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Adaptive behavior is guided by integrated representations of controlled and non-controlled information.

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  1. [1] § Methods › EEG data acquisition and preprocessing ↔ scripts/Preprocessing.m, the whole file · a weak match · score 0.92 · 0.1–50 Hz, 200–1500 ms, 500–1500 ms, EEGLAB, ICA, band
  2. [2] § Results › Decodable and separate representations of controlled and non-controlled associations ↔ scripts/Preprocessing.m, the whole file · a weak match · score 0.61 · linear discriminant, event, 1500 ms, ERP, channel, LDA

Paper

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The authors' code

MATLAB · 131 lines · 5.2 KB · no license · 2 matches

  1. clear;
  2. Datapath = ''; % edit to specify location
  3. eeglabPath = ''; % edit to specify location
  4. scriptPath = '';
  6. addpath(genpath(scriptPath))
  7. % of EEGlab
  8. addpath(genpath(eeglabPath))
  9. % run EEGlab
  10. [ALLEEG, EEG, CURRENTSET, ALLCOM] = eeglab;
  12. subjects = {'sub01', 'sub06', 'sub07', 'sub08', 'sub09', 'sub11', 'sub13', 'sub14', 'sub18', 'sub19', 'sub20', 'sub22', 'sub27', 'sub29', 'sub30', 'sub31', 'sub32', 'sub33', 'sub34', 'sub36', 'sub38', 'sub39', 'sub40', 'sub41'};
  13. %subjects = {'sub59', 'sub60'};
  14. %subjects = {'sub59'};
  16. event_types = {
  17. 'S1_MC_con',{'S111', 'S112'}; 'S1_MC_inc',{'S113', 'S114'};
  18. 'S1_MI_con',{'S125', 'S126'}; 'S1_MI_inc',{'S127', 'S128'};
  19. 'S2_MC_con',{'S215', 'S216'}; 'S2_MC_inc',{'S217', 'S218'};
  20. 'S2_MI_con',{'S221', 'S222'}; 'S2_MI_inc',{'S223', 'S224'};
  21. };
  23. correct_responses = {
  24. 'S1_MC_con',{'S131', 'S132'}; 'S1_MC_inc',{'S133', 'S134'};
  25. 'S1_MI_con',{'S145', 'S146'}; 'S1_MI_inc',{'S147', 'S148'};
  26. 'S2_MC_con',{'S235', 'S236'}; 'S2_MC_inc',{'S237', 'S238'};
  27. 'S2_MI_con',{'S241', 'S242'}; 'S2_MI_inc',{'S243', 'S244'};
  28. };
  29. filter = 1;
  30. filter_band = [0.1, 50]; % >0
  31. re_reference = 1;
  32. ica_epoch = 1;
  33. ica_epoch_interval = [-.5 1.5];
  34. ica = 1;
  35. epoch = 1;
  36. epoch_interval = [-.2 1.5];
  37. erps =[];
  39. for nsub=1:length(subjects)
  40. if filter
  41. dataFile = [subjects{nsub} '.vhdr'];
  42. EEG = pop_loadbv(Datapath, dataFile);
  43. EEG = pop_eegfilt( EEG, filter_band(1), 0, [], [0],[0],[0],['fir1'],[0]);
  44. EEG = pop_eegfilt( EEG, 0, filter_band(2), [], [0],[0],[0],['fir1'],[0]);
  45. EEG.filename = [subjects{nsub} '_filtered.set'];
  46. EEG.filepath = Datapath;
  47. EEG = pop_saveset(EEG, 'filename', EEG.filename, 'filepath', EEG.filepath);
  49. end;
  51. if re_reference
  52. dataFile = [subjects{nsub} '_filtered.set'];
  53. EEG = pop_loadset(dataFile, Datapath);
  55. %add a zero data channel
  56. EEG.data(end+1,:) = 0;
  57. EEG.nbchan = size(EEG.data,1);
  58. EEG.chanlocs(end+1).labels = 'Pz';
  59. %load chanloc information
  60. elecfile = 'D:\Apps\toolbox\eeglab2023.1\plugins\dipfit\standard_BESA\standard-10-5-cap385.elp';
  61. EEG = pop_chanedit(EEG, 'lookup',elecfile);
  62. %re-refer to average reference
  63. EEG = pop_reref( EEG, [], 'refstate',0);
  65. % elecfile = 'D:\Apps\toolbox\eeglab2023.1\plugins\dipfit\standard_BESA\standard-10-5-cap385.elp';
  66. % EEG = pop_chanedit(EEG, 'append',63,'changefield',{64,'labels','Pz'},'lookup',elecfile,'setref',{'1:64','Pz'});
  67. % EEG = pop_reref( EEG, [],'refloc',struct('labels',{'Pz'},'type',{'EEG'},'theta',{180},'radius',{0.25338},'X',{-60.7385},'Y',{-7.4383e-15},'Z',{59.4629},'sph_theta',{-180},'sph_phi',{44.392},'sph_radius',{85},'urchan',{64},'ref',{''},'datachan',{0}));
  68. %
  69. EEG.filename = [subjects{nsub} '_rerefered.set'];
  70. EEG.filepath = Datapath;
  71. EEG = pop_saveset(EEG, 'filename', EEG.filename, 'filepath', EEG.filepath);
  72. end;
  74. if ica_epoch
  75. dataFile = [subjects{nsub} '_rerefered.set'];
  76. EEG = pop_loadset(dataFile, Datapath);
  77. all_events ={};
  78. for n=1:length(event_types)
  79. all_events = [all_events{:} event_types{n,2}];
  80. end;
  81. EEG = pop_epoch( EEG, all_events, ica_epoch_interval, 'newname', EEG.comments, 'epochinfo', 'yes');
  82. % set the original trial index for single trial analysis
  83. for n=1:EEG.trials
  84. EEG.epoch(n).orig_trial_index = n;
  85. end;
  86. EEG.filename = [subjects{nsub} '_epoch_before_ica.set'];
  87. EEG.filepath = Datapath;
  88. EEG = pop_saveset(EEG, 'filename', EEG.filename, 'filepath', EEG.filepath);
  89. end;
  91. if ica
  92. dataFile = [subjects{nsub} '_epoch_before_ica.set'];
  93. EEG = pop_loadset(dataFile, Datapath);
  94. EEG = pop_runica( EEG, 'icatype', 'runica', 'extended', 1, 'stop', 1E-7);
  95. EEG.filename = [subjects{nsub} '_epoch_after_ica.set'];
  96. EEG.filepath = Datapath;
  97. EEG = pop_saveset(EEG, 'filename', EEG.filename, 'filepath', EEG.filepath);
  98. end;
  100. if epoch
  101. dataFile = [subjects{nsub} '_epoch_after_ica_deleted.set'];
  102. EEG = pop_loadset(dataFile, Datapath);
  103. epoch_EEG = EEG;
  105. % output the all trials except for extreme values
  106. all_trials_EEG = EEG;
  107. all_events ={};
  108. for n=1:length(event_types)
  109. all_events = [all_events{:} event_types{n,2}];
  110. end;
  111. EEG = pop_epoch(all_trials_EEG, all_events, epoch_interval, 'newname', EEG.comments, 'epochinfo', 'yes');
  112. for n=1:EEG.trials
  113. EEG.epoch(n).trial_index = n;
  114. end;
  115. % remove baseline
  116. EEG = pop_rmbase(EEG, [epoch_interval(1)*1000 0]);
  118. %Reject extreme values for linear discriminate analysis (LDA)
  119. rej_all_EEG = EEG;
  120. rejthresh = 80;
  121. [EEG extreme_indices] = pop_eegthresh(rej_all_EEG, 1, 1:64, -1*rejthresh, rejthresh, epoch_interval(1)*1000, epoch_interval(2)*1000, 0, 0);
  122. EEG = pop_rejepoch(rej_all_EEG, extreme_indices, 0);
  124. EEG.filename = [subjects{nsub} '_epoch_reject_extreme.set'];
  125. EEG.filepath = Datapath;
  126. EEG = pop_saveset(EEG, 'filename', EEG.filename, 'filepath', EEG.filepath);
  128. end;
  130. end;

Preprocessing.m, no license · at the source

Overview

Authors: Bingfang Huang1,2, Harrison Ritz3,4, Jiefeng Jiang1,2,5
ORCID iDs: Bingfang Huang, Harrison Ritz, Jiefeng Jiang
  1. Department of Psychological and Brain Sciences, University of Iowa, Iowa City, United States
  2. Cognitive Control Collaborative, University of Iowa, Iowa City, United States
  3. Princeton Neuroscience Institute, Princeton University, Princeton, United States
  4. Centre for Neuroscience Studies, Queen’s University, Kingston, Canada
  5. Iowa Neuroscience Institute, University of Iowa, Iowa City, United States
Institutions: University of Iowa (United States); Princeton University (United States); Queen's University (Canada)
Journal: eLife, volume 14, article RP108673
Dates: published online 26 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.108673 · PMID 42647058 · PMCID PMC13516694 · OpenAlex W4414785560
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials, Physiology & signal measures
Keywords: Human
MeSH: Adaptation, Psychological*, Brain*, Adult, Electroencephalography, Female, Humans, Male, Young Adult (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIMH NIH HHS (R01MH131559, R01 MH131559)
Citations: not cited yet (Europe PMC); 97 references in the paper

Abstract

Understanding how task knowledge is encoded neurally is crucial for uncovering the mechanisms underlying adaptive behavior. Here, we test the theory that all task information is integrated into a conjunctive task representation by investigating whether this representation simultaneously includes two types of associations that can guide behavior: stimulus–response (non-controlled) associations and stimulus–control (controlled) associations that inform how task focus should be adjusted to achieve goal-directed behavior. We extended the classic item-specific proportion congruency paradigm to dissociate the electroencephalographic (EEG) representations of controlled and non-controlled associations. Behavioral data replicated previous findings of association-driven adaptive behaviors. Decoding analyses of EEG data further showed that associations of controlled and non-controlled information were represented concurrently and differentially. Brain-behavioral analyses also showed that the strength of both associations was associated with faster responses. These findings provide initial evidence supporting the idea that controlled and non-controlled associations are governed by an integrated task representation to guide adaptive behaviors 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 2 matches between paragraphs and lines of code.

OSF tzcn8

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Python (4), MATLAB (1), Jupyter (1)
Size: 11 files, 6 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (5 files), seaborn (5 files), NumPy (4 files), pandas (4 files), MNE-Python (2 files), SciPy (2 files), statsmodels (2 files), EEGLAB (1 file), lme4 (1 file), Pingouin (1 file), rpy2 (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
6 files
At the source: osf.io/tzcn8/overview

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;
  • 6 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 and analysis scripts have been uploaded to https://osf.io/tzcn8/overview.

The following dataset was generated:

Huang B, Ritz H, Jiang J. 2026. Adaptive behavior is simultaneously guided by associations of controlled and non-controlled information. Open Science Framework. tzcn8

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 1 keyword, 8 MeSH terms, 1 funder, 94 references.

Cite

This paper

Huang, B., Ritz, H., & Jiang, J. (2026). Adaptive behavior is guided by integrated representations of controlled and non-controlled information. eLife, 14, RP108673. https://doi.org/10.7554/elife.108673

BibTeX

@article{huang2026adaptive,
author = {Huang, Bingfang and Ritz, Harrison and Jiang, Jiefeng},
title = {{Adaptive behavior is guided by integrated representations of controlled and non-controlled information}},
journal = {eLife},
year = {2026},
month = aug,
volume = {14},
pages = {RP108673},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.108673},
url = {https://doi.org/10.7554/elife.108673},
pmid = {42647058},
pmcid = {PMC13516694}
}

RIS

TY - JOUR
AU - Huang, Bingfang
AU - Ritz, Harrison
AU - Jiang, Jiefeng
TI - Adaptive behavior is guided by integrated representations of controlled and non-controlled information
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/08/26
VL - 14
SP - RP108673
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.108673
UR - https://doi.org/10.7554/elife.108673
LA - en
ER -

CSL-JSON

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"container-title-short": "Elife",
"volume": "14",
"page": "RP108673",
"DOI": "10.7554/elife.108673",
"PMID": "42647058",
"PMCID": "PMC13516694",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.108673",
"language": "en",
"issued": {
"date-parts": [
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2026,
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}
}

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