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individual_correlation_main.m
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individual_correlation_main.m
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function alldat = individual_correlation_main(Gsq, sz)
% 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
close all; clc;
addpath(genpath('~/code/Tools'));
global mypath datasets datasetnames colors
cnt = 1;
if ~exist('Gsq', 'var'), Gsq = 0; end
if ~exist('sz', 'var'), sz = 0; end
% ============================================ %
% ONE LARGE PLOT WITH PANEL FOR EACH DATASET
% ============================================ %
doText = false;
switch sz
case 1
whichmdls = ['stimcodingst'];
case 0
whichmdls = ['stimcoding'];
end
% color in different grouos
tmpcolors = cbrewer('qual', 'Paired', 10);
transitioncolors = [[0.5 0.5 0.5]; tmpcolors([7 9], :)];
meancolors = [0 0 0; tmpcolors([8 10], :)];
markers = {'o', 'v', '^'}; %also indicate with different markers
close all;
for d = length(datasets):-1:1
disp(datasets{d});
% colors = [8 141 165; 141 165 8; 150 150 150] ./ 256;
if Gsq,
results = readtable(sprintf('%s/summary/%s/allindividualresults_Gsq.csv', mypath, datasets{d}));
else
results = readtable(sprintf('%s/summary/%s/allindividualresults.csv', mypath, datasets{d}));
end
results = results(results.session == 0, :);
allresults = struct(); alltitles = {};
switch datasets{d}
case {'Bharath_fMRI', 'Anke_MEG', 'Anke_merged', 'Anke_2afc_sequential'}
% DOES THIS DATASET HAVE MULTIPLE TRANSITION PROBABILITIES?
% THEN PLOT THESE SEPARATELY
% use the stimcoding difference only from alternating
allresults(1).z_prevresp = results.(['z_1_50_0__' whichmdls 'dczprevresp']) - results.(['z_2_50_0__' whichmdls 'dczprevresp']);
allresults(1).v_prevresp = results.(['dc_1_50_0__' whichmdls 'dczprevresp']) - results.(['dc_2_50_0__' whichmdls 'dczprevresp']);
allresults(1).criterionshift = results.repetition_neutral;
allresults(1).subjnr = results.subjnr;
alltitles{1} = {datasetnames{d}{1}, '- Neutral'};
allresults(1).marker = markers{1};
allresults(1).meancolor = meancolors(1, :);
allresults(1).scattercolor = transitioncolors(1, :);
allresults(2).z_prevresp = results.(['z_1_20_0__' whichmdls 'dczprevresp']) - results.(['z_2_20_0__' whichmdls 'dczprevresp']);
allresults(2).v_prevresp = results.(['dc_1_20_0__' whichmdls 'dczprevresp']) - results.(['dc_2_20_0__' whichmdls 'dczprevresp']);
allresults(2).criterionshift = results.repetition_alternating;
allresults(2).subjnr = results.subjnr;
alltitles{2} = {datasetnames{d}{1}, '- Alternating'};
allresults(2).marker = markers{2};
allresults(2).meancolor = meancolors(2, :);
allresults(2).scattercolor = transitioncolors(2, :);
allresults(3).z_prevresp = results.(['z_1_80_0__' whichmdls 'dczprevresp']) - results.(['z_2_80_0__' whichmdls 'dczprevresp']);
allresults(3).v_prevresp = results.(['dc_1_80_0__' whichmdls 'dczprevresp']) - results.(['dc_2_80_0__' whichmdls 'dczprevresp']);
allresults(3).criterionshift = results.repetition_repetitive;
allresults(3).subjnr = results.subjnr;
alltitles{3} = {datasetnames{d}{1}, '- Repetitive'};
allresults(3).marker = markers{3};
allresults(3).meancolor = meancolors(3, :);
allresults(3).scattercolor = transitioncolors(3, :);
otherwise
if sz == 1,
results.z_prevresp = ...
results.z_1__stimcodingdczprevrespst - results.z_2__stimcodingdczprevrespst;
results.v_prevresp = ...
results.dc_1__stimcodingdczprevrespst - results.dc_2__stimcodingdczprevrespst;
else
try
% use the stimcoding difference
results.z_prevresp = ...
results.(['z_1__' whichmdls 'dczprevresp']) - results.(['z_2__' whichmdls 'dczprevresp']);
results.v_prevresp = ...
results.(['dc_1__' whichmdls 'dczprevresp']) - results.(['dc_2__' whichmdls 'dczprevresp']);
catch
results.z_prevresp = ...
results.(['z_1_0__' whichmdls 'dczprevresp']) - results.(['z_2_0__' whichmdls 'dczprevresp']);
results.v_prevresp = ...
results.(['dc_1_0__' whichmdls 'dczprevresp']) - results.(['dc_2_0__' whichmdls 'dczprevresp']);
end
end
results.criterionshift = results.repetition;
% assign to structure
allresults(1).z_prevresp = results.z_prevresp;
allresults(1).v_prevresp = results.v_prevresp;
allresults(1).criterionshift = results.criterionshift;
allresults(1).marker = markers{1};
allresults(1).meancolor = meancolors(1, :);
allresults(1).scattercolor = transitioncolors(1, :);
alltitles{1} = {datasetnames{d}{1} datasetnames{d}{2}}; % use only the dataset title
end
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;
try
ss = suplabel(cat(2, datasetnames{d}{1}, ' ', datasetnames{d}{2}), 't');
catch
ss = suplabel(datasetnames{d}{1}, 't');
end
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, :), 'ycolor', 'k');
xlabel(sp2, 'History shift in v_{bias}');
set(sp2, 'xcolor', colors(2, :), 'ycolor', 'k');
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
tt = title(txt, 'fontweight', 'normal', 'fontsize', 6, 'horizontalalignment', 'left');
tt.Position(2) = tt.Position(2) - 0.008;
end
tightfig;
if Gsq,
print(gcf, '-dpdf', sprintf('~/Data/serialHDDM/figure1c_Gsq_modelfree_stimcoding_sz%d_d%d.pdf', sz, d));
else
print(gcf, '-dpdf', sprintf('~/Data/serialHDDM/figure1c_HDDM_modelfree_stimcoding_sz%d_d%d.pdf', sz, d));
%print(gcf, '-depsc', sprintf('~/Data/serialHDDM/figure1c_HDDM_modelfree_stimcoding_d%d.eps', d));
end
for a = 1:length(allresults),
% 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).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));
alldat(cnt).datasets = datasets{d};
alldat(cnt).datasetnames = alltitles{a};
% also add the difference in correlation, steigers test
[r,p,rlo,rup] = spearmans(allresults(a).v_prevresp, allresults(a).z_prevresp);
[rhodiff, rhodiffci, pval] = rddiffci(alldat(cnt).corrz, alldat(cnt).corrv, ...
r, numel(allresults(a).v_prevresp), 0.05);
alldat(cnt).corrdiff = rhodiff;
alldat(cnt).corrdiff_ci = rhodiffci;
alldat(cnt).pdiff = pval;
% plotting layout for forestPlot
alldat(cnt).marker = allresults(a).marker;
alldat(cnt).scattercolor = allresults(a).scattercolor;
alldat(cnt).meancolor = allresults(a).meancolor;
cnt = cnt + 1;
end
end
end