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individual_correlation_repeaters_vs_alternators.m
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individual_correlation_repeaters_vs_alternators.m
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function alldat = individual_correlation_repeaters_vs_alternators
% Code to fit the history-dependent drift diffusion models as described in
% Urai AE, de Gee JW, Tsetsos K, Donner TH (2019) Choice history biases subsequent evidence accumulation. eLife, in press.
%
% MIT License
% Copyright (c) Anne Urai, 2019
% anne.urai@gmail.com
% from the csv table, make an overview of repetition behaviour
% get a huge list with values for each participant
% can then work with this dataframe
close all; clc;
addpath(genpath('~/code/Tools'));
global mypath datasets datasetnames colors
cnt = 1;
% ============================================ %
% ONE LARGE PLOT WITH PANEL FOR EACH DATASET
% ============================================ %
doText = false;
close all;
for d = length(datasets):-1:1
disp(datasets{d});
results = readtable(sprintf('%s/summary/%s/allindividualresults.csv', mypath, datasets{d}));
results = results(results.session == 0, :);
allresults = struct(); alltitles = {};
if all(results.repetition >= 0.5),
continue;
end
% ============================================ %
% RECODE INTO HISTORY SHIFT POINT ESTIMATES
% ============================================ %
% % use the stimcoding difference
% assert(1==0)
% try
% results.z_prevresp = ...
% results.(['z_1__stimcodingdczprevresp']) - results.(['z_2__stimcodingdczprevresp']);
% results.v_prevresp = ...
% results.(['dc_1__stimcodingdczprevresp']) - results.(['dc_2__stimcodingdczprevresp']);
% catch
% results.z_prevresp = ...
% results.(['z_1_0__stimcodingdczprevresp']) - results.(['z_2_0__stimcodingdczprevresp']);
% results.v_prevresp = ...
% results.(['dc_1_0__stimcodingdczprevresp']) - results.(['dc_2_0__stimcodingdczprevresp']);
% end
try
results.z_prevresp_repeaters = ...
results.z_1_0_1_0__stimcodingdczprevrespgroupsplit - results.z_1_0_2_0__stimcodingdczprevrespgroupsplit;
results.z_prevresp_alternators = ...
results.z_2_0_1_0__stimcodingdczprevrespgroupsplit - results.z_2_0_2_0__stimcodingdczprevrespgroupsplit;
results.v_prevresp_repeaters = ...
results.dc_1_0_1_0__stimcodingdczprevrespgroupsplit - results.dc_1_0_2_0__stimcodingdczprevrespgroupsplit;
results.v_prevresp_alternators = ...
results.dc_2_0_1_0__stimcodingdczprevrespgroupsplit - results.dc_2_0_2_0__stimcodingdczprevrespgroupsplit;
catch
results.z_prevresp_repeaters = ...
results.z_1_1__stimcodingdczprevrespgroupsplit - results.z_1_2__stimcodingdczprevrespgroupsplit;
results.z_prevresp_alternators = ...
results.z_2_1__stimcodingdczprevrespgroupsplit - results.z_2_2__stimcodingdczprevrespgroupsplit;
results.v_prevresp_repeaters = ...
results.dc_1_1__stimcodingdczprevrespgroupsplit - results.dc_1_2__stimcodingdczprevrespgroupsplit;
results.v_prevresp_alternators = ...
results.dc_2_1__stimcodingdczprevrespgroupsplit - results.dc_2_2__stimcodingdczprevrespgroupsplit;
end
colorss = cbrewer('qual', 'Paired', 10);
% assign to structure
allresults(1).z_prevresp = results.z_prevresp_repeaters;
allresults(1).v_prevresp = results.v_prevresp_repeaters;
allresults(1).criterionshift = results.repetition;
allresults(1).criterionshift(allresults(1).criterionshift < 0.5) = NaN;
allresults(1).marker = 'o'; % purple
allresults(1).meancolor = colorss(10, :);
allresults(1).scattercolor = colorss(9, :);
% also after error choices
allresults(2).z_prevresp = results.z_prevresp_alternators;
allresults(2).v_prevresp = results.v_prevresp_alternators;
allresults(2).criterionshift = results.repetition;
allresults(2).criterionshift(allresults(2).criterionshift > 0.5) = NaN;
allresults(2).marker = 's';
allresults(2).meancolor = colorss(8, :); % orange
allresults(2).scattercolor = colorss(7, :);
disp(datasets{d}); disp(numel(unique(results.subjnr)));
close all;
% PLOT
sp1 = subplot(4,4,1); hold on;
[rho1, tt1] = plotScatter(allresults, 'z_prevresp', 0.585, doText);
ylabel('P(repeat)');
sp2 = subplot(4,4,2); hold on;
[rho2, tt2, handles] = plotScatter(allresults, 'v_prevresp', 0.05, doText);
set(gca, 'yticklabel', []);
set(sp2, 'ylim', get(sp1, 'ylim'), 'ytick', get(sp1, 'ytick'));
% compute the difference in correlation
[rho3, pval3] = corr(cat(1, allresults(:).v_prevresp), cat(1, allresults(:).z_prevresp), ...
'rows', 'complete', 'type', 'spearman');
if pval3 < 0.05,
fprintf('warning %s: rho = %.3f, pval = %.3f \n', datasets{d}, rho3, pval3);
end
[rhodiff, ~, pval] = rddiffci(rho1,rho2,rho3,numel(~isnan( cat(1, allresults(:).criterionshift))), 0.05);
% move together
sp2.Position(1) = sp2.Position(1) - 0.08;
ss = suplabel(cat(2, datasetnames{d}{1}, ' ', datasetnames{d}{2}), 't');
set(ss, 'fontweight', 'normal');
ss.FontWeight = 'normal';
ss.Position(2) = ss.Position(2) - 0.03;
% add colored axes after suplabel (which makes them black)
xlabel(sp1, 'History shift in z');
set(sp1, 'xcolor', colors(1, :));
xlabel(sp2, 'History shift in v_{bias}');
set(sp2, 'xcolor', colors(2, :));
if doText,
%% add line between the two correlation coefficients
txt = {sprintf('\\Delta\\rho(%d) = %.3f, p = %.3f', length(find(~isnan(cat(1, allresults(:).criterionshift) )))-3, rhodiff, pval)};
if pval < 0.001,
txt = {sprintf('\\Delta\\rho(%d) = %.3f, p < 0.001', length(find(~isnan(cat(1, allresults(:).criterionshift) )))-3, rhodiff)};
end
title(txt, 'fontweight', 'normal', 'fontsize', 8, 'horizontalalignment', 'left');
end
tightfig;
print(gcf, '-dpdf', sprintf('~/Data/serialHDDM/figure1c_HDDM_modelfree_repeatersalternators_d%d.pdf', d));
for a = 1:length(allresults),
% % SAVE CORRELATIONS FOR OVERVIEW PLOT
% [r,p,rlo,rup] = corrcoef(allresults(a).z_prevresp, allresults(a).criterionshift);
% alldat(cnt).corrz = r(1,2);
% alldat(cnt).corrz_ci = [rlo(1,2) rup(1,2)];
% alldat(cnt).pz = p(1,2);
% alldat(cnt).bfz = corrbf(r(1,2), numel(allresults(a).z_prevresp));
% [r,p,rlo,rup] = corrcoef(allresults(a).v_prevresp, allresults(a).criterionshift);
% alldat(cnt).corrv = r(1,2);
% alldat(cnt).corrv_ci = [rlo(1,2) rup(1,2)];
% alldat(cnt).pv = p(1,2);
% alldat(cnt).bfv = corrbf(r(1,2), numel(allresults(a).v_prevresp));
% SAVE CORRELATIONS FOR OVERVIEW PLOT
% COMPUTE THE SPEARMANS CORRELATION AND ITS CONFIDENCE INTERVAL!
[alldat(cnt).corrz, alldat(cnt).corrz_ci, alldat(cnt).pz, alldat(cnt).bfz] = ...
spearmans(allresults(a).z_prevresp, allresults(a).criterionshift);
[alldat(cnt).corrv, alldat(cnt).corrv_ci, alldat(cnt).pv, alldat(cnt).bfv] = ...
spearmans(allresults(a).v_prevresp, allresults(a).criterionshift);
alldat(cnt).datasets = datasets{d};
alldat(cnt).datasetnames = datasetnames{d};
% alldat(cnt).datasetnames = allresults(a).title;
% also add the difference in r, Steigers test
[r,p,rlo,rup] = corrcoef(allresults(a).v_prevresp(~isnan(allresults(a).v_prevresp)),...
allresults(a).z_prevresp(~isnan(allresults(a).v_prevresp)));
[rhodiff, rhodiffci, pval] = rddiffci(alldat(cnt).corrz,alldat(cnt).corrv, ...
r(1,2), ...
sum(~isnan(allresults(a).v_prevresp)), 0.05);
assert(~isnan(pval));
alldat(cnt).corrdiff = rhodiff;
alldat(cnt).corrdiff_ci = rhodiffci;
alldat(cnt).pdiff = pval;
alldat(cnt).nsubj = numel(allresults(a).criterionshift);
alldat(cnt).marker = allresults(a).marker;
alldat(cnt).scattercolor = allresults(a).scattercolor;
alldat(cnt).meancolor = allresults(a).meancolor;
cnt = cnt + 1;
end
end
end