","04","05","06","07","08","09","10","11","12","13","14","15","17","18","20","21","22"];%02 single eye, others no eyelink data
  213. % for p = 1:size(x_pos_unmatched,4)
  214. % for tr = 1:size(x_pos_unmatched,1)
  215. % x_pos_uncorrected(tr, :, :, p) = x_pos_unmatched(tr, : ,:, p);
  216. % y_pos_uncorrected(tr, :, :, p) = y_pos_unmatched(tr, :, :, p);
  217. % end
  218. % end
  219. % disp("(3/7) Marking trials with NaNs");
  220. %
  221. % % Identify timepoints exceeding threshold
  222. % for p = 1:length(participants)
  223. % for tr = 1:size(x_pos_unmatched, 1)
  224. % nan_or_not = anynan(x_pos_uncorrected(tr, :, :, p));
  225. % nan_trials(tr, p) = nan_or_not;
  226. % end
  227. % disp("--- Participant "+num2str(p)+" had NaNs in "+num2str(nnz(nan_trials(:, p)/size(x_pos_unmatched, 1)))+" trials out of "+size(x_pos_unmatched, 1))
  228. % nan_trials_p = nan_trials(:, p);
  229. % end
  230. %
  231. %
  232. % %% NaN Corrections
  233. %
  234. % min_nan_length = 0.02; % sec, patches < this will be interpolated
  235. %
  236. % % Prepare variables
  237. % x_pos_unbaselined = x_pos_uncorrected; y_pos_unbaselined = y_pos_uncorrected;
  238. % disp("(5/7) Interpolating short periods of NaNs")
  239. %
  240. % % Interpolate patches that are small enough
  241. % fix_counter = 0;
  242. % for p = 1:length(participants)
  243. % counter_sh = 0; counter_lo = 0;
  244. % for tr = 1:size(x_pos_unmatched,1)
  245. % nans_logical = isnan(squeeze(x_pos_uncorrected(tr, :, :, 1)));
  246. %
  247. % for eye = 1:2
  248. % state_changes = diff([0 nans_logical(:, eye)' 0]); % when this is 1/-1, a NaN block has started/ended
  249. %
  250. % % Find NaN start/ends
  251. % idxs_NaN_starts{tr} = find(state_changes==1);
  252. % idxs_NaN_ends{tr} = find(state_changes==-1);
  253. %
  254. % % If NaNs are present,
  255. % if ~isempty(idxs_NaN_starts{tr})
  256. % % then for each NaN block,
  257. % for i = 1:length(idxs_NaN_starts{tr})
  258. % % which is within acceptable range,
  259. % block_length = idxs_NaN_ends{tr}(i)-idxs_NaN_starts{tr}(i);
  260. %
  261. % if block_length < min_nan_length*f_s
  262. % % interpolate the block.
  263. % fix_counter = fix_counter + 1;
  264. %
  265. % if idxs_NaN_starts{tr}(i) == 1
  266. % % if trial start is already a NaN, interpolate with block end point
  267. % x_pos_unbaselined(tr, idxs_NaN_starts{tr}(i):idxs_NaN_ends{tr}(i)-1, eye, p) = ...
  268. % linspace(x_pos_uncorrected(tr, idxs_NaN_ends{tr}(i), eye, p), x_pos_uncorrected(tr, idxs_NaN_ends{tr}(i), eye, p), block_length);
  269. % y_pos_unbaselined(tr, idxs_NaN_starts{tr}(i):idxs_NaN_ends{tr}(i)-1, eye, p) = ...
  270. % linspace(y_pos_uncorrected(tr, idxs_NaN_ends{tr}(i), eye, p), y_pos_uncorrected(tr, idxs_NaN_ends{tr}(i), eye, p), block_length);
  271. %
  272. % elseif idxs_NaN_ends{tr}(i) == length(state_changes)
  273. % % if trial end is a NaN, interpolate with block start point
  274. % x_pos_unbaselined(tr, idxs_NaN_starts{tr}(i):idxs_NaN_ends{tr}(i)-1, eye, p) = ...
  275. % linspace(x_pos_uncorrected(tr, idxs_NaN_starts{tr}(i)-1, eye, p), x_pos_uncorrected(tr, idxs_NaN_starts{tr}(i)-1, eye, p), block_length);
  276. % y_pos_unbaselined(tr, idxs_NaN_starts{tr}(i):idxs_NaN_ends{tr}(i)-1, eye, p) = ...
  277. % linspace(y_pos_uncorrected(tr, idxs_NaN_starts{tr}(i)-1, eye, p), y_pos_uncorrected(tr, idxs_NaN_starts{tr}(i)-1, eye, p), block_length);
  278. %
  279. % else
  280. % % if both start and end point are available
  281. % x_pos_unbaselined(tr, idxs_NaN_starts{tr}(i)-1:idxs_NaN_ends{tr}(i), eye, p) = ...
  282. % linspace(x_pos_uncorrected(tr, idxs_NaN_starts{tr}(i)-1, eye, p), x_pos_uncorrected(tr, idxs_NaN_ends{tr}(i), eye, p), block_length+2);
  283. % y_pos_unbaselined(tr, idxs_NaN_starts{tr}(i)-1:idxs_NaN_ends{tr}(i), eye, p) = ...
  284. % linspace(y_pos_uncorrected(tr, idxs_NaN_starts{tr}(i)-1, eye, p), y_pos_uncorrected(tr, idxs_NaN_ends{tr}(i), eye, p), block_length+2);
  285. %
  286. % end
  287. % end
  288. % end
  289. % end
  290. % end
  291. % end
  292. % end
  293. %
  294. % %%
  295. % %% Baseline Correction
  296. % disp("(6/7) Baseline correcting and converting to dva")
  297. %
  298. % % Baseline period
  299. % baseline_int = [0.2,1]; % sec, wrt. cue onset, + cue latency(1)
  300. % % Screen info
  301. % dist_from_screen = 72; screen_dim = [48 27.2]; % cm
  302. % screen_pxs = [1920 1080];
  303. % screen_dva = rad2deg(2*atan((0.5*screen_dim)/dist_from_screen));
  304. % dva_per_px = screen_dva(1)/screen_pxs(1);
  305. % % Prepare variables
  306. % x_pos_px = x_pos_unbaselined; y_pos_px = y_pos_unbaselined;
  307. %
  308. % x_pos_px = x_pos_unbaselined - repmat(mean(x_pos_unbaselined(:, floor(baseline_int(1)*f_s):floor(baseline_int(2)*f_s), :, :), 2, "omitnan"), 1, size(x_pos_unbaselined, 2), 1, 1);
  309. % y_pos_px = y_pos_unbaselined - repmat(mean(y_pos_unbaselined(:, floor(baseline_int(1)*f_s):floor(baseline_int(2)*f_s), :, :), 2, "omitnan"), 1, size(y_pos_unbaselined, 2), 1, 1);
  310. %
  311. % % DVA conversion
  312. % x_pos_dva = x_pos_px.*dva_per_px;
  313. % y_pos_dva = y_pos_px.*dva_per_px;
  314. %
  315. %
  316. % % Note trials with outlier baselines
  317. % baseline_outlier_trials = [];
  318. % for p = 1:length(participants)
  319. % baselines = mean(x_pos_unbaselined(:, floor(baseline_int(1)*f_s):floor(baseline_int(2)*f_s), :, :), 2, "omitnan");
  320. % baselines_avgd = squeeze(mean(baselines(:, :, :, p), 3, "omitnan"));
  321. % baselines_y = mean(y_pos_unbaselined(:, floor(baseline_int(1)*f_s):floor(baseline_int(2)*f_s), :, :), 2, "omitnan");
  322. % baselines_avgd_y = squeeze(mean(baselines_y(:, :, :, p), 3, "omitnan"));
  323. %
  324. % % Find stdev
  325. % threshold = 2*std(baselines_avgd, "omitnan"); avg_base = mean(baselines_avgd, "omitnan");
  326. % threshold_y = 2*std(baselines_avgd_y, "omitnan"); avg_base_y = mean(baselines_avgd_y, "omitnan");
  327. %
  328. % % Trial idxs
  329. % baseline_outlier = ((baselines_avgd) > (avg_base + threshold)) | ((baselines_avgd) < (avg_base - threshold));
  330. % baseline_outlier_y = ((baselines_avgd_y) > (avg_base_y + threshold_y)) | ((baselines_avgd_y) < (avg_base_y - threshold_y));
  331. %
  332. % baseline_outlier_trials(:, p) = baseline_outlier | baseline_outlier_y;
  333. % end
  334. % %%

Part6_GazeBias.m, no license · at the source

Overview

Authors: Dan Wang1, Kabir Arora1, Jan Theeuwes2,3,4, Stefan Van der Stigchel1, Surya Gayet1, Samson Chota1
ORCID iDs: Dan Wang, Jan Theeuwes, Surya Gayet
  1. Department of Experimental Psychology, Helmholtz Institute, Utrecht University,Utrecht, Netherlands
  2. Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam,Amsterdam, Netherlands
  3. Institute Brain and Behavior Amsterdam (iBBA), Amsterdam, Netherlands
  4. William James Center for Research, ISPA-Instituto Universitario,Lisbon, Portugal
Institutions: Utrecht University (Netherlands); Vrije Universiteit Amsterdam (Netherlands); William James Center for Research (Portugal)
Journal: Communications biology, volume 9, issue 1, article 779
Dates: received 23 September 2025; accepted 24 March 2026; published online 9 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-09992-2 · PMID 41957277 · PMCID PMC13249964 · OpenAlex W4413752546
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.08.22.671530
Status: code verified
Categories: EEG (modality), human (organism)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Connectivity, Preprocessing, fMRI & imaging, Physiology & signal measures
Keywords: Attention, Sensory processing
MeSH: Attention*, Goals*, Visual Cortex*, Visual Perception*, Electroencephalography, Female, Humans, Male, Photic Stimulation (* major topic)
Topic: Visual Attention and Saliency Detection (Computer Vision and Pattern Recognition, Computer Science), according to OpenAlex
Funding: China Scholarship Council (202308510050); J.T. is supported by a European Research Council (ERC) advanced grant 833029 [LEARNATTEND] and by a NWO Open competition grant 25 406.21.GO.034
Citations: cited by 2 papers (Europe PMC); 48 references in the paper

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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.

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State: the link answers, verified on 29 September 2026
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Size: 18 files, 9 scripts
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Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (7 files), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
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At the source: osf.io/szuxa/

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Wang, D., Arora, K., Theeuwes, J., Van der Stigchel, S., Gayet, S., & Chota, S. (2026). Dynamic competition between bottom-up saliency and top-down goals in early visual cortex. Communications biology, 9(1), 779. https://doi.org/10.1038/s42003-026-09992-2

BibTeX

@article{wang2026dynamic,
author = {Wang, Dan and Arora, Kabir and Theeuwes, Jan and Van der Stigchel, Stefan and Gayet, Surya and Chota, Samson},
title = {{Dynamic competition between bottom-up saliency and top-down goals in early visual cortex}},
journal = {Communications biology},
year = {2026},
month = apr,
volume = {9},
number = {1},
pages = {779},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-09992-2},
url = {https://doi.org/10.1038/s42003-026-09992-2},
pmid = {41957277},
pmcid = {PMC13249964}
}

RIS

TY - JOUR
AU - Wang, Dan
AU - Arora, Kabir
AU - Theeuwes, Jan
AU - Van der Stigchel, Stefan
AU - Gayet, Surya
AU - Chota, Samson
TI - Dynamic competition between bottom-up saliency and top-down goals in early visual cortex
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/04/09
VL - 9
IS - 1
SP - 779
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-09992-2
UR - https://doi.org/10.1038/s42003-026-09992-2
LA - en
ER -

CSL-JSON

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"container-title-short": "Commun Biol",
"volume": "9",
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"DOI": "10.1038/s42003-026-09992-2",
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"PMCID": "PMC13249964",
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"issued": {
"date-parts": [
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}

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