OSCR

Visual exposure to masked faces benefits personally familiar but not famous face recognition.

Code ↔ Paper

7 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 7 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and methods › Procedure ↔ Experiment Code/face_exp_new.m, lines 82–95 · score 0.73 · 800–1000 ms, fixation cross, 800 ms
  2. [2] § Materials and methods › Data analysis › Neural data analysis ↔ Experiment Code/runFaceNew.m, the whole file · a weak match · score 0.64 · 110 ms, 140 ms, 230 ms, 320 ms, MATLAB, window
  3. [3] § Materials and methods › Data analysis › Behavioral data analysis ↔ Experiment Code/face_exp_new.m, lines 1–79 · score 0.58 · mouse button, mouse click, paradigm, RT, 15 %, stimuli
  4. [4] § Materials and methods › Procedure ↔ Experiment Code/face_exp_new.m, lines 1–79 · score 0.56 · pseudo random, sequence, click, respond, block, mouse
  5. [5] § Materials and methods › Neural data acquisition and preprocessing ↔ Analysis and Figure Code/EEG Analysis/preprocess.m, lines 5–52 · score 0.52 · stimulus onset, preprocessed, filtered, EEG
  6. [6] § Materials and methods › Data analysis › Behavioral data analysis ↔ Experiment Code/practice_face_new.m, lines 1–70 · score 0.51 · mouse button, mouse click, paradigm, RT
  7. [7] § Materials and methods › Procedure ↔ Experiment Code/runFaceNew.m, the whole file · a weak match · score 0.51 · pseudo random, sequence, paradigm, block, mouse

Paper

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

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The authors' code

MATLAB · 315 lines · 24 KB · CC-BY-4.0 · 3 matches

  1. function rating = face_exp_new (subject,window,screenNumber,width,height,session,pth,p1,p2,p3,ind,Index_Matrix,h)
  3. % check for Opengl compatibility, abort otherwise:
  4. AssertOpenGL;
  5. KbName('UnifyKeyNames');
  6. KbCheck;
  7. escape = KbName('ESCAPE');
  8. responseKey = 1; % X mouse button
  9. % h = IOPort('OpenSerialPort', 'com3','BaudRate=19200');
  10. % IOPort('Write', h, 'a');
  12. % load data
  13. folder = dir ('C:/Users/Dr. Koel Das/Documents/MATLAB/Srijita/Stim_test_SK/New Paradigm/FINAL STIMULI (MASKED + UNMASKED, INDEXED)/*.jpg');
  14. folder = natsortfiles(folder);
  15. %Index_Matrix = [32 133 222 205 314 17 324 76 332 173 72 337 38 325 299 146 350 9 212 263 59 191 105 164; 294 359 31 176 217 48 134 153 331 114 237 273 68 265 21 282 229 43 98 67 304 198 238 338; 160 80 180 241 308 143 93 25 94 227 209 297 353 122 85 4 345 302 3 280 186 149 284 108; 100 140 181 151 330 88 358 6 335 124 71 269 63 255 300 188 339 44 197 260 95 145 119 220; 56 148 185 169 290 74 348 20 327 152 65 259 49 245 258 224 355 8 193 296 33 161 111 206; 2 166 182 139 292 110 344 24 283 172 41 295 27 253 320 183 357 84 199 274 89 157 115 232; 178 113 147 75 242 215 12 343 218 306 101 275 267 103 156 333 102 352 34 328 131 187 138 106; 340 16 137 319 126 70 341 189 117 30 334 298 51 315 226 112 61 351 163 200 175 81 128 270; 82 29 211 54 289 288 1 208 249 109 135 42 194 159 168 266 243 201 278 79 310 170 46 301; 329 223 257 35 184 11 22 190 347 272 354 346 107 55 246 116 165 303 26 154 78 150 231 239; 291 305 228 322 69 141 136 40 123 250 120 240 45 221 286 23 92 15 271 216 316 203 349 66; 60 247 268 167 18 73 99 252 83 307 104 326 313 195 210 236 321 171 225 97 86 162 262 230; 57 261 342 47 254 192 244 279 158 36 118 214 233 251 28 174 13 129 19 177 336 62 309 235; 5 360 14 318 202 155 64 281 77 87 144 276 317 52 132 10 248 207 287 125 219 311 37 196; 96 50 90 53 323 285 312 142 58 264 356 277 130 213 127 256 121 234 179 39 293 91 7 204]; % pseudo-random image sequence, each row for a block
  16. [r,c]=size(Index_Matrix); % r = no. of blocks, c = no. of trials
  17. num = c; % num = no. of trials
  18. break_text = 'Please take a mandatory break for 2 minutes';
  19. wait_text = 'Wait for a few seconds ...';
  20. % for fixation cross
  21. x1 = (width/2)-10;
  22. y1 = (height/2);
  23. x2 = (width/2)+10;
  24. y2 = (height/2);
  25. xy1 = [x1 x2; y1 y2];
  26. x3 = (width/2);
  27. y3 = (height/2)-10;
  28. x4 = (width/2);
  29. y4 = (height/2)+10;
  30. xy2 = [x3 x4; y3 y4];
  32. try
  34. rating = zeros(num, 4); % to store stim_ref, RT, mouse-button clicked and points per subject per block: col1 = stim_list, col2: confidence ratin, col3: RT
  35. FixCr=zeros(500,2);
  36. fixcross = Screen('MakeTexture', window, FixCr);
  38. FixCr2=ones(40,40)*240;
  39. FixCr2(20:21,:)=0;
  40. FixCr2(:,20:21)=0; %try imshow(FixCr2) to display the result in Matlab
  41. fixcross2 = Screen('MakeTexture', window, FixCr2);
  43. ShowCursor(0,window);
  46. introText = 'Please read carefully: \n\n 1. You will be shown 30 images of faces you may or may not know.\n\n \n\n 2. Your task is to respond with a left mouse click (click only once, as soon as possible), \n\n in a 2-back manner, for images of the SAME INDIVIDUAL \n\n \n\n 3. Click on the LEFT mouse button AFTER you see the SECOND repeated image\n\n \n\n 4. Click to respond only when the "Response" text appears\n\n \n\n Click mouse button to continue ...';
  47. Screen('FillRect', window, 240);
  48. DrawFormattedText(window, introText, 'center', 'center', 0);
  49. % IOPort('Write',h,'t');WaitSecs(0.01);% IOPort('Write',h,'t');
  51. % IOPort('Write',h,'b'); % T1
  52. Screen('Flip', window);
  54. IOPort('Write',h,'b'); % T1
  56. while (1) %wait for user response
  57. [x,y,buttons] = GetMouse(screenNumber);
  58. if buttons(1) || KbCheck
  59. break;
  60. end
  61. end
  64. Jitter = 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  65. Jitter2 = 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  66. trials = Index_Matrix(session,:);
  68. % start trials
  70. for n = 1:c
  71. %HideCursor;
  72. %start_time_trial = GetSecs;
  73. idx = trials (n);
  74. if n>2
  75. idx_copy = trials(n-2);
  76. end
  77. I = imread([folder(idx).name]);
  78. theImage = imresize(I, 0.75); % resizing
  79. %theImage = I;
  82. %% fixation cross (Jitter1)
  84. %t_in = randi([1,4],1);
  85. t_fc = Jitter(ind);
  86. Screen('FillRect', window, [240, 240, 240]);
  87. %Screen('DrawTexture', window, fixcross2);
  88. Screen('DrawLines', window, xy1, 1, 0);
  89. Screen('DrawLines', window, xy2, 1, 0);
  90. Screen('Flip', window);
  91. IOPort('Write',h,'b'); % T2
  92. % wait for 800 - 1000 ms
  93. WaitSecs(t_fc);
  95. %start_time = GetSecs;
  97. %% Present stimuli (100 ms)
  99. Screen('FillRect', window, [240 240 240])
  101. % Make the image into a texture
  102. imageTexture = Screen('MakeTexture', window, theImage);
  104. % Draw the image to the screen
  105. Screen('DrawTexture', window, imageTexture, [], [], 0);
  106. Screen('DrawLines', window, xy1, 1, 0);
  107. Screen('DrawLines', window, xy2, 1, 0);
  110. % Flip to the screen
  111. Screen('Flip', window);
  112. IOPort('Write',h,'b'); % T3
  114. WaitSecs(0.1);
  116. %% pre-response screen
  117. t_fc2 = Jitter2(ind);
  118. Screen('FillRect', window, [240 240 240])
  119. % fixation cross
  120. Screen('DrawLines', window, xy1, 1, 0);
  121. Screen('DrawLines', window, xy2, 1, 0);
  122. %Screen('DrawTexture', window, fixcross2);
  123. % Flip to the screen
  124. Screen('Flip', window);
  125. IOPort('Write',h,'b'); % T4
  127. % Wait for 300-400 ms
  128. WaitSecs(t_fc2);
  130. %% inter-stimulus-interval/Response Screen
  132. responseText = 'Response';
  134. Screen('FillRect', window, [240 240 240]);
  135. if n>2
  136. DrawFormattedText(window, responseText, 'center', (height/2)-70, 0);
  137. end
  138. Screen('DrawLines', window, xy1, 1, 0);
  139. Screen('DrawLines', window, xy2, 1, 0);
  140. % Flip to the screen
  141. Screen('Flip', window);
  142. start_time = GetSecs;
  143. IOPort('Write',h,'b'); % T5
  145. t0 = GetSecs;
  146. answer = 0;
  147. while answer == 0
  148. [x,y,buttons,focus,valuators,valinfo] = GetMouse(screenNumber);
  149. secs = GetSecs;
  150. %WaitSecs(1);
  151. if buttons(responseKey) == 1
  152. answer = 1;
  153. end
  154. if secs - t0 > 1
  155. break
  156. end
  157. end
  158. restime(n) = secs - start_time;
  159. result(n) = answer;
  162. %% saving stimuli-wise performance
  163. if n==1 && answer == 1 % Go on non-task trial
  164. points(n)=0;
  165. elseif n==2 && answer == 1 % Go on non-task trial
  166. points(n)=0;
  167. elseif n>2 && idx == (idx_copy+2) && answer==1 % Go on task-trials
  168. points(n)=1;
  169. elseif n>2 && idx == (idx_copy+4) && answer==1 % Go on task-trials
  170. points(n)=1;
  171. elseif n>2 && idx == (idx_copy-2) && answer==1 % Go on task-trials
  172. points(n)=1;
  173. elseif n>2 && idx == (idx_copy-4) && answer==1 % Go on task-trials
  174. points(n)=1;
  175. elseif n==1 && answer == 0 % No-go on non-task trial
  176. points(n)=1;
  177. elseif n==2 && answer == 0 % No-go on non-task trial
  178. points(n)=1;
  179. elseif n>2 && idx == (idx_copy+2) && answer==0 % No-go on task-trials
  180. points(n)=0;
  181. elseif n>2 && idx == (idx_copy+4) && answer==0
  182. points(n)=0;
  183. elseif n>2 && idx == (idx_copy-2) && answer==0
  184. points(n)=0;
  185. elseif n>2 && idx == (idx_copy-4) && answer==0
  186. points(n)=0;
  187. elseif n>2 && idx ~= (idx_copy+2) && answer==0 % No-go on non task-trials
  188. points(n)=1;
  189. elseif n>2 && idx ~= (idx_copy+4) && answer==0
  190. points(n)=1;
  191. elseif n>2 && idx ~= (idx_copy-2) && answer==0
  192. points(n)=1;
  193. elseif n>2 && idx ~= (idx_copy-4) && answer==0
  194. points(n)=1;
  195. elseif n>2 && idx ~= (idx_copy+2) && answer==1 % Go on non-task task-trials
  196. points(n)=0;
  197. elseif n>2 && idx ~= (idx_copy+4) && answer==1
  198. points(n)=0;
  199. elseif n>2 && idx ~= (idx_copy-2) && answer==1
  200. points(n)=0;
  201. elseif n>2 && idx ~= (idx_copy-4) && answer==1
  202. points(n)=0;
  204. % elseif n>2 && idx~=idx_copy && answer==0 % No-go on non-task trials
  205. % points(n)=1;
  206. % elseif n>2 && idx ~= idx_copy && answer==1 % Go on non-task trials
  207. % points(n)=0;
  208. % elseif n>2 && idx == idx_copy && answer==0 % No-go on task trials
  209. % points(n)=0;
  210. end
  212. ind = ind+1;
  213. end
  214. %filename = strcat(subject,'log',num2str(session), '.mat');'
  215. % result = result';
  216. % restime = restime';
  217. rating(:,1)=trials';
  218. rating(:,2)=result;
  219. rating(:,3)=restime;
  220. rating(:,4)=points;
  221. %filename = strcat('Log', {' '}, subject, {'_'}, num2str(session), '.mat');
  222. filename = sprintf('Log_%s_block_%d.mat', subject, session);
  223. save([pth filename], 'rating');
  226. session=session+1;
  227. trial = sprintf('Session \n\n %d',session);
  228. if session == 8
  229. Screen('FillRect', window, 240);
  230. DrawFormattedText(window, break_text, 'center', 'center', 0);
  231. % IOPort('Write',h,'t');WaitSecs(0.01);% IOPort('Write',h,'t');
  232. Screen('Flip', window);
  233. KbPressWait();
  234. else
  235. Screen('FillRect', window, 240);
  236. DrawFormattedText(window, wait_text, 'center', 'center', 0);
  237. % IOPort('Write',h,'t');WaitSecs(0.01);% IOPort('Write',h,'t');
  238. Screen('Flip', window);
  239. KbPressWait();
  240. end
  241. Screen('FillRect', window, 100);
  242. % Screen(window,'FillRect',[100,100,100],[100,200,300,500]);
  243. % Screen('DrawText', window, 'Practice', 150, 350, [0 0 0]);
  244. Screen(window,'FillRect',130,p1);
  245. DrawFormattedText(window, trial,p1(1)+50,p1(2)+100,[0,0,0]);
  246. Screen(window,'FillRect',130,p3);
  247. Screen('DrawText', window, 'Abort', p3(1)+50, p3(2)+100, [0 0 0]);
  248. %if session == 8
  249. %DrawFormattedText(window, break_text, 'center', (height/2)-250, 0);
  250. %end
  251. Screen('Flip',window);
  253. switch ind
  255. case 451
  256. introText = 'Thank You For Your Time and Contribution Towards Science.\n\n We truly appreciate your participation!\n\n Press Esc to exit';
  257. Screen('FillRect', window, 100);
  258. DrawFormattedText(window, introText, 'center', 'center', 0);
  259. Screen('Flip', window);
  260. % Wait for keystroke:
  261. [~, keycode] = KbStrokeWait;
  262. % Escape exits:
  263. if keycode(escape)
  264. Screen('CloseAll');
  265. ShowCursor;
  266. % Screen('Preference', 'Verbosity', oldVerbosityLevel);
  267. % Screen('Preference', 'VisualDebugLevel', oldVisualDebugLevel);
  268. end
  269. otherwise
  271. while 1
  272. while buttons(1) %if already pressed, wait for release
  273. [x, y, buttons] = GetMouse;
  274. end
  275. while ~buttons(1) %wait for press
  276. [x, y, buttons] = GetMouse;
  277. end
  278. while buttons(1) %wait for release
  279. [x, y, buttons] = GetMouse;
  281. end
  284. if IsInRect(x,y,p1)
  285. % Screen('CloseAll');
  286. close all;
  287. ShowCursor;
  288. % session=session+1;
  289. face_exp_new(subject,window,screenNumber,width,height,session,pth,p1,p2,p3,ind,Index_Matrix,h);
  290. % Screen('Preference', 'Verbosity', oldVerbosityLevel);
  291. % Screen('Preference', 'VisualDebugLevel', oldVisualDebugLevel);
  292. break;
  294. end
  296. if IsInRect(x,y,p3)
  297. Screen('CloseAll');
  298. ShowCursor;
  299. % Screen('Preference', 'Verbosity', oldVerbosityLevel);
  300. % Screen('Preference', 'VisualDebugLevel', oldVisualDebugLevel);
  301. break;
  303. end
  305. end
  306. end
  308. catch
  309. Screen('CloseAll');
  310. ShowCursor;
  311. % Screen('Preference', 'Verbosity', oldVerbosityLevel);
  312. % Screen('Preference', 'VisualDebugLevel', oldVisualDebugLevel);
  313. psychrethrow(psychlasterror);
  314. end
  315. end

face_exp_new.m at commit 4d9fae0, under CC-BY-4.0 · at the source

Overview

Authors: Srijita Karmakar1,2, Subhajit Das2, Koel Das2
ORCID iDs: Srijita Karmakar
  1. Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, United States
  2. Indian Institute of Science Education and Research Kolkata, Kolkata, West Bengal, India
Institutions: Indian Institute of Science Education and Research Kolkata (India); University of California, Santa Barbara (United States)
Journal: Frontiers in psychology, volume 17, article 1671509
Dates: received 23 July 2025; accepted 20 February 2026; published online 17 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fpsyg.2026.1671509 · PMID 42077297 · PMCID PMC13134374 · OpenAlex W7154783770
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), other condition (population), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Machine learning, Preprocessing, Evoked potentials, Physiology & signal measures
Keywords: EEG, face familiarity, face masks, face recognition, N170, N250, COVID-19
Topic: Face Recognition and Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 79 references in the paper

Abstract

Introduction: The introduction of face masks during the recent COVID-19 pandemic presented a potential challenge for human face perception and recognition. Our exploratory study investigates the effect of face masks in face recognition by probing the neuropsychological mechanisms of the same. We also aim to explicate the effect of general exposure to masked faces and visual experience with specific masked faces of personally familiar individuals on face recognition ability.

Methods: Participants detected personally familiar, famous, and unfamiliar Indian faces in masked and unmasked conditions in a 2-back test.

Results: Statistical analyses revealed significant main effects of familiarity and mask conditions on performance accuracy and reaction time (RT). The highest performance accuracy in correctly detecting the target face was observed for familiar and unmasked faces, and the lowest for unfamiliar and masked ones. Notably, RTs were not different between unmasked and masked personally familiar faces, while masked famous faces elicited significantly greater RT than their unmasked counterpart. EEG analysis was consistent with behavioral results. Specifically, we show neural evidence for increased effort in processing masked famous faces that is similar to that for masked unfamiliar faces, but absent for masked familiar faces. This increased effort to process masked faces of unfamiliar or famous individuals is suggestive of a beneficial effect of visual exposure to, and experience with, specific masked faces.

Conclusion: In sum, our study presents behavioral and neural evidence that personal familiarity with masked faces aids perceptual learning and eventual recognition, and this learning does not generalize to otherwise well-known faces.

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 7 matches between paragraphs and lines of code.

srijitakarmakar/MaskedFacePerception

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 4d9fae095ca3597833aea2961e7597ad932787bb, 8 February 2026
Languages: MATLAB (25)
Size: 936 files, 25 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (15 files), Psychtoolbox (3 files), EEGLAB (1 file), Image Processing Toolbox (1 file), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
27 files
At the source: github.com/srijitakarmakar/MaskedFacePerception.git

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;
  • 25 scripts, each with its path and the digest of its content;
  • 7 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

Datasets cited

Data availability statement

The raw and processed behavioral data, processed EEG data, and codes for experiment presentation, analysis, and figure generation are made publicly available on a GitHub repository: https://github.com/srijitakarmakar/MaskedFacePerception.git.

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, 7 keywords, 70 references.

Cite

This paper

Karmakar, S., Das, S., & Das, K. (2026). Visual exposure to masked faces benefits personally familiar but not famous face recognition. Frontiers in psychology, 17, 1671509. https://doi.org/10.3389/fpsyg.2026.1671509

BibTeX

@article{karmakar2026visual,
author = {Karmakar, Srijita and Das, Subhajit and Das, Koel},
title = {{Visual exposure to masked faces benefits personally familiar but not famous face recognition}},
journal = {Frontiers in psychology},
year = {2026},
month = apr,
volume = {17},
pages = {1671509},
publisher = {Frontiers Media SA},
issn = {1664-1078},
doi = {10.3389/fpsyg.2026.1671509},
url = {https://doi.org/10.3389/fpsyg.2026.1671509},
pmid = {42077297},
pmcid = {PMC13134374}
}

RIS

TY - JOUR
AU - Karmakar, Srijita
AU - Das, Subhajit
AU - Das, Koel
TI - Visual exposure to masked faces benefits personally familiar but not famous face recognition
T2 - Frontiers in psychology
J2 - Front Psychol
PY - 2026
DA - 2026/04/17
VL - 17
SP - 1671509
SN - 1664-1078
PB - Frontiers Media SA
DO - 10.3389/fpsyg.2026.1671509
UR - https://doi.org/10.3389/fpsyg.2026.1671509
LA - en
ER -

CSL-JSON

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"id": "10.3389/fpsyg.2026.1671509",
"type": "article-journal",
"title": "Visual exposure to masked faces benefits personally familiar but not famous face recognition",
"container-title": "Frontiers in psychology",
"author": [
{
"family": "Karmakar",
"given": "Srijita"
},
{
"family": "Das",
"given": "Subhajit"
},
{
"family": "Das",
"given": "Koel"
}
],
"container-title-short": "Front Psychol",
"volume": "17",
"page": "1671509",
"DOI": "10.3389/fpsyg.2026.1671509",
"PMID": "42077297",
"PMCID": "PMC13134374",
"ISSN": "1664-1078",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fpsyg.2026.1671509",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
17
]
]
}
}

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