Contributions of insula and superior temporal sulcus to interpersonal guilt and responsibility in social decisions.

Code ↔ Paper

27 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 27 matches · 2 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted

[1] § Results › Behaviour › Momentary happiness: links to reward ↔ Code/master_behavAnalysis_revised.m, lines 1015 to 1143 · score 0.87 · Responsibility Redux model, partner_pRPE, likelihood ratio, social_pRPE, Responsibility model fitted, sRPE · Report a mismatch
[2] § Results › Behaviour › Choices: risk preferences ↔ Code/bin/fittingUtilityFunction/fitCARAmodelToRiskyChoices.m, lines 1 to 67 · score 0.81 · constant absolute risk, risk averse, loss trials, decision maker, expected utility, risk aversion · Report a mismatch
[3] § Results › Behaviour › Choices: risk preferences ↔ Code/bin/fittingUtilityFunction/fitCARAmodelToRiskyChoices2conds.m, lines 1 to 67 · score 0.81 · constant absolute risk, risk averse, loss trials, decision maker, expected utility, risk aversion · Report a mismatch
[4] § Results › BOLD signal › Functional connectivity ↔ Code/master_fMRI_showResults.m, lines 247 to 302 · score 0.80 · left IFG, right IFG, left STS, left insula, connectivity changes, PPI · Report a mismatch
[5] § Results › Behaviour › Momentary happiness: links to reward ↔ Code/bin/compModels/fit_happy_model_responsibility_redux.m, lines 1 to 94 · score 0.80 · ResponsibilityRedux, likelihood ratio, social_pRPE, sRPE, model fitted, AIC · Report a mismatch
[6] § Materials and methods › Momentary happiness › Computational models of happiness variations ↔ Code/master_behavAnalysis_revised.m, lines 1015 to 1143 · score 0.79 · Responsibility Redux model, partner_pRPE, social_pRPE, sRPE, computational models, variations · Report a mismatch
[7] § Materials and methods › Statistical analysis › Decisions › Estimating risk attitude 2: CARA model ↔ Code/bin/fittingUtilityFunction/fitCARAmodelToRiskyChoices.m, lines 1 to 67 · score 0.75 · exponential utility function, Constant Absolute Risk, Expected utility, risk aversion, preferences, model · Report a mismatch
[8] § Materials and methods › Statistical analysis › Decisions › Estimating risk attitude 2: CARA model ↔ Code/bin/fittingUtilityFunction/fitCARAmodelToRiskyChoices2conds.m, lines 1 to 67 · score 0.75 · exponential utility function, Constant Absolute Risk, Expected utility, risk aversion, preferences, model · Report a mismatch
[9] § Materials and methods › fMRI data analysis › PPI analysis ↔ Code/fMRI/c_gPPI.m, lines 18 to 49 · score 0.74 · PPI models, STS cluster, insula cluster, ran, seed, Solo · Report a mismatch
[10] § Materials and methods › Decision task ↔ Code/socialDecisionRespo_fMRI.m, lines 52 to 108 · score 0.73 · ISI, loss, interval, position, mixed, Social · Report a mismatch
[11] § Results › BOLD signal › Comparison with multivariate neural guilt signature () ↔ Code/fMRI/f_apply_YuKoban_guilt_signature_map.m, lines 37 to 57 · score 0.73 · dot product, guilt responses, Yu, signature, Cliff, Delta · Report a mismatch
[12] § Materials and methods › Statistical analysis › Decisions › Generalized linear mixed logistic regression ↔ Code/master_behavAnalysis.m, lines 577 to 651 · score 0.69 · random slopes, random intercepts, explanatory, Cond, minus, mixed · Report a mismatch
[13] § Materials and methods › Statistical analysis › Decisions › Generalized linear mixed logistic regression ↔ Code/master_behavAnalysis_revised.m, lines 588 to 636 · score 0.69 · random slopes, random intercepts, explanatory, Cond, minus, mixed · Report a mismatch
[14] § Materials and methods › Momentary happiness › Computational models of happiness variations ↔ Code/bin/compModels/fit_happy_model_responsibility_redux.m, lines 1 to 94 · score 0.68 · social_pRPE, Responsibility Redux, sRPE, variance, gamma, CR · Report a mismatch
[15] § Materials and methods › Likelihood ratio test ↔ Code/bin/compModels/fit_happy_model_basic.m, lines 1 to 94 · score 0.67 · Chi square distribution, likelihood ratio, L0, L1, freedom, model · Report a mismatch
[16] § Materials and methods › fMRI data analysis › Fixed-effects models ↔ Code/master_behavAnalysis_revised.m, lines 1176 to 1302 · score 0.66 · sRPE, best fitting, risky choice, lottery outcome, coefficients, chosen · Report a mismatch
[17] § Materials and methods › Statistical analysis › Decisions › Estimating risk attitude 1: risk premium ↔ Code/master_behavAnalysis.m, lines 114 to 156 · score 0.59 · indicate risk aversion, risk seeking, indifferent, lottery, fitting, model · Report a mismatch
[18] § Materials and methods › Statistical analysis › Decisions › Estimating risk attitude 1: risk premium ↔ Code/master_behavAnalysis_revised.m, lines 114 to 156 · score 0.59 · indicate risk aversion, risk seeking, indifferent, lottery, fitting, model · Report a mismatch
[19] § Materials and methods › fMRI data analysis › PPI analysis ↔ Code/master_fMRI_showResults.m, lines 247 to 302 · score 0.58 · left STS, left insula, PPI, seed, interaction, cluster · Report a mismatch
[20] § Materials and methods › Parameter recovery › LMM regressions of happiness variations ↔ Code/fMRI/bb_onsetMaker.m, the whole file · a weak match · score 0.54 · risky social, happiness ratings, event, chosen, dummy, linear · Report a mismatch
[21] § Materials and methods › Momentary happiness › Computational models of happiness variations ↔ Code/bin/CharnessRabinModel.m, the whole file · a weak match · score 0.54 · disadvantageous inequality, variations, partner, happiness, models · Report a mismatch
[22] § Results › Behaviour › Momentary happiness: links to reward ↔ Code/master_behavAnalysis.m, lines 69 to 112 · score 0.54 · Guilt envy, Responsibility Redux, computational models, Rutledge, inequality, modelled · Report a mismatch
[23] § Results › Behaviour › Momentary happiness: links to reward ↔ Code/master_behavAnalysis_revised.m, lines 69 to 112 · score 0.54 · Guilt envy, Responsibility Redux, computational models, Rutledge, inequality, modelled · Report a mismatch
[24] § Materials and methods › Momentary happiness ↔ Code/socialDecisionRespo_fMRI.m, lines 495 to 561 · score 0.53 · button press, happiness ratings, cursor · Report a mismatch
[25] § Results › Behaviour › Momentary happiness: effects of agency, responsibility, and guilt ↔ Code/master_behavAnalysis_revised.m, lines 350 to 433 · score 0.52 · partner outcome, lottery choices, agency, CI, guilt, Solo · Report a mismatch
[26] § Results › BOLD signal › Brain regions engaged during receipt of outcomes ↔ Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/create_pain_pathways_brainnetwork.m, lines 71 to 134 · score 0.51 · fine grained, anterior insula, gyrus, cluster · Report a mismatch
[27] § Results › BOLD signal › Brain regions engaged during receipt of outcomes ↔ Atlases_and_parcellations/2024_CANLab_atlas/create_pain_pathways2024_brainnetwork.m, lines 99 to 168 · score 0.51 · fine grained, anterior insula, gyrus, cluster · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The paper is loaded by your browser, with JavaScript: read it at doi.org or on Europe PMC.

The authors' code

MATLAB · 1,385 lines · 68 KB · MIT · 7 matches

%% Script to load and fit behavioural data from the Responsibility project experiments
%% General setup
clear; close all; clc
owd = pwd;
colors = niceColorsDark(3); % the colors for the 3 experimental conditions
fontSize = 20;
set(0,'defaultAxesFontSize',fontSize)
addpath('fittingUtilityFunction')
%% get datafiles
% By hand:
% During fMRI data:
mfilepath = [fileparts(mfilename('fullpath')) filesep];
addpath(genpath(mfilepath))
% baseDir = '/Users/johannesschultz/sciebo/CURRENT WORK/Responsibility/';
baseDir = [fileparts(fileparts(mfilepath)) filesep]; % should go 1 level higher than Code
%% select which study(ies) to show
study = {'Behav','fMRI','both (-> paper)'};
% try selection = bttnChoiceDialog(study, 'Which dataset?', 3,'',[1 2 3]);
% study = study{selection};
% end
study = 'Behav';
study = 'fMRI';
study = 'both (-> paper)';
switch study
case 'Behav'
paperFigure = 0;
datasets{1} = 'Behav';
pathnames{1} = [baseDir 'BehaviouralData/BehaviourOnly']; % Behav only data
case 'fMRI'
paperFigure = 0;
datasets{1} = 'fMRI';
pathnames{1} = [baseDir 'BehaviouralData/fMRIstudy']; % fMRI expt data
case 'both (-> paper)'
paperFigure = 1;
datasets{1} = 'Behav';
datasets{2} = 'fMRI';
pathnames{1} = [baseDir 'BehaviouralData/BehaviourOnly']; % Behav only data
pathnames{2} = [baseDir 'BehaviouralData/fMRIstudy']; % fMRI expt data
end
%% Definition of condition names
% conditionNames = {'You decide for both','Partner decides for both','You decide for yourself'};
conditionNames = {'Social','Partner','Solo'};
hap_typeNames_soc = {'high | high','high | low','low | high','low | low'}; % first participant, then partner
hap_typeNames_nonsoc = {'high','low'};
%% display options
happyFigClassicOrDotPlotFlag = 2; % 0 -> no fig; 1 -> meansembar; 2 -> dotPlot. Choose 1 if want sequentialFiguresForTalk to be 1
sequentialFiguresForTalk = 0;
useDiffHappinessForSimplePlot = 1; % uses changes in subsequent happiness ratings for the simple behavioural happiness plot
if paperFigure
studyOffsets = [0 .45 0 0;0 0 0 0];
paperFig1 = figure('name','Figure 2 - Choices','position',[5 5 1600 840]);
decPanelPos = [.06 .1 .40 .3];
riskPremPanelPos = [.55 .1 .16 .3];
rhoPanelPos = [.79 .1 .16 .3];
paperFig2 = figure('name','Figure 3 - Happiness','position',[5 5 1600 840]);
happyRewardPanelPos1 = [.06 .13 .14 .26];
happyRewardPanelPos2 = [.28 .13 .14 .26];
happyPredPanelPos = [.50 .13 .14 .26];
happyLotteryPanelPos = [.72 .13 .22 .26];
end
%% Happiness data analysis settings
% Zscore the happiness results separately for each run
zscoreSepEachRun = 'Yes';
% % For the non-computational analysis, use only the trials directly before the happiness ratings?
useAllTrialsForNonComp = 'no';
% Computational modeling of happiness: do it or not?
compModelling = 1;
showIndivmodelNameFitFigs = 0;
useTauForFMRIreg = 1;
% Settings for computational modeling a la Rutledge
% Use which modelName?
if compModelling
addpath('TDmodels')
compModelName = {'None','Compare','ParamRecovery','Basic','InequalSimple','GuiltEnvy','otherRPEactPass','Responsibility','ResponsibilityRedux'};
try selection = bttnChoiceDialog(compModelName,'',1,'Use which model name for computational analysis?',[3 3]);
compModelName = compModelName{selection};
end
if strcmpi(compModelName,'None'), compModelling = 0; end
else; compModelName = 'None';
end
whichTrials = 'all';
% Create fMRI regressors?
compToDoList = []; % nothing per default
createfMRIreg = 'No'; % not per default
if compModelling && ~strcmpi(compModelName,'Compare') && ~strcmpi(compModelName,'ParamRecovery') && ~strcmpi(compModelName,'None')
createfMRIreg = {'Yes','No'};
try selection = bttnChoiceDialog(createfMRIreg,'',1,'Create fMRI regressors?',[1 2]);
createfMRIreg = createfMRIreg{selection};
end
end
% Determine comp modelling to dos
if compModelling, compToDoList = 1; end % default for comp modelling
if compModelling && strcmpi(compModelName,'ParamRecovery'), compToDoList = 0; end % no real fitting needed, but loading of fitted data and simulations
if strcmpi(createfMRIreg,'Yes')
compToDoList = [1 2]; % modelling AND make fMRI regressors
spm12 % put spm functions in path
initialISIandSetupValues = dlmread([mfilepath 'bin/ResponsibilityExp_startTime.txt']);
end
%% Decision data analysis settings
% Task: choice between safe option and lottery with 50% probability of high
% vs. low outcome, in 2 contexts: decide for self only, or for self and
% other.
% Aim of analysis: calculate the certainty equivalents (CEs) of the
% lotteries in the 2 choice conditions. CEs are defined as the amount of
% reward for which the participant was choice-indifferent with regards to
% said lotteries; the CE therefore indicates the subjective value of the
% lottery in the currency of the safe reward. Lottery CEs larger than said
% lotteries' objective expected value (EV) reflect risk-seeking behavior;
% CEs smaller than the lotteries' EV indicate risk aversion.
%
% Then, estimate the contribution of utility to the subjective
% values in both choice conditions.
% analyseDecisions = {'Yes','No'};
% try selection = bttnChoiceDialog(analyseDecisions,'',1,'Analyse decisions?',[1 2]);
% analyseDecisions = analyseDecisions{selection};
% end
analyseDecisions = 'Yes'; % general problem: I have few trials and people are not always consistent. See table decTab for a subject's decisions as function of values of risky and safe options
flag.fitBinnedEVdiffs = 1; % the first type of decision analysis I did, abandoned for better alternatives
flag.fitBinnedSVdiffs = 1; % same, but based on subjective value. Only works if I have CARA rhos for each trialtype, so need flag.findCARArhoInSearchSpace=1
flag.fitProbitFunction = 1; % fits the choices fine as function of EVrisky-Vsafe, but (1) is this a valid independent value? 2) I can't directly interpret the estimated parameters
flag.findCARArhoInSearchSpace = 1; % this is the best so far: finds the risk aversion parameter that best explains the choices.
rhoToTest = -.1:0.001:.1; % the values of rho to test
flag.checkRiskAversionConsist = 1; % Only works if fitProbitFunctionFlag and findCARArhoInSearchSpaceFlag are both = 1
binEdges = -50:20:50;
choiceLMMs = {}; % we will collect choice probit linear mixed models in here
trialTypeColors = niceColors70s(3);
% Exponential utility function for the CARA model
% NOTE: rho is a constant that represents the degree of risk preference
% (rho>0 for risk aversion, rho=0 for risk-neutrality, and rho<0 for
% risk-seeking). c is a commodity, i.e. something the decision-maker likes
u_CARA = @(c, rho) (rho ~= 0) .* (1 - exp(-rho .* c)) ./ ( rho + (rho == 0) .* 0.000001) + ...
(rho == 0) .* c;
% ---- Expected utility function, which is the utility times the probability
% c1 and c2 are the high and low amounts of the risky option
EU_CARA = @(c1, c2, p1, rho) p1 .* u_CARA(c1, rho) + (1 - p1) .* u_CARA(c2, rho);
%% ------------------------------------------------------------------------
%
% END SETTINGS, NOW GET DATA
%
% -------------------------------------------------------------------------
% which dataset to use has been defined above
for currentStudy = 1:length(datasets)
pathname = pathnames{currentStudy};
dataset = datasets{currentStudy};
studyName = sprintf('Study %d', currentStudy);
if strcmpi(study,'both (-> paper)') & paperFigure
studyOffset = studyOffsets(currentStudy,:);
end
fprintf(1,' =========== Dataset %s ============ \n',dataset)
clear R RR subjNumbTemp initialISIandSetup filesCell
files = dir([pathname '/*.mat']); filesCell = {};
for f = 1:length(files); filesCell{f} = files(f).name; end; files = filesCell;
% if ~iscell(files); ff{1} = files; files = ff; clear ff; end % needed for Windows I think
% sort or not?
if length(files) > 1
% sortOrNot = {'sort','don''t sort'};
% try selection = bttnChoiceDialog(sortOrNot, 'Sort according to subject number or not?', '','',[1 2]);
% sortOrNot = sortOrNot{selection};
% end
sortOrNot = 'sort';
switch sortOrNot
case 'sort'
files = sortrows(char(files));
case 'don''t sort'
files = char(files);
end
else
files = char(files);
end
% files = files(1:10,:);
%% load data
for f = 1:size(files,1)
Raw = load([pathname filesep deblank(files(f,:))]);
Raw.results(1).subjName = deblank(files(f,:));
Raw.results(1).Ntrials = length(Raw.results);
ii = findstr(Raw.results(1).subjName,'_sub');
temp = Raw.results(1).subjName(ii+5:ii+7);
if strcmpi(temp(1),'M') || strcmpi(temp(1),'W') % then it's from Julia's experiment
temp2 = str2num(temp(2:end));
if strcmpi(temp(1),'M')
temp2 = 100 + temp2;
elseif strcmpi(temp(1),'W')
temp2 = 200 + temp2;
end
temp = temp2;
end % then it's not Julia's experiment, we should have the number
if isempty(temp)
temp = Raw.results(1).subjName(ii(end-2)+1:ii(end-1)-1);
end
if ischar(temp); temp=str2num(temp); end
for str_loop = 1:length(Raw.results)
gl=[Raw.results.amountThisTrialSubject];
Raw.results(str_loop).Agl_Abs=sum(gl(1:str_loop-1));
riskchoices=Raw.results(str_loop).riskyOption;
riskEV=(riskchoices*0.5);
choicesEV=[riskEV Raw.results(str_loop).safeOption];
minEV=min(choicesEV);
Raw.results(str_loop).MinEV=minEV;
if Raw.results(str_loop).play==1
choiceEV=sum(riskchoices*0.5);
Raw.results(str_loop).ChoiceEV=choiceEV;
Raw.results(str_loop).Ch_diff=choiceEV-minEV;
elseif Raw.results(str_loop).play==0
choiceEV=Raw.results(str_loop).safeOption;
Raw.results(str_loop).ChoiceEV=choiceEV;
Raw.results(str_loop).Ch_diff=choiceEV-minEV;
end
Raw.results(str_loop).PE=(gl(str_loop)-choiceEV);
end
subjNumbers(f) = temp;
R{f} = Raw.results;
if strcmpi(zscoreSepEachRun,'Yes')
% zscore happiness (separately for each run)
happ = [R{f}.happiness];
idx = find(~isnan(happ));
happ = zscore(happ(idx));
for i = 1:length(idx)
R{f}(idx(i)).happiness = happ(i);
end
end
end % files
% combine results structures?
clear initialISIandSetup
if size(files,1) > 1
combineOrNot = 'combine';
switch combineOrNot
case 'combine' % then decide how many files to group
if strcmpi(dataset,'Behav'), N = 3; else; N = 2; end
for g = 1:size(files,1) / N % number of groups
idx = [1:N] + (g-1)*N;
RR{g} = horzcat(R{idx});
subjNumbTemp(g) = subjNumbers(idx(1));
% if need to create fMRI regressors, load the initial ISI and setup time
if strcmpi(createfMRIreg,'Yes')
idx = find(initialISIandSetupValues(:,1,1) == subjNumbTemp(g));
if isempty(idx)
warning(sprintf('no ISI values found for subject %d',subjNumbTemp(g)));
initialISIandSetup(g,:) = NaN;
else
initialISIandSetup(g,:) = initialISIandSetupValues(idx,2:end); % go to the correct run and pick its number
end
end
end
R = RR;
subjNumbers = subjNumbTemp;
case 'don''t combine' % nothing to do
end
else; error('No data files found')
end
%% If Study 2, I can choose to analyse data from all the people with fMRI data (44 subjects), or all the people with good fMRI data (40 subjects)
% We scanned 44 people, and excluded 4 that moved too much. I will report
% the behavioural data from 44 people, and the fMRI data from 40 people.
if strcmpi(dataset,'fMRI')
personsWithFMRIdata = [ 301 302 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 347 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349];
% personsWithGoodfMRIdata = [ 301 302 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 347 334 335 336 337 338 339 340 ];
subjNumbersOrig = subjNumbers;
[subjNumbers,idx] = intersect(subjNumbers,personsWithFMRIdata);
R = R(idx);
end
%% display number of subjects included:
fprintf(1,' --- N subjects in this dataset: %d --- \n',length(subjNumbers))
%% ------------------------------------------------------------------------
%
% GOT DATA, NOW COLLECT AND DISPLAY HAPPINESS DATA - ANALYSIS l.1165+
%
% -------------------------------------------------------------------------
%% Get happiness ratings for simple analysis - LMM analysis further below
clear happiness* hap_idx* decisions decisionsCounts
for i = 1:length(R) % for each subject
happinessTemp = [R{i}(:).happiness];
if strcmpi(zscoreSepEachRun,'No')
idx = find(~isnan(happinessTemp));
happ = zscore(happinessTemp(idx));
happinessTemp(idx) = happ;
end
% analysis of the behavioural data
% get the happiness values for the different outcomes
hap_whoWon_soc = [1 1;1 0;0 1;0 0]; % first subject, then partner
hap_whoWon_nonsoc = [1;0]; % only subject
subjectWon = [R{i}(:).subjectWon];
partnerWon = [R{i}(:).partnerWon];
conditions = [R{i}(:).condition];
happiness = [R{i}(:).happiness];
dhappiness = NaN(size(happiness)); dhappiness(~isnan(happiness)) = [0 diff(happiness(2:2:end))];
% Use difference in happiness for analysis
if useDiffHappinessForSimplePlot
happiness = dhappiness;
end
hap_idx = {};
for ii = 1:length(hap_typeNames_soc) % for all 4 outcomes
for cc = 1:2 % social and partner conditions
hap_idx{ii,cc} = find(subjectWon == hap_whoWon_soc(ii,1) & partnerWon == hap_whoWon_soc(ii,2) & conditions == cc);
if strcmpi(useAllTrialsForNonComp,'yes')
try happiness_soc(i,ii,cc) = nanmean([happiness(hap_idx{ii,cc}) happiness(hap_idx{ii,cc}-1)]); catch happiness_soc(i,ii,cc) = NaN; end
else
try happiness_soc(i,ii,cc) = nanmean([happiness(hap_idx{ii,cc})]); catch happiness_soc(i,ii,cc) = NaN; end
end
end
end
for ii = 1:length(hap_typeNames_nonsoc)
hap_idx{ii,1} = find(subjectWon == hap_whoWon_nonsoc(ii) & conditions==3);
if strcmpi(useAllTrialsForNonComp,'yes')
try happiness_nonsoc(i,ii,1) = nanmean([happiness(hap_idx{ii,1}) happiness(hap_idx{ii,1}-1)]); catch happiness_nonsoc(i,ii,1) = NaN; end
else
try happiness_nonsoc(i,ii,1) = nanmean([happiness(hap_idx{ii,1})]); catch happiness_nonsoc(i,ii,1) = NaN; end
end
end
end % for each subject
%% Report happiness ratings
if sequentialFiguresForTalk; figsToDo = 1:6; else; figsToDo = 6; end
for ff = figsToDo % for the different stages of the figure
happiness = NaN(length(R),4,3); % dimensions: subjects; outcome [high|high, high|low, low|high, low|low]; condition [solo, social, partner]
happinessNonsoc = NaN(length(R),4,3); % same size, just for the controls
switch ff
case 1 % first stage: only background
case 2 % second stage: only non-soc
happinessNonsoc(:,2:3,1) = happiness_nonsoc;
case 3 % only add win-win
happinessNonsoc(:,2:3,1) = happiness_nonsoc;
happiness(:,1,2:3) = happiness_soc(:,1,:);
case 4 % add win-lose
happinessNonsoc(:,2:3,1) = happiness_nonsoc;
happiness(:,1:2,2:3) = happiness_soc(:,1:2,:);
case 5 % add lose-win
happinessNonsoc(:,2:3,1) = happiness_nonsoc;
happiness(:,1:3,2:3) = happiness_soc(:,1:3,:);
case 6 % add the remainder
happinessNonsoc(:,2:3,1) = happiness_nonsoc;
happiness(:,:,2:3) = happiness_soc;
end
if happyFigClassicOrDotPlotFlag == 1
figure('name','Happiness','position',[324 378 746 420]);
if i>1 % more than 1 subject
[ll1] = meansembar(1:3,happiness,1,niceColorsWarm(4),14,.2,'CI'); drawnow
hold on
[ll2] = meansembar(1:3,happinessNonsoc,1,[0 0 0;1 1 1;.5 .5 .5;0 0 0],14,.2,'CI'); drawnow
else
bar(squeeze(happiness)'); drawnow
end
if ~sequentialFiguresForTalk; legend([ll1 ll2(2:3)],[hap_typeNames_soc hap_typeNames_nonsoc],'location','eastoutside'); end
ylabel('Happiness (z-scored)')
set(gca,'position',[0.1300 0.2167 0.6998 0.7083],'ylim',[-1 1],'xlim',[.5 3.5],'xtick',1:3,'xticklabel',conditionNames([3 1 2]))
grid on; box off; drawnow
end
end
% For each subject, measure the impact of agency on happiness after negative outcomes for the partner
responsibilityEffect = nanmean(squeeze(happiness(:,[2 4],2)-happiness(:,[2 4],3)),2)
guiltEffect = responsibilityEffect; subjNumb = subjNumbers'; Tg = table(subjNumb,guiltEffect);
writetable(Tg,[mfilepath 'csv/' dataset ' - BehavGuiltEffect.csv']);
% Print happiness values to paper figure
if happyFigClassicOrDotPlotFlag == 2
if paperFigure
figure(paperFig2)
ah1 = axes('position',happyLotteryPanelPos+studyOffset);
else
figure('name',[dataset ' Happiness ratings dotPlots'],'position',[322 427 552 295]); ah1 = gca;
end
% Show happiness figure with dotplots
% as soon as partner loses, participants feel better if partner took the decision
axes(ah1)
hh = []; S = []; happiness2 = [];
happiness2(:,:,1:4) = permute(happiness(:,[1 3 2 4],2:3),[1 3 2]);
S.colors = niceColorsWarm2(3); S.colors = S.colors(2:3,:);
S.errorType = 'CI';
S.markersize = 4;
S.y = happiness2;
hh = dotPlot(S);
set(gca,'ylim',[-3.5 3.5],'ytick',-3:1:3,'xlim',[.2 8.8],'xtick',[1.5:2:8],'xticklabel',{'High\newlineHigh','Low\newlineHigh','High\newlineLow','Low\newlineLow',})
box on
hold on
% add stars for "guilt effect": contrasting low partner outcomes in Social and Partner
plot([5 6],[-3 -3],'k','linewidth',2)
plot([7 8],[-3 -3],'k','linewidth',2)
if strcmpi(dataset,'Behav') % p values are < 0.001 and = 0.002
t_hdle=text(5.5,-2.8,'***'); t_hdle.HorizontalAlignment='center'; t_hdle.FontSize=20;
t_hdle=text(7.5,-2.8,'**'); t_hdle.HorizontalAlignment='center'; t_hdle.FontSize=20;
elseif strcmpi(dataset,'fMRI') % p values are = 0.01 and < 0.001
t_hdle=text(5.5,-2.8,'**'); t_hdle.HorizontalAlignment='center'; t_hdle.FontSize=20;
t_hdle=text(7.5,-2.8,'***'); t_hdle.HorizontalAlignment='center'; t_hdle.FontSize=20;
end
legend([hh(1:2).mu],{'Social','Partner'});
ylabel('\Delta Happiness [std]')
xlabel('Outcome participant (top) and partner (bottom)')
title([studyName ': Happiness after lottery choices'])
end
%% ------------------------------------------------------------------------
%
% NOW COLLECT, DISPLAY AND ANALYSE DECISIONS
%
% -------------------------------------------------------------------------
%% Analyse decisions, but only for conditions 1 & 3, as an algorithm took decisions in condition 2
if strcmpi(analyseDecisions,'Yes'); progressbar(0,0,'Decisions')
EVdiff_str = {'EV_r_i_s_k_y - V_s_a_f_e'};
Ntodo = length(R);
for i = 1:Ntodo % for each subject
conditions = [R{i}(:).condition];
trialTypes = {'gain trials','mixed trials','loss trials'};
for c = [1 3] % don't analyse computer decisions
% ------ 0) Calculate basic variables ------
idx_cond = find(conditions == c);
Vsafe = [R{i}(idx_cond).safeOption]';
temp = reshape([R{i}(idx_cond).riskyOption],2,[]);
riskyHigh = temp(1,:)'; riskyLow = temp(2,:)';
EVrisky = (riskyHigh + riskyLow) / 2;
varRisky = riskyHigh - riskyLow;
EVriskyMinusVsafe = EVrisky-Vsafe;
chooseRisky = [R{i}(idx_cond).play]';
choseSafe = 1-chooseRisky;
% we sort the trials into gain, mixed, and loss trials, because we assume different risk preferences for them
idx_gain = find(riskyLow >= 0 & Vsafe >= 0);
idx_mixed = find(Vsafe == 0);
idx_loss = find(riskyHigh <= 0 & Vsafe <= 0);
trialType = []; trialType(idx_gain) = 1; trialType(idx_mixed) = 2; trialType(idx_loss) = 3; trialType = trialType';
trialType_indices = {idx_gain,idx_mixed,idx_loss};
% Make a table with the variables we need
Tdec = table(trialType, riskyHigh, riskyLow, EVrisky, varRisky, Vsafe, EVriskyMinusVsafe, chooseRisky, choseSafe);
Tdec_allS{i,c} = Tdec; % gather all the decision data tables
% ------ 1) Fit decisions as function of SVrisky-Vsafe with cumulative Gaussian ------
if showIndivmodelNameFitFigs
figure('name',sprintf('Subject %d',subjNumbers(i)));
end
if flag.fitBinnedEVdiffs
[~,binEdges,ee] = histcounts(EVriskyMinusVsafe,binEdges);
for iii = 1:length(binEdges)-1
decisions(i,iii,c) = nanmean(chooseRisky(ee==iii));
decisionsCounts(i,iii,c) = length(chooseRisky(ee==iii));
end
binCenters = (binEdges(1:end-1)+binEdges(2:end))/2;
if showIndivmodelNameFitFigs
if length(vars)>1; subplot(2,2,v); end
hold on
fitPsychFunction_js(binCenters/10,decisions(i,:,c),'plotFlag',gca,'functType',functType,'lowerBounds',lowerBounds,'upperBounds',upperBounds,'showText',0,'plotDottedLines','none','color',colors(c,:),'linewidth',2);
title(EVdiff_str)
% ylabel('P(risky), \bar{x}\pm SEM','interpreter','tex')
ylabel('P(risky) [M+-SEM]')
grid on
end
end
% ------ 2) Fit CARA model ------
% now, new approach to model the choices people made.
% We're using the constant absolute risk aversion (CARA) model to
% obtain certainty equivalents for the risky options.
if flag.findCARArhoInSearchSpace
plotFlag = 0;
for t = 1:length(trialTypes)
idx = trialType_indices{t};
CARA_rho(i,t,c) = findCARArhoInSearchSpace(riskyHigh(idx),riskyLow(idx),Vsafe(idx),chooseRisky(idx),rhoToTest,plotFlag);
end
CARA_rho_searchSpace(i,c) = findCARArhoInSearchSpace(riskyHigh,riskyLow,Vsafe,chooseRisky,rhoToTest,plotFlag);
end
% ------ 3) Fit probit function to model choices as function of difference in expected values between safe and risky:
if flag.fitProbitFunction
xForFit = -80:80; % delta EV (EVrisky - Vsafe) to use for display and calculation of risk premium
yfit = {};
[B,DEV,STATStemp] = glmfit(Tdec.EVriskyMinusVsafe,Tdec.chooseRisky,'binomial','link','probit');
% Deviance test to see if this model fits better than one with just a constant term (see see https://ch.mathworks.com/help/stats/glmfit.html):
[~,dev_noconstant] = glmfit(ones(size(Tdec.EVriskyMinusVsafe)),Tdec.chooseRisky,'binomial','link','probit','Constant','off');
D = dev_noconstant - DEV; D = dev_noconstant - DEV; % D has a chi-square distribution with degrees of freedom equal to the difference in the number of estimated parameters in the model corresponding to DEV and the number of estimated parameters in the constant model.
STATStemp.pModel = 1 - chi2cdf(D,length(B)-1); % Find the p-value for a deviance test.
STATS_probitFitAllT(i,c) = STATStemp;
yfit{1} = glmval(B,xForFit, 'probit');
% Find 0-crossing point, which is a measure of the risk premium:
[~,iii] = min((yfit{1}-.5).^2);
riskPremiumProbitFit(i,c) = xForFit(iii);
end
drawnow
% ------ 5) Calculate the SV of the risky option for each trial based
% on CARA_rho, to see if choices are related to that now. Relate SV to happiness?
if flag.findCARArhoInSearchSpace
SVrisky = [];
for t = 1:length(trialTypes)
idx = Tdec.trialType == t;
SVrisky(idx) = EU_CARA(Tdec.riskyHigh(idx),Tdec.riskyLow(idx),0.5,CARA_rho(i,t,c)); % subjective value of risky option under CARA given estimated rho
end; SVrisky = SVrisky';
Tdec.SVrisky = SVrisky;
Tdec_allS{i,c}.SVrisky = SVrisky;
SVdiff = Tdec.SVrisky-Tdec.Vsafe;
Tdec.SVdiff = SVdiff;
Tdec_allS{i,c}.SVdiff = SVdiff;
end % flag.findCARArhoInSearchSpace
% ------ 6) Fit decisions as function of SVrisky-SVsafe with cumulative Gaussian ------
if flag.fitBinnedSVdiffs && ( flag.findCARArhoInSearchSpace )
[~,binCenters,ee] = histcounts(SVdiff,binEdges);
for iii = 1:length(binEdges)-1
decisions(i,iii,c) = nanmean(chooseRisky(ee==iii));
end
binCenters = (binCenters(1:end-1)+binCenters(2:end))/2;
end % flag.fitBinnedSVdiffs
end % each experimental condition
if showIndivmodelNameFitFigs; legend(conditionNames); end
progressbar(i/Ntodo,0,'Decisions')
end % each subject
end % analyseDecisions
%% Do participants choose on average the option with the higher EV?
propRiskyChoicesIfEVdiffPos = [];
for i = 1:Ntodo
% first, define GLM design matrix
X = [...
Tdec_allS{i,1}.EVriskyMinusVsafe;...
Tdec_allS{i,3}.EVriskyMinusVsafe...
];
y = [Tdec_allS{i,1}.chooseRisky; Tdec_allS{i,3}.chooseRisky]; % risky choices
propRiskyChoicesIfEVdiffPos(i) = mean([ y(X(:,1)>0); 1-y(X(:,1)<0) ]);
end
str = ttest_nice(propRiskyChoicesIfEVdiffPos-0.5);
fprintf('Choices of option with higher EV: mean = %.1f, SD = %.1f, %s\n',mean(propRiskyChoicesIfEVdiffPos)*100,std(propRiskyChoicesIfEVdiffPos)*100,str)
%% Check variations of CARA rho as a function of trialtype and expt. condition
if flag.findCARArhoInSearchSpace
disp('2-way ANOVA on CARA rho:')
anova2_repmeas_autodesign(CARA_rho(:,:,[1 3]));
disp('T-test on CARA rho obtained over all trials through search in space, comparing the 2 conditions:')
ttest_nice(CARA_rho_searchSpace(:,1),CARA_rho_searchSpace(:,3));
end
%% Compare rho for self-for-both vs rho for self only
figure('name',[dataset ' \rho across conditions (left=avg over trialtypes)'],'position',[221 240 815 345])
rho = squeeze(mean(CARA_rho,2));
subplot(1,2,1);
regress_display(rho(:,1),rho(:,3),'regressType','robust ','inputNames',{'rho social','rho solo'},'axesHandle',gca);
subplot(1,2,2);
regress_display(makerow(CARA_rho(:,:,1)),makerow(CARA_rho(:,:,3)),'regressType','robust','inputNames',{'rho social','rho solo'},'axesHandle',gca);
%% Fit generalized linear mixed-effects model to risky choices
% Explanations here:
% https://ch.mathworks.com/help/stats/generalized-linear-mixed-effects-models.html
% https://ch.mathworks.com/help/stats/fit-a-generalized-linear-mixed-effects-model.html
% 1) Make big table with data from all subjects
Tdec = [];
for i = 1:Ntodo
temp1 = Tdec_allS{i,1}; % the self-for-both condition data
temp1.subject = i * ones(size(temp1.chooseRisky));
temp1.condition = ones(size(temp1.chooseRisky));
temp2 = Tdec_allS{i,3}; % the self-only condition data
temp2.subject = i * ones(size(temp2.chooseRisky));
temp2.condition = 2 * ones(size(temp2.chooseRisky));
Tdec = [Tdec; temp1; temp2];
end
% Mean-correct all terms first, to help interpret the coefficients.
Tdec.condition = Tdec.condition-1;
Tdec.EVdiffMC = ( Tdec.EVriskyMinusVsafe-mean(Tdec.EVriskyMinusVsafe) ); % !! do NOT change Euro cent into Euros
Tdec.condMC = Tdec.condition-mean(Tdec.condition);
Tdec.EVcond_int_MC = Tdec.condMC .* Tdec.EVdiffMC - mean(Tdec.condMC .* Tdec.EVdiffMC);
% Write table out to verify model in R
writetable(Tdec,[mfilepath 'csv/' dataset ' - Choices_singleTrialData.csv']);
% 2) Make a simple linear mixed model with EVriskyMinusVsafe, condition and
% their interaction as independent variables, subject as random term
% factor, with random intercept.
% NOTE June 2023: models with random slopes fit even better!
% model = 'chooseRisky ~ 1 + EVdiffMC + condMC + EVcond_int_MC + (1 | subject)';
% modelRS1 = 'chooseRisky ~ 1 + EVdiffMC + condMC + EVcond_int_MC + (1 + EVdiffMC | subject)';
% modelRS2 = 'chooseRisky ~ 1 + EVdiffMC + condMC + EVcond_int_MC + (1 + EVdiffMC + condMC | subject)';
model = 'chooseRisky ~ 1 + EVdiffMC * condMC + (1 + EVdiffMC + condMC | subject)';
% 3) Fit a probit model
disp('Fitting probit and linear probability models to Choice data...')
glme = fitglme(Tdec,...
model,...
'Distribution','Binomial','Link','probit','FitMethod','Laplace',...
'DummyVarCoding','effects');
% 4) Fit a linear probability model, so that the coefficients can be better
% interpreted
lm = fitglme(Tdec,...
model);
% collect glme and lm for econ-style table
choiceLMMs{currentStudy,1} = glme;
choiceLMMs{currentStudy,2} = lm;
%% 5) Make a figure with fitted data for the probit model
%
% MUPRED = PREDICT(GLME) computes a vector MUPRED of predictions of the
% conditional mean of the response given the random effects at the
% original predictors used to create GLME. MUPRED includes contributions
% from both estimated fixed effects and approximate empirical Bayes
% predictors (EBPs) of the random effects.
%
% MUPRED = PREDICT(GLME,T) uses predictor variables from the table T to
% predict the conditional mean of the response. T must contain all of the
% predictor variables used to create GLME.
%
% [MUPRED,MUCI] = PREDICT(...) also returns the two-column matrix MUCI
% containing 95% pointwise confidence intervals for the predicted values.
% The lower limits of the bounds are in column 1, and the upper limits
% are in column 2.
% Compute histograms of decisions at each EVdiff bin, for solo and social conditions
clear hdle
if paperFigure
figure(paperFig1), axes('position',decPanelPos+studyOffset)
% [hh,hhhh] = meansemplotWithDots(binCenters/100,decisions(:,:,[1 3])); hh(1).Visible = 'off'; hh(2).Visible = 'off'; hold on
% colors = [0 0 .5; 0 0 0; .5 0 0]; % dark blue for solo and dark red for social
colors = [.6 .6 1; 0 0 0; 1 .6 .6]; % dark blue for solo and dark red for social
markers = '^ov'; % upper triangle for solo and lower triangle for social
for c = [1 3]; for i=1:length(binCenters), [n,v]=hist(decisions(:,i,c)); keep=find(n);
scatter(binCenters(i)*ones(length(keep),1),v(keep),n(keep)*10,colors(c,:),'filled',markers(c));
hold on, end, end
else
figure('Name',[dataset ' GLME fit'],'position',[ 360 427 346 271]);
end
% Print probit-model fits to figure
% Nsubj = length(subjNumbers);
Nd = 100; % N datapoints to model
EVdiffMC = linspace(-50,50,Nd)';
subject = zeros(Nd,1);
colors = [0 0 1;1 0 0];
for c = 1:2 % both conditions
condMC = -1*(c-1.5) * ones(Nd,1);
Tnew = table(EVdiffMC,condMC,subject);
x = EVdiffMC;
[mupred,muci] = predict(glme,Tnew); hold on
hdle(c,:) = errorarea(x,mupred,muci,colors(c,:));
end
xlabel([EVdiff_str{1} '[Euro cents]'])
ylabel('{\it p} (risky)')
set(gca,'xlim',[-50 50])
title([studyName ': Choices'])
legend(hdle(:,1),conditionNames([3 1]),'location','NorthWest');
%% If I assessed risk aversion using both CARA and the 0-crossing of the fitted probit function, I can check whether the derived measures are related:
if flag.checkRiskAversionConsist && flag.fitProbitFunction && flag.findCARArhoInSearchSpace
% check correlation between horizontal deviation in the probit fit
% and rho
% riskPremiumProbitFit(abs(riskPremiumProbitFit)>50)=NaN;
riskPrem = riskPremiumProbitFit / 100; % !! Transform Euro cent into Euro
inputNames = {'\rho','Risk premium'};
% regress_display(CARA_rho(:),riskPremiumProbitFit_clean(:))
figure('name',[dataset ' Consistency risk aversion measurements'],'position',[221 240 815 345])
cc = [1 3]; % conditions to report on
for c = 1:length(cc)
subplot(1,2,c);
riskPrem_temp = riskPrem(:,cc(c));
rho_temp = mean(CARA_rho(:,:,cc(c)),2);
regress_display(rho_temp,riskPrem_temp,...
'axesHandle',gca,'inputNames',inputNames,'titlePrefix',conditionNames{cc(c)},'regressType','robust'); grid on
end
% Compare risk preference measure rho for Solo and Social:
if paperFigure
figure(paperFig1)
else,
figure('name',[dataset ' Risk preference across conditions'],'position',[360 399 546 299]);
end
facecolor = [.5 .5 .5]; withmdn = 0; style = 2;
% Print estimated risk premium values to paper figure
if paperFigure, figure(paperFig1), axes('position',riskPremPanelPos+studyOffset), else, subplot(1,2,2); end
riskPrem_temp = riskPrem; riskPrem_temp(abs(riskPrem_temp)>.7) = 0;
% hh = distributionPlot(riskPrem_temp(:,[3 1]),'color',[.8 .8 .8],'showMM',4); hold on;
% hh{2}(1).LineWidth=2; hh{2}(2).LineWidth=2; hh{2}(1).Marker='.';
violin(1,riskPrem_temp(:,3),'facecolor',facecolor,'withmdn',withmdn,'style',style,'scaling',1); hold on
violin(2,riskPrem_temp(:,1),'facecolor',facecolor,'withmdn',withmdn,'style',style,'scaling',1); hold on
% hh{2}(1).Marker='o'; hh{2}(1).MarkerFaceColor=[1 0 0];
% hh{2}(2).Marker='^'; hh{2}(2).MarkerFaceColor=[0 1 0];
plot(riskPrem_temp(:,[3 1])','k'); set(gca,'xtick',[1 2],'xticklabel',conditionNames([3 1]),'xlim',[.5 2.5]);
m = mean(riskPrem_temp(:,[3 1]));
CI = ci_t(riskPrem_temp(:,[3 1]),95); % find 95% confidence intervals for the mean, assuming normally-distributed data
errL = m - CI(1,:);
errH = CI(2,:) - m;
% previously: err = std(riskPrem_temp(:,[3 1]))/sqrt(size(riskPrem_temp,1))
errorbar([1 2],m,errL,errH,'.-r','linewidth',2);
title([studyName ': Risk premium'])%,'position',[1 0.0027473])
ylabel([EVdiff_str{1} ' [Euro]'])
ttest_nice(riskPrem_temp(:,1),riskPrem_temp(:,3),'paired',1);
% Print estimated risk aversion parameter rho values to paper figure
if paperFigure, figure(paperFig1), axes('position',rhoPanelPos+studyOffset), else,
subplot(1,2,1);
end
violin(1,rho(:,3),'facecolor',facecolor,'withmdn',withmdn,'style',style,'scaling',1); hold on
violin(2,rho(:,1),'facecolor',facecolor,'withmdn',withmdn,'style',style,'scaling',1); hold on
plot(rho(:,[3 1])','k'); set(gca,'xtick',[1 2],'xticklabel',conditionNames([3 1]),'xlim',[.5 2.5])
m = mean(rho(:,[3 1]));
CI = ci_t(rho(:,[3 1]),95); % find 95% confidence intervals for the mean, assuming normally-distributed data
errL = m - CI(1,:);
errH = CI(2,:) - m;
% previously: errorbar([1 2],mean(rho(:,[3 1])),std(rho(:,[3 1]))/sqrt(size(rho,1)),'.-r','linewidth',2);
errorbar([1 2],m,errL,errH,'.-r','linewidth',2);
title([studyName ': \rho'],'fontsize',24)
ylabel('risk seeking \leftrightarrow risk averse')
ttest_nice(rho(:,1),rho(:,3),'paired',1);
end
%% ------------------------------------------------------------------------
%
% ANALYSED DECISIONS, NOW COMPUTATIONAL MODELING OF HAPPINESS
%
% -------------------------------------------------------------------------
%% Rutledge-like computational analysis
for i = 1:length(R) % for each subject
% first, prepare the variables for the computational analysis
choseSafe = [R{i}(:).play];
% choseGamble = 1-choseSafe;
try, nonsocialTrial = ([R{i}(:).condition] == 3);
catch; nonsocialTrial = ~isnan([R{i}(:).amountThisTrialPartner]); end
try, socialActive = ([R{i}(:).condition] == 1);
catch; socialActive = [R{i}.condition]; end
try, socialPassive = ([R{i}(:).condition] == 2); end
amountReceivedBySubject = [R{i}.amountThisTrialSubject];
CR = amountReceivedBySubject.*choseSafe;
EV = mean(reshape([R{i}(:).riskyOption],2,[])) .* (1-choseSafe);
RPE = EV-amountReceivedBySubject.*(1-choseSafe);
happinessTemp = [R{i}(:).happiness];
if strcmpi(zscoreSepEachRun,'No')
idx = find(~isnan(happinessTemp));
happ = zscore(happinessTemp(idx));
happinessTemp(idx) = happ;
end
matrix = zeros(length(R{i}),20);
matrix(:,1) = [R{i}(:).condition];
matrix(:,3) = [R{i}(:).safeOption];
matrix(:,4:5) = reshape([R{i}(:).riskyOption],2,[])';
matrix(:,7) = [R{i}(:).play];
matrix(:,8) = [R{i}(:).amountThisTrialSubject];
matrix(:,10) = happinessTemp;
matrix(:,13) = isnan([R{i}(:).amountThisTrialPartner])+1;
matrix(:,16) = [R{i}(:).amountThisTrialPartner];
matrix(:,17) = [R{i}(:).Agl_Abs];
matrix(:,18)= [R{i}(:).MinEV];
matrix(:,19)= [R{i}(:).ChoiceEV];
matrix(:,20)= [R{i}(:).Ch_diff];
matrix(:,21) = [R{i}(:).PE];
switch whichTrials
case 'all'
idx = 1:size(matrix,1);
case 'social'
idx = find(~nonsocialTrial);
case 'non-social'
idx = find(nonsocialTrial);
case 'social-active'
idx = find(socialActive);
case 'social-passive'
idx = find(socialPassive);
case 'social-passive and non-social'
idx = [find(socialPassive) find(nonsocialTrial)];
case 'social-active and non-social'
idx = [find(socialActive) find(nonsocialTrial)];
end
matrix = matrix(idx,:);
alldata(i).socdata{1} = matrix;
% alldata(i).Ntrials = [R{i}.Ntrials];
end % subject
%% Run Rutledge-like computational analysis
clear compResults mtxs ff
for whatToDo = compToDoList % 1 = fit the data, 2 = create fMRI regressors; 0 = parameter recovery; done after having fitted real data.
disp('Fitting computational models for participant number: ')
% first, fit behavioural data, then create fMRI regressors if desired
for n=1:length(alldata), % n subjects
fprintf(1,'%d,',n);
for m=1:length(alldata(n).socdata), % m runs?
% temp is the data, rows are trials, columns are variables:
% 3: safe values
% 4 to 5: risky values
% 7: 1 if gambled, 0 if chose safe option
% 8: reward obtained by self
% 10: happiness score
% 13: 1 if participant chose, 2 if other person chose
% 16: other person reward
% 17: Agl_Abs
% 18: MinEV
% 19: ChoiceEV
% 20: Ch_diff
% 21: PE
eval(sprintf('temp = alldata(%d).socdata{%d};',n,m)); temp(1,10:12)=nan; %collect matrices and toss first rating
t2 = temp(:,10); rawhappy = t2(~isnan(t2)); happyind = find(~isnan(t2)); temp(isnan(temp(:,16)),16)=0; %other reward=0 when solo trial
nrate=length(rawhappy); ntrial=size(temp,1); x=zeros(nrate,ntrial);
evmtx=x; certainmtx=x; rpemtx=x; rewardmtx=x; othercertainmtx=x; otherrewardmtx=x; otherrpemtx=x; ipemtx=x; envymtx=x; guiltmtx=x; notenvymtx=x; notguiltmtx=x; youchosemtx=x; theychosemtx=x;
Agl_Absmtx=x; MinEVmtx=x; ChoiceEVmtx=x; Ch_diffmtx=x; PEmtx=x;
switch whatToDo
case 0 % parameter recovery; same trials as for fitting
trialsToFit = happyind;
case 1 % fit the data
trialsToFit = happyind;
case 2 % create fMRI regressors
trialsToFit = 1:ntrial;
end
for q = 1:length(trialsToFit)
temp2 = temp(1:trialsToFit(q),:); %clip out all trials up to rating
tempev = mean(temp2(:,4:5),2) .* double(temp2(:,7)==1); %0 if no gamble or error, ev if gambled
temprpe = temp2(:,8) .* double(temp2(:,7)==1) - tempev; %0 if no gamble or error, rpe if gambled
tempreward = temp2(:,8) .* double(temp2(:,7)==1); % gamble rewards (0 if chose certain)
tempcertain = temp2(:,3) .* double(temp2(:,7)==0); % 0 if gambled, otherwise certain amount
tempothercertain = temp2(:,16) .* double(temp2(:,7)==0) .* double(temp2(:,13)==2); % other reward if no gamble
tempotherreward = temp2(:,16) .* double(temp2(:,7)==1) .* double(temp2(:,13)==2); % other reward if gamble
tempotherrpe = temp2(:,16) .* double(temp2(:,7)==1) - tempev; % rpe for other
tempipe = (sign(temprpe)) .* double(temp2(:,1)<3) .* (tempotherreward-tempev); %sign or your rpe x sign of theirs
tempenvy = (sign(temprpe)<0) .* double(temp2(:,1)<3) .* (tempotherreward-tempreward); %you lost and they got this much more than you
tempguilt = (sign(temprpe)>0) .* double(temp2(:,1)<3) .* (tempreward-tempotherreward); %you won and you got this much more than them
tempnotenvy = (sign(temprpe)<0) .* double(temp2(:,1)<3) .* double(~(tempotherreward-tempreward)); %you lost and they also lost dummy
tempnotguilt = (sign(temprpe)>0) .* double(temp2(:,1)<3) .* double(~(tempreward-tempotherreward)); %you won and they also won dummy
tempyouchose = double(temp2(:,13)==1);
temptheychose = double(temp2(:,13)==2);
evmtx(q,1:length(tempev)) = fliplr(transpose(tempev)) / 100; %convert to ?
certainmtx(q,1:length(tempcertain)) = fliplr(transpose(tempcertain)) / 100;
rpemtx(q,1:length(temprpe)) = fliplr(transpose(temprpe)) / 100;
rewardmtx(q,1:length(tempreward)) = fliplr(transpose(tempreward)) / 100;
ipemtx(q,1:length(tempipe)) = fliplr(transpose(tempipe)) / 100;
othercertainmtx(q,1:length(tempothercertain)) = fliplr(transpose(tempothercertain)) / 100;
otherrewardmtx(q,1:length(tempotherreward)) = fliplr(transpose(tempotherreward)) / 100;
otherrpemtx(q,1:length(temprpe)) = fliplr(transpose(tempotherrpe)) / 100;
envymtx(q,1:length(tempenvy)) = fliplr(transpose(tempenvy)) / 100;
guiltmtx(q,1:length(tempguilt)) = fliplr(transpose(tempguilt)) / 100;
notenvymtx(q,1:length(tempnotenvy)) = fliplr(transpose(tempnotenvy)); %dummy
notguiltmtx(q,1:length(tempnotguilt)) = fliplr(transpose(tempnotguilt)); %dummy
youchosemtx(q,1:length(tempyouchose)) = fliplr(transpose(tempyouchose)); %1 if you chose
theychosemtx(q,1:length(temptheychose)) = fliplr(transpose(temptheychose)); %1 if they chose
Agl_Abs=double(temp2(:,17));
MinEV=double(temp2(:,18));
ChoiceEV=double(temp2(:,19));
Ch_diff=double(temp2(:,20));
PE=double(temp2(:,21));
Agl_Absmtx(q,1:length(Agl_Abs)) = fliplr(transpose(Agl_Abs)) / 100; %convert to ?
MinEVmtx(q,1:length(MinEV)) = fliplr(transpose(MinEV)) / 100; %convert to ?
ChoiceEVmtx(q,1:length(ChoiceEV)) = fliplr(transpose(ChoiceEV)) / 100; %convert to ?
Ch_diffmtx(q,1:length(Ch_diff)) = fliplr(transpose(Ch_diff)) / 100; %convert to ?
PEmtx(q,1:length(PE)) = fliplr(transpose(PE)) / 100; %convert to ?
end;
clear mtx; mtx.certainmtx=single(certainmtx); mtx.evmtx=single(evmtx); mtx.rpemtx=single(rpemtx); mtx.rewardmtx=single(rewardmtx);
mtx.ipemtx=single(ipemtx); mtx.othercertainmtx=single(othercertainmtx); mtx.otherrewardmtx=single(otherrewardmtx); mtx.otherrpemtx=single(otherrpemtx);
mtx.envymtx=single(envymtx); mtx.guiltmtx=single(guiltmtx); mtx.youchosemtx=single(youchosemtx); mtx.theychosemtx=single(theychosemtx);
mtx.notenvymtx=single(notenvymtx); mtx.notguiltmtx=single(notguiltmtx);
mtx.Agl_Abs=single(Agl_Absmtx); mtx.MinEV=single(MinEVmtx); mtx.PE=single(PEmtx);
mtx.ChoiceEV=single(ChoiceEVmtx); mtx.Ch_diff=single(Ch_diffmtx);
mtx.conds=temp(1:trialsToFit(end),1);
eval(sprintf('alldata(%d).mtx{%d}=mtx;',n,m));
eval(sprintf('alldata(%d).rawhappy{%d}=rawhappy;',n,m));
eval(sprintf('temp = alldata(%d).socdata{%d};',n,m)); temp(1,10:12)=nan; %toss first rating
t2 = temp(:,10); zhappy = t2(~isnan(t2));
eval(sprintf('alldata(%d).zhappy{%d}=zhappy;',n,m))
% do computational modelling
if whatToDo == 1 %2 & strcmpi(createfMRIreg,'Yes')
if strcmpi(compModelName,'ActPass')
compResults(n,1) = fit_happy_model_inequalityActPass(mtx,zhappy,NaN);
elseif strcmpi(compModelName,'otherRPEactPass')
compResults(n,1) = fit_happy_model_otherRPEactPass(mtx,zhappy,NaN);
elseif strcmpi(compModelName,'GuiltEnvy')
compResults(n,1) = fit_happy_model_guiltenvy(mtx,zhappy,NaN);
elseif strcmpi(compModelName,'InequalSimple')
compResults(n,1) = fit_happy_model_inequalitySimple(mtx,zhappy,NaN);
elseif strcmpi(compModelName,'Basic')
compResults(n,1) = fit_happy_model_basic(mtx,zhappy,NaN);
elseif strcmpi(compModelName,'Responsibility')
compResults(n,1) = fit_happy_model_responsibility(mtx,zhappy,NaN);
elseif strcmpi(compModelName,'ResponsibilityRedux')
compResults(n,1) = fit_happy_model_responsibility_redux(mtx,zhappy,NaN);
elseif strcmpi(compModelName,'GuiltEnvy+UtilityFunction')
compResults(n,1) = fit_happy_model_guiltenvy_utl(mtx,zhappy,NaN);
elseif strcmpi(compModelName,'Compare')
compResults(n,1) = fit_happy_model_basic(mtx,zhappy,NaN);
compResults(n,2) = fit_happy_model_inequalitySimple(mtx,zhappy,NaN);
compResults(n,3) = fit_happy_model_guiltenvy(mtx,zhappy,NaN);
compResults(n,4) = fit_happy_model_responsibility(mtx,zhappy,NaN);
compResults(n,5) = fit_happy_model_responsibility_redux(mtx,zhappy,NaN);
end
if showIndivmodelNameFitFigs
for m = 1:size(compResults,2)
figure;
subplot(2,1,1)
plot(zhappy,'b');
hold on; plot(compResults(n,m).happypred,'r')
dataNames = {'happiness','predicted happiness'};
legend(dataNames);
title([R{n}(1).subjName ' ' whichTrials], 'interpreter','none')
ylabel(sprintf('R2 = %.2f',compResults(n,m).r2));
subplot(2,1,2)
bar(compResults(n,m).b)
xlabel_oblique(compResults(n,m).paramNames)
title([compResults(n,m).compModelName ' - parameters'], 'interpreter','none')
drawnow
end
end
% regress_display(zhappy,results(n).happypred,'inputNames',dataNames)
end % whatToDo == 1
% use tau to create fMRI regressors if desired
if whatToDo == 2 && strcmpi(createfMRIreg,'Yes')
idx = strmatch('decay',compResults(n,1).paramNames); % look for the decay parameter
tau = compResults(n,1).b(idx); %decay constant
if useTauForFMRIreg
[fMRIregressors,fMRIregressor_names] = create_fMRIregressors_otherRPEactPass(tau,mtx,R{n},initialISIandSetup(n,:)); % use results of the fit to create fMRI regressors
% [fMRIregressors,fMRIregressor_names] = create_fMRIregressors(tau,mtx,R{n},initialISIandSetup(n,:)); % use results of the fit to create fMRI regressors
else
[fMRIregressors,fMRIregressor_names] = create_fMRIregressors(0,mtx,R{n},initialISIandSetup(n,:)); % use results of the fit to create fMRI regressors
end
q=R{n}(1).subjName; us = findstr(q,'_');
subjNo = q(us(5)+1:us(6)-1);
if double(subjNo(1)) > 57
subjNo = q(us(4)+1:us(5)-1);
end
for ses = 1:length(fMRIregressors)
dlmwrite(['fMRIregressors_' subjNo '_' num2str(ses) '.txt'],fMRIregressors{ses})
figure; imagesc(fMRIregressors{ses});
set(gca,'xtick',[1:size(fMRIregressors{ses},2)]+.4)
xlabel_oblique(fMRIregressor_names(1:size(fMRIregressors{ses},2)),15,12,1);
set(gca,'position',[0.1300 0.1800 0.7750 0.7450])
ylabel('Trial from start of run1')
title(['fMRIregressors for subject ' subjNo ', run ' num2str(ses)])
end % fMRI ses
end % fMRI regressors
end % runs
mtxs{n} = mtx;
zhappyAll(n).zhappy = zhappy;
subjectNos(n).subjectNo = n*ones(length(zhappy),1);
end % subjects
end % whatToDo
fprintf(1,'done\n');
% Make table with computational results, write it out
if compModelling && whatToDo ~= 0 % so no parameter recovery
for m = 1:size(compResults,2)
predictedHappiness = vertcat(compResults(:,m).happypred);
realHappiness = vertcat(zhappyAll(:).zhappy);
subjectNo = vertcat(subjectNos(:).subjectNo);
ThapComp = table(realHappiness,predictedHappiness,subjectNo);
writetable(ThapComp,[mfilepath 'csv/' dataset ' - Happiness and happiness predicted by ' compResults(1,m).modelName '.csv'])
end
end
if ~compModelling || whatToDo == 0 % then read already computed predicted happiness values
ThapComp = readtable([mfilepath 'csv/' dataset ' - Happiness and happiness predicted by ResponsibilityRedux.csv']);
end
% Show fit quality of model
if strcmpi(study,'both (-> paper)')
figure(paperFig2)
axes('position',happyPredPanelPos+studyOffset);
else
figure
end
regress_display(ThapComp.realHappiness,ThapComp.predictedHappiness,'axesHandle',gca,'inputNames',{'Reported \Delta happiness [std]','Pred. \Delta happiness [std]'},'regressType','robust')
title([studyName ': Fit of comp. model'])
%% Show results of computational modelling
try disp('Individual fits of the models tested (R2):')
disp(reshape([compResults.r2],n,[])); end
if exist('compResults','var')
% combine the results in a matrix
clear resMtx
f1 = figure('name','RL model parameters','position',[440 139 560 659]);
for m = 1:size(compResults,2)
for n = 1:size(compResults,1)
resMtx{m}(n,:) = [subjNumbers(n) compResults(n,m).r2 compResults(n,m).r2adj compResults(n,m).aic compResults(n,m).bic compResults(n,m).b]; % with aic and bic
end
resMtxVarnames{m} = [{'Subject number', 'R2', 'R2adj', 'aic', 'bic'}, compResults(n,m).paramNames];
% display
if length(alldata) > 1
% group results
figure(f1); subplot(size(compResults,2),1,m)
showWhat = [6:size(resMtx{m},2)];
plot([.5 length(showWhat)+.5],[0 0],'k')
try violinDots(resMtx{m}(:,showWhat));
catch, meansemplot(resMtx{m}(:,showWhat));
end
try xlabel_oblique(resMtxVarnames{m}(showWhat)); end
title(compResults(1,m).modelName)
% individual variation
figure('name',['Individual variation of parameters for compModelName ' compResults(1,m).modelName]);
try
subplot(4,1,1)
bar(resMtx{m}(:,2)); title('R2')
subplot(4,1,2)
bar(responsibilityEffect); title('Responsibility effect: Effect of self - other''s decision on happiness in other loss trials')
subplot(4,1,3)
bar(resMtx{m}(:,end-1)); title(compResults(n,m).paramNames(end-1))
subplot(4,1,4)
bar(resMtx{m}(:,end)); title(compResults(n,m).paramNames(end))
catch
subplot(4,1,1)
plot(resMtx{m}(:,2)); title('R2')
subplot(4,1,2)
plot(responsibilityEffect); title('Responsibility effect: Effect of self - other''s decision on happiness in other loss trials')
subplot(4,1,3)
plot(resMtx{m}(:,end-1)); title(compResults(n,m).paramNames(end-1))
subplot(4,1,4)
plot(resMtx{m}(:,end)); title(compResults(n,m).paramNames(end))
end
% write out data to analyse in SPSS or such
dlmwrite(['ComputFitData_' compResults(n,m).modelName '.csv'],resMtx{m})
end
disp('Mean and median R2:')
disp([mean(resMtx{m}(:,2)) median(resMtx{m}(:,2))])
end
else; disp('No computational modelling done')
end
if strcmpi(compModelName,'compare') % then make a table showing the comparison results and do some stats on the parameters
modelNames = {compResults(1,:).modelName};
Nmodels = length(modelNames);
figure('name','RL model comparison','position',[954 480 446 325])
for m = 1:Nmodels
aic(m) = sum(resMtx{m}(:,3));
bic(m) = sum(resMtx{m}(:,4));
end
subplot(2,1,1); bar(bic); title('BIC'); set(gca,'xtick',1:Nmodels)
subplot(2,1,2); bar(aic); title('AIC'); set(gca,'xtick',1:Nmodels,'xticklabel',modelNames)
% do statistics on the model parameters:
disp('Testing if the model parameters CR, EV, RPE and decay are above 0, and if EV < RPE:')
for m = 1:Nmodels; disp(modelNames{m});
for tt = 5:8; ttest_nice(resMtx{m}(:,tt)); end;
ttest_nice(resMtx{m}(:,6),resMtx{m}(:,7));
end
% save the calculated model parameters for each model, and do stats on the Responsibilty model parameters:
for m = 1:Nmodels
params = vertcat(compResults(:,m).b);
paramNames = compResults(1,m).paramNames;
ParamFitTable = array2table(params,'VariableNames',paramNames);
writetable(ParamFitTable,[mfilepath 'csv/' dataset ' - ' modelNames{m} ' - fittedParameters.csv']);
% stats on the Responsibiltiy model parameters:
if m == 4 % the Responsibility model
disp('Comparing the model parameters sRPE > social_pRPE:')
str = signrank_nice(ParamFitTable.sRPE, ParamFitTable.social_pRPE); disp(str)
disp('Comparing the model parameters social_pRPE > 0:')
str = signrank_nice(ParamFitTable.social_pRPE); disp(str)
disp('Comparing the model parameters social_pRPE > partner_pRPE:')
str = signrank_nice(ParamFitTable.social_pRPE, ParamFitTable.partner_pRPE); disp(str)
elseif m == 5 % the ResponsibilityRedux model
disp('Comparing the model parameters sRPE > social_pRPE:')
str = signrank_nice(ParamFitTable.sRPE, ParamFitTable.social_pRPE); disp(str)
disp('Comparing the model parameters social_pRPE > 0:')
str = signrank_nice(ParamFitTable.social_pRPE); disp(str)
end
end
% make paper table
varIdxToReport = [2 3 5 4]; % R2, adjR2, BIC, AIC
for m = 1:length(modelNames)
dataForTable(m,:) = [mean(resMtx{m}(:,2)) mean(resMtx{m}(:,3)) round(sum(resMtx{m}(:,5))) round(sum(resMtx{m}(:,4)))];
end
Tcomp = array2table(dataForTable,'VariableNames', resMtxVarnames{1}(varIdxToReport));
Tcomp.modelName = modelNames';
writetable(Tcomp,[mfilepath 'csv/' dataset ' - computational models of happiness.xlsx']);
% do some stats to compare the model fits
disp('Results of likelihood ratio test comparing the different models:')
% run likelihood ratio tests (LR = -2 * ln(L0/L1) = -2 * (logL1 - logL0)), follows Chi-squared distribution, with DF = diff in N params:
% See for example here: https://www.geeksforgeeks.org/r-language/likelihood-ratio-test/
comparisons = [[1 4];[2 4];[3 4];[5 4]]; % the numbers of the models to be compared: compare Responsibility (model 4) to all others
for pair = 1:size(comparisons,1)
m0 = comparisons(pair,1); m1 = comparisons(pair,2); % m0 is the simpler, m1 the more complex model
% LR = -2 * ([compResults(:,m0).logL] - [compResults(:,m1).logL]); % for each participant separately
LR = -2 * (sum([compResults(:,m0).logL]) - sum([compResults(:,m1).logL])); % summing over participants
df = length(compResults(1,m1).b) - length(compResults(1,m0).b);
pval = chi2test(LR,df); % LRs follow a Chi-square distribution
fprintf('Is model "%s" better than model "%s"? LikelihoodRatioTest = %.2f, p = %f\n', modelNames{m1}, modelNames{m0}, LR, pval)
end
end % compare models
if strcmpi(compModelName,'ParamRecovery')
%% Run parameter recovery analysis, only for ResponsibilityRedux model. Need to load real parameters and compute mtx matrices (done above).
% First, load the real fitted parameters
paramDir = 'csv/';
params = readtable([paramDir dataset ' - ResponsibilityRedux - fittedParameters.csv']);
% Next, for each subject of each study, simulate data based on real
% parameters, fit those data, and check what parameters come out. Vary the
% noise (units: SD of happiness). Display results.
noiseLevel = 1; Nrep = 10; displayFlag = 0;
if ~(length(mtxs) == size(params,1)); error('Data and parameter matrices have different sizes - check which study we''re processing here'); end
progress_report(0,'Parameter recovery')
recov_params_all = [];
for sub = 1:length(mtxs)
progress_report(sub/length(mtxs),'Parameter recovery')
mtx = mtxs{sub};
paramsThisSub = table2array(params(sub,:));
recov_params = parameterRecovery_happy_model_responsibility_redux(mtx,paramsThisSub,noiseLevel,Nrep,displayFlag);
recov_params_all(:,:,sub) = recov_params; % dimensions: Nrep, paramNo, subNo.
end % for each subject
save(['bin/compModels/Recovered model parameters for ' dataset ' study ' num2str(Nrep) 'iter.mat'],'recov_params_all','params')
% Now, display
figure('Name',['Model parameter recovery for ' dataset ' study']);
for p = 1:size(params,2)
subplot(2,3,p);
% plot(table2array(params(:,p)),squeeze(mean(recov_params_all(:,p,:))),'.') % x axis: real parameters across subjects, y axis: recovered parameters
plot(repmat(table2array(params(:,p)),1,Nrep)',squeeze(recov_params_all(:,p,:)),'.') % x axis: real parameters across subjects, y axis: recovered parameters
title(params.Properties.VariableNames{p},'interpreter','none')
end
end % parameter recovery
%% ------------------------------------------------------------------------
%
% NOW LINEAR MIXED MODEL ANALYSIS OF HAPPINESS DATA
%
% -------------------------------------------------------------------------
data = [];
for i = 1:length(R) % for each subject
for j = 1:length(R{i}) % for each trial
dat = [...
R{i}(j).happiness...
R{i}(j).condition...
R{i}(j).safeOption/100 ...
R{i}(j).riskyOption/100 ...
R{i}(j).play...
R{i}(j).subjectWon...
R{i}(j).partnerWon...
R{i}(j).amountThisTrialSubject/100 ...
R{i}(j).amountThisTrialPartner/100 ...
i...
];
data = [data; dat];
end
end
Thap = array2table(data,'VariableNames',{'happiness' 'cond' 'Vsafe' 'riskyHi' 'riskyLo' 'choseRisky' 'subjectWon' 'partnerWon' 'rewardSubj' 'rewardPart' 'subject'});
Thap.EV = ( ( Thap.riskyHi + Thap.riskyLo ) / 2 ) .* Thap.choseRisky + ( Thap.Vsafe .* ( 1 - Thap.choseRisky) );
ineq = NaN(size(data,1),1); ineq(Thap.rewardSubj ~= Thap.rewardPart) = 1; ineq(Thap.rewardSubj == Thap.rewardPart) = 0; ineq(isnan(Thap.rewardPart)) = NaN; % social trials in which participants got different outcomes vs same outcomes
ineqAdv = NaN(size(data,1),1); ineqAdv(Thap.subjectWon==1 & Thap.partnerWon==0) = 1; ineqAdv(Thap.subjectWon==1 & Thap.partnerWon==1) = 0; % trials in which participant only won vs. both won
ineqDisadv = NaN(size(data,1),1); ineqDisadv(Thap.subjectWon==0 & Thap.partnerWon==1) = 1; ineqDisadv(Thap.subjectWon==1 & Thap.partnerWon==1) = 0; % trials in which partner only won vs. both won
Thap.ineq = ineq;
Thap.ineqAdv = ineqAdv;
Thap.ineqDisadv = ineqDisadv;
Thap.sRPE = Thap.rewardSubj - Thap.EV;
Thap.pRPE = Thap.rewardPart - Thap.EV;
Thap.subjDecided = Thap.cond ~= 2;
Thap.socialTrial = Thap.cond < 3;
% Write table out to verify model in R - remove trials with happiness =
% NaN, trials were the safe option was chosen, and non-social trials
Thap2 = Thap(~isnan(Thap.happiness) & ~isnan(Thap.partnerWon),:);
writetable(Thap2,[mfilepath 'csv/' dataset ' - Happiness_singleTrialData_socialRiskyChoicesOnly.csv']);
% Make a nice simple plot relating reward to happiness change
if strcmpi(study,'both (-> paper)')
figure(paperFig2)
axes('position',happyRewardPanelPos1+studyOffset); regress_display(Thap.rewardSubj, Thap.happiness,'axesHandle',gca,'colors',colors(1,:),'inputNames',{'Participant reward [Euro]','\Delta Happiness [std]'},'regressType','robust');
title({[studyName ':'],'Impact of participant','reward on happiness'})
axes('position',happyRewardPanelPos2+studyOffset); regress_display(Thap.rewardPart, Thap.happiness,'axesHandle',gca,'colors',colors(2,:),'inputNames',{'Partner reward [Euro]','\Delta Happiness [std]'},'regressType','robust');
% title(' Impact of partner\newlinereward on happiness')
title({[studyName ':'],'Impact of partner','reward on happiness'})
else
figure
subplot(1,2,1); regress_display(Thap.rewardSubj, Thap.happiness,'axesHandle',gca,'colors',colors(1,:),'inputNames',{'Participant reward [Euro]','\Delta Happiness [std]'},'regressType','robust');
title('Impact of participant\newlinereward on happiness')
subplot(1,2,2); regress_display(Thap.rewardPart, Thap.happiness,'axesHandle',gca,'colors',colors(2,:),'inputNames',{'Partner reward [Euro]','\Delta Happiness [std]'},'regressType','robust');
title(' Impact of partner\newlinereward on happiness')
end
% Testing simple models relating happiness to reward
model = {}; modelName = {}; lm_data = [];
model{1} = 'happiness ~ 1 + rewardSubj + rewardPart + (1|subject)'; % modelName{1} = 'Subject and partner reward';
model{2} = 'happiness ~ 1 + rewardSubj + rewardPart + ineq + (1|subject)'; % modelName{2} = 'Subject reward + inequality (lotteries only)';
lm = {};
for m = 1:length(model)
lm{m} = fitglme(Thap,model{m});
end
for m = 2:length(model)
results = compare(lm{1},lm{m})%,'CheckNesting',true)
end
[~,best] = min(results.AIC);
disp('========= Linear mixed model regressing happiness on reward; best model was:')
lm{best}
printEconomicsStyleRegressionTableForGLMs(lm, modelName, [dataset ' - LMM happiness on reward'], [mfilepath 'csv/' dataset ' - LMM happiness on reward.csv']);
% show residuals of best-fitting model
[~,best] = min(results.AIC);
figure('position',[319 137 889 798],'name','Residuals of LM on happiness, simple and responsibility models');
[H,p,jbstat] = jbtest(lm{best}.residuals);
if H; str = 'Residuals not normally distributed'; else; str = 'Residuals normally distributed'; end
tit = sprintf('Jarque-Bera stat = %.1f, p = %.3f',jbstat,p);
subplot(2,2,1); plot(Thap.happiness,lm{best}.residuals,'.'); axis tight; xlabel('Happiness'); ylabel('Residuals'); title(str)
subplot(2,2,2); qqplot(lm{best}.residuals); xlabel('Normal quantiles'); ylabel('Residuals'); title(tit)
% Condition models
model = {}; lm = {}; modelName = {};
model{1} = 'happiness ~ 1 + cond + (1|subject)';
model{2} = 'happiness ~ 1 + subjDecided + (1|subject)';
model{3} = 'happiness ~ 1 + socialTrial + (1|subject)';
model{4} = 'happiness ~ 1 + subjDecided + socialTrial + (1|subject)';
for m = 1:length(model)
lm{m} = fitglme(Thap,model{m});
disp([lm{m}.LogLikelihood lm{m}.Rsquared.Adjusted]);
end
for m = 2:length(model)
results = compare(lm{1},lm{m})%,'CheckNesting',true)
end
[~,best] = min(results.AIC);
disp('========= Linear mixed model regressing happiness on subjDecided and social or not; best model was:')
lm{best}
printEconomicsStyleRegressionTableForGLMs(lm, modelName, [dataset ' - LMM happiness on conditions'], [mfilepath 'csv/' dataset ' - LMM happiness on conditions.csv']);
% Responsibility models
model = {}; lm = {}; modelName = {};
model{1} = 'happiness ~ 1 + subjectWon + partnerWon + subjDecided + (1|subject)';
model{2} = 'happiness ~ 1 + subjectWon + partnerWon + subjectWon:partnerWon + subjDecided + subjDecided:subjectWon + subjDecided:partnerWon + (1|subject)';
for m = 1:length(model)
lm{m} = fitglme(Thap,model{m});
disp([lm{m}.LogLikelihood lm{m}.Rsquared.Adjusted]);
end
for m = 2:length(model)
results = compare(lm{1},lm{m})%,'CheckNesting',true)
end
disp('========= Linear mixed model regressing happiness after lottery outcomes based on who won and who decided; best model was:')
lm{end}
% show residuals of best-fitting Responsibility LMM model
[~,best] = min(results.AIC);
[H,p,jbstat] = jbtest(lm{best}.residuals);
if H; str = 'Residuals not normally distributed'; else; str = 'Residuals normally distributed'; end
tit = sprintf('Jarque-Bera stat = %.1f, p = %.3f',jbstat,p);
subplot(2,2,3); plot(Thap.happiness,lm{best}.residuals,'.'); axis tight; xlabel('Happiness'); ylabel('Residuals'); title(str)
subplot(2,2,4); qqplot(lm{best}.residuals); xlabel('Normal quantiles'); ylabel('Residuals'); title(tit)
% show effect of outcome for partner X decision-maker on happiness
q = lm{end}.Coefficients(7,:);
fprintf('Study %d, happiness, linear model t statistic for %s: t(%d) = %.2f, p value = %f, beta = %.3f, 95%% CI = [%.3f %.3f]\n', currentStudy, q{1,1}, q{1,5}, q{1,4}, q{1,6}, q{1,2}, q{1,7}, q{1,8})
printEconomicsStyleRegressionTableForGLMs(lm, {}, [dataset ' - LMM happiness on outcomes and responsibility'], [mfilepath 'csv/' dataset ' - LMM happiness on outcomes and responsibility.csv']);
%% ------------------------------------------------------------------------
%
% NOW INTER-INDIVIDUAL DIFFERENCES
% EXPLORATION, NOT REPORTED IN PAPER BECAUSE TOO LOW N
%
% -------------------------------------------------------------------------
%% Relate responsibilityEffect to some outcomes of the computational analysis
if strcmpi(modelName,'Compare')
q=[responsibilityEffect resMtx{4}(:,9:10) resMtx{4}(:,9)-resMtx{4}(:,10)];
idx=~isnan(responsibilityEffect);
names={'responsibility','otherRPEact','otherRPEpass','diff'};
disp(names)
[rr,pp]=corr(q(idx,:))
end
%% Check if measures of risk aversion relate to the responsibility effect:
if flag.fitProbitFunction && flag.findCARArhoInSearchSpace
figure('name','Does risk aversion relate to the responsibility effect?','position',[202 83 840 612])
respo = 'Respons. effect happiness';
subplot(2,2,1);
inputNames = {respo,'\rho'};
regress_display(responsibilityEffect,mean(CARA_rho(:,:,1),2),'regressType','robust','axesHandle',gca,'inputNames',inputNames)
subplot(2,2,2);
inputNames = {respo,'Risk premium'};
regress_display(responsibilityEffect,mean(riskPremiumProbitFit(:,1),2),'regressType','robust','axesHandle',gca,'inputNames',inputNames)
subplot(2,2,3);
inputNames = {respo,'\Delta \rho (social-solo)'};
regress_display(responsibilityEffect,mean(CARA_rho(:,:,1)-CARA_rho(:,:,3),2),'regressType','robust','axesHandle',gca,'inputNames',inputNames)
subplot(2,2,4);
inputNames = {respo,'\Delta Risk premium (social-solo)'};
regress_display(responsibilityEffect,mean(riskPremiumProbitFit(:,1)-riskPremiumProbitFit(:,3),2),'regressType','robust','axesHandle',gca,'inputNames',inputNames)
end
end % which dataset
%% If output for paper, report mixed-model regressions on choices and make econ-style mixed model output tables
if paperFigure
disp('Stats for probit models of choices')
for st = 1:2 % Study 1 or 2
% Stats for effect of EVdiff in probit model of choices
q = choiceLMMs{st,1}.Coefficients(2,:); % Probit model
fprintf('Study %d, probit model t statistic for %s: t(%d) = %.2f, p value = %d, beta = %.3f, 95%% CI = [%.3f %.3f]\n', st, q{1,1}, q{1,5}, q{1,4}, q{1,6}, q{1,2}, q{1,7}, q{1,8})
% Stats for effect of condition (social vs solo)
q = choiceLMMs{st,1}.Coefficients(3,:); % Probit model for Study 1
fprintf('Study %d, probit model t statistic for %s: t(%d) = %.2f, p value = %d, beta = %.3f, 95%% CI = [%.3f %.3f]\n', st, q{1,1}, q{1,5}, q{1,4}, q{1,6}, q{1,2}, q{1,7}, q{1,8})
end
% Print all model stats to file
modelNames = {'Study 1 probit','Study 1 linear','Study 2 probit','Study 2 linear'};
printEconomicsStyleRegressionTableForGLMs(choiceLMMs([1 3 2 4]), modelNames, 'Choices', [mfilepath 'csv/LMMs choices.csv']);
end
%% If paperFigure, add letters and print as SVG
panLetterFontSize = 30; % 40 previously
if paperFigure
figure(paperFig1)
axes('position',[0 0 1 1],'visible','off')
text(repmat([.02 .49 .73],1,2),kron([.89 .44],[1 1 1]),cellstr(['ABCDEF']'),'fontsize',panLetterFontSize)
cd([mfilepath 'Figures/'])
printSVG
printfig
figure(paperFig2)
axes('position',[0 0 1 1],'visible','off') %.41
text(repmat([.02 .23 .44 .67],1,2),kron([.89 .44],[1 1 1 1]),cellstr(['ABCDEFGH']'),'fontsize',panLetterFontSize)
printSVG
printfig
cd(mfilepath)
end
%% Show parameter recovery
% A nice supplementary materials figure of the parameter recovery results
compModelParameterRecoveryFigure
%% POSSIBLE TO DOs
set(0,'defaultAxesFontSize',12) % back to normal defaults
disp('Could check whether the variance of the lotteries correlates with their EV or EV diff to alternative safe option, as both variance and deltaEV can affect choices')
disp('Could relate sympathy levels to responsibilityEffect, if I had more participants')

master_behavAnalysis_revised.m at commit 11854fe, under MIT · at the source

Overview

Authors: Maria Gädeke¹, Tom Eric Willems^1,2, Omar Salah Ahmed^1,3,4, Bernd Weber^3,4, Rene Hurlemann⁵, Johannes Schultz^3,4

ORCID iDs: Tom Eric Willems, Johannes Schultz

Masters in Neuroscience Program, University of Bonn, Bonn, Germany
Institute of Psychology, University of Bern, Bern, Switzerland
Center for Economics and Neuroscience, University of Bonn, Bonn, Germany
Institute of Experimental Epileptology and Cognition Research, Medical Faculty, University of Bonn, Bonn, Germany
Department of Psychiatry, University of Oldenburg, Oldenburg, Germany

Institutions: University of Bonn (Germany); University of Bern (Switzerland); Carl von Ossietzky Universität Oldenburg (Germany)

Journal: eLife, volume 14, article RP105391

Dates: published online 24 March 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.7554/elife.105391 · PMID 41873443 · PMCID PMC13012722 · OpenAlex W4408798353

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: human (organism), cognitive (subfield)

Methods: Connectivity, Statistics, Machine learning, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging

Keywords: Human

MeSH: Decision Making*, Guilt*, Insular Cortex*, Interpersonal Relations*, Social Behavior*, Temporal Lobe*, Adult, Brain Mapping, Female, Happiness, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)

Topic: Emotions and Moral Behavior (Social Psychology, Psychology), according to OpenAlex

Funding: Rheinische Friedrich-Wilhelms-Universität Bonn

Citations: not cited yet (Europe PMC); 73 references in the paper

Research resources: MATLAB RRID:SCR_001622, R RRID:SCR_001905, Psychtoolbox RRID:SCR_002881, SPM RRID:SCR_007037, RRID:SCR_014703, lme4 RRID:SCR_015654, JASP RRID:SCR_015823

Abstract

This study investigated the neural mechanisms involved in feelings of interpersonal guilt and responsibility evoked by social decisions in humans. In two studies (one during fMRI), participants repeatedly chose between safe and risky monetary outcomes in social contexts. Across conditions, each participant chose for both themselves and a partner (Social condition), or the partner chose for both themselves and the participant (Partner condition), or the participant chose just for themselves (Solo condition, control). If the risky option was chosen in the Social or Partner condition, participant and partner could each receive either the high or the low outcome of a lottery with 50% probability, independently of each other. Participants were shown the outcomes for themselves and for their partner on each trial and reported their momentary happiness every few trials. As expected, participant happiness decreased following both low lottery outcomes for themselves and for the partner. Crucially, happiness decreases following low outcomes for the partner were larger when the participant rather than their partner had made the choice, which fits an operational definition of guilt. This guilt effect was associated with BOLD signal increase in the left anterior insula. Connectivity between this region and the right inferior frontal gyrus varied depending on choice and experimental condition, suggesting that this part of prefrontal cortex is sensitive to guilt-related information during social choices. Variations in happiness were well explained by computational models based on participants’ and partners’ rewards and reward prediction errors. A model-based analysis revealed a left superior temporal sulcus cluster that tracked partner reward prediction errors that followed participant choices. Our findings identify neural mechanisms of guilt and social responsibility during social decisions under risk.

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 27 matches between paragraphs and lines of code.

canlab/neuroimaging_pattern_masks

License: GPL-3.0

State: the link answers, verified on 29 October 2026

Evidence: files inventoried

Commit: 961d0033f7074ef8d6c25c6ac318d016a92bba12, 23 October 2026

Languages: MATLAB (264), Python (45), Shell (26), JavaScript (7)

Size: 5,409 files, 342 scripts

Software Heritage: not archived

Found in: the text, “Comparison with multivariate neural guilt signat”

Holds: environment (Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/setup.py), continuous integration, documentation, 4 notebooks

Not found: README, license file, CITATION.cff, tests

Tools: NumPy (22 files), NiBabel (21 files), Statistics and Machine Learning Toolbox (15 files), SciPy (12 files), FSL (9 files), pandas (6 files), ANTs (5 files), scikit-learn (5 files), fMRIPrep (4 files), Parallel Computing Toolbox (4 files), Nilearn (4 files), Connectome Workbench (4 files), FreeSurfer (3 files), SPM (3 files), Image Processing Toolbox (2 files), Nipype (2 files), Pillow (2 files), abagen (1 file), Matplotlib (1 file), MRtrix3 (1 file), statsmodels (1 file)

Availability: 2 checks, the latest on 29 October 2026: the link answers

29 October 2026: the link answers
28 September 2026: the link answers

344 files

Atlases_and_parcellations/2006_desikan_killiany/create_deskian_killiany_atlas.m, MATLAB, 151 lines
Atlases_and_parcellations/2006_desikan_killiany/src/single_subject_registration_fusion.sh, Shell, 73 lines
Atlases_and_parcellations/2006_desikan_killiany/visualize_contents.m, MATLAB, 22 lines
Atlases_and_parcellations/2009_destrieux/create_destrieux_atlas.m, MATLAB, 152 lines
Atlases_and_parcellations/2009_destrieux/src/single_subject_registration_fusion.sh, Shell, 73 lines
Atlases_and_parcellations/2009_destrieux/visualize_contents.m, MATLAB, 22 lines
Atlases_and_parcellations/2011_Buckner_7networks/visualize_contents.m, MATLAB, 21 lines
Atlases_and_parcellations/2011_Yeo_17networks/visualize_contents.m, MATLAB, 24 lines
Atlases_and_parcellations/2012_desikan_killiany_tourville/create_dkt_atlas.m, MATLAB, 151 lines
Atlases_and_parcellations/2012_desikan_killiany_tourville/visualize_contents.m, MATLAB, 23 lines
Atlases_and_parcellations/2013_Shen_Constable_NIMG_268_parcellation/Shen_MNI152NLin2009cAsym_create_atlas_object.m, MATLAB, 143 lines
Atlases_and_parcellations/2013_Shen_Constable_NIMG_268_parcellation/Shen_MNI152NLin6Asym_create_atlas_object.m, MATLAB, 143 lines
Atlases_and_parcellations/2013_Shen_Constable_NIMG_268_parcellation/Shen_create_atlas_object.m, MATLAB, 143 lines
Atlases_and_parcellations/2013_Shen_Constable_NIMG_268_parcellation/visualize_contents.m, MATLAB, 23 lines
Atlases_and_parcellations/2014_Keuken_7T_subcortex/Keuken_create_atlas_object.m, MATLAB, 147 lines
Atlases_and_parcellations/2014_Keuken_7T_subcortex/visualize_contents.m, MATLAB, 21 lines
Atlases_and_parcellations/2016_CIT168_Amygdala_v1.0.3/CIT168_MNI152NLin2009cAsym_create_atlas_object.m, MATLAB, 149 lines
Atlases_and_parcellations/2016_CIT168_Amygdala_v1.0.3/CIT168_MNI152NLin6Asym_create_atlas_object.m, MATLAB, 149 lines
Atlases_and_parcellations/2016_CIT168_Amygdala_v1.0.3/visualize_contents.m, MATLAB, 22 lines
Atlases_and_parcellations/2016_Fan_Brainnetome_r273_parcellation/Brainnetome_create_atlas_object.m, MATLAB, 119 lines
Atlases_and_parcellations/2016_Fan_Brainnetome_r273_parcellation/get_brainnetome_regions_by_name.m, MATLAB, 39 lines
Atlases_and_parcellations/2016_Fan_Brainnetome_r273_parcellation/get_brainnetome_regions_by_name_copy.m, MATLAB, 31 lines
Atlases_and_parcellations/2016_Fan_Brainnetome_r273_parcellation/visualize_contents.m, MATLAB, 21 lines
Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/create_glasser_atlas.m, MATLAB, 118 lines
Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/diagnostics/compare_registration_fusion_with_old_glasser.m, MATLAB, 274 lines
Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/old/GlasserHCP_create_atlas_object.m, MATLAB, 157 lines
Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/src/assemble_multi_subjects.sh, Shell, 40 lines
Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/src/single_subject_registration_fusion.sh, Shell, 119 lines
Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/visualize_contents.m, MATLAB, 22 lines
Atlases_and_parcellations/2018_CIT168_Reinf_Learn_v1.0.0/CIT168_create_atlas_object.m, MATLAB, 129 lines
Atlases_and_parcellations/2018_CIT168_Reinf_Learn_v1.0.0/CIT168_create_atlas_object_old.m, MATLAB, 95 lines
Atlases_and_parcellations/2018_CIT168_Reinf_Learn_v1.0.0/MNI152_2009c_nonlin_asym_1mm/cit2mni.sh, Shell, 42 lines
Atlases_and_parcellations/2018_CIT168_Reinf_Learn_v1.0.0/visualize_contents.m, MATLAB, 21 lines
Atlases_and_parcellations/2018_CIT168_Reinf_Learn_v1.1.0/CIT168_MNI152NLin2009cAsym_create_atlas_object.m, MATLAB, 148 lines
Atlases_and_parcellations/2018_CIT168_Reinf_Learn_v1.1.0/CIT168_MNI152NLin6Asym_create_atlas_object.m, MATLAB, 147 lines
Atlases_and_parcellations/2018_CIT168_Reinf_Learn_v1.1.0/visualize_contents.m, MATLAB, 22 lines
Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/compare_with_leadDBS_version.m, MATLAB, 167 lines
Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/compare_with_morel.m, MATLAB, 142 lines
Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/iglesias_MNI152NLin2009cAsym_create_atlas.m, MATLAB, 184 lines
Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/iglesias_MNI152NLin6Asym_create_atlas.m, MATLAB, 184 lines
Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/src/subnuclear_parcellation_test_2.m, MATLAB, 329 lines
Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/visualize_contents.m, MATLAB, 22 lines
Atlases_and_parcellations/2018_Schaefer_Yeo_multires_cortical_parcellation/Schaefer_create_atlas_object.m, MATLAB, 176 lines
Atlases_and_parcellations/2018_Schaefer_Yeo_multires_cortical_parcellation/schaefer_atlas_create_17_networks_LR.m, MATLAB, 109 lines
Atlases_and_parcellations/2018_Schaefer_Yeo_multires_cortical_parcellation/visualize_contents.m, MATLAB, 22 lines
Atlases_and_parcellations/2018_Wager_combined_atlas/create_basal_ganglia_atlas.m, MATLAB, 99 lines
Atlases_and_parcellations/2018_Wager_combined_atlas/create_brainstem_atlas.m, MATLAB, 249 lines
Atlases_and_parcellations/2018_Wager_combined_atlas/create_thalamus_atlas.m, MATLAB, 88 lines
Atlases_and_parcellations/2018_Wager_combined_atlas/create_thalamus_atlas_old.m, MATLAB, 86 lines
Atlases_and_parcellations/2018_Wager_combined_atlas/plugin_canlab_atlas_2018_relabel_larger_units.m, MATLAB, 243 lines
Atlases_and_parcellations/2018_Wager_combined_atlas/script_2018_Wager_combined_atlas.m, MATLAB, 247 lines
Atlases_and_parcellations/2018_Wager_combined_atlas/visualize_contents.m, MATLAB, 31 lines
Atlases_and_parcellations/2019_Cartmell_NAcCoreShell/NAcCoreShell_MNI152NLin20009cAsym_create_atlas_object.m, MATLAB, 145 lines
Atlases_and_parcellations/2019_Cartmell_NAcCoreShell/NAcCoreShell_MNI152NLin6Asym_create_atlas_object.m, MATLAB, 145 lines
Atlases_and_parcellations/2019_Cartmell_NAcCoreShell/visualize_contents.m, MATLAB, 22 lines
Atlases_and_parcellations/2019_Kragel_PAG/Kragel_create_atlas_object.m, MATLAB, 137 lines
Atlases_and_parcellations/2019_Kragel_PAG/visualize_contents.m, MATLAB, 21 lines
Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/Surface_and_pathway_visualization/painpathways_multi_surface_demo.m, MATLAB, 215 lines
Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/Surface_and_pathway_visualization/painpathways_surface_rendering1.m, MATLAB, 296 lines
Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/Surface_and_pathway_visualization/pbn_amy_painpathways_demo.m, MATLAB, 178 lines
Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/Surface_and_pathway_visualization/plot_pain_pathways_2019.m, MATLAB, 303 lines
Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/add_origlabel_atlas_to_pain_pathways_brainnetwork.m, MATLAB, 146 lines
Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/create_pain_pathways_brainnetwork.m, MATLAB, 705 lines, 1 match
Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/pain2019_plugin_correlation_matrix_plots.m, MATLAB, 46 lines
Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/pain2019_remove_pain_unrelated_nuisance.m, MATLAB, 119 lines
Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/painpathways_local_cPDM_example_STdatabase.m, MATLAB, 148 lines
Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/painpathways_local_nps_example_BMRK3.m, MATLAB, 144 lines
Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/painpathways_local_nps_example_STdatabase.m, MATLAB, 144 lines
Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/prep1_extract_single_trial_pain_data.m, MATLAB, 684 lines
Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/prep1_extract_single_trial_pain_data_cpdm_only.m, MATLAB, 276 lines
Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/windsorize_matrix_columnwise.m, MATLAB, 27 lines
Atlases_and_parcellations/2019_Wager_pain_pathways/visualize_contents.m, MATLAB, 22 lines
Atlases_and_parcellations/2019_Zheng_Pain_Connectivity_Modules/code/CRsubj_net.m, MATLAB, 101 lines
Atlases_and_parcellations/2019_Zheng_Pain_Connectivity_Modules/code/mean_data_cross_trials.m, MATLAB, 138 lines
Atlases_and_parcellations/2019_Zheng_Pain_Connectivity_Modules/code/preparation_extract_data.m, MATLAB, 107 lines
Atlases_and_parcellations/2019_Zheng_Pain_Connectivity_Modules/code/single_trial_rescale_multistudy_data_by_mad.m, MATLAB, 42 lines
Atlases_and_parcellations/2019_Zheng_Pain_Connectivity_Modules/visualize_contents.m, MATLAB, 27 lines
Atlases_and_parcellations/2020_JulichBrain_v3.0.3/julich_fmriprep20_create_atlas_object.m, MATLAB, 171 lines
Atlases_and_parcellations/2020_JulichBrain_v3.0.3/julich_fsl6_create_atlas_object.m, MATLAB, 183 lines
Atlases_and_parcellations/2020_JulichBrain_v3.0.3/parseXML.m, MATLAB, 70 lines
Atlases_and_parcellations/2020_JulichBrain_v3.0.3/visualize_contents.m, MATLAB, 22 lines
Atlases_and_parcellations/2020_JulichBrain_v3.0.3/warp_to_MNI152NLin6Asym.sh, Shell, 21 lines
Atlases_and_parcellations/2020_JulichBrain_v3.0.3/warp_to_MNI152NLin6Asym0.sh, Shell, 20 lines
Atlases_and_parcellations/2020_Pandora_white_matter_atlas/visualize_contents.m, MATLAB, 28 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/__init__.py, Python, 1 line
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bash/tools/GW_interface_pipeline.sh, Shell, 32 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bash/tools/copy_fmri.sh, Shell, 12 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bash/tools/create_masks.sh, Shell, 26 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bash/tools/display_axial_mask_mni.sh, Shell, 44 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bash/tools/display_slices.sh, Shell, 66 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bash/tools/mask_vol.sh, Shell, 11 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bash/tools/mrstats_master.sh, Shell, 12 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bash/tools/mrstats_median.sh, Shell, 31 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bash/tools/preproc_dti.sh, Shell, 2 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/Disruptome/__init__.py, Python, 1 line
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/Functionnectome/__init__.py, Python, 1 line
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/Parcellotron3000/__init__.py, Python, 1 line
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/TDI_Disco/__init__.py, Python, 1 line
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/TDI_Disco/tdi_disco.py, Python, 16 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/__init__.py, Python, 1 line
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/gui/__init__.py, Python, 1 line
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/scripts/__init__.py, Python, 1 line
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/scripts/bcbseg.py, Python, 33 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/scripts/effectsize_T2R.py, Python, 44 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/scripts/generate_synth_lesions.py, Python, 169 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/scripts/pick_up_matched_synth_lesions.py, Python, 217 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/scripts/tck2trk.py, Python, 62 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/scripts/trk2tck.py, Python, 47 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/PCA.py, Python, 297 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/__init__.py, Python, 1 line
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/brain_extract.py, Python, 22 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/constants.py, Python, 36 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/datamined_kruskal.py, Python, 105 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/disconnectome.py, Python, 8 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/divide_KMeans.py, Python, 385 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/divide_mask.py, Python, 90 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/entropy.py, Python, 65 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/funcon.py, Python, 136 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/mat_transform.py, Python, 171 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/nii_stats.py, Python, 55 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/read_log_anacom.py, Python, 149 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/segment.py, Python, 29 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/split_clusters.py, Python, 81 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/setup.py, Python, 37 lines
Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/visualize_contents.m, MATLAB, 29 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/Group-Parcellation/create_tian_atlas.m, MATLAB, 26 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/Tian_3T_S4_MNI152NLin2009cAsym_create_atlas_object.m, MATLAB, 342 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/Tian_3T_S4_MNI152NLin6Asym_create_atlas_object.m, MATLAB, 341 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/Tian_3T_S1_MNI152NLin2009cAsym_2mm_create_atlas_object.m, MATLAB, 139 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/Tian_3T_S1_MNI152NLin2009cAsym_create_atlas_object.m, MATLAB, 146 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/Tian_3T_S1_MNI152NLin6Asym_2mm_create_atlas_object.m, MATLAB, 139 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/Tian_3T_S1_MNI152NLin6Asym_create_atlas_object.m, MATLAB, 139 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/Tian_3T_S2_MNI152NLin2009cAsym_2mm_create_atlas_object.m, MATLAB, 139 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/Tian_3T_S2_MNI152NLin2009cAsym_create_atlas_object.m, MATLAB, 146 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/Tian_3T_S2_MNI152NLin6Asym_2mm_create_atlas_object.m, MATLAB, 139 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/Tian_3T_S2_MNI152NLin6Asym_create_atlas_object.m, MATLAB, 139 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/Tian_3T_S3_MNI152NLin2009cAsym_2mm_create_atlas_object.m, MATLAB, 139 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/Tian_3T_S3_MNI152NLin2009cAsym_create_atlas_object.m, MATLAB, 146 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/Tian_3T_S3_MNI152NLin6Asym_2mm_create_atlas_object.m, MATLAB, 139 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/Tian_3T_S3_MNI152NLin6Asym_create_atlas_object.m, MATLAB, 139 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/Tian_3T_S4_MNI152NLin2009cAsym_2mm_create_atlas_object.m, MATLAB, 139 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/Tian_3T_S4_MNI152NLin6Asym_2mm_create_atlas_object.m, MATLAB, 139 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/Tian_3T_S4_MNI152NLin6Asym_create_atlas_object.m, MATLAB, 139 lines
Atlases_and_parcellations/2020_Tian_subcortical_v1.1/visualize_contents.m, MATLAB, 22 lines
Atlases_and_parcellations/2020_iglesias_hypothalamus/iglesias_MNI152NLin2009cAsym_create_atlas.m, MATLAB, 139 lines
Atlases_and_parcellations/2020_iglesias_hypothalamus/iglesias_MNI152NLin6Asym_create_atlas.m, MATLAB, 149 lines
Atlases_and_parcellations/2020_iglesias_hypothalamus/visualize_contents.m, MATLAB, 22 lines
Atlases_and_parcellations/2022_Hansen_PET_tracer_maps/canlab_explore_neuromaps_pet_images.mlx, MATLAB, to read at its source
Atlases_and_parcellations/2022_Hansen_PET_tracer_maps/canlab_explore_neuromaps_pet_images_backup1.mlx, MATLAB, to read at its source
Atlases_and_parcellations/2022_Hansen_PET_tracer_maps/canlab_prep_neuromaps_pet_images.mlx, MATLAB, to read at its source
Atlases_and_parcellations/2022_Hansen_PET_tracer_maps/canlab_prep_neuromaps_pet_images_backup1.mlx, MATLAB, to read at its source
Atlases_and_parcellations/2022_Hansen_PET_tracer_maps/image_object_weighted_average.m, MATLAB, 57 lines
Atlases_and_parcellations/2022_Hansen_PET_tracer_maps/visualize_contents.m, MATLAB, 55 lines
Atlases_and_parcellations/2023_Bianciardi_BrainstemNavigatorV0.9/biancia_create_atlas_objects_macro.m, MATLAB, 137 lines
Atlases_and_parcellations/2023_Bianciardi_BrainstemNavigatorV0.9/bianciardi_create_atlas_obj.m, MATLAB, 315 lines
Atlases_and_parcellations/2023_Bianciardi_BrainstemNavigatorV0.9/visualize_contents.m, MATLAB, 25 lines
Atlases_and_parcellations/2023_CANLab_atlas/create_CANLab2023_atlas.m, MATLAB, 355 lines
Atlases_and_parcellations/2023_CANLab_atlas/create_CANLab2023_atlas_cifti.sh, Shell, 106 lines
Atlases_and_parcellations/2023_CANLab_atlas/dilate.m, MATLAB, 77 lines
Atlases_and_parcellations/2023_CANLab_atlas/lateralize.m, MATLAB, 48 lines
Atlases_and_parcellations/2023_CANLab_atlas/setup_canlab2023.m, MATLAB, 29 lines
Atlases_and_parcellations/2023_CANLab_atlas/src/create_CANLab2023_CIFTI_subctx.m, MATLAB, 110 lines
Atlases_and_parcellations/2023_CANLab_atlas/src/create_CANLab2023_atlas_macro.m, MATLAB, 61 lines
Atlases_and_parcellations/2023_CANLab_atlas/src/create_CANLab2023_atlas_prep.sh, Shell, 11 lines
Atlases_and_parcellations/2023_CANLab_atlas/src/create_CANLab2023_unrestricted.m, MATLAB, 357 lines
Atlases_and_parcellations/2023_CANLab_atlas/src/create_bg2023_atlas.m, MATLAB, 159 lines
Atlases_and_parcellations/2023_CANLab_atlas/src/create_brainstem2023_atlas_unrestricted.m, MATLAB, 269 lines
Atlases_and_parcellations/2023_CANLab_atlas/src/create_thalamus2023_atlas.m, MATLAB, 135 lines
Atlases_and_parcellations/2023_CANLab_atlas/visualize_contents.m, MATLAB, 28 lines
Atlases_and_parcellations/2023_CANlab_atlas_CIFTI/visualize_contents.m, MATLAB, 26 lines
Atlases_and_parcellations/2023_harvard_aan_brainstem_atlas/aan_MNI152NLin2009cAsym_create_atlas.m, MATLAB, 166 lines
Atlases_and_parcellations/2023_harvard_aan_brainstem_atlas/aan_MNI152NLin6Asym_create_atlas.m, MATLAB, 166 lines
Atlases_and_parcellations/2023_harvard_aan_brainstem_atlas/compare_with_bianciardi.m, MATLAB, 60 lines
Atlases_and_parcellations/2023_harvard_aan_brainstem_atlas/visualize_contents.m, MATLAB, 22 lines
Atlases_and_parcellations/2023_levinson_bari_limbic_brainstem_atlas/LBLBA_MNI152NLin2009cAsym_create_atlas.m, MATLAB, 150 lines
Atlases_and_parcellations/2023_levinson_bari_limbic_brainstem_atlas/LBLBA_MNI152NLin6Asym_create_atlas.m, MATLAB, 150 lines
Atlases_and_parcellations/2023_levinson_bari_limbic_brainstem_atlas/compare_with_bianciardi.m, MATLAB, 51 lines
Atlases_and_parcellations/2023_levinson_bari_limbic_brainstem_atlas/visualize_contents.m, MATLAB, 22 lines
Atlases_and_parcellations/2024_CANLab_atlas/create_CANLab2024_atlas.m, MATLAB, 802 lines
Atlases_and_parcellations/2024_CANLab_atlas/create_pain_pathways2024_brainnetwork.m, MATLAB, 741 lines, 1 match
Atlases_and_parcellations/2024_CANLab_atlas/setup_canlab2024.m, MATLAB, 76 lines
Atlases_and_parcellations/2024_CANLab_atlas/src/canlab2024_add_labels_5_networks_and_large_structures.m, MATLAB, 33 lines
Atlases_and_parcellations/2024_CANLab_atlas/src/create_CANLab2024_CIFTI_subctx.m, MATLAB, 110 lines
Atlases_and_parcellations/2024_CANLab_atlas/src/create_CANLab2024_atlas_cifti.sh, Shell, 132 lines
Atlases_and_parcellations/2024_CANLab_atlas/src/create_CANLab2024_atlas_macro.m, MATLAB, 39 lines
Atlases_and_parcellations/2024_CANLab_atlas/src/create_CANLab2024_atlas_prep.sh, Shell, 15 lines
Atlases_and_parcellations/2024_CANLab_atlas/src/create_bg2024_atlas.m, MATLAB, 251 lines
Atlases_and_parcellations/2024_CANLab_atlas/src/create_brainstem2024_atlas_unrestricted.m, MATLAB, 481 lines
Atlases_and_parcellations/2024_CANLab_atlas/src/create_openCANLab2024.m, MATLAB, 550 lines
Atlases_and_parcellations/2024_CANLab_atlas/src/create_openCANLab2024_CIFTI_subctx.m, MATLAB, 110 lines
Atlases_and_parcellations/2024_CANLab_atlas/src/create_openCANLab2024_atlas_cifti.sh, Shell, 132 lines
Atlases_and_parcellations/2024_CANLab_atlas/src/create_thalamus2024_atlas.m, MATLAB, 94 lines
Atlases_and_parcellations/2024_CANLab_atlas/visualize_contents.m, MATLAB, 29 lines
Atlases_and_parcellations/2026_Pourmajidian_mitochondrial energetic capacity_Map/visualize_contents.m, MATLAB, 27 lines
CANlab_Meta_analysis_maps/2003_Wager_Emotion_64_studies/visualize_contents.m, MATLAB, 25 lines
CANlab_Meta_analysis_maps/2003_Wager_Working_memory_60_studies/visualize_contents.m, MATLAB, 23 lines
CANlab_Meta_analysis_maps/2004_Wager_Attention_switching_31_studies/visualize_contents.m, MATLAB, 21 lines
CANlab_Meta_analysis_maps/2007_Etkin_AJP_Anxiety_disorders/visualize_contents.m, MATLAB, 22 lines
CANlab_Meta_analysis_maps/2007_Nee_Inhibition_47_studies/visualize_contents.m, MATLAB, 25 lines
CANlab_Meta_analysis_maps/2008_Kober_Emotion_163_studies/visualize_contents.m, MATLAB, 25 lines
CANlab_Meta_analysis_maps/2011_Agency_Meta_analysis/visualize_contents.m, MATLAB, 24 lines
CANlab_Meta_analysis_maps/2011_Meissner_Placebo_meta_analysis_masks/visualize_contents.m, MATLAB, 23 lines
CANlab_Meta_analysis_maps/2011_Yarkoni_Neurosynth_Original/visualize_contents.m, MATLAB, 24 lines
CANlab_Meta_analysis_maps/2012_Denny_SOMA_self_other_meta/plot_points_on_slices_script.m, MATLAB, 41 lines
CANlab_Meta_analysis_maps/2012_Denny_SOMA_self_other_meta/visualize_contents.m, MATLAB, 24 lines
CANlab_Meta_analysis_maps/2014_BuhleSilvers_Reappraisal_Meta/visualize_contents.m, MATLAB, 25 lines
CANlab_Meta_analysis_maps/2015_Satpute_Barett_Emotion_Valence/visualize_contents.m, MATLAB, 28 lines
CANlab_Meta_analysis_maps/2015_Wager_Kang_etal_Emotion_Meta_BSPP/visualize_contents.m, MATLAB, 25 lines
CANlab_Meta_analysis_maps/2016_Neurosynth_Wager_SocAffective/visualize_contents.m, MATLAB, 25 lines
CANlab_Meta_analysis_maps/2016_Pauli_Basal_Ganglia_Parcels/scripts/pauli2016_create_atlas_object.m, MATLAB, 147 lines
CANlab_Meta_analysis_maps/2016_Pauli_Basal_Ganglia_Parcels/scripts/pauli2016_plot_bucknerlab_similarity.m, MATLAB, 78 lines
CANlab_Meta_analysis_maps/2016_Pauli_Basal_Ganglia_Parcels/visualize_contents.m, MATLAB, 31 lines
CANlab_Meta_analysis_maps/2016_delaVega_JN_neurosynth-mfc_parcellation/visualize_contents.m, MATLAB, 22 lines
CANlab_Meta_analysis_maps/2017_Ashar_Placebo_Review/visualize_contents.m, MATLAB, 30 lines
CANlab_Meta_analysis_maps/2017_delaVega_Neurosynth_cortical_parcellation/delavega_create_atlas_object.m, MATLAB, 151 lines
CANlab_Meta_analysis_maps/2017_delaVega_Neurosynth_cortical_parcellation/visualize_contents.m, MATLAB, 25 lines
CANlab_Meta_analysis_maps/2018_Kraynak_Gianaros_immunemeta/visualize_contents.m, MATLAB, 29 lines
CANlab_Meta_analysis_maps/2018_Sha_BiolPsych_Common_Networks_Psychopathology/scripts/publish_sha_2018_maps.m, MATLAB, 24 lines
CANlab_Meta_analysis_maps/2018_Sha_BiolPsych_Common_Networks_Psychopathology/scripts/sha_2018_visualize_maps.m, MATLAB, 59 lines
CANlab_Meta_analysis_maps/2018_Sha_BiolPsych_Common_Networks_Psychopathology/visualize_contents.m, MATLAB, 29 lines
CANlab_Meta_analysis_maps/2021_Zunhammer_n603_Pain_Placebo/scripts/viz zunhammer placebo decreases.m, MATLAB, 70 lines
CANlab_Meta_analysis_maps/2021_Zunhammer_n603_Pain_Placebo/visualize_contents.m, MATLAB, 30 lines
CANlab_Meta_analysis_maps/2024_Quah_Saggar_factor_maps/publish_quah_factor_map_report.m, MATLAB, 14 lines
CANlab_Meta_analysis_maps/2024_Quah_Saggar_factor_maps/quah_factor_map_montages.m, MATLAB, 80 lines
CANlab_Meta_analysis_maps/2024_Quah_Saggar_factor_maps/visualize_contents.m, MATLAB, 26 lines
CANlab_Meta_analysis_maps/scripts_summary/canlab_create_meta_analysis_summary_tables.m, MATLAB, 98 lines
Individual_study_maps/2024_Bo_EmotionRegulation_BayesFactor/visualize_contents.m, MATLAB, 24 lines
Individual_study_maps/2026_Bo_Amygdala_FIR_Clusters/Bo2026_AmygdalaFIR_create_atlas_object.m, MATLAB, 351 lines
Individual_study_maps/2026_Bo_Amygdala_FIR_Clusters/visualize_contents.m, MATLAB, 77 lines
Individual_study_maps/2026_Miao_Social-ToM_BayesFactor/visualize_contents.m, MATLAB, 29 lines
Multivariate_signature_patterns/2011_Wager_JNeuro_placebo_prediction/visualize_contents.m, MATLAB, 26 lines
Multivariate_signature_patterns/2015_Chang_PLoSBiology_PINES/readme_apply_PINES_signature.mlx, MATLAB, to read at its source
Multivariate_signature_patterns/2015_Chang_PLoSBiology_PINES/visualize_contents.m, MATLAB, 23 lines
Multivariate_signature_patterns/2015_Kragel_emotionClassificationBPLS/compare_kragel_wager_emometa.m, MATLAB, 51 lines
Multivariate_signature_patterns/2015_Kragel_emotionClassificationBPLS/visualize_contents.m, MATLAB, 24 lines
Multivariate_signature_patterns/2015_Woo_NatureComms_Rejection/visualize_contents.m, MATLAB, 24 lines
Multivariate_signature_patterns/2016_Eisenbarth_JNeuro_autonomic_patterns/visualize_contents.m, MATLAB, 24 lines
Multivariate_signature_patterns/2016_Krishnan_eLife_VPS/readme_apply_signature.m, MATLAB, 36 lines
Multivariate_signature_patterns/2016_Krishnan_eLife_VPS/visualize_contents.m, MATLAB, 23 lines
Multivariate_signature_patterns/2017_Ashar_care_distress/visualize_contents.m, MATLAB, 24 lines
Multivariate_signature_patterns/2017_Lopez_Sola_Fibromyalgia/visualize_contents.m, MATLAB, 25 lines
Multivariate_signature_patterns/2017_Rosenberg_sustained_attention/test_saCPM.m, MATLAB, 90 lines
Multivariate_signature_patterns/2017_Rosenberg_sustained_attention/visualize_contents.m, MATLAB, 54 lines
Multivariate_signature_patterns/2017_Woo_SIIPS1/load_siips_subregions.m, MATLAB, 93 lines
Multivariate_signature_patterns/2017_Woo_SIIPS1/visualize_contents.m, MATLAB, 26 lines
Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/visualize_contents.m, MATLAB, 29 lines
Multivariate_signature_patterns/2018_Reddan_Threat_Conditioning_ImEx/marianne_tmp_script_tor_save_threat_pattern_results.m, MATLAB, 81 lines
Multivariate_signature_patterns/2018_Reddan_Threat_Conditioning_ImEx/visualize_contents.m, MATLAB, 23 lines
Multivariate_signature_patterns/2019_Kragel_Emotion_Schemas/visualize_contents.m, MATLAB, 29 lines
Multivariate_signature_patterns/2019_Lee_JPain_backpain/S1/make_S1back_ROIs_Lee2018.m, MATLAB, 18 lines
Multivariate_signature_patterns/2019_Lee_JPain_backpain/apply_LeeCBP_S1_marker.m, MATLAB, 31 lines
Multivariate_signature_patterns/2019_Lee_JPain_backpain/visualize_contents.m, MATLAB, 26 lines
Multivariate_signature_patterns/2019_Matthewson_Woo_SCR_pain/visualize_contents.m, MATLAB, 33 lines
Multivariate_signature_patterns/2019_Yu_Koban_Guilt/apply_guilt_signature_example.m, MATLAB, 17 lines
Multivariate_signature_patterns/2019_Yu_Koban_Guilt/tables_and_figures/yu_koban_save_sig_results.m, MATLAB, 47 lines
Multivariate_signature_patterns/2019_Yu_Koban_Guilt/visualize_contents.m, MATLAB, 21 lines
Multivariate_signature_patterns/2020_Geuter_pain_multivariate_mediation_PDM/visualize_contents.m, MATLAB, 23 lines
Multivariate_signature_patterns/2020_Silvestrini_Rainville_Pain_CogControl_interaction_aMCC/Silvestrini_Readme.mlx, MATLAB, to read at its source
Multivariate_signature_patterns/2020_Silvestrini_Rainville_Pain_CogControl_interaction_aMCC/visualize_contents.m, MATLAB, 23 lines
Multivariate_signature_patterns/2020_VanOudenhove_Kragel_somatovisceral_pain/classify_somatovisceral_pain.m, MATLAB, 49 lines
Multivariate_signature_patterns/2020_VanOudenhove_Kragel_somatovisceral_pain/visualize_contents.m, MATLAB, 50 lines
Multivariate_signature_patterns/2020_Zhou_general_vicarious_pain/visualize_contents.m, MATLAB, 26 lines
Multivariate_signature_patterns/2021_Ceko_MPA2_multiaversive/apply_mpa2_to_kragel270.m, MATLAB, 72 lines
Multivariate_signature_patterns/2021_Ceko_MPA2_multiaversive/apply_multiaversive_mpa2_patterns.m, MATLAB, 142 lines
Multivariate_signature_patterns/2021_Ceko_MPA2_multiaversive/visualize_contents.m, MATLAB, 25 lines
Multivariate_signature_patterns/2021_Zhou_Subjective_Fear/visualize_contents.m, MATLAB, 21 lines
Multivariate_signature_patterns/2021_vantHoff_BASIC_sexual_image_classifier/region_table/BASIC_result.m, MATLAB, 265 lines
Multivariate_signature_patterns/2021_vantHoff_BASIC_sexual_image_classifier/visualize_contents.m, MATLAB, 21 lines
Multivariate_signature_patterns/2022_Koban_NCS_Craving/Data_code_public/NCS_weightmaps/Thresholded_NCS_wmaps/ncs_provenance_create_thresholded_maps.m, MATLAB, 29 lines
Multivariate_signature_patterns/2022_Koban_NCS_Craving/Data_code_public/scripts/Apply_NCS/apply_ncs.m, MATLAB, 13 lines
Multivariate_signature_patterns/2022_Koban_NCS_Craving/Data_code_public/scripts/ImageFeatures/imagefeatures_table.m, MATLAB, 79 lines
Multivariate_signature_patterns/2022_Koban_NCS_Craving/Data_code_public/scripts/Prediction/Apply_cvNCSmaps_stats_2022.m, MATLAB, 340 lines
Multivariate_signature_patterns/2022_Koban_NCS_Craving/Data_code_public/scripts/Prediction/PersonLevel_Covariates_2022.m, MATLAB, 143 lines
Multivariate_signature_patterns/2022_Koban_NCS_Craving/Data_code_public/scripts/Prediction/predict_craving_10fold_2022Apr_N99_boot.m, MATLAB, 54 lines
Multivariate_signature_patterns/2022_Koban_NCS_Craving/Data_code_public/scripts/Prediction/split_and_merge_CravingData.m, MATLAB, 12 lines
Multivariate_signature_patterns/2022_Koban_NCS_Craving/visualize_contents.m, MATLAB, 24 lines
Multivariate_signature_patterns/2022_coll_pain_monetary_reward_decision_value/visualize_contents.m, MATLAB, 29 lines
Multivariate_signature_patterns/2023_Speer_Brain_Reward_Signature_BRS/explore_speer_BRS.mlx, MATLAB, to read at its source
Multivariate_signature_patterns/2023_Speer_Brain_Reward_Signature_BRS/visualize_contents.m, MATLAB, 21 lines
Multivariate_signature_patterns/2024_FEPS_Facial_Expressions_of_Pain_Signature/visualize_contents.m, MATLAB, 22 lines
Multivariate_signature_patterns/2026_Acil_Mentalizing_Self_Other/apply_mental2.m, MATLAB, 18 lines
Multivariate_signature_patterns/2026_Acil_Mentalizing_Self_Other/visualize_contents.m, MATLAB, 24 lines
Multivariate_signature_patterns/2026_Murillo_PiFoneM/visualize_contents.m, MATLAB, 23 lines
Multivariate_signature_patterns/apply_all_signatures.m, MATLAB, 233 lines
Multivariate_signature_patterns/apply_siips.m, MATLAB, 359 lines
Neurosynth_maps/2016_Neurosynth_100_topics/Prep_v4_v5_Neurosynth_Topics.mlx, MATLAB, to read at its source
Neurosynth_maps/2016_Neurosynth_100_topics/neurosynth_topics_clusters.m, MATLAB, 82 lines
Neurosynth_maps/2016_Neurosynth_100_topics/neurosynth_topics_prep_factor_maps.mlx, MATLAB, to read at its source
Neurosynth_maps/2016_Neurosynth_100_topics/visualize_contents.m, MATLAB, 55 lines
Neurosynth_maps/assign_unique_cliques_and_average.m, MATLAB, 292 lines
Neurosynth_maps/assign_unique_cliques_from_maximal_cliques.m, MATLAB, 320 lines
Neurosynth_maps/maximalCliques.m, MATLAB, 68 lines
Neurosynth_maps/mkda/visualize_contents.m, MATLAB, 21 lines
Neurosynth_maps/neurosynth_interregion_coactivation.m, MATLAB, 303 lines
Neurosynth_maps/neurosynth_seed_coactivation_map.m, MATLAB, 274 lines
Neurosynth_maps/scripts/generate_topic_embeddings.py, Python, 143 lines
Neurosynth_maps/scripts/montage_tables_provincial_connector_hubs.m, MATLAB, 30 lines
Neurosynth_maps/scripts/neurosynth_cluster_defModeA_by_connectivity.m, MATLAB, 94 lines
Neurosynth_maps/scripts/neurosynth_default_mode_analysis_tor_dec_2019.m, MATLAB, 226 lines
Neurosynth_maps/scripts/neurosynth_viz_overall_prob_activation.m, MATLAB, 81 lines
Neurosynth_maps/scripts/ns_matlab_prep_MKDA.m, MATLAB, 158 lines
Neurosynth_maps/scripts/topic_embedding_similarity_analysis.m, MATLAB, 242 lines
Neurosynth_maps/scripts/tor_script_prepdata_2022_stub_(use_older_database).m, MATLAB, 439 lines
docs/canlab_render_atlas.m, MATLAB, 106 lines
docs/canlab_render_patterns.m, MATLAB, 127 lines
docs/figure_source/build_overview.py, Python, 129 lines
docs/figure_source/build_taxonomy.py, Python, 282 lines
docs/figure_source/build_thumbnails.py, Python, 120 lines
docs/figure_source/common.py, Python, 125 lines
docs/run_all_multivariate.m, MATLAB, 65 lines
site/scripts/build.py, Python, 133 lines
site/scripts/prepare_brand.py, Python, 56 lines
site/scripts/prepare_labels.py, Python, 60 lines
site/scripts/prepare_shared.py, Python, 19 lines
site/scripts/seed_catalog.py, Python, 94 lines
site/scripts/validate.py, Python, 60 lines
site/src/app.js, JavaScript, 120 lines
site/src/atlas.js, JavaScript, 8 lines
site/src/graph.js, JavaScript, 63 lines
site/src/hero.js, JavaScript, 30 lines
site/src/state.js, JavaScript, 33 lines
site/vendor/canlab_niivue_viewer.js, JavaScript, 717 lines
site/vendor/niivue.js, JavaScript, 910 lines
spatial_basis_functions/hcp_91k/visualize_contents.m, MATLAB, 62 lines
spatial_basis_functions/hcp_groupICAs/visualize_contents.m, MATLAB, 23 lines
spatial_basis_functions/margulies/extras/margulies_load_and_view_gradients.m, MATLAB, 8 lines
spatial_basis_functions/margulies/visualize_contents.m, MATLAB, 22 lines
spatial_basis_functions/mitochondrial_profile_maps/scripts/explore_mitochondrial_maps.mlx, MATLAB, to read at its source
spatial_basis_functions/mitochondrial_profile_maps/visualize_contents.m, MATLAB, 26 lines
spatial_basis_functions/transcriptomic_gradients/create_gradients.py, Python, 75 lines
spatial_basis_functions/transcriptomic_gradients/visualize_contents.m, MATLAB, 22 lines
templates/cerebellum/visualize_contents.m, MATLAB, 30 lines
templates/transforms/code/0_populate_ants_and_convert_to_fsl.sh, Shell, 5 lines
templates/transforms/code/1_ants_to_spm_mat.sh, Shell, 5 lines
templates/transforms/code/2_ants_to_spm_warps.py, Python, 56 lines
templates/transforms/code/ants_to_spm_warps.py, Python, 27 lines
templates/transforms/code/apply_spm_warp.m, MATLAB, 147 lines
templates/transforms/code/ea_antsmat2mat.m, MATLAB, 51 lines
templates/transforms/code/fmriprep_to_fsl.sh, Shell, 36 lines
templates/transforms/code/fsl_to_fmriprep.sh, Shell, 39 lines
templates/transforms/code/subctx_alignment.sh, Shell, 50 lines
templates/transforms/download_warpfield.m, MATLAB, 34 lines
LICENSE, License, 674 lines
README.md, Text, 57 lines

At the source: github.com/canlab/Neuroimaging_Pattern_Masks/tree/master/Multivariate_signature_patterns/2019_Yu_Koban_Guilt

bonnsocialneuroscienceunit/responsibilityexperiment

License: MIT

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: 11854fee85b7b75e23b24cbf98b284c44f6de94d, 12 March 2026

Languages: MATLAB (54)

Size: 455 files, 54 scripts

Software Heritage: not archived

Found in: the references

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Statistics and Machine Learning Toolbox (18 files), Optimization Toolbox (13 files), SPM (8 files), Psychtoolbox (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

56 files

Code/bin/CharnessRabinModel.m, MATLAB, 80 lines, 1 match
Code/bin/anova1_repmeas_onMatrix.m, MATLAB, 79 lines
Code/bin/anova2_repmeas.m, MATLAB, 135 lines
Code/bin/anova2_repmeas_autodesign.m, MATLAB, 42 lines
Code/bin/anovan_autoDesign.m, MATLAB, 116 lines
Code/bin/bttnChoiceDialog.m, MATLAB, 233 lines
Code/bin/ci_t.m, MATLAB, 31 lines
Code/bin/cohens_d.m, MATLAB, 60 lines
Code/bin/compModelParameterRecoveryFigure.m, MATLAB, 73 lines
Code/bin/compModels/create_fMRIregressors.m, MATLAB, 74 lines
Code/bin/compModels/create_fMRIregressors_otherRPEactPass.m, MATLAB, 80 lines
Code/bin/compModels/fit_happy_model_Omar_Social.m, MATLAB, 335 lines
Code/bin/compModels/fit_happy_model_basic.m, MATLAB, 135 lines, 1 match
Code/bin/compModels/fit_happy_model_guiltenvy.m, MATLAB, 139 lines
Code/bin/compModels/fit_happy_model_guiltenvy_utl.m, MATLAB, 233 lines
Code/bin/compModels/fit_happy_model_inequalityActPass.m, MATLAB, 142 lines
Code/bin/compModels/fit_happy_model_inequalitySimple.m, MATLAB, 137 lines
Code/bin/compModels/fit_happy_model_otherRPE.m, MATLAB, 102 lines
Code/bin/compModels/fit_happy_model_otherRPEactPass.m, MATLAB, 139 lines
Code/bin/compModels/fit_happy_model_partnerRPE.m, MATLAB, 102 lines
Code/bin/compModels/fit_happy_model_responsibility.m, MATLAB, 139 lines
Code/bin/compModels/fit_happy_model_responsibility_redux.m, MATLAB, 138 lines, 2 matches
Code/bin/compModels/parameterRecovery_happy_model_responsibility_redux.m, MATLAB, 139 lines
Code/bin/correl.m, MATLAB, 26 lines
Code/bin/dotPlot.m, MATLAB, 333 lines
Code/bin/fittingUtilityFunction/exponUtilityFunction.m, MATLAB, 23 lines
Code/bin/fittingUtilityFunction/findCARArhoInSearchSpace.m, MATLAB, 71 lines
Code/bin/fittingUtilityFunction/fitCARAmodelToRiskyChoices.m, MATLAB, 327 lines, 2 matches
Code/bin/fittingUtilityFunction/fitCARAmodelToRiskyChoices2conds.m, MATLAB, 333 lines, 2 matches
Code/bin/fittingUtilityFunction/fitCARAmodelToRiskyChoices_workFile.m, MATLAB, 283 lines
Code/bin/fittingUtilityFunction/fitExponUtilityFunction.m, MATLAB, 12 lines
Code/bin/fittingUtilityFunction/fitUtilityFunction.m, MATLAB, 54 lines
Code/bin/fittingUtilityFunction/test_prospectTheory.m, MATLAB, 25 lines
Code/bin/meanErrPlotDots.m, MATLAB, 133 lines
Code/bin/meansembar.m, MATLAB, 106 lines
Code/bin/niceColorsWarm2.m, MATLAB, 33 lines
Code/bin/printEconomicsStyleRegressionTableForGLMs.m, MATLAB, 120 lines
Code/bin/printSVG.m, MATLAB, 78 lines
Code/bin/printfig.m, MATLAB, 78 lines
Code/bin/regress_display.m, MATLAB, 334 lines
Code/bin/signrank_nice.m, MATLAB, 239 lines
Code/bin/stars.m, MATLAB, 10 lines
Code/bin/ttest_nice.m, MATLAB, 130 lines
Code/fMRI/a_preprocessing.m, MATLAB, 213 lines
Code/fMRI/b_FFX.m, MATLAB, 635 lines
Code/fMRI/bb_onsetMaker.m, MATLAB, 92 lines, 1 match
Code/fMRI/c_gPPI.m, MATLAB, 191 lines, 1 match
Code/fMRI/d_RFX_fullFact.m, MATLAB, 275 lines
Code/fMRI/e_RFX_pairedTtest_guiltEffect.m, MATLAB, 109 lines
Code/fMRI/f_apply_YuKoban_guilt_signature_map.m, MATLAB, 87 lines, 1 match
Code/master_behavAnalysis.m, MATLAB, 1,351 lines, 3 matches
Code/master_behavAnalysis_revised.m, MATLAB, 1,385 lines, 7 matches
Code/master_fMRI_showResults.m, MATLAB, 302 lines, 2 matches
Code/socialDecisionRespo_fMRI.m, MATLAB, 634 lines, 2 matches
LICENSE, License, 21 lines
README.md, Text, 30 lines

At the source: archive.softwareheritage.org/swh:1:dir:ffdb46a05612c94626e7dae978b0d5a32f577529;origin=https://github.com/BonnSocialNeuroscienceUnit/ResponsibilityExperiment;visit=swh:1:snp:6670a774dba086cd3a7a09fbcb2af6fa92fef0d6;anchor=swh:1:rev:11854fee85b7b75e23b24cbf98b284c44f6de94d

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;
396 scripts, each with its path and the digest of its content;
27 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

Datasets cited

doi:10.18112/openneuro.ds005588.v1.0.0, at OpenNeuro; found in “Data availability”

Data availability

All behavioural data and group fMRI results maps needed to reproduce all the results and the figures of this study are freely available at GitHub (https://github.com/BonnSocialNeuroscienceUnit/ResponsibilityExperiment) (copy archived at Schultz, 2026). The entire raw dataset (anonymous data) is publicly available on OpenNeuro (https://doi.org/10.18112/openneuro.ds005588.v1.0.0). All the code used to replicate the experiment, analyse the data and produce the figures of this study is freely available at GitHub (https://github.com/BonnSocialNeuroscienceUnit/ResponsibilityExperiment) (Schultz, 2026).

The following previously published dataset was used:

Schultz J. 2024. SoDec - Responsibility fMRI experiment dataset. OpenNeuro.

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, pages, dates, 6 authors, 1 keyword, 14 MeSH terms, 1 funder, 70 references, 7 RRIDs.

Cite

This paper

Gädeke, M., Willems, T. E., Ahmed, O. S., Weber, B., Hurlemann, R., & Schultz, J. (2026). Contributions of insula and superior temporal sulcus to interpersonal guilt and responsibility in social decisions. eLife, 14, RP105391. https://doi.org/10.7554/elife.105391

BibTeX

@article{gadeke2026contributions, author = {Gädeke, Maria and Willems, Tom Eric and Ahmed, Omar Salah and Weber, Bernd and Hurlemann, Rene and Schultz, Johannes}, title = {{Contributions of insula and superior temporal sulcus to interpersonal guilt and responsibility in social decisions}}, journal = {eLife}, year = {2026}, month = mar, volume = {14}, pages = {RP105391}, publisher = {eLife Sciences Publications, Ltd}, issn = {2050-084X}, doi = {10.7554/elife.105391}, url = {https://doi.org/10.7554/elife.105391}, pmid = {41873443}, pmcid = {PMC13012722} }

RIS

TY - JOUR AU - Gädeke, Maria AU - Willems, Tom Eric AU - Ahmed, Omar Salah AU - Weber, Bernd AU - Hurlemann, Rene AU - Schultz, Johannes TI - Contributions of insula and superior temporal sulcus to interpersonal guilt and responsibility in social decisions T2 - eLife J2 - eLife PY - 2026 DA - 2026/03/24 VL - 14 SP - RP105391 SN - 2050-084X PB - eLife Sciences Publications, Ltd DO - 10.7554/elife.105391 UR - https://doi.org/10.7554/elife.105391 LA - en ER -

CSL-JSON

{ "id": "10.7554/elife.105391", "type": "article-journal", "title": "Contributions of insula and superior temporal sulcus to interpersonal guilt and responsibility in social decisions", "container-title": "eLife", "author": [ { "family": "Gädeke", "given": "Maria" }, { "family": "Willems", "given": "Tom Eric" }, { "family": "Ahmed", "given": "Omar Salah" }, { "family": "Weber", "given": "Bernd" }, { "family": "Hurlemann", "given": "Rene" }, { "family": "Schultz", "given": "Johannes" } ], "container-title-short": "eLife", "volume": "14", "page": "RP105391", "DOI": "10.7554/elife.105391", "PMID": "41873443", "PMCID": "PMC13012722", "ISSN": "2050-084X", "publisher": "eLife Sciences Publications, Ltd", "URL": "https://doi.org/10.7554/elife.105391", "language": "en", "issued": { "date-parts": [ [ 2026, 3, 24 ] ] } }

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

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Add the badge to the code's README

If you are an author or a maintainer of this paper's code, you can add this badge. It says what OSCR knows of the paper (its code, its validated tracing map) and links the code to this page. Paste one of these snippets under the README's title. Keep the two marks around it, so that the badge can be replaced in place and is never added twice. OSCR changes nothing in your repository.

Markdown

<!-- oscr-badge:start -->
[![OSCR](https://openscicode.org/badge/doi/10.7554/elife.105391.svg)](https://openscicode.org/badge/doi/10.7554/elife.105391)
<!-- oscr-badge:end -->

reStructuredText

.. oscr-badge:start

.. image:: https://openscicode.org/badge/doi/10.7554/elife.105391.svg
   :target: https://openscicode.org/badge/doi/10.7554/elife.105391
   :alt: OSCR

.. oscr-badge:end

HTML

<!-- oscr-badge:start -->
<a href="https://openscicode.org/badge/doi/10.7554/elife.105391"><img src="https://openscicode.org/badge/doi/10.7554/elife.105391.svg" alt="OSCR"></a>
<!-- oscr-badge:end -->

On GitHub, open the README, choose Edit (the pencil), paste the snippet under the title, then "Commit changes".

canlab/neuroimaging_pattern_masks on GitHub (add a README there)

If you choose to, you can have the badge kept up to date for you, with oscr badge --write from the command line or with the GitHub Action in your repository's workflows. Every other form and option (a card, AsciiDoc, LaTeX, shields.io) is on the badge page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Report a mismatch between the code and the paper · Report a code error (signed in)

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Report a reproduction failure (signed in)

Versions of its code: the releases of its code tied to a version of this paper (the preprint, the accepted manuscript, the version of record…), each with the tracing map versioned with it.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.