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Reply to: "No evidence of neural feature-specific pre-activation during the prediction of an upcoming stimulus".

Code ↔ Paper

3 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 3 matches
  1. [1] § Results and Discussion › Alternative approach to probe sound frequency specific neural preactivation patterns ↔ alajulienne/om_decode_timegen_trSNDteSND_alajulienne.m, lines 3–24 · score 0.70 · prediction related neural, random sequences, activation, complementary, activity, repetition
  2. [2] § Results and Discussion › Alternative approach to probe sound frequency specific neural preactivation patterns ↔ alajulienne/om_decode_timegen_trSNDteSND_alajulienne.m, lines 3–24 · score 0.61 · LDA classifier, random sequence, relabeled, transitions, probable, repetitions
  3. [3] § Results and Discussion › Novel data demonstrating feature-specific anticipatory auditory predictions ↔ 4and8tones/plot_stat_sequences.R, lines 184–228 · score 0.53 · mass, Intervals, probability, position, HDI, ROPE

Paper

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

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The authors' code

MATLAB · 381 lines · 14 KB · no license · 2 matches

  1. function om_decode_timegen_trSNDteSND_alajulienne(subJ,cfg_in)
  2. %% om_decode_timegen_trSNDteSND_alajulienne.m
  3. %%
  4. % This script implements two complementary decoding approaches to investigate prediction-related patterns
  5. % in the brain, as described more in details in Schubert et al. (2023) and
  6. % Topalidis et al. (2025) (https://doi.org/10.1016/j.cub.2025.03.064).
  7. %
  8. % In summary:
  9. % - We train a multiclass LDA classifier on tones from the random sequence, excluding tone repetitions (bottom-up classifier).
  10. % - We test this classifier on tone repetitions from both random and increasingly predictable sequences to detect pre-activation of stimulus-specific activity.
  11. % - We also relabel tone repetitions in the test set to the most likely (mp='most probable') expected next tone, capturing prediction-related neural activity.
  12. % - Separately, we train a second classifier (top-down classifier) on forward tone transitions from the predictable ('ordered') sequence (e.g., A/B, B/C).
  13. % - This top-down classifier is tested on random and increasingly more predictable most probable tone repetitions to identify top-down representations of expected transitions.
  16. %clear all; close all; restoredefaultpath; matlabrc;
  17. disp('Those are the inputs:')
  18. cfg_in
  21. OLDCFG = cfg_in;
  22. % Add user's version of FieldTrip or obob_ownft (update this path as needed)
  23. addpath('/path/to/your/fieldtrip'); % or obob_ownft
  24. ft_defaults;
  26. %% MVPALight
  27. % Add user's version of MVPA-Light (update this path as needed)
  28. addpath(genpath('/path/to/your/MVPA-Light/'));
  30. %%% the rest
  31. % Add user's analysis code directories (update these paths as needed)
  32. addpath('/path/to/your/omissionMarkov/decoding');
  33. addpath('/path/to/your/omissionMarkov/decoding/functions/');
  35. %% out things
  36. % Set user's data and output directories (update these paths as needed)
  37. fileDir = '/path/to/your/data/sssoriginal/';
  38. outDirTGSND = '/path/to/your/data/decoding/matlab/alajulienne/';
  40. %%
  41. clear tmpdata data % to stay on the safe side
  42. conds={'random*','midminus*','midplus*','ordered*'};
  43. trialinfos = [];
  45. %%
  46. for iFile=1:length(conds) %only on orderer if 4
  48. tmpFile= dir([fileDir,'*',subJ,'_block*',conds{iFile}]);
  49. cur_file = [tmpFile.folder,'/',tmpFile.name];
  51. cfg = [];
  52. cfg.dataset = cur_file;
  53. cfg.trialdef.triallength = Inf; % infinite trial length for one trial
  54. cfg.trialdef.ntrials = 1; % one trial
  55. cfg = ft_definetrial(cfg);
  57. cfg.channel = cfg_in.chanType; % select channel type
  58. cfg.hpfilter = 'yes'; % high-pass filter
  59. cfg.hpfreq = 0.1;
  60. cfg.hpinstabilityfix = 'split';
  61. tmpdata = ft_preprocessing(cfg);
  63. cfg = [];
  64. cfg.channel = cfg_in.chanType;
  65. cfg.dataset=cur_file ;
  66. cfg.trialdef.prestim = 1;
  67. cfg.trialdef.poststim = 1;
  68. cfg.trialdef.eventtype = 'Trigger';
  69. cfg.trialdef.eventvalue = [1 2 3 4 10 20 30 40]; % only sounds needed
  70. cfg_wtrials = ft_definetrial(cfg);
  71. data{iFile}= ft_redefinetrial(cfg_wtrials, tmpdata);
  72. clear tmpdata;
  74. data{iFile}.trialinfo(:,2)= iFile;
  75. %%%
  76. % Low-pass filter at 30 Hz
  77. cfg=[];
  78. cfg.lpfilter = 'yes'; %usually is yes here
  79. cfg.lpfreq = 30;
  80. data{iFile} = ft_preprocessing(cfg,data{iFile});
  82. %% fix the stimulus delay
  83. cfg = [];
  84. cfg.offset = -24; % 24 samples at 1kHz (approx 23.5 ms delay), in the original study with the original pneumatic tubes
  85. data{iFile} = ft_redefinetrial(cfg,data{iFile});
  87. % Downsample if needed (if fsample > 100 (files on Zenode have Fs = 100))
  88. if data{iFile}.fsample > 100
  89. cfg=[];
  90. if iscell(cfg_in.Fs)
  91. cfg.resamplefs=str2num(cfg_in.Fs{1});
  92. else
  93. cfg.resamplefs=str2num(cfg_in.Fs); %
  94. end
  95. data{iFile}=ft_resampledata(cfg, data{iFile});
  96. else end
  98. trialinfos=[trialinfos; data{iFile}.trialinfo];
  100. if ~strcmp(cfg_in.chanType,'MEGMAG') % combine planar if not MEGMAG
  101. cfg=[];
  102. data{iFile}=ft_combineplanar(cfg, data{iFile});
  103. else end
  105. end
  106. %% Remove empty blocks if it happens and append data
  107. if max(size(data(~cellfun('isempty',data))))>1 % do not count the empty elements
  108. cfg = [];
  109. cfg.appenddim = 'rpt';
  110. data=ft_appenddata(cfg, data{:});
  111. else
  112. foo = data(~cellfun('isempty',data)); %take out the only non empty one
  113. data = foo{1};
  114. clear foo;
  115. end
  117. %% Remove cfg field to save disk space
  118. data = rmfield(data,'cfg');
  121. %% MVPA part
  123. clear acc* result* cfg*
  124. %% init the magic ....
  125. startup_MVPA_Light;
  127. %% find the indices of the different sounds and omissions
  128. allIdxRD = find(data.trialinfo(:,2)==1); %rd sounds and omissions
  129. allIdxMM = find(data.trialinfo(:,2)==2); %mm sounds and omissions
  130. allIdxMP = find(data.trialinfo(:,2)==3); %mp sounds and omissions
  131. allIdxOR = find(data.trialinfo(:,2)==4); %or sounds and omissions
  133. %% find the self repetitions, see the function below
  134. tmpidx = findSelfRepetitions(data.trialinfo(allIdxRD,1));
  135. selfRepRD = allIdxRD(tmpidx);
  137. % same for the other entropies
  138. tmpidx = findSelfRepetitions(data.trialinfo(allIdxMM,1));
  139. selfRepMM = allIdxMM(tmpidx);
  140. tmpidx = findSelfRepetitions(data.trialinfo(allIdxMP,1));
  141. selfRepMP = allIdxMP(tmpidx);
  142. tmpidx = findSelfRepetitions(data.trialinfo(allIdxOR,1));
  143. selfRepOR = allIdxOR(tmpidx);
  145. % find the omissions and the trials immediately after
  146. % RD
  147. [tmpidx, tmpidy] = detectOmissions(data.trialinfo(allIdxRD,1));
  148. omIdxRD = allIdxRD(tmpidx); postOmIdxRD = allIdxRD(tmpidy);
  150. % MM
  151. [tmpidx, tmpidy] = detectOmissions(data.trialinfo(allIdxMM,1));
  152. omIdxMM = allIdxMM(tmpidx); postOmIdxMM = allIdxMM(tmpidy);
  154. % MP
  155. [tmpidx, tmpidy] = detectOmissions(data.trialinfo(allIdxMP,1));
  156. omIdxMP = allIdxMP(tmpidx); postOmIdxMP = allIdxMP(tmpidy);
  158. % OR
  159. [tmpidx, tmpidy] = detectOmissions(data.trialinfo(allIdxOR,1));
  160. omIdxOR = allIdxOR(tmpidx); postOmIdxOR = allIdxOR(tmpidy);
  163. %% create the 'classic' training set, i.e. wo omissions, and post omissions
  165. % RD
  166. normalIdxRD = setdiff(allIdxRD, union(omIdxRD, postOmIdxRD));
  167. cfg=[];
  168. cfg.keeptrials='yes';
  169. cfg.trials = [normalIdxRD];
  170. data_tl_snd_rd=ft_timelockanalysis(cfg, data);
  171. time=data_tl_snd_rd.time;
  173. % MM
  174. normalIdxMM = setdiff(allIdxMM, union(omIdxMM, postOmIdxMM));
  175. cfg=[];
  176. cfg.keeptrials='yes';
  177. cfg.trials = [normalIdxMM]; %
  178. data_tl_snd_mm=ft_timelockanalysis(cfg, data);
  180. % MP
  181. normalIdxMP = setdiff(allIdxMP, union(omIdxMP, postOmIdxMP));
  182. cfg=[];
  183. cfg.keeptrials='yes';
  184. cfg.trials = [normalIdxMP]; %
  185. data_tl_snd_mp=ft_timelockanalysis(cfg, data);
  187. % OR
  188. normalIdxOR = setdiff(allIdxOR, union(omIdxOR, postOmIdxOR));
  189. cfg=[];
  190. cfg.keeptrials='yes';
  191. cfg.trials = [normalIdxOR]; %
  192. data_tl_snd_or=ft_timelockanalysis(cfg, data);
  195. %% create the 'forward' training set, i.e. the one without self repetitions and omissions, and post omissions
  196. % RD
  197. forwardIdxRD = setdiff(allIdxRD, union(selfRepRD, union(omIdxRD, postOmIdxRD)));
  198. cfg=[];
  199. cfg.keeptrials='yes';
  200. cfg.trials = [forwardIdxRD]; %
  201. data_tl_rdFW=ft_timelockanalysis(cfg, data);
  203. % MM
  204. forwardIdxMM = setdiff(allIdxMM, union(selfRepMM, union(omIdxMM, postOmIdxMM)));
  205. cfg=[];
  206. cfg.keeptrials='yes';
  207. cfg.trials = [forwardIdxMM]; %
  208. data_tl_mmFW=ft_timelockanalysis(cfg, data);
  210. % MP
  211. forwardIdxMP = setdiff(allIdxMP, union(selfRepMP, union(omIdxMP, postOmIdxMP)));
  212. cfg=[];
  213. cfg.keeptrials='yes';
  214. cfg.trials = [forwardIdxMP]; %
  215. data_tl_mpFW=ft_timelockanalysis(cfg, data);
  217. % OR
  218. forwardIdxOR = setdiff(allIdxOR, union(selfRepOR, union(omIdxOR, postOmIdxOR)));
  219. cfg=[];
  220. cfg.keeptrials='yes';
  221. cfg.trials = [forwardIdxOR]; %
  222. data_tl_orFW=ft_timelockanalysis(cfg, data);
  224. %% create the self repetitions test set, i.e. the one with only self repetitions
  225. % RD
  226. SRidxRD = setdiff(selfRepRD, union(omIdxRD, postOmIdxRD)); %remove OMs as well
  227. cfg=[];
  228. cfg.keeptrials='yes';
  229. cfg.trials = SRidxRD;%
  230. data_tl_rdSR=ft_timelockanalysis(cfg, data);
  232. % MM
  233. SRidxMM = setdiff(selfRepMM, union(omIdxMM, postOmIdxMM)); %remove OMs as well
  234. cfg=[];
  235. cfg.keeptrials='yes';
  236. cfg.trials = [SRidxMM]; %
  237. data_tl_mmSR=ft_timelockanalysis(cfg, data);
  239. % MP
  240. SRidxMP = setdiff(selfRepMP, union(omIdxMP, postOmIdxMP)); %remove OMs as well
  241. cfg=[];
  242. cfg.keeptrials='yes';
  243. cfg.trials = [SRidxMP]; %
  244. data_tl_mpSR=ft_timelockanalysis(cfg, data);
  246. % OR
  247. SRidxOR = setdiff(selfRepOR, union(omIdxOR, postOmIdxOR)); %remove OMs as well
  248. cfg=[];
  249. cfg.keeptrials='yes';
  250. cfg.trials = [SRidxOR]; %
  251. data_tl_orSR=ft_timelockanalysis(cfg, data);
  253. %% 'classical' way a la Demarchi et al. 2019
  254. % RD train/test
  255. cfg = [];
  256. cfg.classifier = 'multiclass_lda';
  257. cfg.metric = 'accuracy';
  258. cfg.preprocessing = 'undersample';
  259. [accTG_SND_RD, result_accTG_SND_RD] = mv_classify_timextime(cfg, data_tl_snd_rd.trial, data_tl_snd_rd.trialinfo(:,1));
  261. % Cross decoding
  262. % Rd_SND to Mm_SND
  263. [accTG_SND_RD_MM, result_accTG_SND_RD_MM] = mv_classify_timextime(cfg, data_tl_snd_rd.trial, data_tl_snd_rd.trialinfo(:,1),data_tl_snd_mm.trial, data_tl_snd_mm.trialinfo(:,1));
  265. % Rd_SND to Mp_SND
  266. [accTG_SND_RD_MP, result_accTG_SND_RD_MP] = mv_classify_timextime(cfg,data_tl_snd_rd.trial, data_tl_snd_rd.trialinfo(:,1),data_tl_snd_mp.trial, data_tl_snd_mp.trialinfo(:,1));
  268. % Rd_SND to Or_SND
  269. [accTG_SND_RD_OR, result_accTG_SND_RD_OR] = mv_classify_timextime(cfg,data_tl_snd_rd.trial, data_tl_snd_rd.trialinfo(:,1),data_tl_snd_or.trial, data_tl_snd_or.trialinfo(:,1));
  272. %% train on ordered forward and test on self repetitions
  273. cfg = [];
  274. cfg.classifier = 'multiclass_lda';
  275. cfg.metric = 'accuracy';
  276. cfg.preprocessing = 'undersample';
  277. [accTG_SND_fwOR_srRD, result_acc_fwOR_srRD] = mv_classify_timextime(cfg, data_tl_orFW.trial, data_tl_orFW.trialinfo(:,1), data_tl_rdSR.trial, data_tl_rdSR.trialinfo(:,1));
  278. [accTG_SND_fwOR_srMM, result_acc_fwOR_srMM] = mv_classify_timextime(cfg, data_tl_orFW.trial, data_tl_orFW.trialinfo(:,1), data_tl_mmSR.trial, data_tl_mmSR.trialinfo(:,1));
  279. [accTG_SND_fwOR_srMP, result_acc_fwOR_srMP] = mv_classify_timextime(cfg, data_tl_orFW.trial, data_tl_orFW.trialinfo(:,1), data_tl_mpSR.trial, data_tl_mpSR.trialinfo(:,1));
  280. [accTG_SND_fwOR_srOR, result_acc_fwOR_srOR] = mv_classify_timextime(cfg, data_tl_orFW.trial, data_tl_orFW.trialinfo(:,1), data_tl_orSR.trial, data_tl_orSR.trialinfo(:,1));
  282. % relabeled / most probable
  283. % this is the one needed for the 'top down'/'negative training times' part of the plot
  284. cfg = [];
  285. cfg.classifier = 'multiclass_lda';
  286. cfg.metric = 'accuracy';
  287. cfg.preprocessing = 'undersample';
  288. [accTG_SND_fwOR_srRDmp, result_acc_fwOR_srRDmp] = mv_classify_timextime(cfg, data_tl_orFW.trial, data_tl_orFW.trialinfo(:,1), data_tl_rdSR.trial, makeMostProbable(data_tl_rdSR.trialinfo(:,1)));
  289. [accTG_SND_fwOR_srMMmp, result_acc_fwOR_srMMmp] = mv_classify_timextime(cfg, data_tl_orFW.trial, data_tl_orFW.trialinfo(:,1), data_tl_mmSR.trial, makeMostProbable(data_tl_mmSR.trialinfo(:,1)));
  290. [accTG_SND_fwOR_srMPmp, result_acc_fwOR_srMPmp] = mv_classify_timextime(cfg, data_tl_orFW.trial, data_tl_orFW.trialinfo(:,1), data_tl_mpSR.trial, makeMostProbable(data_tl_mpSR.trialinfo(:,1)));
  291. [accTG_SND_fwOR_srORmp, result_acc_fwOR_srORmp] = mv_classify_timextime(cfg, data_tl_orFW.trial, data_tl_orFW.trialinfo(:,1), data_tl_orSR.trial, makeMostProbable(data_tl_orSR.trialinfo(:,1)));
  293. %% training on the random forward
  294. cfg = [];
  295. cfg.classifier = 'multiclass_lda';
  296. cfg.metric = 'accuracy'% {'accuracy', 'confusion', 'f1', 'mae'}; %
  297. cfg.output_type = 'dval'; %
  298. cfg.preprocessing = 'undersample';
  299. [accTG_SND_fwRD_srRD, result_acc_fwRD_srRD] = mv_classify_timextime(cfg, data_tl_rdFW.trial, data_tl_rdFW.trialinfo(:,1), data_tl_rdSR.trial, data_tl_rdSR.trialinfo(:,1));
  300. [accTG_SND_fwRD_srMM, result_acc_fwRD_srMM] = mv_classify_timextime(cfg, data_tl_rdFW.trial, data_tl_rdFW.trialinfo(:,1), data_tl_mmSR.trial, data_tl_mmSR.trialinfo(:,1));
  301. [accTG_SND_fwRD_srMP, result_acc_fwRD_srMP] = mv_classify_timextime(cfg, data_tl_rdFW.trial, data_tl_rdFW.trialinfo(:,1), data_tl_mpSR.trial, data_tl_mpSR.trialinfo(:,1));
  302. [accTG_SND_fwRD_srOR, result_acc_fwRD_srOR] = mv_classify_timextime(cfg, data_tl_rdFW.trial, data_tl_rdFW.trialinfo(:,1), data_tl_orSR.trial, data_tl_orSR.trialinfo(:,1));
  304. % relabeled / most probable
  305. % this is the one needed for the 'bottom up'/'positive training times' part of the plot
  306. cfg = [];
  307. cfg.classifier = 'multiclass_lda';
  308. cfg.metric = 'accuracy'% {'accuracy', 'confusion', 'f1', 'mae'}; %
  309. cfg.output_type = 'dval'; %
  310. cfg.preprocessing = 'undersample';
  311. [accTG_SND_fwRD_srRDmp, result_acc_fwRD_srRDmp] = mv_classify_timextime(cfg, data_tl_rdFW.trial, data_tl_rdFW.trialinfo(:,1), data_tl_rdSR.trial, makeMostProbable(data_tl_rdSR.trialinfo(:,1)));
  312. [accTG_SND_fwRD_srMMmp, result_acc_fwRD_srMMmp] = mv_classify_timextime(cfg, data_tl_rdFW.trial, data_tl_rdFW.trialinfo(:,1), data_tl_mmSR.trial, makeMostProbable(data_tl_mmSR.trialinfo(:,1)));
  313. [accTG_SND_fwRD_srMPmp, result_acc_fwRD_srMPmp] = mv_classify_timextime(cfg, data_tl_rdFW.trial, data_tl_rdFW.trialinfo(:,1), data_tl_mpSR.trial, makeMostProbable(data_tl_mpSR.trialinfo(:,1)));
  314. [accTG_SND_fwRD_srORmp, result_acc_fwRD_srORmp] = mv_classify_timextime(cfg, data_tl_rdFW.trial, data_tl_rdFW.trialinfo(:,1), data_tl_orSR.trial, makeMostProbable(data_tl_orSR.trialinfo(:,1)));
  318. %% and save!
  320. cfg_in = OLDCFG; % to keep the original cfg_in
  322. if iscell(cfg_in.Fs)
  323. Fs = cfg_in.Fs{1};
  324. else
  325. Fs = cfg_in.Fs;
  326. end
  328. outFile = [ subJ '_' cfg_in.chanType '_timegen_trRdSND_teSND_Fs' num2str(Fs) '_alajulienne.mat' ];
  330. if iscell(outFile)
  331. outFile = [outFile{:}];
  332. else end
  334. save(fullfile(outDirTGSND, outFile),'acc*','result*','time','cfg_in' ,'-v7.3');
  335. disp(['Saved: ',fullfile(outDirTGSND, outFile)]);
  337. end
  340. %% local functions
  341. function repetition_indices = findSelfRepetitions(sequence)
  342. % Initialize an empty array to store indices of the first self-repetitions
  343. repetition_indices = [];
  344. % Flag to track if we're in a repetition sequence
  345. inRepetition = false;
  346. % Loop through the sequence starting from the second element
  347. for i = 2:length(sequence)
  348. % Check if the current element is equal to the previous element
  349. if sequence(i) == sequence(i-1)
  350. % If not already in a repetition, this is the first repetition
  351. if ~inRepetition
  352. repetition_indices = [repetition_indices, i];
  353. inRepetition = true; % Mark that we're now in a repetition
  354. end
  355. else
  356. % If the current element is different, reset the repetition flag
  357. inRepetition = false;
  358. end
  359. end
  360. end
  363. function [omIdx, postOmIdx] = detectOmissions(sequence)
  364. % Find indices where elements are 10 or more (detect omissions)
  365. omIdx = find(sequence >= 10);
  367. % Initialize array to store the indices following the omissions
  368. postOmIdx = omIdx + 1;
  370. % Ensure indices are within bounds
  371. postOmIdx(postOmIdx > length(sequence)) = [];
  373. end
  375. function shifted_sequence = makeMostProbable(sequence)
  376. % mod to perform the cyclic shift
  377. % 2 -> 1, 3 -> 2, 4 -> 3, 1 -> 4, ...
  378. shifted_sequence = mod(sequence - 2, 4) + 1;
  379. end

om_decode_timegen_trSNDteSND_alajulienne.m at commit 25f86b2, no license · at the source

Overview

Authors: Gianpaolo Demarchi1,2, Thomas Hartmann1, Anne Hauswald1, Juliane Schubert1, Lisa Reisinger1, Pavlos Topalidis1, Kaja Benz1, Fabian Schmidt1, Quirin Gehmacher1, Nathan Weisz1,2
ORCID iDs: Gianpaolo Demarchi, Pavlos Topalidis, Fabian Schmidt, Quirin Gehmacher, Nathan Weisz
  1. Centre for Cognitive Neuroscience, Department of Psychology, Paris-Lodron-University of Salzburg, Salzburg, Austria
  2. Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University Salzburg, Salzburg, Austria
Institutions: University of Salzburg (Austria); Paracelsus Medical University (Austria); Christian Doppler Klinik (Austria)
Journal: Nature communications, volume 17, issue 1, article 4638
Dates: received 27 January 2025; accepted 8 May 2026; published online 26 May 2026
Type: Letter · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-73567-2 · PMID 42191730 · PMCID PMC13212686 · OpenAlex W7162464188
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism)
Methods: Connectivity, Statistics, Machine learning, Preprocessing
Keywords: Cortex, Neural encoding, Human behaviour
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Austrian Science Fund FWF (W 1233)
Citations: not cited yet (Europe PMC); 9 references in the paper

Abstract

No abstract was found for this paper: the paper, at the publisher.

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Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

gdemarchi/Markov2.0

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 25f86b286e5d5fdabbb35a450d40e5f8caef79d9, 12 April 2026
Languages: MATLAB (6), R (1)
Size: 10 files, 7 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (4 files), ggplot2 (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
8 files
At the source: github.com/gdemarchi/Markov2.0

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

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Demarchi, G., Hartmann, T., Hauswald, A., Schubert, J., Reisinger, L., Topalidis, P., Benz, K., Schmidt, F., Gehmacher, Q., & Weisz, N. (2026). Reply to: "No evidence of neural feature-specific pre-activation during the prediction of an upcoming stimulus". Nature communications, 17(1), 4638. https://doi.org/10.1038/s41467-026-73567-2

BibTeX

@article{demarchi2026reply,
author = {Demarchi, Gianpaolo and Hartmann, Thomas and Hauswald, Anne and Schubert, Juliane and Reisinger, Lisa and Topalidis, Pavlos and Benz, Kaja and Schmidt, Fabian and Gehmacher, Quirin and Weisz, Nathan},
title = {{Reply to: "No evidence of neural feature-specific pre-activation during the prediction of an upcoming stimulus"}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {4638},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73567-2},
url = {https://doi.org/10.1038/s41467-026-73567-2},
pmid = {42191730},
pmcid = {PMC13212686}
}

RIS

TY - JOUR
AU - Demarchi, Gianpaolo
AU - Hartmann, Thomas
AU - Hauswald, Anne
AU - Schubert, Juliane
AU - Reisinger, Lisa
AU - Topalidis, Pavlos
AU - Benz, Kaja
AU - Schmidt, Fabian
AU - Gehmacher, Quirin
AU - Weisz, Nathan
TI - Reply to: "No evidence of neural feature-specific pre-activation during the prediction of an upcoming stimulus"
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/26
VL - 17
IS - 1
SP - 4638
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73567-2
UR - https://doi.org/10.1038/s41467-026-73567-2
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-73567-2",
"type": "article-journal",
"title": "Reply to: \"No evidence of neural feature-specific pre-activation during the prediction of an upcoming stimulus\"",
"container-title": "Nature communications",
"author": [
{
"family": "Demarchi",
"given": "Gianpaolo"
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{
"family": "Hartmann",
"given": "Thomas"
},
{
"family": "Hauswald",
"given": "Anne"
},
{
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"given": "Juliane"
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{
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"given": "Lisa"
},
{
"family": "Topalidis",
"given": "Pavlos"
},
{
"family": "Benz",
"given": "Kaja"
},
{
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"given": "Fabian"
},
{
"family": "Gehmacher",
"given": "Quirin"
},
{
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"given": "Nathan"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "4638",
"DOI": "10.1038/s41467-026-73567-2",
"PMID": "42191730",
"PMCID": "PMC13212686",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-73567-2",
"language": "en",
"issued": {
"date-parts": [
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]
}
}

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