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Regime shift detection and neurocomputational substrates for under and overreactions to change.

Code ↔ Paper

1 match 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 1 match · it ties a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and methods › General Linear Models of BOLD signals › Independent regions-of-interest (ROIs) analysis ↔ 2.Analysis/fMRI/code/run_read_beta_rg1_paper_GLM_2.m, the whole file · a weak match · score 0.60 · dmPFC, LOSO ROI, beta, masks, IFG, cluster

Paper

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

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The authors' code

MATLAB · 80 lines · 2.2 KB · no license · 1 match

  1. % Script for running read_beta
  2. %
  3. %
  4. clear all;close all
  6. analysis_dir = '~/Muchen/Regime_experiment1/Analysis';
  7. ROI_type = 'LOSO_ROI';
  9. %beta_dir='~/Muchen/Regime_experiment1/Analysis/model_17_motionE_6mm/copeMask/masked_PE/model_5_motionE_6mm/';
  10. %beta_file='PE_subjects_cope2_control_B.txt'
  11. %beta_dir='~/Muchen/Regime_experiment1/Analysis/model_17_motionE_6mm/cope_masks/cope9/masked_PE/ppi_model_5_motionE_6mm_seed_sphere_6mm_GM_Bartra_fig09_vmpfc'
  12. %beta_dir='~/Muchen/Regime_experiment1/Analysis/model_5_motionE_6mm/LOSO_ROI/GM_Bartra_fig09_vmpfc/masked_PE/model_20_motionE_6mm'
  14. % Mask info
  15. mask_model = 'paper_GLM_2_check_3';
  17. %mask_copeNo=15;roi = {'dmPFC','l_IFG','r_IFG','l_IPS','r_IPS'};
  18. %mask_copeNo=11;roi = {'r_aINS'}; %logD
  19. mask_copeNo=10;roi = {'r_fusiform'}; %Qt odds
  20. n_roi = length(roi);
  22. % PE info
  23. PE_model = 'paper_GLM_2_check_3';
  24. PE_copeNo=15; %cope9,11,or 15
  26. n_subj=30;
  27. sigma=0.05;
  28. UB=0.2;
  29. LB=-0.2;
  31. for i=1:n_roi
  32. cluster_name = ['cluster_cope' num2str(mask_copeNo) '_' roi{i}];
  33. PE_dir=fullfile(analysis_dir,mask_model,ROI_type,cluster_name,'masked_PE',PE_model)
  34. PE_file=['PE_subjects_cope' num2str(PE_copeNo) '.txt'];
  36. PE0 = read_beta(PE_dir,PE_file,n_subj);
  37. n_realSubj = length(PE0);
  38. PE(1:n_realSubj,i)=PE0;
  39. PE(n_realSubj+1:n_subj,i)=NaN;
  41. [H(i),P(i),CI,STATS{i}] = ttest(PE(:,i));
  42. t(i) = STATS{i}.tstat;
  44. end
  46. H
  47. P
  48. t
  49. mu_PE = nanmean(PE);
  50. sem_PE = nanstd(PE)./sqrt(n_realSubj);
  51. t_stat = mu_PE./sem_PE;
  52. t_threshold = icdf('t',0.95,n_subj-1);
  56. figure(1);clf
  57. hold on;
  58. x_gap = 2;
  59. x_center = 1:x_gap:(n_roi*x_gap-1);
  60. bar(x_center,mu_PE,'FaceColor',[1 1 1],'EdgeColor',[0 0 0],'LineWidth',1.5);
  61. hold on;
  62. for i=1:n_roi
  63. l = plot([x_center(i) x_center(i)],[mu_PE(i)-sem_PE(i) mu_PE(i)+sem_PE(i)],'linewidth',3);hold on;
  64. l.Color = '#0080FF';
  65. x_grid = x_center(i) + unifrnd(LB,UB,n_subj,1);
  66. %x_grid = x_center(i) + normrnd(0,sigma,n_subj,1);
  67. plot(x_grid,PE(:,i),'k.','markersize',10);
  68. end
  69. ylabel('mean PE')
  70. title(['cope ' num2str(PE_copeNo)]);
  72. data.PE_model = PE_model;
  73. data.PE_copeNo = PE_copeNo;
  74. data.script_name = 'run_read_beta_paper_GLM_2_fig5.m';
  75. data.H = H;
  76. data.P = P;
  77. data.t = t;
  78. data.STATS = STATS;
  79. fname = ['beta_' PE_model '_cope_' num2str(PE_copeNo) '.mat'];
  80. save(fullfile('~/Muchen/advanced/data/',fname),'data');

run_read_beta_rg1_paper_GLM_2.m, no license · at the source

Overview

Authors: Mu-Chen Wang1, George Wu2, Shih-Wei Wu1,3
ORCID iDs: Mu-Chen Wang, Shih-Wei Wu
  1. Institute of Neuroscience, National Yang Ming Chiao Tung University Taipei Taiwan
  2. Booth School of Business, University of Chicago Chicago United States
  3. Brain Research Center, National Yang Ming Chiao Tung University Taipei Taiwan
Institutions: National Yang Ming Chiao Tung University (Taiwan); University of Chicago (United States)
Journal: eLife, volume 14, article RP104684
Dates: published online 11 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.104684 · PMID 42112672 · PMCID PMC13160555 · OpenAlex W4406898815
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.7554/elife.104684.4
Status: code verified
Categories: fMRI (modality), human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Machine learning, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: change detection, regime shift, system neglect, underreactions to change, overreactions to change, decision making, fMRI, Bayesian modeling, Human
MeSH: Brain*, Decision Making*, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Journal subjects: Neuroscience
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Science and Technology Council (108-2410-H-010-012-MY3, 110-2410-H-A49A-504 -MY3)
Citations: not cited yet (Europe PMC); 65 references in the paper

Abstract

The world constantly changes, with the underlying state of the world shifting from one regime to another. The ability to detect a regime shift, such as the onset of a pandemic or the end of a recession, significantly impacts individual decisions, as well as governmental policies. However, determining whether a regime has changed is usually not obvious, as signals are noisy and reflective of the volatility of the environment. We designed an fMRI paradigm that examines a stylized regime-shift detection task. Human participants showed systematic overreaction and underreaction: Overreaction was most commonly seen when signals were noisy, but when environments were stable and change is possible but unlikely. By contrast, underreaction was observed when signals were precise but when environments were unstable and hence change was more likely. These behavioral signatures are consistent with the system-neglect computational hypothesis, which posits that sensitivity or lack thereof to system parameters (noise and volatility) is central to these behavioral biases. Guided by this computational framework, we found that individual subjects’ sensitivity to system parameters was represented by two distinct brain networks. Whereas a frontoparietal network selectively represented individuals’ sensitivity to signal noise but not environment volatility, the ventromedial prefrontal cortex (vmPFC) showed the opposite pattern. Further, these two networks were involved in different aspects of regime-shift computations: while vmPFC correlated with subjects’ beliefs about change, the frontoparietal network represented the strength of evidence in favor of regime shifts. Together, these results suggest that regime-shift detection recruits belief-updating and evidence-evaluation networks and that under- and overreactions arise from how sensitive these networks are to the system parameters.

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 1 match between paragraphs and lines of code.

OSF xh7dy

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (5), Shell (3)
Size: 27 files, 8 scripts
Software Heritage: not checked
Found in: the text, “Materials and methods”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Psychtoolbox (10 files), Statistics and Machine Learning Toolbox (3 files), FSL (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
19 files
At the source: osf.io/xh7dy/

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;
  • 19 scripts, each with its path and the digest of its content;
  • 1 match 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

All data, including behavioral and fMRI, and analysis code are available at Open Science Framework: https://osf.io/xh7dy/.

The following dataset was generated:

WangM-C WuG S-WWu 2026System Neglect and the Neurocomputational Substrates for Over- and Underreactions to ChangesOpen Science Frameworkxh7dy

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 9 keywords, 9 MeSH terms, 1 funder, 62 references.

Cite

This paper

Wang, M.-C., Wu, G., & Wu, S.-W. (2026). Regime shift detection and neurocomputational substrates for under and overreactions to change. eLife, 14, RP104684. https://doi.org/10.7554/elife.104684

BibTeX

@article{wang2026regime,
author = {Wang, Mu-Chen and Wu, George and Wu, Shih-Wei},
title = {{Regime shift detection and neurocomputational substrates for under and overreactions to change}},
journal = {eLife},
year = {2026},
month = may,
volume = {14},
pages = {RP104684},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.104684},
url = {https://doi.org/10.7554/elife.104684},
pmid = {42112672},
pmcid = {PMC13160555}
}

RIS

TY - JOUR
AU - Wang, Mu-Chen
AU - Wu, George
AU - Wu, Shih-Wei
TI - Regime shift detection and neurocomputational substrates for under and overreactions to change
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/05/11
VL - 14
SP - RP104684
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.104684
UR - https://doi.org/10.7554/elife.104684
LA - en
ER -

CSL-JSON

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"container-title": "eLife",
"author": [
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"family": "Wang",
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"given": "Shih-Wei"
}
],
"container-title-short": "eLife",
"volume": "14",
"page": "RP104684",
"DOI": "10.7554/elife.104684",
"PMID": "42112672",
"PMCID": "PMC13160555",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.104684",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
11
]
]
}
}

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