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Doing the math: detection and neural processing of auditory task-unrelated stimuli while solving arithmetic tasks.

Code ↔ Paper

2 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 2 matches
  1. [1] § Methods and materials › Statistical analyses ↔ EEG Data/MultiverseANOVA.m, lines 34 to 138 · score 0.87 · P3b amplitude, 600 ms, 800 ms, 700 ms, ANOVAs, widths · Report a mismatch
  2. [2] § Methods and materials › Statistical analyses ↔ EEG Data/MultiverseANOVA.m, lines 34 to 138 · score 0.74 · N1 peak, P3b amplitudes, 159 ms, 700 ms, ANOVAs, component · Report a mismatch

Paper

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The authors' code

MATLAB · 249 lines · 9.4 KB · no license · 2 matches

  1. %% Multiverse Analysis
  3. % Add fieldtrip.
  4. ft_defaults;
  7. %% Load data
  9. % Load one of the four data sets (2 filters, 2 references).
  10. % Add path!!
  11. load('~\data_0.1Hz_avg.mat');
  12. load('brewermap.mat')
  14. % Remove 4 IDs in the following three data sets:
  15. % data_0.1Hz_avg.mat, data_1Hz_avg.mat, data_1Hz_mastoids.mat
  16. % Leave out this step for the following data set:
  17. % data_0.1Hz_mastorids
  18. % erpArray_chan_samp_sub_cond = erpArray_chan_samp_sub_cond(:,:,defaultInclusion,:);
  20. t = round(t);
  22. %% Normalisation
  24. % In case of normalisation:
  25. diffErpArray_chan_samp_sub_cond = erpArray_chan_samp_sub_cond(:,:,:,2:4) - repmat(erpArray_chan_samp_sub_cond(:,:,:,1),1,1,1,3);
  26. diffErpArray_chan_samp_sub_cond(:,:,:,4:6) = erpArray_chan_samp_sub_cond(:,:,:,6:8) - repmat(erpArray_chan_samp_sub_cond(:,:,:,5),1,1,1,3);
  27. diffConditionList = conditionList([2,3,4,6,7,8]);
  29. % In case of no normalisation:
  30. % diffErpArray_chan_samp_sub_cond = erpArray_chan_samp_sub_cond(:,:,:,2:4);
  31. % diffErpArray_chan_samp_sub_cond(:,:,:,4:6) = erpArray_chan_samp_sub_cond(:,:,:,6:8);
  32. % diffConditionList = conditionList([2,3,4,6,7,8]);
  34. %% Compute multiverse
  36. % N1 Amplitude
  37. comp{1}.name = 'N1';
  39. % N1-peaks for average reference
  40. % comp{1}.peaksPerConditionPerIteration = [209,207,181;209,207,181;209,207,181];
  42. % N1-peaks for mastoid reference
  43. comp{1}.peaksPerConditionPerIteration = [220,201,159;220,201,159;220,201,159];
  44. comp{1}.widthPerIteration = [30,50,70];
  45. comp{1}.yLabels = {'30 ms','(original) 50 ms','70 ms'};
  46. comp{1}.xLabels = {'narrow','original','wide'};
  48. % channel names for average reference
  49. % comp{1}.channelsPerIteration = {{'FC3_avr','FC4_avr','C3_avr','C4_avr','C5_avr','C6_avr'},...
  50. % {'F3_avr','F4_avr','F5_avr','F6_avr','FC3_avr','FC4_avr','FC5_avr','FC6_avr','C3_avr','C4_avr','C5_avr','C6_avr'},...
  51. % {'F3_avr','F4_avr','F5_avr','F6_avr','FC3_avr','FC4_avr','FC5_avr','FC6_avr','C3_avr','C4_avr','C5_avr','C6_avr','CP3_avr','CP4_avr','C1_avr','C2_avr'}};
  53. % channel names for mastoid reference
  54. comp{1}.channelsPerIteration = {{'FC3_Av..nce','FC4_Av..nce','C3_Ave..nce','C4_Ave..nce','C5_Ave..nce','C6_Ave..nce'},...
  55. {'F3_Ave..nce','F4_Ave..nce','F5_Ave..nce','F6_Ave..nce','FC3_Av..nce','FC4_Av..nce','FC5_Av..nce','FC6_Av..nce','C3_Ave..nce','C4_Ave..nce','C5_Ave..nce','C6_Ave..nce'},...
  56. {'F3_Ave..nce','F4_Ave..nce','F5_Ave..nce','F6_Ave..nce','FC3_Av..nce','FC4_Av..nce','FC5_Av..nce','FC6_Av..nce','C3_Ave..nce','C4_Ave..nce','C5_Ave..nce','C6_Ave..nce','CP3_Av..nce','CP4_Av..nce','C1_Ave..nce','C2_Ave..nce'}};
  58. % P3b Amplitude
  59. comp{2}.name = 'P3b';
  61. % P3-"peaks" for average and mastoid reference
  62. comp{2}.peaksPerConditionPerIteration = [450,450,450;500,500,500;550,550,550];
  63. comp{2}.widthPerIteration = [150,200,250];
  64. comp{2}.yLabels = {'[300 600]','(original) [300 700]','[300 800]'};
  65. comp{2}.xLabels = {'narrow','medium','original'};
  67. % channel names for average reference
  68. % comp{2}.channelsPerIteration = {{'Pz_avr'},...
  69. % {'P1_avr','P2_avr','Pz_avr','POz_avr','CPz_avr','CP1_avr','CP2_avr'},...
  70. % {'CP1_avr','CP2_avr','CP3_avr','CP4_avr','CPz_avr','P1_avr','P2_avr','P3_avr','P4_avr','Pz_avr','POz_avr','PO3_avr','PO4_avr'}};
  72. % channel names for mastoid reference
  73. comp{2}.channelsPerIteration = {{'Pz_Ave..nce'},...
  74. {'P1_Ave..nce','P2_Ave..nce','Pz_Ave..nce','POz_Av..nce','CPz_Av..nce','CP1_Av..nce','CP2_Av..nce'},...
  75. {'CP1_Av..nce','CP2_Av..nce','CP3_Av..nce','CP4_Av..nce','CPz_Av..nce','P1_Ave..nce','P2_Ave..nce','P3_Ave..nce','P4_Ave..nce','Pz_Ave..nce','POz_Av..nce','PO3_Av..nce','PO4_Av..nce'}};
  77. nComps = numel(comp);
  78. T = [];
  79. for c = 1:nComps
  80. nIOIs = numel(comp{c}.widthPerIteration);
  81. nChannelGroups = numel(comp{c}.channelsPerIteration);
  82. for i = 1:nIOIs
  83. for g = 1:nChannelGroups
  85. isChannel = ismember(channels,comp{c}.channelsPerIteration{g});
  86. find(isChannel)
  87. avg = nan(37,3,2);
  88. for l = 1:2 %load
  89. for v = 1:3 % sound volume
  90. peakMs = comp{c}.peaksPerConditionPerIteration(i,v);
  91. width = comp{c}.widthPerIteration(i);
  92. ioi = [peakMs-width peakMs+width];
  93. allIOIs{c,i} = ioi;
  94. isInIoi = t >= ioi(1) & t <= ioi(2);
  95. theCondition = (l-1)*3 + v;
  96. ampl = squeeze(mean(mean(diffErpArray_chan_samp_sub_cond(isChannel,isInIoi,:,theCondition),1),2));
  97. avg(:,v,l) = ampl;
  99. N = length(ampl);
  100. subject = (1:N)';
  101. component = repmat({comp{c}.name},N,1);
  102. volume = ones(N,1)*v;
  103. loadLevel = ones(N,1)*l;
  104. ioiIndex = ones(N,1)*i;
  105. channelGroup = ones(N,1)*g;
  107. theT = table(subject,ampl,component,volume,loadLevel,ioiIndex,channelGroup);
  108. T = [T;theT];
  110. end
  111. end
  113. ll_int1 = avg(:,1,1);
  114. ll_int2 = avg(:,2,1);
  115. ll_int3 = avg(:,3,1);
  116. hl_int1 = avg(:,1,2);
  117. hl_int2 = avg(:,2,2);
  118. hl_int3 = avg(:,3,2);
  120. % Add path!!
  121. writetable(table(ll_int1,ll_int2,ll_int3,hl_int1,hl_int2,hl_int3),['~\',comp{c}.name,'_IOI',num2str(i),'_ChannGroup',num2str(g),'.csv'])
  123. result{c,i,g} = mb_repeatedMeasuresANOVA(avg,[3,2],{'Intensity','Load'});
  125. FMat_mainIntens{c}(i,g) = result{c,i,g}.reducedTable{2,4};
  126. FMat_mainLoad{c}(i,g) = result{c,i,g}.reducedTable{3,4};
  127. FMat_IntXLoad{c}(i,g) = result{c,i,g}.reducedTable{4,4};
  129. pMat_mainIntens{c}(i,g) = result{c,i,g}.reducedTable{2,5};
  130. pMat_mainLoad{c}(i,g) = result{c,i,g}.reducedTable{3,5};
  131. pMat_IntXLoad{c}(i,g) = result{c,i,g}.reducedTable{4,5};
  133. end
  134. end
  135. end
  137. % Add path!!
  138. save('~\multiverseAmplitudes.mat','allIOIs','comp','result','T','FMat*','pMat*');
  141. %% Make illustrations
  143. % Add path!!
  144. load('~\multiverseAmplitudes.mat','comp','result','T','FMat*','pMat*');
  146. nComps = numel(comp);
  147. myMap = ones(1000,3).*0.7;
  148. myMap(51:100,:) = mb_colorGradient([0 0.44706 0.74118],[0.7,0.7,0.7],50);
  149. myMap(1:50,:) = mb_colorGradient([1,1,0.5],[0.8,0.078,0.18],50);
  151. figure('Position',[375 422 1009 384]);
  153. subplot(2,3,1)
  154. h = heatmap(round(pMat_mainIntens{1},3),'ColorLimits',[0,1],'ColorScaling','scaled','Colormap',myMap);
  155. h.XDisplayLabels = comp{1}.xLabels;
  156. h.YDisplayLabels = comp{1}.yLabels;
  157. title('SNR')
  158. ylabel('N1')
  160. subplot(2,3,2)
  161. h = heatmap(round(pMat_mainLoad{1},3),'ColorLimits',[0,1],'ColorScaling','scaled','Colormap',myMap);
  162. h.XDisplayLabels = comp{1}.xLabels;
  163. h.YDisplayLabels = comp{1}.yLabels;
  164. title('Load')
  166. subplot(2,3,3)
  167. h = heatmap(round(pMat_IntXLoad{1},3),'ColorLimits',[0,1],'ColorScaling','scaled','Colormap',myMap);
  168. h.XDisplayLabels = comp{1}.xLabels;
  169. h.YDisplayLabels = comp{1}.yLabels;
  170. title('Load x SNR')
  172. subplot(2,3,4)
  173. h = heatmap(round(pMat_mainIntens{2},3),'ColorLimits',[0,1],'ColorScaling','scaled','Colormap',myMap);
  174. h.XDisplayLabels = comp{2}.xLabels;
  175. h.YDisplayLabels = comp{2}.yLabels;
  176. title('SNR')
  177. ylabel('P3b')
  179. subplot(2,3,5)
  180. h = heatmap(round(pMat_mainLoad{2},3),'ColorLimits',[0,1],'ColorScaling','scaled','Colormap',myMap);
  181. h.XDisplayLabels = comp{2}.xLabels;
  182. h.YDisplayLabels = comp{2}.yLabels;
  183. title('Load')
  185. subplot(2,3,6)
  186. h = heatmap(round(pMat_IntXLoad{2},3),'ColorLimits',[0,1],'ColorScaling','scaled','Colormap',myMap);
  187. h.XDisplayLabels = comp{2}.xLabels;
  188. h.YDisplayLabels = comp{2}.yLabels;
  189. title('Load x SNR')
  192. %% Add topographies
  194. for c = 1:nComps
  195. figure
  196. nIOIs = numel(comp{c}.widthPerIteration);
  197. nChannelGroups = numel(comp{c}.channelsPerIteration);
  198. for i = 1:nIOIs
  199. for g = 1:nChannelGroups
  201. selectedChannels = ismember(channels,comp{c}.channelsPerIteration{g});
  203. timelock = [];
  204. timelock.time = t;
  205. timelock.label = lay.label;
  206. timelock.avg = squeeze(mean(mean(diffErpArray_chan_samp_sub_cond(:,:,:,:),3),4));
  208. timelock.dimord = 'chan_time';
  210. subplot(nIOIs,nChannelGroups,g+(i-1)*nChannelGroups)
  212. layOfInterest = lay;
  213. layOfInterest.pos = lay.pos(selectedChannels,:);
  214. layOfInterest.width = lay.width(selectedChannels,:);
  215. layOfInterest.height = lay.height(selectedChannels,:);
  216. layOfInterest.label = lay.label(selectedChannels);
  218. cfg = [];
  219. cfg.xlim= allIOIs{c,i};
  220. cfg.zlim = [];
  221. cfg.gridscale = [];
  222. cfg.marker = 'on';
  223. cfg.markersize = 3;
  224. cfg.parameter = 'avg';
  225. cfg.channel = [];
  226. cfg.highlight = 'on';
  228. cfg.highlightchannel = lay.label(selectedChannels);
  229. cfg.highlightcolor = 'k';
  230. cfg.highlightsize = 8;
  231. cfg.highlightsymbol = '.';
  232. cfg.figure = 0;
  233. cfg.comment =' ';
  234. cfg.commentpos = 'title';
  235. cfg.layout = lay;
  236. cfg.style = 'straight';
  237. cfg.interactive = 'off';
  238. cfg.colormap = brewermap;
  240. ft_topoplotER(cfg,timelock);
  242. end
  243. end
  244. end

MultiverseANOVA.m, no license · at the source

Overview

Authors: Laura Brockhoff1, Xenia Alteköster1, Maximilian Bruchmann1,2, Sebastian Schindler1,2, Robert Moeck1, Thomas Straube1,2
ORCID iDs: Laura Brockhoff, Maximilian Bruchmann, Sebastian Schindler
  1. Institute of Medical Psychology and Systems Neuroscience, University of Münster, Von-Esmarch-Str. 52, D-48149 Münster, Germany
  2. Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Fliednerstr. 21, D-48149 Münster, Germany
Institutions: University of Münster (Germany)
Journal: Neuroscience of consciousness, volume 2026, issue 1, article niag059
Dates: received 22 December 2025; accepted 7 July 2026; published online 18 September 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/nc/niag059 · PMID 42761790 · PMCID PMC13587093 · OpenAlex W7112681917
Open access: gold, a free copy (OpenAlex)
Preprint: osf.io/5fehs
Status: code verified (how the code is found and verified)
Categories: EEG (modality), cognitive (subfield)
Methods: Statistics, Preprocessing, Evoked potentials, Physiology & signal measures
Keywords: Load Theory, cognitive load, attentional bottleneck, task-unrelated stimuli, event-related potentials
Topic: Cognitive and developmental aspects of mathematical skills (Developmental and Educational Psychology, Psychology), according to OpenAlex
Citations: not cited yet (Europe PMC); 80 references in the paper

Abstract

Load Theory proposes that perceptual load impairs the processing of task-unrelated stimuli. However, there is increasing evidence that not only perceptual demands but also cognitive task load affect the processing of task-unrelated stimuli. The current event-related potential (ERP) study investigated the effects of cognitive load induced by arithmetic operations on the detection and neural processing of intensity-modulated beep tones. The tasks were based on different instructions regarding perceptually identical displays of numbers. Results showed decreased stimulus detection and reduced P3b in the ERP to auditory stimuli under high compared with low load across different signal-to-noise ratios (SNR). However, the N1 amplitude was unaffected by load based on our typical analytical pipeline using an average reference. Using a linked mastoids reference and strong filtering revealed an effect of load on the N1 for the highest SNR. Thus, analytical pipelines may affect the results. Nevertheless, the lower probability of reported awareness of task-unrelated auditory stimuli during difficult mathematical tasks across SNR levels seems to be driven predominantly by a late neural effect.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

OSF 5fehs

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Languages: SPSS (2), MATLAB (1)
Size: 50 files, 3 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
3 files
At the source: osf.io/5fehs/?view_only=a0e6759a4e29465a93b111633ae87de7

The paper's code and data availability statement is in the Data section.

Tracing map

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Data

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

Data availability

Data and code are available on the Open Science Framework and are accessible via https://osf.io/5fehs/?view_only=a0e6759a4e29465a93b111633ae87de7.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 77 references.

Cite

This paper

Brockhoff, L., Alteköster, X., Bruchmann, M., Schindler, S., Moeck, R., & Straube, T. (2026). Doing the math: detection and neural processing of auditory task-unrelated stimuli while solving arithmetic tasks. Neuroscience of consciousness, 2026(1), niag059. https://doi.org/10.1093/nc/niag059

BibTeX

@article{brockhoff2026doing,
author = {Brockhoff, Laura and Alteköster, Xenia and Bruchmann, Maximilian and Schindler, Sebastian and Moeck, Robert and Straube, Thomas},
title = {{Doing the math: detection and neural processing of auditory task-unrelated stimuli while solving arithmetic tasks}},
journal = {Neuroscience of consciousness},
year = {2026},
month = sep,
volume = {2026},
number = {1},
pages = {niag059},
publisher = {Oxford University Press},
issn = {2057-2107},
doi = {10.1093/nc/niag059},
url = {https://doi.org/10.1093/nc/niag059},
pmid = {42761790},
pmcid = {PMC13587093}
}

RIS

TY - JOUR
AU - Brockhoff, Laura
AU - Alteköster, Xenia
AU - Bruchmann, Maximilian
AU - Schindler, Sebastian
AU - Moeck, Robert
AU - Straube, Thomas
TI - Doing the math: detection and neural processing of auditory task-unrelated stimuli while solving arithmetic tasks
T2 - Neuroscience of consciousness
J2 - Neurosci Conscious
PY - 2026
DA - 2026/09/18
VL - 2026
IS - 1
SP - niag059
SN - 2057-2107
PB - Oxford University Press
DO - 10.1093/nc/niag059
UR - https://doi.org/10.1093/nc/niag059
LA - en
ER -

CSL-JSON

{
"id": "10.1093/nc/niag059",
"type": "article-journal",
"title": "Doing the math: detection and neural processing of auditory task-unrelated stimuli while solving arithmetic tasks",
"container-title": "Neuroscience of consciousness",
"author": [
{
"family": "Brockhoff",
"given": "Laura"
},
{
"family": "Alteköster",
"given": "Xenia"
},
{
"family": "Bruchmann",
"given": "Maximilian"
},
{
"family": "Schindler",
"given": "Sebastian"
},
{
"family": "Moeck",
"given": "Robert"
},
{
"family": "Straube",
"given": "Thomas"
}
],
"container-title-short": "Neurosci Conscious",
"volume": "2026",
"issue": "1",
"page": "niag059",
"DOI": "10.1093/nc/niag059",
"PMID": "42761790",
"PMCID": "PMC13587093",
"ISSN": "2057-2107",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/nc/niag059",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
18
]
]
}
}

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