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Replicability of representational similarity and its role in successful memory retrieval.

Code ↔ Paper

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  1. [1] § Methods › Procedure ↔ Custom Scripts/ManipulationCheckOnlyAnalysis.m, lines 91–121 · score 0.66 · correct answers, manipulation check, campfire, yes, skipping

Paper

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The authors' code

MATLAB · 277 lines · 11 KB · no license · 1 match

  1. %% ERICA'S VERSION – Cleaned Full Script for Behavioral Data Analysis (Memory + Manipulation Check)
  3. % Define the subjects to process
  4. subjnames = {'62'};
  6. % Initialize output matrix
  7. nummatrix = [];
  9. % Define correct manipulation check answers
  10. correct_1 = {'aceofdiamond.png','apron.png','sponge01.png','tent.png','campfire.png','corn02.png' };
  11. %Correct YES
  12. correct_2 = {'barn.png','tree.png','africanelephant.png', 'fingerprint.png','puzzlepiece.png','feather03a.png'};
  13. %Correct NO
  16. % Main loop for each subject
  17. for i = 1:length(subjnames)
  18. name = subjnames{i};
  19. fprintf('\n📂 Starting Subject %s\n', name);
  21. % Change directory: ERICA'S COMPUTER
  22. %cd('E:\BehavioralTask_OtherRachelOneDriveStuff\cmd');
  25. %LAB COMPUTERS: update the lines below so that the script finds the data files on your
  26. %machine
  27. %Update the lines below so that the script finds the data files on your machine
  28. addpath('\\192.168.76.14\dianalab\DroboC\RSA_Replication_Study_drobo\RawBehavData');
  30. %RACHEL'S OFFICE: update the lines below so that the script finds the data files on your machine
  31. % Update the lines below so that the script finds the data files on your machine
  32. %addpath('\\128.173.168.149\dianalab\DroboC\RSA_Replication_Study_drobo\RawBehavData');
  36. %% ------------------------ LOAD STUDY LOGS AND IDENTIFY MANIPULATION CHECK ITEMS ------------------------
  38. study_files = { strcat(name,'-Run_1_study_3_2.log'), strcat(name,'-Run_2_study_3_2.log'), ...
  39. strcat(name,'-Run_3_study_3_2.log'), strcat(name,'-Run_4_study_3_2.log') };
  41. mc_trials = {}; % to store manipulation check trials
  43. for f = 1:length(study_files)
  44. fid = fopen(study_files{f});
  45. if fid == -1
  46. warning('❌ Could not open file: %s', study_files{f});
  47. continue;
  48. end
  50. data = textscan(fid, '%*d%d%s%s%*d%*d%*d%*d%*[^\n]', 'HeaderLines', 5, 'Delimiter', '\t');
  51. fclose(fid);
  53. trialnum = data{1};
  54. event = data{2};
  55. code = data{3};
  57. for j = 1:length(code)
  58. if contains(event{j}, 'Picture') && contains(code{j}, 'Study,') && ...
  59. ~contains(code{j}, 'Study_Response') && contains(code{j}, '.png')
  60. parts = strsplit(strtrim(code{j}), ',');
  61. if length(parts) >= 3
  62. [~, baseimg, ~] = fileparts(strtrim(parts{2}));
  63. img_name = [baseimg, '.png'];
  64. question = strtrim(parts{3});
  66. if any(strcmp(img_name, correct_1)) || any(strcmp(img_name, correct_2))
  67. cur_trial = trialnum(j);
  68. response = '0'; % default to skipped
  70. % Look ahead until the first Response event in a *new* trial AND it's 7/8/9
  71. for k = j+1:min(j+15, length(trialnum))
  72. if trialnum(k) ~= cur_trial && contains(event{k}, 'Response')
  73. resp_code = strtrim(code{k});
  74. if ismember(resp_code, {'7','8','9'})
  75. response = resp_code;
  76. break;
  77. end
  78. end
  79. end
  81. mc_trials = [mc_trials; {img_name, question, response}];
  82. fprintf('🧪 MC item: %-20s | Response: %s\n', img_name, response);
  83. end
  84. end
  85. end
  86. end
  87. end
  90. %% ------------------------ SCORE MC TRIALS ------------------------
  91. %% ------------------------ SCORE MANIPULATION CHECKS (EXTERNAL ONLY) ------------------------
  93. % Define correct answers
  94. could_not_carry = {'barn.png','tree.png','africanelephant.png'}; % NO
  95. could_carry = {'aceofdiamond.png','apron.png','sponge01.png'}; % YES
  96. not_useful = {'fingerprint.png','puzzlepiece.png','feather03a.png'}; % NO
  97. is_useful = {'tent.png','campfire.png','corn02.png'}; % YES
  99. % Create lookup map
  100. valid_items = [could_not_carry, could_carry, not_useful, is_useful];
  101. correct_answers_map = containers.Map;
  102. for img = could_not_carry, correct_answers_map(img{1}) = 'no'; end
  103. for img = could_carry, correct_answers_map(img{1}) = 'yes'; end
  104. for img = not_useful, correct_answers_map(img{1}) = 'no'; end
  105. for img = is_useful, correct_answers_map(img{1}) = 'yes'; end
  107. % Scoring variables
  108. unique_trials = containers.Map;
  109. maybe_count = 0;
  110. consistent_count = 0;
  111. correct_count = 0;
  112. skipped = 0;
  114. for r = 1:size(mc_trials,1)
  115. img = mc_trials{r,1};
  116. resp = strtrim(mc_trials{r,3});
  118. % Skip if not valid MC image
  119. if ~ismember(img, valid_items)
  120. continue;
  121. end
  123. %%% TEMPORARILY COUNT DUPLICATES
  124. % Skip duplicates
  125. % if isKey(unique_trials, img)
  126. % continue;
  127. % end
  128. %%%%%%%%%%%%%%%%%%%%%%%%%%
  130. % Check response validity
  131. if ~ismember(resp, {'7','8','9'})
  132. skipped = skipped + 1;
  133. continue;
  134. end
  136. % Map numeric response to text
  137. if strcmp(resp, '7')
  138. resp_val = 'yes';
  139. elseif strcmp(resp, '9')
  140. resp_val = 'no';
  141. elseif strcmp(resp, '8')
  142. maybe_count = maybe_count + 1;
  143. unique_trials(img) = 'maybe';
  144. continue; % 'maybe' is tracked but not scored
  145. end
  147. % Compare to correct
  148. correct_val = correct_answers_map(img);
  149. if strcmp(resp_val, correct_val)
  150. correct_count = correct_count + 1;
  151. end
  152. unique_trials(img) = resp_val;
  153. end
  155. % Final scoring
  156. incorrect = 16 - correct_count;
  158. fprintf('\n✅ External consistency: %d / 16\n', consistent_count);
  159. fprintf('❔ Maybe responses: %d\n', maybe_count);
  160. fprintf('🚫 Skipped responses: %d\n', skipped);
  161. fprintf('❌ Incorrect responses: %d\n', incorrect);
  164. %% ------------------------ SCORE MANIPULATION CHECKS (INTERNAL ONLY) ------------------------
  165. % Count internal inconsistencies
  166. imgnames = mc_trials(:,1);
  167. resps = mc_trials(:,3);
  168. unique_imgs = unique(imgnames);
  169. inconsistent = 0;
  171. for u = 1:length(unique_imgs)
  172. matches = strcmp(imgnames, unique_imgs{u});
  173. all_resps = unique(resps(matches));
  174. % Only consider valid responses
  175. all_resps = intersect(all_resps, {'7','8','9'});
  176. if length(all_resps) > 1
  177. inconsistent = inconsistent + 1;
  178. end
  179. end
  181. %% ------------------------ LOAD TEST SESSION ------------------------
  182. % testname = strcat(name, '-Test_scenario.log');
  183. % fid = fopen(testname);
  184. % data = textscan(fid, '%d%d%s%s%d%d%d%d%*[^\n]', 'HeaderLines', 5, 'Delimiter', '\t');
  185. % fclose(fid);
  186. %% ------------------------ LOAD TEST SESSION (with file existence check) ------------------------
  187. testname = strcat(name, '-Test_scenario.log');
  189. if exist(testname, 'file')
  190. fprintf('📄 Found test file: %s\n', testname);
  191. fid = fopen(testname);
  192. data = textscan(fid, '%d%d%s%s%d%d%d%d%*[^\n]', 'HeaderLines', 5, 'Delimiter', '\t');
  193. fclose(fid);
  195. trial = data{2};
  196. event = data{3};
  197. code = data{4};
  198. ttime = data{6};
  200. types = {}; pics = {}; conditions = {}; resps = {}; resp_ttimes = [];
  202. for j = 1:length(code)
  203. if contains(event{j}, 'Picture') && contains(code{j}, '.png')
  204. parts = strsplit(strtrim(code{j}), ',');
  205. if length(parts) >= 3
  206. types = [types; strtrim(parts{1})];
  207. pics = [pics; strtrim(parts{2})];
  208. conditions = [conditions; strtrim(parts{3})];
  209. end
  211. % Find the next response
  212. if j+1 <= length(event) && contains(event{j+1}, 'Response')
  213. resps = [resps; code(j+1)];
  214. resp_ttimes = [resp_ttimes; ttime(j+1)];
  215. elseif j+2 <= length(event) && contains(event{j+2}, 'Response')
  216. resps = [resps; code(j+2)];
  217. resp_ttimes = [resp_ttimes; ttime(j+2)];
  218. else
  219. resps = [resps; {'0'}];
  220. resp_ttimes = [resp_ttimes; NaN];
  221. end
  222. end
  223. end
  225. %% ------------------------ SCORE MEMORY RESPONSES ------------------------
  226. resps = cellfun(@num2str, resps, 'UniformOutput', false);
  227. unstudied = []; same = []; var = []; oldresp = []; newresp = [];
  229. for k = 1:length(pics)
  230. if contains(conditions{k}, 'new'), unstudied = [unstudied, k];
  231. elseif contains(conditions{k}, 'sameA') || contains(conditions{k}, 'sameB'), same = [same, k];
  232. elseif contains(conditions{k}, 'varAB') || contains(conditions{k}, 'varBA'), var = [var, k];
  233. end
  235. if strcmp(resps{k}, '1'), oldresp = [oldresp, k];
  236. elseif strcmp(resps{k}, '2'), newresp = [newresp, k];
  237. end
  238. end
  240. same_hit = intersect(same, oldresp);
  241. var_hit = intersect(var, oldresp);
  242. same_miss = intersect(same, newresp);
  243. var_miss = intersect(var, newresp);
  244. fa = intersect(unstudied, oldresp);
  246. prop_same_hit = length(same_hit) / length(same);
  247. prop_var_hit = length(var_hit) / length(var);
  248. prop_same_miss = length(same_miss) / length(same);
  249. prop_var_miss = length(var_miss) / length(var);
  250. prop_fa = length(fa) / length(unstudied);
  252. total_hits = length(same_hit) + length(var_hit);
  253. total_miss = length(same_miss) + length(var_miss);
  254. HR = min(max(total_hits / (length(same)+length(var)), 0.01), 0.99);
  255. FAR = min(max(length(fa) / length(unstudied), 0.01), 0.99);
  256. dprime = norminv(HR) - norminv(FAR);
  258. else
  259. fprintf('⚠️ Test file missing: %s — skipping memory scoring.\n', testname);
  260. % Set placeholder values
  261. same_hit = []; var_hit = []; same_miss = []; var_miss = []; fa = [];
  262. prop_same_hit = NaN; prop_var_hit = NaN; prop_same_miss = NaN;
  263. prop_var_miss = NaN; prop_fa = NaN; total_hits = NaN;
  264. dprime = NaN; total_miss = NaN;
  265. end
  267. %% ------------------------ STORE RESULTS ------------------------
  269. % Adjust incorrect count (optional, per your note)
  270. incorrect = incorrect - maybe_count;
  272. % nummatrixrow = [length(same_hit), length(var_hit), length(same_miss), length(var_miss), length(fa), ...
  273. % prop_same_hit, prop_var_hit, prop_same_miss, prop_var_miss, prop_fa, ...
  274. % total_hits, dprime, total_miss, skipped, inconsistent, maybe_count, incorrect];
  275. nummatrixrow = [skipped, inconsistent, maybe_count, incorrect];
  276. nummatrix = [nummatrix; nummatrixrow];
  277. end

ManipulationCheckOnlyAnalysis.m, no license · at the source

Overview

Authors: Ece Yuksel1, Erica S Shafer1, Madeline Netto1, Rachel A Diana1
ORCID iDs: Ece Yuksel, Rachel A Diana
  1. Department of Psychology, Virginia Tech, Blacksburg, VA, United States
Institutions: Virginia Tech (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1214
Dates: received 18 March 2026; accepted 18 March 2026; published online 24 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1214 · PMID 42052507 · PMCID PMC13112209 · OpenAlex W7143441916
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), none (in silico) (organism), cognitive (subfield)
Methods: Statistics, Connectivity, fMRI & imaging, Machine learning
Keywords: fMRI, representational similarity analysis, episodic memory, hippocampus, context variability, encoding variability
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Science Foundation (1850674); Directorate for Social, Behavioral and Economic Sciences (1850674)
Citations: not cited yet (Europe PMC); 55 references in the paper
Research resources: RRID:SCR_005994

Abstract

Studies of hippocampal pattern similarity during event encoding and its relationship to subsequent memory retrieval have revealed inconsistent results. Our laboratory recently found evidence that differences in cognitive processing during encoding can modulate the relationship between hippocampal pattern similarity and recognition success. This finding is consistent with the theoretical proposal that hippocampal representations have a dynamic relationship to memory retrieval in which cognitive goals are influential. However, there have been few attempts to replicate representational similarity findings from functional magnetic resonance imaging (fMRI) and, to our knowledge, no evidence of successful replication in either the hippocampus or subsequent memory studies. In order to draw strong theoretical conclusions from our findings, or others in the literature, it is important to demonstrate that those findings are robust. The current study attempted a direct replication of our prior experiment with the exception of minor modifications in the neuroimaging parameters, which were intended to assess the degree to which representational similarity analyses are influenced by reasonable technical differences in data collection. We did not replicate the finding that cognitive variability interacts with future recognition success in the hippocampus. Overall, we failed to replicate 9 out of 12 significant F-test results from the Lim et al. study. The three findings that were replicated can be explained by minor visual differences in stimulus presentation on variable cognitive context trials. In addition, we found three new significant effects in the current study that did not previously appear in our earlier study. Therefore, we conclude that fMRI studies using representational similarity analysis of subsequent memory performance are sensitive to minor methodological variation. Further tests of the replicability of these findings are needed prior to drawing theoretical conclusions from their results. Preregistered Stage 1 protocol: https://osf.io/njzhq (date of in-principle acceptance: August 1, 2025) Final recommended Stage 2 manuscript and materials: https://doi.org/10.17605/OSF.IO/ZUYNA PCI:RR Stage 1 recommendation: https://rr.peercommunityin.org/articles/rec?id=873 PCI:RR Stage 2 recommendation: https://rr.peercommunityin.org/articles/rec?id=1278

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 1 match between paragraphs and lines of code.

OSF zuyna

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: MATLAB (21)
Size: 96 files, 21 scripts
Software Heritage: not checked
Found in: “Data and Code Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (12 files), Image Processing Toolbox (3 files), Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
21 files
At the source: doi.org/10.17605/OSF.IO/ZUYNA

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;
  • 21 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 and Code Availability

All raw data, custom scripts, RSA output, and statistical output are available at: https://doi.org/10.17605/OSF.IO/ZUYNA

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 6 keywords, 2 funders, 55 references, 1 RRID.

Cite

This paper

Yuksel, E., Shafer, E. S., Netto, M., & Diana, R. A. (2026). Replicability of representational similarity and its role in successful memory retrieval. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1214. https://doi.org/10.1162/imag.a.1214

BibTeX

@article{yuksel2026replicability,
author = {Yuksel, Ece and Shafer, Erica S and Netto, Madeline and Diana, Rachel A},
title = {{Replicability of representational similarity and its role in successful memory retrieval}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {4},
pages = {IMAG.a.1214},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1214},
url = {https://doi.org/10.1162/imag.a.1214},
pmid = {42052507},
pmcid = {PMC13112209}
}

RIS

TY - JOUR
AU - Yuksel, Ece
AU - Shafer, Erica S
AU - Netto, Madeline
AU - Diana, Rachel A
TI - Replicability of representational similarity and its role in successful memory retrieval
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/04/24
VL - 4
SP - IMAG.a.1214
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1214
UR - https://doi.org/10.1162/imag.a.1214
LA - en
ER -

CSL-JSON

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"id": "10.1162/imag.a.1214",
"type": "article-journal",
"title": "Replicability of representational similarity and its role in successful memory retrieval",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Yuksel",
"given": "Ece"
},
{
"family": "Shafer",
"given": "Erica S"
},
{
"family": "Netto",
"given": "Madeline"
},
{
"family": "Diana",
"given": "Rachel A"
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1214",
"DOI": "10.1162/imag.a.1214",
"PMID": "42052507",
"PMCID": "PMC13112209",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1214",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
24
]
]
}
}

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