OSCR

Decision processes underlying effort avoidance and their relationship with metacognition.

Code ↔ Paper

11 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 11 matches
  1. [1] § Methods › Independence from visual, reaction-time, and motor confounds ↔ Code_RSA/COSMO_07_pressing_responselocked.m, lines 1–103 · score 0.70 · cue locked, response locked, threshold, LDA, class, AUC
  2. [2] § Methods › Independence from visual, reaction-time, and motor confounds ↔ Code_RSA/COSMO_08_pressing_cuelocked.m, lines 1–87 · score 0.70 · cue locked accept, response locked, threshold, LDA, AUC, press
  3. [3] § Methods › Behavioral analysis ↔ Matlab/fit_meta_d_mcmc_group.m, lines 1–60 · score 0.69 · detection theory, metacognitive sensitivity, confidence ratings, hierarchical, discriminability, Behavioral
  4. [4] § Methods › Behavioral analysis ↔ Matlab/fit_meta_d_mcmc.m, lines 1–23 · score 0.67 · detection theory, metacognitive sensitivity, confidence ratings, discriminability, Behavioral, MATLAB
  5. [5] § Results › Reward and effort representations are not explained by visual, reaction-time, or motor confounds ↔ Code_RSA/COSMO_07_pressing_responselocked.m, lines 1–103 · score 0.67 · Motor cross decoding, cue locked, response locked, transfer, AUC, press
  6. [6] § Results › Reward and effort representations are not explained by visual, reaction-time, or motor confounds ↔ Code_RSA/COSMO_08_pressing_cuelocked.m, lines 1–87 · score 0.66 · Motor cross decoding, cue locked, response locked, axis, transfer, AUC
  7. [7] § Methods › Independence from visual, reaction-time, and motor confounds ↔ Code_RSA/COSMO_10_RSA_reward_vs_color.m, lines 1–27 · score 0.66 · reward magnitude, neural RDM, CIELAB, medium, Spearman, perceptually
  8. [8] § Methods › Statistical analysis ↔ 99_Code_plot/plot_own_vs_other_TFCE.m, lines 1–53 · score 0.65 · threshold free cluster, enhancement, dh, TFCE, RSA
  9. [9] § Results › Reward and effort representations are not explained by visual, reaction-time, or motor confounds ↔ Code_RSA/COSMO_10_RSA_reward_vs_color.m, lines 1–27 · score 0.63 · reflects reward magnitude, early reward, neural RDM, perception, window, correlated
  10. [10] § Methods › Independence from visual, reaction-time, and motor confounds ↔ Code_RSA/COSMO_09_RSA_own_vs_other_SV.m, lines 1–30 · score 0.62 · Spearman correlation, sv, neural RDM, transformed, component, Fisher
  11. [11] § Methods › Independence from visual, reaction-time, and motor confounds ↔ Code_RSA/COSMO_09_RSA_own_vs_other_SV.m, lines 1–30 · score 0.52 · Spearman correlations, neural RDM, geometries, EEG, model, reward

Paper

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

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The authors' code

MATLAB · 230 lines · 9.6 KB · no license · 2 matches

  1. %% COSMO_07_pressing_responselocked.m
  2. % =========================================================================
  3. % Response-locked motor cross-decoding, scored with AUC (threshold-free).
  4. %
  5. % PURPOSE
  6. % Establishes whether the keypress motor pattern transfers across tasks at
  7. % all. This is the precondition for interpreting the cue-locked analysis
  8. % (control_pressing_cuelocked.m): only if a shared motor pattern exists can
  9. % its absence at a given cue-locked latency be read as evidence that the
  10. % representation there is motor-free.
  11. %
  12. % DESIGN
  13. % Train : counting-task keypress (key 9 vs 0), response-locked, per time point.
  14. % Test : decision keypress (accept = 9 vs avoid = 0), same response-locked
  15. % time point.
  16. % Also reports WITHIN-counting decodability (k-fold, dashed line in the
  17. % figure) as a sanity check: it bounds what cross-decoding could achieve if
  18. % the two tasks shared a pattern completely.
  19. %
  20. % Classifier: Fisher LDA with Ledoit-Wolf analytic shrinkage (data-driven),
  21. % same as the cue-locked analysis. Training classes are balanced by
  22. % subsampling the majority class (nRep repeats, averaged).
  23. %
  24. % =========================================================================
  26. clear; clc;
  28. cfg.inDir = fullfile('..','00_response_locked'); % Name of directory
  29. cfg.subDir = 'sub%02d';
  30. cfg.countFile = 'subject%02d_count_resp.mat'; % Name of file
  31. cfg.decisionFile = 'subject%02d_decision_resp.mat';
  32. cfg.subjects = [1 2 3]; % set subjects here
  33. cfg.nFold = 5; % within-counting CV folds
  34. cfg.nRep = 5; % balanced-subsample repeats
  35. cfg.nPerm = 5000; cfg.alpha = 0.05;
  36. cfg.rng = 1; rng(cfg.rng);
  38. nSub = numel(cfg.subjects);
  40. % time grid: 52 ms window, 20 ms step (matches the main analysis)
  41. cfg.binCentersMs = -400:20:200;
  42. cfg.binHalfMs = 26; % +-26 ms = 52 ms window
  43. tms = cfg.binCentersMs; nTime = numel(tms);
  45. crossAUC = nan(nSub, nTime);
  46. withinAUC = nan(nSub, nTime);
  47. lamAll = [];
  49. for si = 1:nSub
  50. sn = cfg.subjects(si);
  51. fc = fullfile(cfg.inDir, sprintf(cfg.subDir,sn), sprintf(cfg.countFile,sn));
  52. fd = fullfile(cfg.inDir, sprintf(cfg.subDir,sn), sprintf(cfg.decisionFile,sn));
  53. if ~isfile(fc) || ~isfile(fd)
  54. warning('sub%02d: input file not found, skipped.', sn); continue;
  55. end
  56. C = load(fc); % counting keypress
  57. D = load(fd); % decision keypress
  58. yc = C.trialinfo(:,1); % 1=key9, 2=key0
  59. yd = D.trialinfo(:,1); % 1=accept(9), 2=avoid(0)
  60. if sum(yd==2) < 2, continue; end
  61. tcv = C.time(:)'*1000; tdv = D.time(:)'*1000; % response-locked time (ms)
  62. folds = kfold(yc, cfg.nFold, cfg.rng + sn);
  64. for t = 1:nTime
  65. cc = cfg.binCentersMs(t);
  66. selc = tcv >= cc-cfg.binHalfMs & tcv <= cc+cfg.binHalfMs; % 52 ms window
  67. seld = tdv >= cc-cfg.binHalfMs & tdv <= cc+cfg.binHalfMs;
  68. Xc = mean(C.trial(:,:,selc), 3); % ntrial_c x nchan (window mean)
  69. Xd = mean(D.trial(:,:,seld), 3); % ntrial_d x nchan
  71. % --- cross-decoding AUC (balanced training, Ledoit-Wolf, nRep) ---
  72. a = zeros(cfg.nRep,1);
  73. for r = 1:cfg.nRep
  74. b = balanced_idx(yc, [1 2], cfg.rng + sn*10 + r);
  75. [w, lam] = lda_lw(Xc(b,:), yc(b)); lamAll(end+1)=lam; %#ok<SAGROW>
  76. [~,~,~,a(r)] = perfcurve(yd, Xd*w, 1); % AUC, class 1 (accept) positive
  77. end
  78. crossAUC(si,t) = mean(a);
  80. % --- within-counting decodability AUC (k-fold, balanced training) ---
  81. sc = nan(numel(yc),1);
  82. for f = 1:cfg.nFold
  83. trI = find(folds~=f); te = folds==f;
  84. b = balanced_idx(yc(trI), [1 2], cfg.rng + sn*10 + f);
  85. w = lda_lw(Xc(trI(b),:), yc(trI(b)));
  86. sc(te) = Xc(te,:) * w;
  87. end
  88. [~,~,~,withinAUC(si,t)] = perfcurve(yc, sc, 1);
  89. end
  90. fprintf('sub%02d done (avoid=%d)\n', sn, sum(yd==2));
  91. end
  93. keep = all(~isnan(crossAUC),2) & all(~isnan(withinAUC),2);
  94. crossAUCv = crossAUC(keep,:);
  95. withinAUCv = withinAUC(keep,:);
  96. subjKept = cfg.subjects(keep);
  97. nValid = sum(keep);
  98. if nValid < 2
  99. error('Fewer than two usable subjects; nothing to test.');
  100. end
  101. if nValid < nSub
  102. fprintf('\n%d of %d subjects excluded at load/decoding stage.\n', nSub-nValid, nSub);
  103. end
  105. %% ------------------------------- stats ----------------------------------
  106. fid = fopen('control_pressing_AUC_stats.txt','w');
  107. pr(fid,'==== response-locked motor cross-decoding, AUC ====\n');
  108. pr(fid,'n=%d metric=AUC ...', nValid, mean(lamAll));
  109. rng(1); cl = sig_clusters_gt(crossAUCv, 0.5, cfg.nPerm, cfg.alpha);
  110. rows = {'analysis','t_start_ms','t_end_ms','mass','p'};
  111. rows = report(fid, 'cross-decoding AUC > chance', cl, tms, rows);
  112. fclose(fid);
  113. writecsv('control_pressing_AUC_stats.csv', rows);
  114. save('control_pressing_AUC_results.mat', 'crossAUC','withinAUC', ...
  115. 'crossAUCv','withinAUCv','subjKept','tms','cl','cfg','lamAll');
  117. %% ------------------------------ figure ----------------------------------
  118. ts = tms/1000;
  119. mC = mean(crossAUCv,1,'omitnan'); sC = std(crossAUCv,0,1,'omitnan')/sqrt(nValid);
  120. mW = mean(withinAUCv,1,'omitnan');
  121. figure('Color','w','Position',[100 100 760 400]); hold on;
  122. yl = [min([mC-sC mW]) max([mC+sC mW])]; pad=0.10*(yl(2)-yl(1)); yl=[yl(1)-pad yl(2)+pad];
  123. yb = yl(1)+0.05*(yl(2)-yl(1)); hSig=[];
  124. for k=1:numel(cl)
  125. hp=plot([ts(cl(k).tstart) ts(cl(k).tend)],[yb yb],'-','Color',[0.95 0.45 0.1],'LineWidth',6);
  126. if isempty(hSig), hSig=hp; end
  127. end
  128. plot(ts,0.5*ones(size(ts)),'k:'); plot([0 0],yl,'k-');
  129. hW = plot(ts, mW, '--','Color',[0.55 0.55 0.55],'LineWidth',1.4); % within (reference)
  130. hC = shaded(ts, mC, sC, [0.10 0.35 0.80]); set(hC,'LineWidth',2.6); % cross (main)
  131. ylim(yl); xlim([ts(1) ts(end)]); set(gca,'FontSize',14);
  132. xlabel('time from keypress (s)','FontSize',14);
  133. ylabel('cross-decoding AUC','FontSize',14);
  134. title(sprintf('motor cross-decoding (AUC) n=%d', nValid),'FontSize',14);
  135. L=[hC hW]; Ls={'cross: count 9/0 \rightarrow accept/avoid','within: count 9 vs 0'};
  136. if ~isempty(hSig), L(end+1)=hSig; Ls{end+1}='cross > chance (p<.05)'; end
  137. lgd=legend(L,Ls,'Location','northwest','FontSize',10);
  138. saveas(gcf,'control_pressing_crossdecode_AUC.png');
  139. delete(lgd); saveas(gcf,'control_pressing_crossdecode_AUC_nolegend.png');
  141. %% --------------------------- per-subject CSV ----------------------------
  142. f=fopen('control_pressing_AUC_persubj.csv','w');
  143. fprintf(f,'subject,metric,%s\n', strjoin(compose('%dms',round(tms)),','));
  144. for si=1:nValid
  145. fprintf(f,'%d,cross,%s\n', subjKept(si), strjoin(compose('%.4f',crossAUCv(si,:)),','));
  146. fprintf(f,'%d,within,%s\n',subjKept(si), strjoin(compose('%.4f',withinAUCv(si,:)),','));
  147. end
  148. fclose(f);
  149. fprintf('\nSaved figure / stats / per-subject CSV / results.mat in %s\n', pwd);
  151. % ============================ local functions ============================
  152. % function f = subfile(cfg, sn, which)
  153. % f = fullfile(cfg.inDir, sprintf('sub%02d',sn), ...
  154. % sprintf('COSMO_subject%02d_%s_resp.mat', sn, which));
  155. % end
  157. function [w, lam] = lda_lw(X, y)
  158. % Fisher LDA with Ledoit-Wolf analytic shrinkage (Ledoit & Wolf 2004).
  159. mu1 = mean(X(y==1,:),1); mu2 = mean(X(y==2,:),1);
  160. Xr = X; Xr(y==1,:) = X(y==1,:)-mu1; Xr(y==2,:) = X(y==2,:)-mu2;
  161. [n,p] = size(Xr);
  162. S = (Xr'*Xr)/n; mu = trace(S)/p;
  163. d2 = sum(sum((S - mu*eye(p)).^2));
  164. rowSS = sum(Xr.^2,2);
  165. b2bar = (sum(rowSS.^2) - 2*sum(sum((Xr*S).*Xr,2)) + n*sum(S(:).^2)) / n^2;
  166. lam = max(0, min(b2bar/d2, 1));
  167. Ss = (1-lam)*S + lam*mu*eye(p);
  168. w = Ss \ (mu1-mu2)';
  169. end
  171. function idx = balanced_idx(y, classes, seed)
  172. rng(seed); nmin = min(arrayfun(@(c) sum(y==c), classes)); idx=[];
  173. for c = classes
  174. ci = find(y==c); ci = ci(randperm(numel(ci))); idx=[idx; ci(1:nmin)]; %#ok<AGROW>
  175. end
  176. idx = sort(idx);
  177. end
  179. function folds = kfold(y, K, seed)
  180. rng(seed); folds = zeros(numel(y),1);
  181. for c = unique(y(:))'
  182. idx = find(y==c); idx = idx(randperm(numel(idx)));
  183. folds(idx) = mod(0:numel(idx)-1, K) + 1;
  184. end
  185. end
  187. function cl = sig_clusters_gt(M, chance, nperm, alpha)
  188. D = M - chance; [n,~] = size(D); tcrit = tinv(1-alpha, n-1);
  189. tstat = @(X) mean(X,1,'omitnan')./(std(X,0,1,'omitnan')/sqrt(n));
  190. obs = pos_clusters(tstat(D), tcrit); mx = zeros(nperm,1);
  191. for p=1:nperm
  192. s=sign(randn(n,1)); s(s==0)=1; c=pos_clusters(tstat(D.*s),tcrit);
  193. if isempty(c), mx(p)=0; else, mx(p)=max([c.mass]); end
  194. end
  195. cl = struct('tstart',{},'tend',{},'mass',{},'p',{});
  196. for k=1:numel(obs)
  197. pv=(1+sum(mx>=obs(k).mass))/(nperm+1);
  198. if pv<alpha, obs(k).p=pv; cl(end+1)=obs(k); end %#ok<AGROW>
  199. end
  200. end
  201. function cl = pos_clusters(tvec, tcrit)
  202. sig=tvec>tcrit; cl=struct('tstart',{},'tend',{},'mass',{},'p',{});
  203. d=diff([0 sig 0]); s=find(d==1); e=find(d==-1)-1;
  204. for k=1:numel(s), idx=s(k):e(k);
  205. cl(end+1)=struct('tstart',s(k),'tend',e(k),'mass',sum(tvec(idx)),'p',NaN); end %#ok<AGROW>
  206. end
  207. function rows = report(fid, name, cl, tms, rows)
  208. pr(fid,'%s:\n', name);
  209. if isempty(cl), pr(fid,' (no significant cluster)\n');
  210. else
  211. for k=1:numel(cl)
  212. pr(fid,' %4.0f-%4.0f ms mass=%.1f p=%.4f\n', tms(cl(k).tstart),tms(cl(k).tend),cl(k).mass,cl(k).p);
  213. rows(end+1,:)={name,tms(cl(k).tstart),tms(cl(k).tend),cl(k).mass,cl(k).p}; %#ok<AGROW>
  214. end
  215. end
  216. pr(fid,'\n');
  217. end
  218. function writecsv(fname, rows)
  219. f=fopen(fname,'w'); fprintf(f,'%s,%s,%s,%s,%s\n',rows{1,:});
  220. for r=2:size(rows,1), fprintf(f,'%s,%g,%g,%g,%g\n',rows{r,:}); end
  221. fclose(f);
  222. end
  223. function h = shaded(x,m,e,col)
  224. x=x(:)'; m=m(:)'; e=e(:)';
  225. patch([x fliplr(x)],[m+e fliplr(m-e)],col,'EdgeColor','none','FaceAlpha',0.20);
  226. h=plot(x,m,'-','Color',col,'LineWidth',2);
  227. end
  228. function pr(fid,fmt,varargin)
  229. s=sprintf(fmt,varargin{:}); fprintf('%s',s); fprintf(fid,'%s',s);
  230. end

COSMO_07_pressing_responselocked.m, no license · at the source

Overview

Authors: Kazuki Yoshida1, Ryuji Saito2, Ryota Hayashi3, Seiichi Watanabe4, Hiroki Okada5
ORCID iDs: Kazuki Yoshida, Ryuji Saito, Ryota Hayashi
  1. Faculty of Health Sciences, Hokkaido University, Sapporo, Japan
  2. Department of Occupational Therapy, School of Rehabilitation Sciences, Health Sciences University of Hokkaido, Ishikari, Japan
  3. Department of Occupational Therapy, Faculty of Rehabilitation, Kansai Medical University, Hirakata, Japan
  4. Medical Corporation Nasukougen Hospital, Nasu-machi, Japan
  5. Department of Occupational Therapy, School of Rehabilitation, Hyogo Medical University, Hyogo, Japan
Institutions: Hokkaido University (Japan); Health Sciences University of Hokkaido (Japan); Kansai Medical University (Japan); Hyogo Medical University (Japan)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1332
Dates: received 20 April 2026; accepted 13 July 2026; published online 14 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1332 · PMID 42609632 · PMCID PMC13479346 · OpenAlex W7169571570
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing
Keywords: electroencephalography (EEG), neural decoding, representational similarity analysis, metacognition, effort-based decision making, effort avoidance
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Japan Society for the Promotion of Science (24K00489)
Citations: not cited yet (Europe PMC); 63 references in the paper

Abstract

Effort-based decision making relies on cost–benefit computations that require individuals to evaluate expected rewards against the effort needed to obtain them. However, the temporal dynamics underlying the neural processing of reward and effort, as well as the influence of metacognitive ability on this process, remain unclear. The present study investigated these dynamics by recording electroencephalogram (EEG) data while participants performed an effort-based decision-making task and by assessing their metacognitive ability. Decoding analyses of the EEG data revealed that neural representations of effort-avoidance decisions emerged early (approximately 260 ms after stimulus onset) and remained stable over time. Crucially, representational similarity analysis showed that the brain processes reward information before effort information. Although both types of information were primarily represented in parietal electrodes, effort processing engaged a broader network that also included frontal regions. Behaviorally, higher metacognitive ability was associated with better task performance and reduced sensitivity to immediate rewards. These findings suggest a sequential decision-making process in which individuals first evaluate potential rewards and subsequently integrate information about the required effort when deciding whether to avoid effort. Furthermore, higher metacognitive ability appears to buffer against immediate reward impulses, thereby promoting long-term goal-directed behavior. Overall, this study provides direct neural evidence for the temporal dynamics of cost–benefit computation and highlights the critical role of metacognition in effort-based decision making.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 11 matches between paragraphs and lines of code.

smfleming/HMM

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 8b6efee71010eedd70fb29681e75298613118da3, 19 April 2026
Languages: MATLAB (34), R (10)
Size: 68 files, 44 scripts
Software Heritage: not archived
Found in: the text, “Behavioral analysis”
Holds: README, license file, 1 notebook
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (14 files), JAGS (9 files), tidyverse (9 files), broom (6 files), ggpubr (6 files), reshape2 (6 files), Optimization Toolbox (1 file), Parallel Computing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
46 files
At the source: github.com/smfleming/HMM

OSF 7egv5

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (13)
Size: 15 files, 13 scripts
Software Heritage: not checked
Found in: “Data and Code Availability”
Holds: 7 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (4 files), CoSMoMVPA (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
13 files
At the source: doi.org/10.17605/OSF.IO/7EGV5

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 57 scripts, each with its path and the digest of its content;
  • 11 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 and Code Availability

The data are available from the corresponding author (K. Y.) upon reasonable request. Custom scripts for the main analyses are available through the following OSF repository: https://doi.org/10.17605/OSF.IO/7EGV5

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 2, 28 September 2026

  • Funding: added Japan Society for the Promotion of Science: 24K00489

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 62 references.

Cite

This paper

Yoshida, K., Saito, R., Hayashi, R., Watanabe, S., & Okada, H. (2026). Decision processes underlying effort avoidance and their relationship with metacognition. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1332. https://doi.org/10.1162/imag.a.1332

BibTeX

@article{yoshida2026decision,
author = {Yoshida, Kazuki and Saito, Ryuji and Hayashi, Ryota and Watanabe, Seiichi and Okada, Hiroki},
title = {{Decision processes underlying effort avoidance and their relationship with metacognition}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = aug,
volume = {4},
pages = {IMAG.a.1332},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1332},
url = {https://doi.org/10.1162/imag.a.1332},
pmid = {42609632},
pmcid = {PMC13479346}
}

RIS

TY - JOUR
AU - Yoshida, Kazuki
AU - Saito, Ryuji
AU - Hayashi, Ryota
AU - Watanabe, Seiichi
AU - Okada, Hiroki
TI - Decision processes underlying effort avoidance and their relationship with metacognition
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/08/14
VL - 4
SP - IMAG.a.1332
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1332
UR - https://doi.org/10.1162/imag.a.1332
LA - en
ER -

CSL-JSON

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"id": "10.1162/imag.a.1332",
"type": "article-journal",
"title": "Decision processes underlying effort avoidance and their relationship with metacognition",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
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"family": "Yoshida",
"given": "Kazuki"
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"family": "Watanabe",
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"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1332",
"DOI": "10.1162/imag.a.1332",
"PMID": "42609632",
"PMCID": "PMC13479346",
"ISSN": "2837-6056",
"publisher": "MIT Press",
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"issued": {
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The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.7554/elife.103846 [code]
Overt visual attention modulates decision-related signals in the frontal cortex.
Journal: eLife
In common: broom, Parallel Computing Toolbox, ggpubr, 2 other tools, cognitive, 2 references
[2] doi:10.1038/s41467-026-75987-6 [code]
Neural and computational mechanisms of effort under the pressure of a deadline.
Journal: Nature communications
In common: Statistics and Machine Learning Toolbox, cognitive, 5 references
[3] doi:10.1002/hbm.70605 [code]
BrainEnrich: Revealing Biological Insights for Imaging-Derived Phenotypes Through Transcriptomic Enrichment.
Journal: Human brain mapping
In common: JAGS, broom, reshape2, 3 other tools
[4] doi:10.7554/elife.103566 [code]
Effort produces after-effects costly for others but valued for self.
Journal: eLife
In common: ggpubr, tidyverse, EEG, 4 references
[5] doi:10.1038/s41586-026-10612-6 [code]
Acquired genetic and cell-state changes in IDH-mutant glioma progression.
Journal: Nature
In common: JAGS, broom, reshape2, 2 other tools
[6] doi:10.1162/imag.a.1258 [code]
Non-specific increase in alpha power during a neurofeedback session targeting its downregulation.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: broom, Parallel Computing Toolbox, reshape2, 3 other tools, EEG
[7] doi:10.1016/j.celrep.2026.117646 [code]
Medial entorhinal-hippocampal desynchronization parallels the emergence of memory impairment in a mouse model of Alzheimer's disease pathology.
Journal: Cell reports
In common: Optimization Toolbox, broom, Parallel Computing Toolbox, 3 other tools
[8] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: Optimization Toolbox, broom, reshape2, 3 other tools
[9] doi:10.1162/imag.a.1201 [code]
All spectral frequencies of neural activity reveal semantic representation in the human anterior ventral temporal cortex.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: Optimization Toolbox, Parallel Computing Toolbox, reshape2, 2 other tools, 1 reference
[10] doi:10.1111/ejn.70601 [code]
Optimising Analysis Choices for Multivariate Decoding: Creating Pseudotrials Using Trial Averaging and Resampling.
Journal: The European journal of neuroscience
In common: CoSMoMVPA, Statistics and Machine Learning Toolbox, 3 references

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