OSCR

A neural signature of adaptive mentalization.

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18 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.

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  1. [1] § Results › Characterizing adaptive mentalization with the CHASE model ↔ source/BAKR_2024_CHASE_model.m, lines 1–31 · score 0.84 · rock paper scissors, strategic player, best respond, recursive reasoning steps, CHASE model, exploitable
  2. [2] § Methods › CHASE model ↔ source/BAKR_2024_CHASE_model.m, lines 1–31 · score 0.81 · Strategic players, best responding, recursive reasoning steps, CHASE model, iteratively, rock
  3. [3] § Methods › Neuroimaging data acquisition and analysis › Univariate analysis ↔ source/BAKR_2024_2L.m, lines 140–212 · score 0.73 · SnPM, cluster forming threshold, SPM, FWE, inference, mask
  4. [4] § Methods › Alternative models › Self-tuning EWA ↔ source/BAKR_2024_CHASE_LR_update.m, lines 74–151 · score 0.73 · change detector function, foregone payoffs, empirical, tuning, EWA, model
  5. [5] § Methods › Neuroimaging data acquisition and analysis › Univariate analysis ↔ source/BAKR_2024_create_nuisance_regressors.m, the whole file · a weak match · score 0.69 · global signal, motion parameters, derivatives, confounds, regressors, uniformity
  6. [6] § Methods › CHASE model › Loss sensitivity and learning differences ↔ source/BAKR_2024_CHASE_model.m, lines 33–144 · score 0.68 · loss aversion, payoff matrix, recursive reasoning, wins
  7. [7] § Methods › CHASE model › Loss sensitivity and learning differences ↔ source/BAKR_2024_ToMk_model.m, lines 6–117 · score 0.68 · loss aversion, payoff matrix, recursive reasoning, wins
  8. [8] § Methods › CHASE model › A3: adaptive players ( > 1) try to infer the level of the opponent ↔ source/BAKR_2024_CHASE_model.m, lines 33–144 · score 0.62 · KL divergence, possible opponent, successive belief, BU, prediction
  9. [9] § Methods › CHASE model › A3: adaptive players ( > 1) try to infer the level of the opponent ↔ source/BAKR_2024_ToMk_model.m, lines 6–117 · score 0.62 · KL divergence, possible opponent, successive belief, BU, prediction
  10. [10] § Methods › Neuroimaging data acquisition and analysis › Multivariate analysis ↔ source/BAKR_2024_apply_pattern.m, the whole file · a weak match · score 0.58 · correlation coefficients, neural activation, bins, vector, adaptive, BU
  11. [11] § Methods › Neuroimaging data acquisition and analysis › Functional connectivity analysis › Denoising ↔ source/BAKR_2024_create_nuisance_regressors.m, the whole file · a weak match · score 0.55 · nuisance regressor, derivatives, confounding, signal
  12. [12] § Methods › Computational models ↔ BAKR_2024_run_model_fitting.m, lines 14–33 · score 0.55 · fictitious play, reinforcement learning, EWA, behavior, models
  13. [13] § Results › Opponent-level BUs can be decoded from neural activity ↔ BAKR_2024_results_and_figures.m, lines 530–596 · score 0.55 · multivariate activation pattern, social brain, overlaps, univariate, ROIs, decoding
  14. [14] § Results › Participants track action frequencies to predict nonstrategic play ↔ source/MERLIN_toolbox/mn_compare.m, lines 218–296 · score 0.54 · protected exceedance probability, model comparison, PXP
  15. [15] § Methods › Alternative models › Experience-weighted attraction (EWA) ↔ source/BAKR_2024_CHASE_LR_update.m, lines 74–151 · score 0.54 · foregone payoffs, forgetting, RL, attraction, EWA, weighted
  16. [16] § Results › CHASE outperforms existing alternative models ↔ BAKR_2024_run_model_fitting.m, lines 14–33 · score 0.52 · fictitious play, reinforcement learning, CHASE model, EWA, behavior
  17. [17] § Methods › Neuroimaging data acquisition and analysis › Replication in an independent dataset ↔ BAKR_2024_results_and_figures.m, lines 530–596 · score 0.52 · activation patterns, social brain, Univariate, Multivariate, correlation, decoding
  18. [18] § Results › Brain regions linked to evaluation and adaptation of mentalization strategies ↔ source/BAKR_2024_2L.m, lines 140–212 · score 0.51 · cluster forming threshold, SPM, FWE, Pos, Neg, mask

Paper

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The authors' code

MATLAB · 380 lines · 16 KB · no license · 4 matches

  1. function [negLL,states,data] = BAKR_2024_CHASE_model(data,model,curr_params,type)
  2. %
  3. % Code to fit the CHASE model (Buergi, Aydogan, Konovalov, & Ruff; under review)
  4. %
  5. % In brief, this model captures mentalization in strategic interactions by assuming that:
  6. % - Non-strategic players (defined as k = 0) have a tendency to repeat their historical action frequencies.
  7. % - Strategic players try to exploit this tendency by performing a limited number of recursive reasoning steps
  8. % (k > 0), i.e. iteratively (and noisily) best-responding to that action distribution.
  9. % - Finally, adaptive players (kappa >= 2) assume that they are facing a strategic player and try to infer
  10. % their level of recursive reasoning, by integrating evidence for the different levels over time.
  11. %
  12. % Inputs
  13. % - data: struct containing the experimental data (i.e. choices in Rock-Paper-Scissors) of both players
  14. % . choice_own: vector containing the chosen actions of the subject (from 1:3)
  15. % . choice_other: vector containing the chosen actions of the opponent (from 1:3)
  16. % - curr_params: vector containing the model parameters
  17. % . alpha: speed of updating attractions (i.e. learning rate of a delta rule over chosen actions)
  18. % . beta: recursive reasoning noise (i.e. softmax inverse decision temperature)
  19. % . gamma: sensitivity to evidence for the sophistication of the opponent
  20. % . lambda: loss aversion (or lack thereof)
  21. % . kappa: depth of mentalization (note: equal to k if kappa < 2)
  22. % Outputs
  23. % - negLL: scalar negative log-likelihood of the data, given the model and the parameters
  24. % - states: struct containing the inferred internal states (for e.g. use in neuroimaging analysis)
  25. % . SV: subjective value of the chosen action, given the current predictions about the opponent
  26. % . APE: opponent action prediction error, i.e. level of surprise about the observed opponent action
  27. % . BU: opponent sophistication belief update, i.e. the Kullback-Leibler divergence between successive
  28. % beliefs
  29. %
  31. % Niklas Buergi, 2024
  33. %% Initialize
  35. switch type
  37. case 'fit'
  39. % set game defaults, if none provided
  40. if ~any(isfield(data,{'strat_space','win','loss','tie'}))
  41. data.win = 1;
  42. data.loss = -1;
  43. data.tie = 0;
  44. data.strat_space = 3;
  45. end
  47. % extract variables
  48. choice_own = data.choice_own;
  49. choice_other = data.choice_other;
  50. % nTrials = size(choice_own,1);
  52. assert(numel(choice_own) > 1 & numel(choice_other) > 1 & numel(data.missing) > 1);
  55. case 'sim'
  57. task = data;
  59. data = struct();
  60. [data,env] = task.fxn(data,task,'init');
  61. data.n_trials = numel(data.trial);
  62. data.missing = zeros(data.n_trials,1);
  64. [data.score_own,data.score_other,choice_own,choice_other] = deal(NaN(data.n_trials,1));
  66. end
  68. nTrials = data.n_trials;
  69. nActions = data.strat_space(1);
  71. % name parameters
  72. params = array2table(curr_params','VariableNames',{model.params.name});
  74. % ======================== adapt to parameterization ========================= %
  76. % add loss aversion, if parameter provided
  77. if any(strcmp(params.Properties.VariableNames,'lambda')) % lossav
  78. data.loss = data.loss*params.lambda;
  79. end
  81. % construct payoff matrices
  82. [pi_own, pi_other] = comp_paymatrix(data);
  84. % set exp_max_k
  85. if strcmp(model.maxK_type,'fitted') % || strcmp(type,'sim')
  86. exp_max_k = params.kappa;
  87. else
  88. exp_max_k = model.exp_max_k; % i.e. if not provided via parameters, take from settings
  89. end
  91. % ========================= pre-allocate variables =========================== %
  93. % main variables
  94. subj_pred = NaN(nTrials,nActions); % subject's estimated probability of the different possible opponent actions
  95. subj_resp = NaN(nTrials,nActions); % subject's response probabilities to the estimate above (based on a noisy best-response)
  97. subj_KL_div = NaN(nTrials,1); % belief update (BU), given by the KL-divergence between successive beliefs
  98. subj_APE = NaN(nTrials,1); % 1 minus the probability assigned to the action chosen by the opponent (from subj_pred)
  99. subj_SV = NaN(nTrials,1); % subjective value of choosing the different actions, given one's predictions of the opponent
  101. % recursive reasoning steps (either from own perspetive ('LK'), or from the perspective of the opponent ('CH-leaky'))
  102. if exp_max_k < 2 || strcmp(model.architecture,'LK')
  103. subj_pred_k = deal(NaN(exp_max_k+1,nActions)); % 1st order beliefs (what subjects think the opponent will play)
  104. subj_resp_k = deal(NaN(exp_max_k+1,nActions)); % subject's response probabilities (BR), in response to their beliefs above
  105. elseif exp_max_k >= 2 && contains(model.architecture,'CH')
  106. opp_pred_oppk = NaN(exp_max_k,nActions); % 2nd order beliefs (what subject thinks opponent thinks about them)
  107. opp_resp_oppk = NaN(exp_max_k,nActions); % opponent's response probabilities, in response to their beliefs above (as perceived by the subject)
  108. subj_beliefs = ones(1,exp_max_k)/exp_max_k; % priors of the subject about the opponents's current level k, bounded by their own sophistication
  109. end
  111. % outputs for figures
  112. beliefs = NaN(nTrials,3);
  113. likelihoods = NaN(nTrials,3);
  115. % initialize attractions
  116. switch model.learning_rule
  117. case 'RW-freq'
  118. [f_mat_own,f_mat_other] = BAKR_2024_CHASE_LR_init(model.learning_rule,nTrials,nActions);
  119. case {'RW-reward','RW-hybrid'}
  120. [f_mat_own,f_mat_other] = BAKR_2024_CHASE_LR_init(model.learning_rule,nTrials,nActions,pi_own,pi_other);
  121. score_own = data.score_own; score_other = data.score_other;
  122. case 'RW-regret'
  123. [f_mat_own,f_mat_other,score_pot_own,score_pot_other] = BAKR_2024_CHASE_LR_init(...
  124. model.learning_rule,nTrials,nActions,...
  125. pi_own,pi_other,...
  126. choice_own,choice_other);
  127. case {'EWA-full','EWA-single'}
  128. [f_mat_own,f_mat_other,score_pot_own,score_pot_other,N_own,N_other] = BAKR_2024_CHASE_LR_init(...
  129. model.learning_rule,nTrials,nActions,...
  130. pi_own,pi_other,...
  131. choice_own,choice_other);
  132. end
  134. % =========================== save initial values ============================ %
  136. f_mat_own_0 = f_mat_own(1,:);
  137. f_mat_other_0 = f_mat_other(1,:);
  138. if exp_max_k >= 2 && contains(model.architecture,'CH')
  139. subj_beliefs_0 = subj_beliefs;
  140. end
  141. if any(strcmp(model.learning_rule,{'EWA-full','EWA-single'}))
  142. N_own_0 = N_own(1);
  143. N_other_0 = N_other(1);
  144. end
  146. %% Trial loop
  147. for iTrial = 1:nTrials
  149. % if any(data.choice_own == 0 | isnan(data.choice_own))
  150. % error('Missing choices - have to adapt first.');
  151. % end
  153. % skip updates if missing trial
  154. if numel(data.missing) > 1 && data.missing(iTrial)
  155. f_mat_own(iTrial,:) = f_mat_own(max(1,iTrial-1),:);
  156. f_mat_other(iTrial,:) = f_mat_other(max(1,iTrial-1),:);
  157. if any(strcmp(model.learning_rule,{'EWA-full','EWA-single'}))
  158. N_own(iTrial,:) = N_own(max(1,iTrial-1),:);
  159. N_other(iTrial,:) = N_other(max(1,iTrial-1),:);
  160. end
  161. continue
  162. end
  164. % re-set initial values (if fitting across blocks)
  165. if data.trial(iTrial) == 1 && iTrial > 1
  166. f_mat_own_old = f_mat_own(iTrial-1,:);
  167. f_mat_other_old = f_mat_other(iTrial-1,:);
  168. f_mat_own(iTrial-1,:) = f_mat_own_0; % need to overwrite old ones to ensure correct update below
  169. f_mat_other(iTrial-1,:) = f_mat_other_0;
  170. if exp_max_k >= 2 && contains(model.architecture,'CH')
  171. subj_beliefs = subj_beliefs_0; % will be overwritten at end of trial
  172. end
  173. if any(strcmp(model.learning_rule,{'EWA-full','EWA-single'}))
  174. N_own_old = N_own(iTrial-1);
  175. N_other_old = N_other(iTrial-1);
  176. N_own(iTrial-1) = N_own_0;
  177. N_other(iTrial-1) = N_other_0;
  178. end
  179. end
  181. % strategic reasoning
  182. if exp_max_k < 2 || strcmp(model.architecture,'LK')
  184. % ======================================================================
  185. % Low-level or static reasoning
  186. % ======================================================================
  188. for k = 0:exp_max_k
  190. % ========================= (Re-)active strategies =================== %
  192. if k == 0 % egocentric strategy (ignoring opponent)
  193. subj_pred_k(1,:) = ones(1,nActions)/nActions;
  194. subj_resp_k(1,:) = softmax_fxn(f_mat_own(max(1,iTrial-1),:),params.beta);
  196. elseif k == 1 % responding to the other's egocentric strategy
  197. subj_pred_k(2,:) = softmax_fxn(f_mat_other(max(1,iTrial-1),:),params.beta);
  198. subj_resp_k(2,:) = softmax_fxn((pi_own*subj_pred_k(2,:)')',params.beta);
  200. elseif k >= 2
  202. % ===================== LK: Higher-level reasoning =================== %
  204. % always add two steps of reasoning to last estimate of same type (i.e. odd vs even)
  205. subj_pred_k(k+1,:) = softmax_fxn((pi_other*subj_resp_k(k+1-2,:)')',params.beta);
  206. subj_resp_k(k+1,:) = softmax_fxn((pi_own*subj_pred_k(k+1,:)')',params.beta);
  208. end
  210. subj_pred(iTrial,:) = subj_pred_k(end,:);
  211. subj_resp(iTrial,:) = subj_resp_k(end,:);
  213. end
  215. elseif exp_max_k >= 2 && contains(model.architecture,'CH')
  217. % ======================================================================
  218. % CH: Belief-based strategies
  219. % ======================================================================
  221. % subject simulates reasoning process *of the opponent* to:
  222. % - form and update beliefs about the opponent's level of reasoning
  223. % - predict the next opponent action, marginalizing over these beliefs
  225. for k = 2:exp_max_k
  227. if k == 2 % opponent levels 0 & 1
  228. opp_pred_oppk(1,:) = ones(1,nActions)/nActions; % i.e. no expectations; used only for computing subject action predictions
  229. opp_resp_oppk(1,:) = softmax_fxn(f_mat_other(max(1,iTrial-1),:),params.beta); % = exp_p_a_opp(2,:)
  231. opp_pred_oppk(2,:) = softmax_fxn(f_mat_own(max(1,iTrial-1),:),params.beta); % = exp_p_a_subj(1,:)
  232. opp_resp_oppk(2,:) = softmax_fxn((pi_other*opp_pred_oppk(2,:)')',params.beta);
  234. elseif k > 2 % higher levels: add two more steps to last action estimate of same type (i.e. odd vs even)
  235. opp_pred_oppk(k,:) = softmax_fxn((pi_own*opp_resp_oppk(k-2,:)')',params.beta); % ..compute their opponent's best response to that..
  236. opp_resp_oppk(k,:) = softmax_fxn((pi_other*opp_pred_oppk(k,:)')',params.beta); % ..and compute their own best response to that
  238. end
  240. end
  242. % ========================= Subject beliefs ========================== %
  244. % subject's weighted opponent action prediction & response to that
  245. subj_pred(iTrial,:) = subj_beliefs * opp_resp_oppk;
  246. subj_resp(iTrial,:) = softmax_fxn(pi_own*subj_pred(iTrial,:)',params.beta);
  248. end
  250. % =========================== Action simulation ========================== %
  252. if strcmp(type,'sim')
  254. % subject
  255. choice_own(iTrial) = find(mnrnd(1,subj_resp(iTrial,:)));
  256. data.choice_own(iTrial) = choice_own(iTrial);
  258. % bot
  259. [data,env] = task.fxn(data,task,'update',env,iTrial); % need only: choice_other(iTrial)
  260. choice_other(iTrial,1) = data.choice_other(iTrial);
  261. score_own(iTrial,1) = data.score_own(iTrial);
  262. score_other(iTrial,1) = data.score_other(iTrial);
  264. end
  266. % ========================= Subject beliefs ========================== %
  268. if exp_max_k >= 2 && contains(model.architecture,'CH')
  270. % belief update
  271. prior = subj_beliefs;
  272. likelihood = softmax_fxn(opp_resp_oppk(:,choice_other(iTrial)),params.gamma);
  273. posterior = (likelihood .* prior') ./ sum(likelihood .* prior');
  274. subj_KL_div(iTrial) = nansum(posterior .* log(posterior ./ prior'));
  276. % save
  277. beliefs(iTrial,1:exp_max_k) = subj_beliefs;
  278. likelihoods(iTrial,1:exp_max_k) = likelihood;
  280. % replace with new belief
  281. subj_beliefs = posterior';
  283. end
  285. % compute internal states
  286. subj_APE(iTrial) = 1 - subj_pred(iTrial,choice_other(iTrial));
  287. subj_SV(iTrial) = pi_own(choice_own(iTrial),:) * subj_pred(iTrial,:)';
  289. % ==========================================================================
  290. % Level-0 update
  291. % ==========================================================================
  293. % L0 representation
  294. switch model.learning_rule
  295. case 'RW-freq'
  296. [f_mat_own,f_mat_other] = BAKR_2024_CHASE_LR_update(model.learning_rule,iTrial,f_mat_own,f_mat_other,...
  297. choice_own,choice_other,params);
  298. case {'RW-reward','RW-hybrid'}
  299. [f_mat_own,f_mat_other] = BAKR_2024_CHASE_LR_update(model.learning_rule,iTrial,f_mat_own,f_mat_other,...
  300. choice_own,choice_other,params,...
  301. score_own,score_other);
  302. case 'RW-regret'
  303. [f_mat_own,f_mat_other] = BAKR_2024_CHASE_LR_update(model.learning_rule,iTrial,f_mat_own,f_mat_other,...
  304. choice_own,choice_other,params,...
  305. score_pot_own,score_pot_other);
  306. case {'EWA-full','EWA-single'}
  307. [f_mat_own,f_mat_other,N_own,N_other] = BAKR_2024_CHASE_LR_update(model.learning_rule,iTrial,f_mat_own,f_mat_other,...
  308. choice_own,choice_other,params,...
  309. score_pot_own,score_pot_other,N_own,N_other);
  310. end
  312. % put overwritten values back (if fitting across blocks) <- cannot do earlier because this is used for update above
  313. if data.trial(iTrial) == 1 && iTrial > 1
  314. f_mat_own(iTrial-1,:) = f_mat_own_old;
  315. f_mat_other(iTrial-1,:) = f_mat_other_old;
  316. if any(strcmp(model.learning_rule,{'EWA-full','EWA-single'}))
  317. N_own(iTrial-1) = N_own_old;
  318. N_other(iTrial-1) = N_other_old;
  319. end
  320. end
  322. end
  324. %% Post-processing
  326. % internal states
  327. states.subj_SV = subj_SV;
  328. states.subj_APE = subj_APE;
  329. states.subj_KL_div = subj_KL_div;
  331. % states for figures
  332. % states.f_mat_own = f_mat_own;
  333. % states.likelihoods = likelihoods;
  334. states.beliefs = beliefs;
  336. switch type
  338. case 'fit'
  340. lik = NaN(nTrials,1);
  341. lik(~data.missing) = subj_resp(sub2ind([nTrials,nActions],find(~data.missing),choice_own(~data.missing))); % chosen action
  342. states.lik = lik;
  343. % states.p_a = subj_resp;
  345. negLL = -sum(log(lik(~data.missing)));
  348. case 'sim'
  350. states.beliefs = beliefs;
  351. states.level_k = env.level_k;
  352. % states.score_own = score_own;
  354. states.score = NaN(nTrials,1);
  355. for i_block = 1:task.n_blocks
  356. idx = (task.n_trials*(i_block-1))+1:(task.n_trials*(i_block));
  357. states.score(idx) = cumsum(score_own(idx));
  358. end
  359. % states.gamma = curr_gamma(1:nTrials);
  361. data.choice_own = choice_own;
  362. data.choice_other = choice_other;
  363. data.score_own = score_own;
  364. data.score_other = score_other;
  366. data.n_blocks = 1;
  368. data.bot_level = task.bot.levels;
  370. negLL = NaN;
  372. end
  374. end
  376. function p_actions = softmax_fxn(inputs,beta)
  378. p_actions = exp(beta*inputs)/(sum(exp(beta*inputs)));
  380. end

BAKR_2024_CHASE_model.m at commit 95c6522, no license · at the source

Overview

Authors: Niklas Buergi1,2, Gökhan Aydogan1,3, Arkady Konovalov1,4, Christian C Ruff1,3,5
ORCID iDs: Niklas Buergi, Gökhan Aydogan, Arkady Konovalov, Christian C Ruff
  1. Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
  2. Max Planck Institute for Biological Cybernetics, Tübingen, Germany
  3. University Research Priority Program ‘Adaptive Brain Circuits in Development and Learning’ (URPP AdaBD), University of Zurich, Zurich, Switzerland
  4. School of Psychology, Centre for Human Brain Health, University of Birmingham, Birmingham, UK
  5. Faculty of Medicine, University of Zurich, Zurich, Switzerland
Institutions: University of Zurich (Switzerland); Max Planck Institute for Biological Cybernetics (Germany); University of Birmingham (United Kingdom)
Journal: Nature neuroscience, volume 29, issue 4, pages 934-944
Dates: received 11 February 2024; accepted 22 January 2026; published online 9 March 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41593-026-02219-x · PMID 41803318 · PMCID PMC13061600 · OpenAlex W7134268805
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, fMRI & imaging
Keywords: Neural decoding, Learning algorithms, Social behaviour, Psychology, Decision
MeSH: Adaptation, Psychological*, Brain*, Mentalization*, Theory of Mind*, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Models, Neurological (* major topic)
Topic: Personality Disorders and Psychopathology (Clinical Psychology, Psychology), according to OpenAlex
Funding: European Research Council (725355); Swiss National Science Foundation (10.006.863)
Citations: cited by 4 papers (Europe PMC); 56 references in the paper

Abstract

Mentalization, inferring others’ emotions and intentions, is crucial for human social interactions and is impaired in various brain disorders. While previous neuroscience research has focused on static mentalization strategies, we know little about how the brain adaptively selects which strategies to use at any given moment. Here we investigate this core aspect of mentalization with computational modeling and functional magnetic resonance imaging (fMRI) during interactive strategic games. We find that most participants can adapt their strategies to the changing sophistication of their opponents, though there are considerable individual differences. Model-based fMRI analyses identify a distributed brain network in which activity and connectivity track this mentalization-belief adaptation. The extent to which people update their beliefs about others’ sophistication can be predicted out of sample from neural activity, providing a neural signature of adaptive mentalization. Our model elucidates the neural basis of mentalization ability and provides a method for assessing these capabilities in healthy and clinical populations.

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 18 matches between paragraphs and lines of code.

ruffgroup/neural_signature_of_mentalization

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 95c6522696ed3129c05e8932ce4f257ffd28ccc8, 13 November 2025
Languages: MATLAB (48), Python (3)
Size: 125 files, 51 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (9 files), Statistics and Machine Learning Toolbox (7 files), Image Processing Toolbox (3 files), CONN (1 file), Optimization Toolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
52 files
At the source: github.com/ruffgroup/neural_signature_of_mentalization

Code availability

The analysis code to produce all main results and figures can be accessed at: https://github.com/ruffgroup/neural_signature_of_mentalization56.

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;
  • 51 scripts, each with its path and the digest of its content;
  • 18 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 Statement

All behavioral data can be accessed at: https://github.com/ruffgroup/neural_signature_of_mentalization56. Preprocessed neural data are available upon request.

The analysis code to produce all main results and figures can be accessed at: https://github.com/ruffgroup/neural_signature_of_mentalization56.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 10 MeSH terms, 2 funders, 44 references.

Cite

This paper

Buergi, N., Aydogan, G., Konovalov, A., & Ruff, C. C. (2026). A neural signature of adaptive mentalization. Nature neuroscience, 29(4), 934-944. https://doi.org/10.1038/s41593-026-02219-x

BibTeX

@article{buergi2026neural,
author = {Buergi, Niklas and Aydogan, Gökhan and Konovalov, Arkady and Ruff, Christian C},
title = {{A neural signature of adaptive mentalization}},
journal = {Nature neuroscience},
year = {2026},
month = mar,
volume = {29},
number = {4},
pages = {934--944},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02219-x},
url = {https://doi.org/10.1038/s41593-026-02219-x},
pmid = {41803318},
pmcid = {PMC13061600}
}

RIS

TY - JOUR
AU - Buergi, Niklas
AU - Aydogan, Gökhan
AU - Konovalov, Arkady
AU - Ruff, Christian C
TI - A neural signature of adaptive mentalization
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/03/09
VL - 29
IS - 4
SP - 934
EP - 944
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02219-x
UR - https://doi.org/10.1038/s41593-026-02219-x
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41593-026-02219-x",
"type": "article-journal",
"title": "A neural signature of adaptive mentalization",
"container-title": "Nature neuroscience",
"author": [
{
"family": "Buergi",
"given": "Niklas"
},
{
"family": "Aydogan",
"given": "Gökhan"
},
{
"family": "Konovalov",
"given": "Arkady"
},
{
"family": "Ruff",
"given": "Christian C"
}
],
"container-title-short": "Nat Neurosci",
"volume": "29",
"issue": "4",
"page": "934-944",
"DOI": "10.1038/s41593-026-02219-x",
"PMID": "41803318",
"PMCID": "PMC13061600",
"ISSN": "1097-6256",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41593-026-02219-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
9
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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