Temporal predictions shape somatosensory perception.

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 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted

[1] § Methods › EEG preprocessing ↔ EEG Preprocessing Pipeline/EEG_PreProcessing_PT.m, lines 97–116 · score 0.56 · 4–34 Hz, EEG preprocessing, 16 Hz
[2] § Methods › Stimuli and task ↔ Experimental Code/PT_Randomize.m, the whole file · a weak match · score 0.52 · consecutive trials, randomized, guarantee, blocks

Paper

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The authors' code

MATLAB · 334 lines · 8.7 KB · no license · 1 match

%% General Configuration
warning('OFF');
%%%List of EEG DATASETS
directory = 'D:\Old USB\Predictive Timing Analysis 13032024\Raw EEG Data\';
list = dir(sprintf('%s*.eeg',directory));
load('timing_matrix.mat');
load('level_matrix.mat');
%% 1. Reading Data and Trial Definition %% /// AllTrials.mat
for VPLoop = 1:35
fpd = {};
cfg = {};
cfg.sub = VPLoop;
cfg.level_matrix = level_matrix;
cfg.timing_matrix = timing_matrix;
cfg.demean = 'yes';
cfg.detrend = 'no'; %SEE detrend, we will not detrend based on different trial lengths.
%%%Channel Location
cfg.dataset = sprintf('%s%s',directory,list(VPLoop).name);
cfg.layout = 'easycapM11.lay';
cfg.hpfilter = 'no';
%%%Trial Definition
cfg.trialdef.pre = 3; % in seconds, we will take 1 seconds before the cue as a baseline, +2 seconds for taper
cfg.trialdef.post1 = 12; % for ICA and to make comparable, we use the same post-trial-def for all trial types
cfg.trialdef.post2 = 12;
cfg.trialdef.post3 = 12; % in seconds, we will need 2sec cue,4 second delay, 4 seconds stimulus, +2 seconds for taper
cfg.trialfun = 'AllTrials_Correction';
cfg = ft_definetrial(cfg);
datp = ft_preprocessing(cfg);
cfg = [];
fpd = datp;
clear datp
%%% The channel number needed to be changed in certain participants
%%% (swapped channels during EEG prep)
if VPLoop == 1
for i = 1:length(fpd.trial)
fpd.trial{i} = fpd.trial{i}([1:61 63 62 64],:);
end
end
if VPLoop == 4
for i = 1:length(fpd.trial)
fpd.trial{i} = fpd.trial{i}([1:36 39 38 37 40:64],:);
end
end
if VPLoop == 19
for i = 1:length(fpd.trial)
fpd.trial{i} = fpd.trial{i}([1:2 4 3 5:64],:);
end
end
if VPLoop == 33
for i = 1:length(fpd.trial)
fpd.trial{i} = fpd.trial{i}([1:5 9 7:8 6 10:64],:);
end
end
cfg = [];
cfg.implicitref = 'FCz';
fpd = ft_preprocessing(cfg,fpd);
cfg=[];
cfg.reref = 'yes';
cfg.refchannel = 'EEG';
fpd = ft_preprocessing(cfg, fpd);
%%% Manually screen if a channel needs interpolation, see interpolation.m
%
% cfg = [];
% cfg.viewmode = 'vertical'
% ft_databrowser(cfg, fpd);
%%% We need to repair ch7 P3 for participants 29-35
for j = 1:2
cfg = [];
if j == 1
cfg.bpfilter = 'yes';
cfg.bpfreq = [4 34];
cfg.bpfilttype = 'but';
cfg.bpfiltord = 6;
elseif j == 2
cfg.hpfilter = 'yes';
cfg.hpfreq = [16];
cfg.hpfilttype = 'but';
cfg.hpfiltord = 6;
end
%
icdata = ft_preprocessing(cfg,fpd);
icdata.cfg = [];
%%% Some summary statistics / change to method "trial" for single trial
%%% evaluation
cfg = {};
cfg.method = 'summary';
cfg.keepchannel = 'yes';
cfg.metric = 'maxzvalue';
cfg.channel = 'EEG';
cfg.elec = ft_read_sens('standard_1005.elc');
ncfg.layout = 'easycapM11.lay';
ncfg.method = 'triangulation';
cfg.neighbours = ft_prepare_neighbours(ncfg, icdata);
icdata2 = ft_rejectvisual(cfg,icdata);
%%% We also used this stage to interpolate channels.
cfg = {};
cfg.method = 'summary';
cfg.keepchannel = 'repair';
cfg.metric = 'maxzvalue';
cfg.channel = 'all';
cfg.elec = ft_read_sens('standard_1005.elc');
ncfg.layout = 'easycapM11.lay';
ncfg.method = 'triangulation';
cfg.neighbours = ft_prepare_neighbours(ncfg, icdata);
icdata2 = ft_rejectvisual(cfg,icdata2);
if j == 1
save(sprintf('icdata2_low_%i.mat',VPLoop),'icdata2');
elseif j == 2
save(sprintf('icdata2_high_%i.mat',VPLoop),'icdata2');
end
end
end
for j = 1:2
for k = 4
if k == 1
sub = 1:6;
elseif k == 2
sub = 7:12;
elseif k == 3
sub = 13:18;
elseif k == 4
sub = 19:22;
elseif k == 5
sub = 23:26;
elseif k == 6
sub = 27:30;
elseif k == 7
sub = 31:35;
end
parfor VPLoop = sub
if j == 1
icdata2 = load(sprintf('icdata2_low_%i.mat',VPLoop));
icdata2 = icdata2.icdata2;
elseif j == 2
icdata2 = load(sprintf('icdata2_high_%i.mat',VPLoop));
icdata2 = icdata2.icdata2;
end
cfg = [];
cfg = [];
cfg.channel = {'all'};
cfg.method = 'runica';
cfg.numcomponent = 64;
cfg.runica.extended = 0;
cfg.runica.maxsteps = 1000;
icdata3{VPLoop} = ft_componentanalysis(cfg,icdata2);
% if j == 1
% save(sprintf('icdata3_low_%i.mat',VPLoop),'icdata3');
% elseif j == 2
% save(sprintf('icdata3_high_%i.mat',VPLoop),'icdata3');
% end
end
for VPLoop = sub;
icdataf = icdata3{VPLoop};
if j == 1
save(sprintf('icdataf_low_%i.mat',VPLoop),'icdataf');
elseif j == 2
save(sprintf('icdataf_high_%i.mat',VPLoop),'icdataf');
end
end
clear icdata3
end
end
%%saving procedure
clear all;
for VPLoop = 1:35
for j = 1:2
if j == 1
load(sprintf('icdataf_low_%i.mat',VPLoop));
else
load(sprintf('icdataf_high_%i.mat',VPLoop));
end
a = icdataf.label;
icdataf.label = icdataf.topolabel(1:64);
cfg = [];
cfg.path = 'D:/';
cfg.layout = 'easycapM11.lay';
cfg.prefix = 'IC';
rej_comp = ft_icabrowser_sls(cfg,icdataf); %%% replace with ft_icabrowser or request file per mail, ft_icabrowser_sls is an adapted version which has trial-by-trial representations and ICA signal over trials
cfg.component = find(rej_comp);
icdataf.label = a;
ICDataNew = ft_rejectcomponent(cfg, icdataf);
if j == 1
save(sprintf('ICDataNew_low_%i',VPLoop),'ICDataNew');
else
save(sprintf('ICDataNew_high_%i',VPLoop),'ICDataNew');
end
end
end
%%% Evaluate on summary / trial level
for VPLoop = 1:35
for j = 1:2
if j == 1
load(sprintf('ICDataNew_low_%i.mat',VPLoop));
else
load(sprintf('ICDataNew_high_%i.mat',VPLoop));
end
cfg = {};
cfg.method = 'summary';
cfg.keepchannel = 'yes';
cfg.metric = 'range';
cfg.channel = 'EEG';
%cfg.latency = [0 8]
cfg.preproc.bpfilter = 'no';
cfg.elec = ft_read_sens('standard_1005.elc');
ncfg.layout = 'easycapM11.lay';
ncfg.method = 'triangulation';
cfg.neighbours = ft_prepare_neighbours(ncfg, ICDataNew);
ICDataNew = ft_rejectvisual(cfg,ICDataNew);
cfg = {};
cfg.method = 'trial';
cfg.keepchannel = 'yes';
cfg.elec = ft_read_sens('standard_1005.elc');
ncfg.layout = 'easycapM11.lay';
ncfg.method = 'triangulation';
cfg.neighbours = ft_prepare_neighbours(ncfg, ICDataNew);
ICDataNew = ft_rejectvisual(cfg,ICDataNew);
% cfg = [];
% cfg.viewmode = 'vertical'
% ncfg = ft_databrowser(cfg, ICDataNew);
%
if j == 1
save(sprintf('ICDataNew_lowC_%i.mat',VPLoop),'ICDataNew');
else
save(sprintf('ICDataNew_highC_%i.mat',VPLoop),'ICDataNew');
end
end
end
%%% Final artifact rejection step
for VPLoop = 1:35
for j = 1:2
if j == 1
load(sprintf('ICDataNew_lowC_%i.mat',VPLoop));
else
load(sprintf('ICDataNew_highC_%i.mat',VPLoop));
end
cfg = {};
cfg.method = 'trial';
cfg.keepchannel = 'yes';
cfg.elec = ft_read_sens('standard_1005.elc');
ncfg.layout = 'easycapM11.lay';
ncfg.method = 'triangulation';
cfg.neighbours = ft_prepare_neighbours(ncfg, ICDataNew);
ICDataNew = ft_rejectvisual(cfg,ICDataNew);
if j == 1
save(sprintf('ICDataNew_lowC_%i.mat',VPLoop),'ICDataNew');
else
save(sprintf('ICDataNew_highC_%i.mat',VPLoop),'ICDataNew');
end
end
end

EEG_PreProcessing_PT.m, no license · at the source

Overview

Authors: Andreas Strube^1,2, Christian Büchel¹

ORCID iDs: Andreas Strube, Christian Büchel

Institutions: Harvard University (United States); McLean Hospital (United States); University Medical Center Hamburg-Eppendorf (Germany)

Journal: Nature communications, volume 17, issue 1, article 3476

Dates: received 6 December 2024; accepted 23 March 2026; published online 14 April 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41467-026-71600-y · PMID 41974688 · PMCID PMC13079782 · OpenAlex W7154111147

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: EEG (modality), human (organism), pain (population), cognitive (subfield)

Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Physiology & signal measures

Keywords: Emotion, Pain, Human behaviour

MeSH: Anticipation, Psychological*, Pain*, Pain Perception*, Somatosensory Cortex*, Adult, Alpha Rhythm, Beta Rhythm, Cold Temperature, Cues, Female, Hot Temperature, Humans, Male, Time Factors, Young Adult (* major topic)

Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: European Research Council (883892, AdG-883892-PainPersist); Deutsche Forschungsgemeinschaft (German Research Foundation) (422744262-TRR 289, 531626241)

Citations: cited by 2 papers (Europe PMC); 58 references in the paper

Abstract

Although intensity expectations have been thoroughly studied in relation to pain, there has been a notable lack of investigation into temporal expectations. One important temporal pain effect, the so-called dread effect, suggests that future pain becomes more aversive with increasing delay. Here we investigated temporal expectations including the dread effect by presenting probabilistically cued painful heat and non-painful cold stimuli after different delay periods. Actual stimulus latency had no effect on perceived intensity in both non-painful cold and painful heat conditions. However, our data clearly show that the expectation of longer delays amplified somatosensory perception, indicating that the dread effect is related to expected and not to experienced delay. Electroencephalography data show that temporal expectations modulate alpha/beta activity during cue presentation, but not during stimulation. Actual stimulus timing is represented in alpha-to-beta frequencies during heat and cold stimulation.

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 2 matches between paragraphs and lines of code.

OSF tajch

License: none: the authors keep all their rights

State: the link answers, verified on 29 September 2026

Evidence: files inventoried

Languages: MATLAB (65)

Size: 142 files, 65 scripts

Software Heritage: not checked

Found in: “Code availability”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Psychtoolbox (17 files), SPM (16 files), FieldTrip (9 files), Statistics and Machine Learning Toolbox (7 files)

Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)

29 September 2026: the link answers (HTTP 200)

65 files

Behavioral Analysis Code/LME.m, MATLAB, 123 lines
Behavioral Analysis Code/LME_z.m, MATLAB, 132 lines
Behavioral Analysis Code/ViolinPlots.m, MATLAB, 36 lines
Behavioral Analysis Code/rmAnova_Analysis.m, MATLAB, 134 lines
Behavioral VBA Analysis Code/Correlation_ParameterswithExpectationEffect.m, MATLAB, 42 lines
Behavioral VBA Analysis Code/ExtractParameters.m, MATLAB, 19 lines
Behavioral VBA Analysis Code/Plotting.m, MATLAB, 65 lines
Behavioral VBA Analysis Code/VBA Analysis with fixed offset parameter/create_cdf.m, MATLAB, 18 lines
Behavioral VBA Analysis Code/VBA Analysis with fixed offset parameter/fit_models_vba6.m, MATLAB, 303 lines
Behavioral VBA Analysis Code/VBA Analysis with fixed offset parameter/g_integrate6.m, MATLAB, 58 lines
Behavioral VBA Analysis Code/VBA Analysis with fixed offset parameter/g_integrate_relaxLinear26.m, MATLAB, 58 lines
Behavioral VBA Analysis Code/VBA Analysis with fixed offset parameter/g_integrate_relaxLinear6.m, MATLAB, 58 lines
Behavioral VBA Analysis Code/VBA Analysis with fixed offset parameter/g_integrate_sharp_prior.m, MATLAB, 58 lines
Behavioral VBA Analysis Code/VBA Analysis with fixed offset parameter/g_integrate_sharp_prior2.m, MATLAB, 58 lines
Behavioral VBA Analysis Code/VBA Analysis with fixed offset parameter/g_integrate_shift26.m, MATLAB, 58 lines
Behavioral VBA Analysis Code/VBA Analysis with fixed offset parameter/g_integrate_shift6.m, MATLAB, 58 lines
Behavioral VBA Analysis Code/VBA Analysis with fixed offset parameter/vba_optimize.m, MATLAB, 167 lines
Behavioral VBA Analysis Code/VBA Analysis with free offset parameter/create_cdf.m, MATLAB, 18 lines
Behavioral VBA Analysis Code/VBA Analysis with free offset parameter/fit_models_vba6.m, MATLAB, 314 lines
Behavioral VBA Analysis Code/VBA Analysis with free offset parameter/g_integrate6.m, MATLAB, 58 lines
Behavioral VBA Analysis Code/VBA Analysis with free offset parameter/g_integrate_relaxLinear26.m, MATLAB, 58 lines
Behavioral VBA Analysis Code/VBA Analysis with free offset parameter/g_integrate_relaxLinear6.m, MATLAB, 58 lines
Behavioral VBA Analysis Code/VBA Analysis with free offset parameter/g_integrate_sharp_prior.m, MATLAB, 58 lines
Behavioral VBA Analysis Code/VBA Analysis with free offset parameter/g_integrate_sharp_prior2.m, MATLAB, 58 lines
Behavioral VBA Analysis Code/VBA Analysis with free offset parameter/g_integrate_shift26.m, MATLAB, 58 lines
Behavioral VBA Analysis Code/VBA Analysis with free offset parameter/g_integrate_shift6.m, MATLAB, 58 lines
Behavioral VBA Analysis Code/VBA Analysis with free offset parameter/simulate.m, MATLAB, 42 lines
Behavioral VBA Analysis Code/VBA Analysis with free offset parameter/vba_optimize.m, MATLAB, 167 lines
Behavioral VBA Analysis Code/diff3_6grps.m, MATLAB, 80 lines
EEG Analysis Code/ClusterActivity_BarGraphs.m, MATLAB, 79 lines
EEG Analysis Code/StatTests.m, MATLAB, 211 lines
EEG Analysis Code/StatTests_Trials10ormore.m, MATLAB, 218 lines
EEG Analysis Code/TFR_Plotting.m, MATLAB, 63 lines
EEG Analysis Code/find_range_min_max.m, MATLAB, 50 lines
EEG Analysis Code/ft_statfun_slopeFTest.m, MATLAB, 194 lines
EEG Analysis Code/topoplotting.m, MATLAB, 121 lines
EEG Preprocessing Pipeline/AllTrials_Correction.m, MATLAB, 108 lines
EEG Preprocessing Pipeline/EEG_PreProcessing_PT.m, MATLAB, 334 lines, 1 match
EEG Preprocessing Pipeline/SaveRawEpochedData.m, MATLAB, 77 lines
EEG Preprocessing Pipeline/freq_transform.m, MATLAB, 144 lines
Experimental Code/PT_Config.m, MATLAB, 63 lines
Experimental Code/PT_Cues.m, MATLAB, 11 lines
Experimental Code/PT_Instructions.m, MATLAB, 49 lines
Experimental Code/PT_Keys.m, MATLAB, 17 lines
Experimental Code/PT_Main.m, MATLAB, 71 lines
Experimental Code/PT_Randomize.m, MATLAB, 63 lines, 1 match
Experimental Code/PT_Salience.m, MATLAB, 139 lines
Experimental Code/PT_Setup.m, MATLAB, 33 lines
Experimental Code/PT_Training.m, MATLAB, 198 lines
Experimental Code/PT_catch.m, MATLAB, 157 lines
Experimental Code/PT_catch_training.m, MATLAB, 158 lines
Experimental Code/PT_present_RatingScale.m, MATLAB, 41 lines
Experimental Code/PT_present_cross.m, MATLAB, 7 lines
Experimental Code/PT_present_cue.m, MATLAB, 11 lines
Experimental Code/PT_present_int.m, MATLAB, 12 lines
Experimental Code/PT_present_salienceRatingScale.m, MATLAB, 44 lines
Experimental Code/PT_startblock.m, MATLAB, 26 lines
Experimental Code/PT_trial.m, MATLAB, 184 lines
Experimental Code/PT_trigger.m, MATLAB, 12 lines
Experimental Code/PT_trigger_reset.m, MATLAB, 8 lines
Experimental Code/PT_window_settings.m, MATLAB, 28 lines
Experimental Code/config_io.m, MATLAB, 13 lines
Experimental Code/config_io32.m, MATLAB, 13 lines
Experimental Code/outp.m, MATLAB, 10 lines
Experimental Code/outp32.m, MATLAB, 10 lines

At the source: osf.io/tajch/

Code availability

Code for this study is publicly available on https://osf.io/tajch/. Project 10.17605/OSF.IO/TAJCH58.

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

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;
65 scripts, each with its path and the digest of its content;
2 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

Data for this study are publicly available on https://osf.io/tajch/. Project 10.17605/OSF.IO/TAJCH58.

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, 2 authors, 3 keywords, 15 MeSH terms, 2 funders, 52 references.

Cite

This paper

Strube, A., & Büchel, C. (2026). Temporal predictions shape somatosensory perception. Nature communications, 17(1), 3476. https://doi.org/10.1038/s41467-026-71600-y

BibTeX

@article{strube2026temporal, author = {Strube, Andreas and Büchel, Christian}, title = {{Temporal predictions shape somatosensory perception}}, journal = {Nature communications}, year = {2026}, month = apr, volume = {17}, number = {1}, pages = {3476}, publisher = {Nature Publishing Group}, issn = {2041-1723}, doi = {10.1038/s41467-026-71600-y}, url = {https://doi.org/10.1038/s41467-026-71600-y}, pmid = {41974688}, pmcid = {PMC13079782} }

RIS

TY - JOUR AU - Strube, Andreas AU - Büchel, Christian TI - Temporal predictions shape somatosensory perception T2 - Nature communications J2 - Nat Commun PY - 2026 DA - 2026/04/14 VL - 17 IS - 1 SP - 3476 SN - 2041-1723 PB - Nature Publishing Group DO - 10.1038/s41467-026-71600-y UR - https://doi.org/10.1038/s41467-026-71600-y LA - en ER -

CSL-JSON

{ "id": "10.1038/s41467-026-71600-y", "type": "article-journal", "title": "Temporal predictions shape somatosensory perception", "container-title": "Nature communications", "author": [ { "family": "Strube", "given": "Andreas" }, { "family": "Büchel", "given": "Christian" } ], "container-title-short": "Nat Commun", "volume": "17", "issue": "1", "page": "3476", "DOI": "10.1038/s41467-026-71600-y", "PMID": "41974688", "PMCID": "PMC13079782", "ISSN": "2041-1723", "publisher": "Nature Publishing Group", "URL": "https://doi.org/10.1038/s41467-026-71600-y", "language": "en", "issued": { "date-parts": [ [ 2026, 4, 14 ] ] } }

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Temporal predictions shape somatosensory perception.

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