OSCR

Action or Stimulus: Individual Beliefs About Learned Associations Influence the Processing of Immediate and Delayed Feedback.

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] § Method › Data Analysis › Behavioral Data Analyses › PE Modelling ↔ MATLAB Scripts/PE_2Alphas6Q_based_on_responses_AI.m, lines 1–46 · score 0.61 · best fitting model, stimulus pairs, unchosen, expectations, hlinger, Albrecht
  2. [2] § Method › Data Analysis › EEG Data Analyses › Preprocessing ↔ MATLAB Scripts/create_single_trial_v1.m, lines 1–67 · score 0.54 · peak latencies, 140 ms, 250 ms, electrodes, amplitudes
  3. [3] § Method › Data Analysis › EEG Data Analyses › Preprocessing ↔ MATLAB Scripts/create_single_trial_v1_diff.m, lines 1–62 · score 0.54 · peak latencies, 140 ms, 250 ms, electrodes, amplitudes
  4. [4] § Method › Data Analysis › EEG Data Analyses › Preprocessing ↔ MATLAB Scripts/create_single_trial_v1.m, lines 1–67 · score 0.51 · peak latency, segments, 200 ms, 400 ms, electrodes, amplitudes
  5. [5] § Method › Data Analysis › EEG Data Analyses › Preprocessing ↔ MATLAB Scripts/create_single_trial_v1_diff.m, lines 1–62 · score 0.51 · peak latency, segments, 200 ms, 400 ms, electrodes, amplitudes
  6. [6] § Method › Participants ↔ MATLAB Scripts/export_peak_max.m, the whole file · a weak match · score 0.50 · Heinrich Heine University, sseldorf, psychological
  7. [7] § Method › Participants ↔ MATLAB Scripts/export_peak_max_end_dyn.m, the whole file · a weak match · score 0.50 · Heinrich Heine University, sseldorf, psychological

Paper

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

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The authors' code

MATLAB · 279 lines · 12 KB · no license · 2 matches

  1. function create_single_trial_v1(folder)
  2. %before starting, it is important to define the length of the segment
  3. %in ms and at what time the event happens (aka point 0)
  4. segmentlength = 1000;
  5. beforemarker = 200;
  7. %this is where I define the conditions (names should be as in the
  8. %filenames used in BVA export) and electrodes. For the BVA export, I
  9. %use txt export and include electrode names as column names.
  10. conditions = {'Immediate_Negative','Immediate_Positive','NoSoundDelayed_Negative','NoSoundDelayed_Positive','WithSoundDelayed_Negative','WithSoundDelayed_Positive'};
  11. electrodes = {'FzFC1FCzFC2Cz','P7','P8'};
  12. pools = {{'Fz','FC1','FCz','FC2','Cz'}};
  13. windows = {'200_400','140_250'};
  14. %this reads in the single-trial data
  15. restructure_Matrices_st(folder,conditions,electrodes,'',segmentlength,beforemarker,pools);
  16. subjects = string(evalin('base','subjects'));
  18. %here I define some things about the output table
  19. output_name = 'st_data.xlsx';
  20. header = {'Subject','Condition','SegmentNum','Electrode','TimeWindow','pospeak','negpeak','peaktopeak'};
  21. assignin('base','dependentvariables',[]);
  22. row = {};
  23. %this adds rows (calculates the ST-values) for each subject (in German:
  24. %Versuchsperson, so "vp" in short)
  25. for i = 1:size(subjects,1)
  26. subjects(i,:)
  27. for j = 1:size(conditions,2)
  28. % %this function is "where the magic happens" so where the values
  29. % %are exported. The last number that is given to the function is
  30. % %"around" and that defines in what area around the peak latency
  31. % %the amplitudes should be exported. If it is set to 0, it is
  32. % %just the latency itself. If it is set to 10, it includes 10ms
  33. % %before and 10ms after the latency.
  34. addrows(conditions{j},electrodes,segmentlength,subjects(i,:),10,windows);
  35. end
  36. end
  37. %now I just combine behavioral and EEG data...
  38. dependentvariables = evalin('base','dependentvariables');
  39. deptable = cell2table(dependentvariables,'VariableNames',header);
  40. deptable.Subject = str2num(str2mat(deptable.Subject));
  41. behavioral = evalin('base','behavioral');
  42. behavioral.Condition = cellstr(behavioral.Condition);
  45. %check if there are any vps in deptable, but not in behavioral or other
  46. %way around
  47. % subjects_behavioral = string(evalin('base','subjects_behavioral'));
  48. % for i = 1:size(subjects,1)
  49. % if ~ismember(subjects(i,:),subjects_behavioral)
  50. % rows = ~(strcmp(deptable.Subject,subjects(i)));
  51. % deptable = deptable(rows,:);
  52. % end
  53. % end
  54. % for i = 1:size(subjects_behavioral,1)
  55. % if ~ismember(subjects_behavioral(i,:),subjects)
  56. % rows = (~strcmp(behavioral.Subject,subjects_behavioral(i,:)));
  57. % behavioral = behavioral(rows,:);
  58. % end
  59. % end
  60. % behavioral.Subject = string(behavioral.Subject);
  61. % deptable.Subject = string(deptable.Subject);
  62. assignin('base','deptable_eeg',deptable);
  63. assignin('base','beh',behavioral);
  64. alltab = join(deptable,behavioral);
  65. writetable(alltab,'dependentvariables_st_v1.xlsx');
  66. assignin('base', 'dependentvariables_st_v1', alltab);
  67. end
  69. function addrows(condition,electrodes,segmentlength,vp,around,windows)
  70. matrix = evalin('base',strcat(condition,'_st'));
  71. rows = (strcmp(matrix.vp,vp));
  72. matrix = matrix(rows,:);
  73. positivepeak = evalin('base','positivepeak');
  74. negativepeak = evalin('base','negativepeak');
  75. dependentvariables = evalin('base','dependentvariables');
  76. subjects = evalin('base','subjects');
  77. subjectindex = find(strcmp(subjects,vp));
  78. subjectindex = subjectindex(1)
  79. row = {};
  80. tempdep = [];
  81. for j = 1:(floor(size(matrix,1)/segmentlength))
  82. rows = (matrix.segmentNum == j);
  83. tempmatrix = matrix(rows,:);
  84. j
  85. %indexadd = Number of previous segments * segment length
  86. indexadd = (j-1) * segmentlength;
  87. peakvals = zeros(size(electrodes,2),3);
  88. countneg = 0;
  89. countpos = 0;
  90. for k = 1:size(peakvals,1)
  91. for m = 1:size(peakvals,2)
  92. peakvals(k,m) = 99999;
  93. end
  94. end
  95. for k = 1:size(electrodes,2)
  96. for m = 1:size(windows,2)
  97. colname = strcat(electrodes{k},'_',condition,'_',windows{m},'_latency');
  98. if ismember(colname,positivepeak.Properties.VariableNames)
  99. if positivepeak{subjectindex,colname} < 99999
  100. rows = (tempmatrix.ms >= positivepeak{subjectindex,colname}-around);
  101. tempmatrix_pos = tempmatrix(rows,:);
  102. rows = (tempmatrix_pos.ms <= positivepeak{subjectindex,colname}+around);
  103. tempmatrix_pos = table2array(tempmatrix_pos(rows,electrodes{k}));
  104. peakvals(k,1) = mean(tempmatrix_pos);
  105. end
  106. if negativepeak{subjectindex,colname} < 99999
  107. rows = (tempmatrix.ms >= negativepeak{subjectindex,colname}-around);
  108. tempmatrix_neg = tempmatrix(rows,:);
  109. rows = (tempmatrix_neg.ms <= negativepeak{subjectindex,colname}+around);
  110. tempmatrix_neg = table2array(tempmatrix_neg(rows,electrodes{k}));
  111. peakvals(k,2) = mean(tempmatrix_neg);
  112. end
  113. if peakvals(k,1) < 99999 && peakvals(k,2) < 99999
  114. peakvals(k,3) = peakvals(k,2) - peakvals(k,1);
  115. end
  116. row = {convertStringsToChars(vp)};
  117. row = [row,condition];
  118. row = [row,j,electrodes{k},windows{m},peakvals(k,1), peakvals(k,2), peakvals(k,3)];
  119. tempdep = [tempdep;row];
  120. end
  121. end
  122. end
  123. end
  124. dependentvariables = [dependentvariables;tempdep];
  125. assignin('base','dependentvariables',dependentvariables);
  126. end
  128. function restructure_Matrices_st(folder,conditions,electrodes,filesel,segmentlength,timebeforeevent,pools)
  129. % Include 'include' or exclude '/exclude' filename parts
  130. %conditions is an array with all conditions as strings. These need to
  131. %be equal as to what they are called in the file names (including upper
  132. %and lower case!)
  133. for i = 1:size(conditions,2)
  134. output_name = strcat(conditions{i},'_st');
  135. restructure(filenames(folder,conditions{i},'/AV','/Report','/08_','/12_','/20_','/23_','/29_','/39_','/47_','/Mast_','/FCz_'),output_name,folder,electrodes,segmentlength,timebeforeevent,pools);
  136. end
  137. end
  139. function restructure(currfilenames,varname,folder,electrodes,segmentlength,timebeforeevent,pools)
  140. subject_index = (1:2);
  141. %if contains( who('vps*') , 'vps' )
  142. % vps = evalin('base','vps');
  143. %else
  144. subjects = [];
  145. %end
  146. maxsize = 0;
  147. all_tab = [];
  148. for i = 1:length(currfilenames)
  149. i = i
  150. filename = strcat(folder,'/',currfilenames{i})
  151. temptab = readtable(filename,'Delimiter','space');
  152. size(pools)
  153. pools
  154. electrode_names = evalin('base','electrode_names_original');
  155. counter = 1;
  156. for j = 1:size(electrode_names,1)
  157. if strcmp(electrode_names{j},temptab.Properties.VariableNames{counter})
  158. counter = counter + 1;
  159. else
  160. temptab.new = NaN(size(temptab,1),1);
  161. variable_names = temptab.Properties.VariableNames;
  162. variable_names{size(variable_names,2)} = electrode_names{j};
  163. temptab.Properties.VariableNames = variable_names;
  164. end
  165. end
  167. for j = 1:size(pools,2)
  168. orwidth = width(temptab);
  169. temptab.pool = mean(temptab{:,pools{1,j}},2,"omitnan");
  170. colname = '';
  171. for k = 1:size(pools{1,j},2)
  172. colname = strcat(colname,pools{1,j}{1,k})
  173. end
  174. temptab.Properties.VariableNames{orwidth+1} = colname;
  175. end
  176. temptab = temptab(:,electrodes);
  177. subject = cell(size(temptab,1),1);
  179. subject(:) = cellstr(currfilenames{i}(subject_index));
  180. %segmentNum = cell(segmentlength,1);
  181. msNumTemp = (timebeforeevent*(-1):segmentlength-timebeforeevent-1);
  182. msNumTemp = num2cell(permute(msNumTemp,[2 1]));
  183. msNum = [];
  184. segmentNumTemp = cell(segmentlength,1);
  185. segmentNum = [];
  186. floor(size(temptab,1)/segmentlength)
  187. for j = 1:floor(size(temptab,1)/segmentlength)
  188. segmentNumTemp(:) = num2cell(j);
  189. if size(msNum,1) == 0
  190. msNum = msNumTemp;
  191. segmentNum = segmentNumTemp;
  192. else
  193. msNum = cat(1,msNum,msNumTemp);
  194. segmentNum = cat(1,segmentNum,segmentNumTemp);
  195. end
  196. end
  197. size(segmentNum)
  198. size(subject)
  199. size(msNum)
  200. size(temptab)
  201. temptab = [temptab subject msNum segmentNum];
  202. temptab.Properties.VariableNames{size(electrodes,2)+1} = 'vp';
  203. temptab.Properties.VariableNames{size(electrodes,2)+2} = 'ms';
  204. temptab.Properties.VariableNames{size(electrodes,2)+3} = 'segmentNum';
  205. if size(all_tab,1) == 0
  206. all_tab = temptab;
  207. else
  208. all_tab = [all_tab;temptab];
  209. end
  210. subjectcode = string(currfilenames{i}(subject_index))
  211. %if ~vps.includes(vpcode)
  212. subjects = [subjects; subjectcode]
  213. %end
  214. end
  216. assignin('base',varname,all_tab);
  217. assignin('base','subjects',subjects);
  218. end
  220. function [filenames]=filenames(folder, varargin)
  221. % Read filenames from a folder and output a cell with all names matching
  222. % the conditions set in varargin.
  223. % by Alexander Seidel - 2016
  224. %
  225. % INPUT
  226. % folder [string] Total or relative path to the folder.
  227. % varargin [string] Arbitrary amount of strings to specifiy
  228. % conditions. Condition strings starting with '/'
  229. % are used to exclude entries from the file name
  230. % list, all others are form a requirement each
  231. % entry must meet. Each entry must meet all
  232. % requirements set by the conditions.
  233. %
  234. % OUTPUT
  235. % files [cell] vertical cell array with all filenames that
  236. % meet the specified conditions.
  238. % Read filenames and remove all entries that are folders
  239. filenames = dir(folder);
  240. filenames = {filenames([filenames.isdir] == 0).name}';
  242. if isempty(filenames)
  243. error('The Folder is empty.');
  244. end
  246. % Remove entries not matching the conditions
  247. if ~isempty(varargin)
  248. for i=1:length(varargin)
  249. if varargin{i}(1) == '/'
  250. rows = ~strfindl(filenames,varargin{i}(2:end));
  251. else
  252. rows = strfindl(filenames,varargin{i});
  253. end
  255. filenames = filenames(rows);
  256. end
  257. end
  259. if isempty(filenames)
  260. error('No files matching the criteria were found.');
  261. end
  262. end
  264. function xlsoverwrite(filename,data)
  265. % Overwrites an excel file instead of just changing
  266. % fields in an existing one
  267. if exist(filename, 'file')
  268. delete (filename);
  269. end
  270. xlswrite(filename,data);
  271. end
  273. function index=strfindl(str, pattern)
  274. %% A logical version of strfind
  276. strfound = strfind(str,pattern);
  277. index = cell2mat(cellfun(@(x) ~isempty(x),strfound,'uni', false));
  279. end

create_single_trial_v1.m, no license · at the source

Overview

Authors: Christine Albrecht1, Marta Ghio1, Christian Bellebaum1
ORCID iDs: Christine Albrecht
  1. Faculty of Mathematics and Natural Sciences Heinrich Heine University Düsseldorf Düsseldorf Germany
Institutions: Heinrich Heine University Düsseldorf (Germany)
Journal: The European journal of neuroscience, volume 63, issue 5, article e70451
Dates: received 22 April 2025; accepted 19 February 2026; published online 6 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/ejn.70451 · PMID 41793036 · PMCID PMC12966775 · OpenAlex W7134124835
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Statistics, Physiology & signal measures
Keywords: feedback delay, feedback‐association types, FRN/N2, N170, prediction error
MeSH: Association Learning*, Brain*, Feedback, Psychological*, Adult, Electroencephalography, Evoked Potentials, Female, Humans, Male, Reward, Young Adult (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG) (467460456)
Citations: cited by 1 paper (Europe PMC); 74 references in the paper

Abstract

Feedback learning seems to involve two systems, the striatal reward system and the medial temporal lobe (MTL), which have both been linked to event‐related potential (ERP) components such as the feedback‐related negativity (FRN)/N2, overlapped by a reward positivity (RewP), and the N170, respectively. In this study, we tested the hypothesis that the former system is more involved in associating the feedback with previous actions and the latter in associating the feedback with previous stimuli. More specifically, we hypothesized that the engagement of these systems depends on individual beliefs in credit assignment, that is, whether participants linked the feedback they received to actions or stimuli, possibly modulated by feedback timing. Electroencephalography (EEG) data were recorded from 43 participants performing an ambiguous feedback–learning task, in which feedback could be attributed to either a performed action or a selected stimulus, according to the instruction. As revealed by an Action Index derived from behavioral data, the focus on stimulus–feedback associations was generally stronger than that on action–feedback associations. We found that both FRN/N2 and N170 were influenced by individual beliefs about learned associations, with the FRN/N2 showing stronger feedback valence coding across feedback delays when participants took action–feedback associations into account. Also prediction error coding in the N170 was more pronounced for stronger action–feedback association learning. The results seem to suggest that both learning systems are recruited, at least to some extent, when action–feedback and stimulus–feedback associations are considered simultaneously.

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.

OSF f3r42

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: MATLAB (30), R (6)
Size: 327 files, 36 scripts
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Optimization Toolbox (6 files), car (1 file), ggplot2 (1 file), ggpubr (1 file), lme4 (1 file), lmerTest (1 file), reshape2 (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
36 files
At the source: doi.org/10.17605/OSF.IO/F3R42

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;
  • 36 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

No dataset and no data link were found in the paper.

Data Availability Statement

The study was preregistered on 10.17605/OSF.IO/Z9MPA (https://doi.org/10.17605/OSF.IO/Z9MPA). The data that support the findings of this study and all analysis scripts are openly accessible through the Open Science Framework at 10.17605/OSF.IO/F3R42 (https://doi.org/10.17605/OSF.IO/F3R42).

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, issue, pages, dates, 3 authors, 5 keywords, 11 MeSH terms, 1 funder, 71 references.

Cite

This paper

Albrecht, C., Ghio, M., & Bellebaum, C. (2026). Action or Stimulus: Individual Beliefs About Learned Associations Influence the Processing of Immediate and Delayed Feedback. The European journal of neuroscience, 63(5), e70451. https://doi.org/10.1111/ejn.70451

BibTeX

@article{albrecht2026action,
author = {Albrecht, Christine and Ghio, Marta and Bellebaum, Christian},
title = {{Action or Stimulus: Individual Beliefs About Learned Associations Influence the Processing of Immediate and Delayed Feedback}},
journal = {The European journal of neuroscience},
year = {2026},
month = mar,
volume = {63},
number = {5},
pages = {e70451},
publisher = {Wiley},
issn = {0953-816X},
doi = {10.1111/ejn.70451},
url = {https://doi.org/10.1111/ejn.70451},
pmid = {41793036},
pmcid = {PMC12966775}
}

RIS

TY - JOUR
AU - Albrecht, Christine
AU - Ghio, Marta
AU - Bellebaum, Christian
TI - Action or Stimulus: Individual Beliefs About Learned Associations Influence the Processing of Immediate and Delayed Feedback
T2 - The European journal of neuroscience
J2 - Eur J Neurosci
PY - 2026
DA - 2026/03/01
VL - 63
IS - 5
SP - e70451
SN - 0953-816X
PB - Wiley
DO - 10.1111/ejn.70451
UR - https://doi.org/10.1111/ejn.70451
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Action or Stimulus: Individual Beliefs About Learned Associations Influence the Processing of Immediate and Delayed Feedback",
"container-title": "The European journal of neuroscience",
"author": [
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"family": "Albrecht",
"given": "Christine"
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"container-title-short": "Eur J Neurosci",
"volume": "63",
"issue": "5",
"page": "e70451",
"DOI": "10.1111/ejn.70451",
"PMID": "41793036",
"PMCID": "PMC12966775",
"ISSN": "0953-816X",
"publisher": "Wiley",
"URL": "https://doi.org/10.1111/ejn.70451",
"language": "en",
"issued": {
"date-parts": [
[
2026,
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]
}
}

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