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Canonical and retinal size in visual working memory.

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The authors' code

MATLAB · 299 lines · 15 KB · no license

  1. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  2. % BigSmallVWM1.m
  3. % Participants encoded 8 objects which appeared in random locations in a
  4. % 4x4 grid for 2s. The objects dissapeared for the encoding interval of 1s
  5. % before reappearing with one object different. Participants were asked to
  6. % click on the object that was different.
  7. %
  8. % In Exp1, objects were intermixed on the display: two objects were large
  9. % retinal and canonical size, two objects were small retinal and canonical
  10. % size, two objects were small retinal and large canonical size, and two
  11. % objects were large retinal and small canonical size
  12. %
  13. % There were 16 participants. Participants completed 12 blocks of 32 trials
  14. % for 384 total trials per participant to counterbalance the objects
  15. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  17. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  18. % Experimental parameters
  19. clear all;
  20. rand('state', sum(100*clock));
  21. Screen('Preference', 'SkipSyncTests', 1);
  23. encodingDuration = 2; retentionInterval = 1;
  25. prompt = {'Outputfile', 'block', 'Subject number', 'age', 'gender', 'memory load'};
  26. defaults = {'BigSmallVWM1', '1', '99', '18', 'F', '8'};
  27. answer = inputdlg(prompt, 'BigSmallVWM1', 2, defaults);
  28. [output, block, subnum, age, gender, nObjects] = deal(answer{:});
  30. nObjects = str2num(nObjects);
  31. stimulusFileName = ['BigSmallVWM1_s' subnum '.xls'];
  32. if exist(stimulusFileName)==2&&(str2num(subnum)~=99)
  33. fileproblem = input('That file already exists! Append a .x (1), overwrite (2), or break (3/default)?');
  34. if isempty(fileproblem) | fileproblem==3
  35. return;
  36. elseif fileproblem==1
  37. stimulusFileName = [stimulusFileName '.x'];
  38. end
  39. end
  40. stimulusFile = fopen(stimulusFileName, 'w');
  42. writeData(stimulusFile, {'subnum', 'age', 'gender', 'trial', 'canonicalSize', 'pixelSize', 'object1',...
  43. 'object2', 'object3', 'object4', 'object5', 'object6', 'object7', 'object8', 'probe', ...
  44. 'probecell', 'probex', 'probey', 'mousex', 'mousey', 'mouseDistance', 'accuracy', 'rt', 'numCorrectSoFar'}, '\t');
  46. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  48. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  49. % Sound feedback
  50. BeepFreq = [800 1300 2000]; BeepDur = [.1 .1 .1];
  51. Beep1 = MakeBeep(BeepFreq(1), BeepDur(1));
  52. Beep2 = MakeBeep(BeepFreq(2), BeepDur(2));
  53. Beep3 = MakeBeep(BeepFreq(3), BeepDur(3));
  54. Beep4 = [Beep1 Beep2 Beep3];
  55. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  57. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  58. % Screen parameters
  59. gray = [127 127 127 ]; white = [255 255 255]; black = [0 0 0]; darkgreen = [0 200 0]; lineWidth = 6;
  60. bgcolor = white; textcolor = black; linecolor = darkgreen;
  62. [mainwin, screenrect] = Screen(0, 'OpenWindow');
  63. Screen('FillRect', mainwin, bgcolor);
  64. center = [screenrect(3)/2 screenrect(4)/2];
  65. Screen(mainwin, 'Flip');
  68. nrow = 4; ncolumn = 4; cellsize = 180;
  69. for ncells = 1:nrow.*ncolumn
  70. xnum = (mod(ncells-1, ncolumn)+1)-ncolumn/2-0.5;
  71. ynum = ceil(ncells/nrow)-nrow/2-0.5;
  72. cellcenter(ncells,1) = center(1)+xnum.*cellsize;
  73. cellcenter(ncells,2) = center(2)+ynum.*cellsize;
  74. end
  76. % Display the grid
  78. startGridx = nrow/2+1; startGridy = ncolumn/2+1;
  79. for i = 1:5
  80. Screen('DrawLine', mainwin, darkgreen, center(1)-(startGridx-i)*cellsize, center(2)-nrow/2*cellsize, center(1)-(startGridx-i)*cellsize, center(2)+nrow/2*cellsize,lineWidth);
  81. Screen('DrawLine', mainwin, darkgreen, center(1)-(ncolumn/2)*cellsize, center(2)-(startGridy-i)*cellsize, center(1)+(ncolumn/2)*cellsize, center(2)-(startGridy-i)*cellsize,lineWidth);
  82. end
  83. for j = 1:nrow.*ncolumn
  84. Screen('DrawText', mainwin, num2str(j),cellcenter(j,1), cellcenter(j,2),textcolor);
  85. end
  86. Screen(mainwin, 'Flip');
  87. WaitSecs(1);
  89. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  91. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  92. % select objects
  93. nObjectsPerSize = 96;
  94. if mod(str2num(subnum),2) == 0 % even numbered participants
  95. tinyPhotoIndex = [1:2:nObjectsPerSize*2]; % odd numbered objects
  96. largePhotoIndex = [2:2:nObjectsPerSize*2]; % even numbered objects
  97. else
  98. tinyPhotoIndex = [2:2:nObjectsPerSize*2];
  99. largePhotoIndex = [1:2:nObjectsPerSize*2];
  100. end
  102. tinyPhotoBigObjectIndex = Shuffle(tinyPhotoIndex);
  103. tinyPhotoSmallObjectIndex = Shuffle(tinyPhotoIndex);
  104. largePhotoBigObjectIndex = Shuffle(largePhotoIndex);
  105. largePhotoSmallObjectIndex = Shuffle(largePhotoIndex);
  107. tinyPhotoSize = 70; largePhotoSize = 140;
  108. nTrialsPerBlock = 32; nBlocks = nObjectsPerSize*4/nTrialsPerBlock;
  109. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  111. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  112. % Instructions
  113. instructions = ['Welcome to the Visual Attention and Memory Study. You will see a display of several objects. Please try to remember as much of the display as possible. A second or so later, another similar array will appear, except that one object has changed. Your task is to identify which object has changed by clicking on the center of the object. \n\n The task is challenging, but please try your best! \n\n There will be 12 blocks of trials, each taking a couple of minutes to complete. You may take a break at the end of each block. Please click the mouse to start.']
  114. DrawFormattedText(mainwin, instructions, 'center', 'center', textcolor, 70);
  115. Screen('Flip', mainwin);
  116. GetClicks;
  117. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  119. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  120. % Trial loop
  122. randTrials = Shuffle(1:nObjectsPerSize*4);
  124. largePhotoBigObjectStart = 0;
  125. largePhotoSmallObjectStart = 0;
  126. tinyPhotoBigObjectStart = 0;
  127. tinyPhotoSmallObjectStart = 0;
  129. randCells = Shuffle(1:nrow*ncolumn);
  131. eightObjectSize = [largePhotoSize, largePhotoSize, tinyPhotoSize, tinyPhotoSize, largePhotoSize, largePhotoSize, tinyPhotoSize, tinyPhotoSize];
  132. numCorrectSoFar = 0;
  133. for i = 1:nObjectsPerSize*4
  135. if str2num(subnum)==99 & i > 4
  136. break; ShowCursor; Screen('CloseAll'); fclose('all');
  137. end
  138. if i > 1 && mod(i,nTrialsPerBlock) == 0 % break every 32 trials or so
  139. whichBlock = floor(i/nTrialsPerBlock);
  140. Screen('FillRect', mainwin, bgcolor);
  141. Screen('DrawText', mainwin, ['You got ' num2str(numCorrectSoFar) ' out of ' num2str(i) ' trials correct so far. Please try your best!'],center(1)-400,center(2)-50,textcolor);
  142. Screen('DrawText', mainwin, ['End of block ' num2str(whichBlock) ' out of ' num2str(nBlocks) '. You may take a short break. Click to continue.'],center(1)-400,center(2)+50,textcolor);
  143. Screen(mainwin, 'Flip');
  144. GetClicks;
  145. end
  147. if mod(randTrials(i),4) == 0
  148. pixelSize = 'largePhoto';
  149. photoSize = largePhotoSize;
  150. objectSize = 'big';
  151. largePhotoBigObjectStart = largePhotoBigObjectStart + 1;
  152. probeLoc = 1;
  153. probeIndex = largePhotoBigObjectIndex(largePhotoBigObjectStart);
  154. otherToChooseFrom = largePhotoBigObjectIndex(find(largePhotoBigObjectIndex~=probeIndex));
  155. templist1 = Shuffle(otherToChooseFrom); templist2 = Shuffle(tinyPhotoBigObjectIndex); templist3 = Shuffle(largePhotoSmallObjectIndex); templist4 = Shuffle(tinyPhotoSmallObjectIndex);
  156. elseif mod(randTrials(i),4) == 1
  157. pixelSize = 'tinyPhoto';
  158. photoSize = tinyPhotoSize;
  159. objectSize = 'big';
  160. tinyPhotoBigObjectStart = tinyPhotoBigObjectStart + 1;
  161. probeLoc = 3;
  162. probeIndex = tinyPhotoBigObjectIndex(tinyPhotoBigObjectStart);
  163. otherToChooseFrom = tinyPhotoBigObjectIndex(find(tinyPhotoBigObjectIndex~=probeIndex));
  164. templist1 = Shuffle(largePhotoBigObjectIndex); templist2 = Shuffle(otherToChooseFrom); templist3 = Shuffle(largePhotoSmallObjectIndex); templist4 = Shuffle(tinyPhotoSmallObjectIndex);
  165. elseif mod(randTrials(i),4) == 2
  166. pixelSize = 'largePhoto';
  167. photoSize = largePhotoSize;
  168. objectSize = 'small';
  169. probeLoc = 5;
  170. largePhotoSmallObjectStart = largePhotoSmallObjectStart + 1;
  171. probeIndex = largePhotoSmallObjectIndex(largePhotoSmallObjectStart);
  172. otherToChooseFrom = largePhotoSmallObjectIndex(find(largePhotoSmallObjectIndex~=probeIndex));
  173. templist1 = Shuffle(largePhotoBigObjectIndex); templist2 = Shuffle(tinyPhotoBigObjectIndex); templist3 = Shuffle(otherToChooseFrom); templist4 = Shuffle(tinyPhotoSmallObjectIndex);
  174. else
  175. pixelSize = 'tinyPhoto';
  176. photoSize = tinyPhotoSize;
  177. objectSize = 'small';
  178. probeLoc = 7;
  179. tinyPhotoSmallObjectStart = tinyPhotoSmallObjectStart + 1;
  180. probeIndex = tinyPhotoSmallObjectIndex(tinyPhotoSmallObjectStart);
  181. otherToChooseFrom = tinyPhotoSmallObjectIndex(find(tinyPhotoSmallObjectIndex~=probeIndex));
  182. templist1 = Shuffle(largePhotoBigObjectIndex); templist2 = Shuffle(tinyPhotoBigObjectIndex); templist3 = Shuffle(largePhotoSmallObjectIndex); templist4 = Shuffle(otherToChooseFrom);
  183. end
  185. listOfEncodingObjects = [templist1(1:2), templist2(1:2), templist3(1:2), templist4(1:2)];
  187. randCells = Shuffle(randCells); % shuffle cell locations
  189. % Draw memory array
  190. Screen('FillRect', mainwin, bgcolor);
  191. for k = 1:5
  192. Screen('DrawLine', mainwin, darkgreen, center(1)-(startGridx-k)*cellsize, center(2)-nrow/2*cellsize, center(1)-(startGridx-k)*cellsize, center(2)+nrow/2*cellsize,lineWidth);
  193. Screen('DrawLine', mainwin, darkgreen, center(1)-(ncolumn/2)*cellsize, center(2)-(startGridy-k)*cellsize, center(1)+(ncolumn/2)*cellsize, center(2)-(startGridy-k)*cellsize,lineWidth);
  194. end
  196. for j = 1:nObjects
  197. if j < 5
  198. imageName = ['./Big/big' num2str(listOfEncodingObjects(j)) '.jpg'];
  199. else
  200. imageName = ['./Small/small' num2str(listOfEncodingObjects(j)) '.jpg'];
  201. end
  202. eachObjectSize = eightObjectSize(j);
  203. thisIm = imread(imageName); imgDim = size(thisIm); diagDim = sqrt(imgDim(1).^2 + imgDim(2).^2); dimCell = eachObjectSize.*sqrt(2); scalingFactor = dimCell./diagDim; photoSize1 = imgDim(2).*scalingFactor; photoSize2 = imgDim(1).*scalingFactor;
  204. thisImObject = Screen('MakeTexture', mainwin, thisIm);
  205. Screen('DrawTexture', mainwin, thisImObject, [], [cellcenter(randCells(j),1)-photoSize1/2, cellcenter(randCells(j),2)-photoSize2/2, cellcenter(randCells(j),1)+photoSize1/2, cellcenter(randCells(j),2)+photoSize2/2],1);
  206. end
  208. Screen('Flip', mainwin);
  210. currentDisplay = Screen('GetImage', mainwin);
  211. imwrite(currentDisplay, ['Exp1ImageA.jpg']); %this will write out a file called MathisFigure.jpg
  213. WaitSecs(encodingDuration);
  215. Screen('FillRect', mainwin, bgcolor);
  216. Screen('Flip', mainwin);
  217. WaitSecs(retentionInterval);
  219. % Draw Test array
  220. for k = 1:5
  221. Screen('DrawLine', mainwin, darkgreen, center(1)-(startGridx-k)*cellsize, center(2)-nrow/2*cellsize, center(1)-(startGridx-k)*cellsize, center(2)+nrow/2*cellsize,lineWidth);
  222. Screen('DrawLine', mainwin, darkgreen, center(1)-(ncolumn/2)*cellsize, center(2)-(startGridy-k)*cellsize, center(1)+(ncolumn/2)*cellsize, center(2)-(startGridy-k)*cellsize,lineWidth);
  223. end
  225. probeName = ['./' objectSize '/' objectSize num2str(probeIndex) '.jpg'];
  226. thisIm = imread(probeName); imgDim = size(thisIm); diagDim = sqrt(imgDim(1).^2 + imgDim(2).^2); dimCell = photoSize.*sqrt(2); scalingFactor = dimCell./diagDim; photoSize1 = imgDim(2).*scalingFactor; photoSize2 = imgDim(1).*scalingFactor;
  227. thisImObject = Screen('MakeTexture', mainwin, thisIm);
  228. Screen('DrawTexture', mainwin, thisImObject, [], [cellcenter(randCells(probeLoc),1)-photoSize1/2, cellcenter(randCells(probeLoc),2)-photoSize2/2, cellcenter(randCells(probeLoc),1)+photoSize1/2, cellcenter(randCells(probeLoc),2)+photoSize2/2],1);
  229. probex = cellcenter(randCells(probeLoc),1);
  230. probey = cellcenter(randCells(probeLoc),2);
  232. for j = 1:nObjects
  233. if j ~= probeLoc
  234. if j < 5
  235. imageName = ['./Big/big' num2str(listOfEncodingObjects(j)) '.jpg'];
  236. else
  237. imageName = ['./Small/small' num2str(listOfEncodingObjects(j)) '.jpg'];
  238. end
  239. eachObjectSize = eightObjectSize(j);
  240. thisIm = imread(imageName); imgDim = size(thisIm); diagDim = sqrt(imgDim(1).^2 + imgDim(2).^2); dimCell = eachObjectSize.*sqrt(2); scalingFactor = dimCell./diagDim; photoSize1 = imgDim(2).*scalingFactor; photoSize2 = imgDim(1).*scalingFactor;
  241. thisImObject = Screen('MakeTexture', mainwin, thisIm);
  242. Screen('DrawTexture', mainwin, thisImObject, [], [cellcenter(randCells(j),1)-photoSize1/2, cellcenter(randCells(j),2)-photoSize2/2, cellcenter(randCells(j),1)+photoSize1/2, cellcenter(randCells(j),2)+photoSize2/2],1);
  243. end
  244. end
  246. timeStart = GetSecs; accuracy = 0; rt = 0;
  247. Screen('Flip', mainwin);
  249. currentDisplay = Screen('GetImage', mainwin);
  250. imwrite(currentDisplay, ['Exp2ImageB.jpg']); %this will write out a file called MathisFigure.jpg
  252. %mousex = 0; mousey = 0; Record response
  253. mouseDistance = 1000;
  255. while 1
  256. [mousex,mousey,buttons] = GetMouse;
  257. while any(buttons) % if already down, wait for release
  258. [mousex,moosey,buttons] = GetMouse;
  259. end
  260. while ~any(buttons) % wait for press
  261. [mousex,mousey,buttons] = GetMouse;
  262. end
  263. while any(buttons) % wait for release
  264. [mousex,mousey,buttons] = GetMouse;
  265. end
  267. if (mousex > 50 && mousey > 50)
  268. rt = (GetSecs - timeStart)*1000; break;
  269. elseif mousex<50&&mousey<50
  270. ShowCursor;
  271. fclose('all');
  272. Screen('CloseAll');
  273. return;
  274. end
  275. end
  277. mouseDistance = sqrt((mousex-probex).^2 + (mousey-probey).^2); % distance between mouse click and probe image;
  279. if mouseDistance < 150
  280. accuracy = 1; numCorrectSoFar = numCorrectSoFar+1; Snd('Play', Beep4);
  281. else
  282. accuracy = 0; Snd('Play', Beep1);
  283. end
  285. writeData(stimulusFile, {subnum, age, gender, i, objectSize, pixelSize, otherToChooseFrom(1),...
  286. otherToChooseFrom(2), otherToChooseFrom(3), otherToChooseFrom(4), otherToChooseFrom(5), otherToChooseFrom(6),...
  287. otherToChooseFrom(7), otherToChooseFrom(8), probeIndex, ...
  288. randCells(probeLoc), probex, probey, mousex, mousey, mouseDistance, accuracy, rt, numCorrectSoFar}, '\t');
  289. Screen('FillRect', mainwin, bgcolor);
  290. Screen('Flip', mainwin);
  292. WaitSecs(0.75);
  293. end
  295. ShowCursor;
  296. Screen('CloseAll');
  297. fclose('all');
  298. disp('Overall accuracy is:');
  299. disp(numCorrectSoFar/(i-1));

BigSmallVWM1.m, no license · at the source

Overview

Authors: William L Gronewald1, Vanessa G Lee1, Roger W Remington1
  1. Department of Psychology, University of Minnesota, S504 Elliott Hall, Minneapolis, MN 55455 USA
Institutions: University of Minnesota (United States)
Journal: Psychonomic bulletin & review, volume 33, issue 5, article 147
Dates: received 25 June 2025; accepted 27 March 2026; published online 21 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3758/s13423-026-02913-8 · PMID 42014599 · PMCID PMC13099791 · OpenAlex W7155077236
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Statistics, Physiology & signal measures
Keywords: Visual working memory, Canonical size, Attention, Change detection
MeSH: Memory, Short-Term*, Pattern Recognition, Visual*, Size Perception*, Visual Perception*, Adult, Female, Humans, Male, Retina, Young Adult (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: McKnight Foundation
Citations: not cited yet (Europe PMC); 23 references in the paper

Abstract

Visual working memory (VWM) is key to many daily tasks, such as remembering visual information about traffic when crossing a busy street. Despite extensive research, the extent to which VWM abstracts out sensory properties not relevant to identification, such as object size, remains unclear. In three experiments, we examined how object size affects VWM, with size defined in two ways: retinal size, referring to the image’s size on the screen (small or large photos), and canonical size, referring to the typical size of objects in the real world, from big (e.g., a tower) to small (e.g., an egg). Experiments 1 and 2 tested memory for real-world objects, classified into four types based on their photo size and canonical size. VWM was better for large rather than small photos—a retinal-size effect—and for canonically small than big objects—a canonical-size effect. These effects were stronger when participants remembered a mix of different-sized objects than when all objects in a display were of the same size. Experiment 3 tested memory for colored squares that were either big or small on the screen. Size had no effect when displays consisted of colored squares of the same size, but big squares were remembered better when mixed with small squares. These results suggest that seemingly irrelevant sensory properties affect VWM, favoring objects that stimulate more neurons. The effect is stronger when size conditions are mixed, indicating that retinally larger objects are better attended.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

OSF hwstm

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: MATLAB (6)
Size: 10 files, 6 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Psychtoolbox (3 files)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
7 files
At the source: osf.io/hwstm/

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;
  • 6 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

These experiments were not preregistered. De-identified subject level data in an aggregated format, scripts, and Supplemental Materials containing additional analyses are available at the Open Science Framework (https://osf.io/hwstm/).

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, issue, pages, dates, 3 authors, 4 keywords, 10 MeSH terms, 1 funder, 15 references.

Cite

This paper

Gronewald, W. L., Lee, V. G., & Remington, R. W. (2026). Canonical and retinal size in visual working memory. Psychonomic bulletin & review, 33(5), 147. https://doi.org/10.3758/s13423-026-02913-8

BibTeX

@article{gronewald2026canonical,
author = {Gronewald, William L and Lee, Vanessa G and Remington, Roger W},
title = {{Canonical and retinal size in visual working memory}},
journal = {Psychonomic bulletin \& review},
year = {2026},
month = apr,
volume = {33},
number = {5},
pages = {147},
publisher = {Springer Science+Business Media},
issn = {1069-9384},
doi = {10.3758/s13423-026-02913-8},
url = {https://doi.org/10.3758/s13423-026-02913-8},
pmid = {42014599},
pmcid = {PMC13099791}
}

RIS

TY - JOUR
AU - Gronewald, William L
AU - Lee, Vanessa G
AU - Remington, Roger W
TI - Canonical and retinal size in visual working memory
T2 - Psychonomic bulletin & review
J2 - Psychon Bull Rev
PY - 2026
DA - 2026/04/21
VL - 33
IS - 5
SP - 147
SN - 1069-9384
PB - Springer Science+Business Media
DO - 10.3758/s13423-026-02913-8
UR - https://doi.org/10.3758/s13423-026-02913-8
LA - en
ER -

CSL-JSON

{
"id": "10.3758/s13423-026-02913-8",
"type": "article-journal",
"title": "Canonical and retinal size in visual working memory",
"container-title": "Psychonomic bulletin & review",
"author": [
{
"family": "Gronewald",
"given": "William L"
},
{
"family": "Lee",
"given": "Vanessa G"
},
{
"family": "Remington",
"given": "Roger W"
}
],
"container-title-short": "Psychon Bull Rev",
"volume": "33",
"issue": "5",
"page": "147",
"DOI": "10.3758/s13423-026-02913-8",
"PMID": "42014599",
"PMCID": "PMC13099791",
"ISSN": "1069-9384",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.3758/s13423-026-02913-8",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
21
]
]
}
}

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