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analysis_all.m
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clear all; close all;
%exp_i = 1;
exp_i = 2;
if exp_i == 1
sbj_list = {'1','2','3','4','5','6','7','8','9','10', '11', '12','13','14','15','16','17','18','19','20','21','22'};
adapt_type_all = [ 1 -1 -1 -1 -1 1 1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1];
elseif exp_i == 2
sbj_list = {'1','2','3','4','5','6','7','8','9','10', '12','13','14','15','16','17','18','19','20','21','22','23'};
% adapt_type_all = -1 for everyone
end
Nsubj = length(sbj_list);
curr_dir = pwd;
Ncond = 4;
Nfiles_per_cond = 2;
if exp_i == 1
model_pred = 1;
model_pred_bm = 0;
elseif exp_i == 2
%mi select the model
mi = 1; %Bayesian insight model
% mi = 2: %prior model
% mi = 3: %insight + prior model
% mi = 4: %response bias k_choice model
% mi = 5: % %response bias + insight model
% mi = 6: %response bias k_choice_and_confidence model
model_pred = 1;
model_pred_bm = 1;
end
load(['alldata_E',num2str(exp_i),'.mat']);
Ntrials = length(alldata(1,1).stims);
%accuracy in cond Adapt-Believe
for si = 1: length(sbj_list)
acc_A_BEL(si) = sum((alldata(si,4).stims>0 & alldata(si,4).resp == 1) |...
(alldata(si,4).stims<0 & alldata(si,4).resp == 0))/ length(alldata(si,4).stims);
end
if exp_i == 1 & model_pred
load('psych_curves_fitting_m2_101_E1.mat')
params_psych_m2_all = NaN(Nsubj, Ncond,3);
for ci = 1:Ncond
params_psych_m2_all(:,ci,1) = mu_est_all(:,ci);
params_psych_m2_all(:,ci,2) = sigma_est_all(:,ci);
if ci == 1
params_psych_m2_all(:,ci,3) = lambda_est_all;
end
end
elseif exp_i == 2 & (model_pred | model_pred_bm)
load('psych_curves_fitting_m2_201_E2.mat')
params_psych_m2_all = NaN(Nsubj, Ncond,3);
for ci = 1:Ncond
params_psych_m2_all(:,ci,1) = mu_est_all(:,ci);
params_psych_m2_all(:,ci,2) = sigma_est_all(:,ci);
if ci == 1
params_psych_m2_all(:,ci,3) = lambda_est_all;
end
end
load(['params_all_models6_E2_Nsubj_', num2str(Nsubj), '.mat'])
params_bm_all = squeeze(params_fit_best_all(:,mi,:,:));
params_bm_allV = params_bm_all;
params_bm_allV(:,:,2) = exp(params_bm_all(:,:,2));% params_bm_all(:,:,2);
end
if model_pred_bm
N_samp = 500;
end
if exp_i == 2
insight = abs((squeeze(params_bm_all(:,4,4))));
end
nbinz = 11;
prop_cw_all = nan(Nsubj,Ncond,nbinz);
prop_cw_w_all = nan(Nsubj,Ncond,nbinz);
prop_cw_pr_all = NaN(Nsubj,Ncond, nbinz);
prop_cw_pred_all = NaN(Nsubj,2, Ncond, nbinz);
prop_cf_pred_all = NaN(Nsubj,2, Ncond ,nbinz);
for si = 1 : Nsubj
datac = alldata(si,:);
for ci = 1:Ncond
if exp_i == 2
rt_all_trials(si,ci,1:121) = [datac(ci).resp_times];
%compute the decision variable
mu_A = params_bm_all(si,ci,1);
sigma_A = exp(params_bm_all(si,ci,2));
prob_right = 0.5;
mu_A_loglike = params_bm_all(si,ci,4);
for sampi = 1: N_samp
x = normrnd(bsxfun(@minus, datac(ci).stims, mu_A), sigma_A);
dv_val_sampi(si,ci,1:121, sampi) = log(prob_right/(1-prob_right)) + log( (normcdf(-mu_A_loglike+0.3-x, 0, sigma_A)-normcdf(-mu_A_loglike-x, 0, sigma_A))./...
(normcdf(-mu_A_loglike-x, 0, sigma_A)- normcdf(-mu_A_loglike+(-0.3)-x,0, sigma_A)));
end
dv_val_sampi(dv_val_sampi>2000) = 2000;
dv_val_sampi(dv_val_sampi<-2000) = -2000;
dv_val(si,ci,1:121)= nanmean(squeeze(dv_val_sampi(si,ci,1:121,:)),2);
end
binz = [];
for j = 1:nbinz
binz(j) = quantile(datac(ci).stims, j/nbinz);
end
binz = [min(datac(ci).stims)*1.001 binz ];
binz_posE(si,ci,:) = (binz(2:end)+binz(1:end-1))/2;
binzz(si,ci,:) = binz;
for j = 1:(nbinz)
indo = (datac(ci).stims>binz(j) & datac(ci).stims<=binz(j+1));
indi = find(datac(ci).stims>binz(j) & datac(ci).stims<=binz(j+1) );
if length(indi)>0
prop_cw_all(si,ci,j) = nansum(datac(ci).resp(indi))/sum(~isnan(datac(ci).resp(indi)));
prop_cf_high_all(si,ci,j) = nansum(datac(ci).conf(indi))/sum(~isnan(datac(ci).conf(indi)));
rt_all(si,ci,j) = nanmedian(datac(ci).resp_times(indi)); %nanmedian(datac(ci).resp_times(indi)); % maybe nanmean? % before it was median
if exp_i == 2
dv_val_binz(si,ci,j) = median(dv_val(si,ci,indi));
end
else
prop_cw_all(si,ci,j) = nan;
prop_cf_high_all(si,ci,j) = nan;
rt_all(si,ci,j) = nan;
if exp_i == 2
dv_val_binz(si,ci,j) = nan;
end
end
indi_right = (datac(ci).resp == 1);
indi_left = (datac(ci).resp == 0);
if length(indo & indi_right)>5
rt_right(si,ci,j) = nanmedian(datac(ci).resp_times(indo & indi_right));
else
rt_right(si,ci,j) = NaN;
end
if length(indo & indi_left)>5
rt_left(si,ci,j) = nanmedian(datac(ci).resp_times(indo & indi_left));
else
rt_left(si,ci,j) = NaN;
end
end
if model_pred
prop_cw_pr_all(si,ci,:) = function_psi(binz_posE(si,ci,:),...
params_psych_m2_all(si,ci,1), params_psych_m2_all(si,ci,2), params_psych_m2_all(si,1,3));
end
% adjustments for participants in experiment 1 who saw the adaptor spiral rotating CW
if exp_i == 1 & adapt_type_all(si) == 1 & ci >= 3
binz = binz - 2*(params_psych_m2_all(si,ci,1));
binz_posE(si,ci,:) = (binz(2:end)+binz(1:end-1))/2;
binzz(si,ci,:) = binz;
params_psych_m2_all(si,ci,1) = - params_psych_m2_all(si,ci,1);
prop_cw_pr_all(si,ci,:) = function_psi(binz_posE(si,ci,:),...
params_psych_m2_all(si,ci,1), params_psych_m2_all(si,ci,2), params_psych_m2_all(si,1,3));
end
if model_pred_bm
% depends on the model, figure out which predict we use
%Predict_bm_alll(params,s, resp,conf,modi, mi,N_samp)
prc = Predict_bm_alll(squeeze(params_bm_all(si,ci,1:end)),squeeze(binz_posE(si,ci,:))', datac(ci).resp, datac(ci).conf, mi,1,N_samp);
prop_cw_pred_all(si,mi,ci,:) = prc(:,1)+ prc(:,2);
prop_cf_pred_all(si,mi,ci,:) = prc(:,1)+ prc(:,3);
end
end
end
%%
colorz = [41 138 8; 152 191 100; 0 0 255; 137 195 255 ]/255;
colorz_shade = (colorz + 3*repmat([228 228 228], 4, 1)/255)/4;
marginsa = [0.08 0.03 0.09 0.1]; %MARGINS = [LEFT RIGHT BOTTOM TOP]
guttera = [0.06 0.06];
sz = [7 6 5 4];
eb_w=0.01;%errorbar width
eb_t=1.2; %errorbar line thickness
tlen1 = 0.02;
tlen2 = 0.02;
fontsz = 12;
binz_pos = binz_posE ;%/0.0167;%settings.ifi;
dashed_linez = 0;
indi_sel = 1:1:Nsubj;
msz_all = [5.5 5.5 5.5 5.5];
%%%%%%%%%%%%%% PSYCH CURVES
figure(3)
set(gcf, 'Position', [100 100 740 400])
tight_subplot(2,9,exp_i,[1 2 3], guttera, marginsa)
for ci = 1:Ncond
he(ci) = fill([mean(squeeze(binz_pos(indi_sel,ci,:)),1) mean(squeeze(binz_pos(indi_sel,ci,end:-1:1)),1)], ...
[mean(squeeze(prop_cw_pr_all(indi_sel,ci,:)),1)-std(squeeze(prop_cw_pr_all(indi_sel,ci,:)),1)./sqrt(length(indi_sel))...
fliplr(mean(squeeze(prop_cw_pr_all(indi_sel,ci,:)),1) + std(squeeze(prop_cw_pr_all(indi_sel,ci,:)),1)./sqrt(length(indi_sel))) ],colorz_shade(ci,:), 'EdgeColor', 'None'); hold on;
plot(mean(squeeze(binz_pos(indi_sel,ci,:)),1), nanmean(squeeze(prop_cw_all(indi_sel,ci,:)),1),'o','MarkerSize', msz_all(ci),'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor', colorz(ci,:) ); hold on;
errorbar(mean(squeeze(binz_pos(indi_sel,ci,:)),1), nanmean(squeeze(prop_cw_all(indi_sel,ci,:)),1), nanstd(squeeze(prop_cw_all(indi_sel,ci,:)),1)/sqrt(length(indi_sel)), 'Color', colorz(ci,:),'Linestyle', 'none','Linewidth',1.5,'CapSize',0); hold on;
end
box off
xlim_min = min(min(min(binz_pos)));
xlim_max = max(max(max(binz_pos)));
ylim_min = 0;
ylim_max = 1.01;
if exp_i == 1
xlim_min = -0.5; xlim_max = 0.5;
elseif exp_i == 2
xlim_min = -0.3; xlim_max = 0.3;
end
xlim([xlim_min xlim_max])
ylim([ylim_min ylim_max])
set(gca, 'FontSize', fontsz)
plot(zeros(1,nbinz), linspace(0,1,nbinz), '--k'); hold on;
box off
set(gca, 'tickdir', 'out')
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',[tlen1 tlen2])
set(gca, 'ytick', [0:0.25:1])
set(gca, 'xtick',[-0.5:0.1:0.5])
if exp_i == 2
xlabel('test stimulus speed clockwise (a.u.)', 'FontName', 'Helvetica', 'FontSize', fontsz)
end
ylabel('proportion response clockwise','FontName', 'Helvetica', 'FontSize', fontsz)
psych_pars_list = {'\mu', '\sigma', '\lambda'};
%since we have 4 planned comparisons
alpha_sidak = 1-(1-0.05).^(1/4); % 0.0127
mszi = 2;
pars_scatter = 0.15;
ylim_minB = -0.14;
nboot = 500;%5000 for final results %for 95% bootstrapped confidence intervals
ci_bnd_low = 0.025;
ci_bnd_high = 0.975;
if exp_i == 1 | exp_i == 2
for pi = 1:2
tight_subplot(2,9,exp_i,[3+2*pi-1 4+2*pi-1], guttera, marginsa)
for ci = 1: 4
paraz = squeeze(params_psych_m2_all(indi_sel,ci,pi));
for kk = 1:nboot
sample = randsample(paraz,length(indi_sel),1);
paraz_samples(kk) = nanmedian(sample);
end
bci_paraz = [quantile(paraz_samples,ci_bnd_low); quantile(paraz_samples,ci_bnd_high)];
hbp(ci)=bar(5*(pi-1)+ci, median(paraz), 'FaceColor', 'None','EdgeColor',colorz(ci,:)); hold on;
plot((5*(pi-1)+ci)*ones(1, length(indi_sel))- pars_scatter+ 2*pars_scatter*rand(1,length(indi_sel)), params_psych_m2_all(indi_sel,ci,pi), 'o','MarkerSize',mszi, 'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor', colorz(ci,:)); hold on;
he(ci) = errorbar(5*(pi-1)+ci, median(paraz), median(paraz)- bci_paraz(1),bci_paraz(2)-median(paraz),'CapSize',0); hold on;
set(he(ci),'Color', [0 0 0], 'Linewidth',2)
errorbarT(he(ci),eb_w,eb_t)
set(hbp(ci), 'BarWidth', 0.8, 'Linewidth', 1)
end
if pi == 1
ylim([-0.3 0.3])
else
ylim([0 0.3])
end
box off
set(gca, 'tickdir', 'out')
set(gca, 'xtick', [])
set(gca,'ticklength', [tlen1 tlen2])
set(gca, 'xticklabels', {})
%title(psych_pars_list{pi})
xlim([5*(pi-1)+0.2 5*(pi-1)+4.8])
set(gca, 'xtick', [2.5 7.5 10.5])
if pi == 1
ylabel('fitted parameter values','FontName', 'Helvetica', 'FontSize', fontsz)
end
set(gca, 'FontSize', fontsz)
end
end
if exp_i == 1 | exp_i == 2 % show median and 95% bootstrapped confidence intervals
for pi = 3 % psych curve parameter 3
ci = 1;
paraz = squeeze(params_psych_m2_all(indi_sel,ci,pi));
for kk = 1:nboot
sample = randsample(paraz,length(indi_sel),1);
paraz_samples(kk) = nanmedian(sample);
end
bci_paraz = [quantile(paraz_samples,ci_bnd_low); quantile(paraz_samples,ci_bnd_high)];
tight_subplot(2,9,exp_i,[8], guttera, marginsa)
hbp(ci)=bar(5*(pi-1)+ci, median(paraz), 'FaceColor', 'None','EdgeColor',[0.5 0.5 0.5]); hold on;
plot((5*(pi-1)+ci)* ones(1, length(indi_sel))- pars_scatter + 2*pars_scatter*rand(1,length(indi_sel)), params_psych_m2_all(indi_sel,ci,pi), 'o','MarkerSize',mszi, 'MarkerFaceColor', [0.5 0.5 0.5], 'MarkerEdgeColor', [0.5 0.5 0.5]); hold on;
he(ci) = errorbar(5*(pi-1)+ci, median(paraz),median(paraz)- bci_paraz(1),bci_paraz(2)-median(paraz) ,'CapSize',0); hold on;
set(he(ci),'Color', [0 0 0], 'Linewidth',2)
errorbarT(he(ci),eb_w,eb_t)
set(hbp(ci), 'BarWidth', 0.8, 'Linewidth', 1)
set(gca, 'FontSize', fontsz)
box off
set(gca, 'tickdir', 'out')
set(gca,'ticklength', [tlen1 tlen2])
set(gca, 'xticklabels', {})
ylim([0 0.21])
end
end
[p,h, stats] = signrank(params_psych_m2_all(:,3,1), params_psych_m2_all(:,1,1));
[effectSize,pValue,CI,bootstrapCI] = BCa_CI_Sr(params_psych_m2_all(:,3,1),params_psych_m2_all(:,1,1),0.05,100000);
%sig_level = 0.05, numBootstraps= 100000
% report bias-corrected and accelerated values : CI
compensation = -(squeeze(params_psych_m2_all(:,3,1))-squeeze(params_psych_m2_all(:,4,1)));
[p,h, stats] = signrank(compensation(indi_sel))
[effectSize,pValue,CI,bootstrapCI] = BCa_CI_Sr(compensation(indi_sel),ones(22,1),0.05,100000);
compensation_baseline = (squeeze(params_psych_m2_all(:,1,1))-squeeze(params_psych_m2_all(:,2,1)));
[p,h] = signrank(compensation_baseline(indi_sel));
[effectSize,pValue,CI,bootstrapCI] = BCa_CI_Sr(compensation_baselin(indi_sel),ones(22,1),0.05,100000);
compensation_norm = compensation./abs(squeeze(params_psych_m2_all(:,3,1)))
[p, h, stats ]= signrank(compensation_norm(indi_sel)-1);
[effectSize,pValue,CI,bootstrapCI] = BCa_CI_Sr(compensation_norm(indi_sel),ones(22,1),0.05,100000);
mid_green = (colorz(1,:)+colorz(2,:))/2;
mid_blue = (colorz(3,:)+colorz(4,:))/2;
for kk = 1:nboot
sample = randsample(compensation_norm,length(indi_sel),1);
comp_norm_samples(kk) = nanmedian(sample);
end
bci_comp_norm = [quantile(comp_norm_samples,ci_bnd_low); quantile(comp_norm_samples,ci_bnd_high)];
tight_subplot(2,9,exp_i,[9], guttera, marginsa)
plot(linspace(0.5,2.5,20), zeros(1,20), '--k'); hold on;
plot(1*ones(1,length(indi_sel))-1/8+1/4*rand(1,length(indi_sel)),compensation_norm(indi_sel), 'o','MarkerSize', mszi, 'MarkerEdgeColor',mid_blue,'MarkerFaceColor',mid_blue); hold on;
xlim([0.5 1.5])
b22 = bar(1, median(squeeze(compensation_norm(indi_sel))), 'FaceColor', 'None','EdgeColor',mid_blue); hold on;
set(b22, 'BarWidth', 0.4, 'Linewidth', 1)
hee2 = errorbar(1, median(squeeze(compensation_norm(indi_sel))),median(squeeze(compensation_norm(indi_sel)))- bci_comp_norm(1),bci_comp_norm(2)-median(squeeze(compensation_norm(indi_sel))) ,'CapSize',0); hold on;
set(hee2,'Color', [0 0 0], 'Linewidth',2)
errorbarT(hee2,eb_w,eb_t)
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',[tlen1 tlen2])
box off
set(gca, 'tickdir', 'out')
set(gca, 'xtick',[1 2])
set(gca, 'xticklabel',{ ''},'FontName', 'Helvetica', 'FontSize', fontsz)
ylim([-2 6])
ylabel(' MAE compensation', 'FontName', 'Helvetica', 'FontSize', fontsz)
[h,p,stats] = ttest(compensation(indi_sel), compensation_baseline(indi_sel))
plot(linspace(0.5,2.5,10),1*ones(1,10), '-k', 'LineWidth',1.5); hold on;
%%
psname =['Fig3_manuscript_nboot_', num2str(nboot),'_exp_',num2str(exp_i),'.pdf']
%print_pdf(psname)
%% prep for confidence plots for figure 4 and supp, based on Maldonado Moscoso et al 2020
gauss_fit_just_mu = @(mu) sum((a_val*exp(-((s0-mu)/c_val).^2)-s1).^2);
options = []; % Reset the OPTIONS struct
options.UncertaintyHandling = 1; % Tell BADS that the objective is noisy
options.NoiseSize = 1;
LB = -2; UB = 2; PLB = -0.2; PUB = 0.2;
LB_all = [0 -0.2 0.01]; UB_all = [10 0.2 10];
for ci = 1: Ncond
binz_all_condi = [];
prop_cf_high_all_condi = [];
rt_all_condi = [];
for si = 1: Nsubj
binz_all_condi = [binz_all_condi; squeeze(binz_posE(si,ci,:))];
prop_cf_high_all_condi = [prop_cf_high_all_condi; 1-squeeze(prop_cf_high_all(si,ci,:))];
rt_si_ci = squeeze(rt_all(si,ci,:));
rt_si_ci = (rt_si_ci - nanmean(rt_si_ci))./nanstd(rt_si_ci);
rt_all_condi = [rt_all_condi; rt_si_ci];
indi_cf = find(squeeze(prop_cf_high_all(si,ci,:))== min(squeeze(prop_cf_high_all(si,ci,:))));
if length(indi_cf) == 1
mu_estim_cf(si,ci) = binz_posE(si,ci,indi_cf(1));
else
mu_estim_cf(si,ci)= mean(binz_posE(si,ci, indi_cf));
end
indi_rt = find(squeeze(rt_all(si,ci,:))== max(squeeze(rt_all(si,ci,:))));
mu_estim_rt(si,ci) = binz_posE(si,ci,indi_rt(1));
end
end
mu_cf_fit_all = mu_estim_cf;
mu_rt_fit_all = mu_estim_rt;
delta_mu_estim_cf_MAE = mu_estim_cf(:,3)-mu_estim_cf(:,1);
delta_mu_estim_rt_MAE = mu_estim_rt(:,3)-mu_estim_rt(:,1);
MAE_strength = -(squeeze(params_psych_m2_all(:,3,1))-squeeze(params_psych_m2_all(:,1,1)));
[r_cf_MAE, p_cf_MAE]= corr(delta_mu_estim_cf_MAE, MAE_strength, 'type', 'Spearman')
[r_rt_MAE, p_rt_MAE]= corr(delta_mu_estim_rt_MAE, MAE_strength, 'type', 'Spearman')
delta_mu_estim_cf_compensation = mu_estim_cf(:,4)-mu_estim_cf(:,3);
delta_mu_estim_rt_compensation = mu_estim_rt(:,4)-mu_estim_rt(:,3);
[r_cf_compensation, p_cf_compensation]= corr(delta_mu_estim_cf_compensation,compensation, 'type', 'Spearman')
[r_rt_compensation, p_rt_compensation]= corr(delta_mu_estim_rt_compensation, compensation, 'type', 'Spearman')
%% Correlations from mus
%close all;
figure(12)
set(gcf, 'Position', [100 100 500 340])
marginsa3 = [0.1200 0.100 0.1400 0.090]; %MARGINS = [LEFT RIGHT BOTTOM TOP]
guttera3 = [ 0.1 0.16];
if exp_i == 1
limis = [- 0.5 0.6];
else
limis = [-0.2 0.3];
end
msz = 3;
for ci = 3: Ncond
tight_subplot(2,2,1,ci-2, guttera3, marginsa3)
plot(squeeze(params_psych_m2_all(indi_sel,ci, 1)), squeeze(mu_cf_fit_all(indi_sel,ci)),'o', 'MarkerSize', msz, 'Color', colorz(ci,:), 'MarkerFaceColor',colorz(ci,:) ); hold on;
axis equal
lsline
box off
set(gca, 'tickdir', 'out')
[r_cf(ci), p_cf(ci)]= corr(squeeze(params_psych_m2_all(indi_sel,ci, 1)), squeeze(mu_cf_fit_all(indi_sel,ci)), 'type', 'Spearman');
title(['\rho =', num2str(r_cf(ci), '%.2f'), ', p = ', num2str(p_cf(ci), '%.2f')]); hold on;
xlim(limis)
ylim(limis)
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',2*[tlen1 tlen2])
if ci == 3
ylabel(' \mu from the confidence curves', 'FontName', 'Helvetica','FontSize', fontsz)
end
tight_subplot(2,2,2,ci-2, guttera3, marginsa3)
plot(squeeze(params_psych_m2_all(indi_sel,ci, 1)), squeeze(mu_rt_fit_all(indi_sel,ci)),'o','MarkerSize', msz, 'Color',colorz(ci,:), 'MarkerFaceColor',colorz(ci,:) ); hold on;
axis equal
lsline
box off
set(gca, 'tickdir', 'out')
[r_rt(ci), p_rt(ci)]= corr(squeeze(params_psych_m2_all(indi_sel,ci, 1)), squeeze(mu_rt_fit_all(indi_sel,ci)), 'type', 'Spearman');
title(['\rho =', num2str(r_rt(ci), '%.2f'), ', p = ', num2str(p_rt(ci), '%.2f')]); hold on;
xlabel(' \mu from the psychometric curve', 'FontName', 'Helvetica','FontSize', fontsz)
xlim(limis)
ylim(limis)
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',2*[tlen1 tlen2])
if ci == 3
ylabel(' \mu from the RT curves', 'FontName', 'Helvetica','FontSize', fontsz)
end
end
%%
psname = ['Corrs_btwn_mu_based_on_peaks_averaged_exp_', num2str(exp_i), '.pdf']
%print_pdf(psname)
%%
% mu estim from the 3 curves
mus_psych = squeeze(params_psych_m2_all(:,:, 1));
% cond 3:
[p,h] = signrank(mus_psych(:,3), mu_cf_fit_all(:,3)); % 0.49
[effectSize,pValue,CI,bootstrapCI] = BCa_CI_Sp(mus_psych(:,3),mu_cf_fit_all(:,3),0.05,100000)
[p,h] = signrank(mus_psych(:,3), mu_rt_fit_all(:,3)); % 0.98
[effectSize,pValue,CI,bootstrapCI] = BCa_CI_Sp(mus_psych(:,3),mu_rt_fit_all(:,3),0.05,100000)
% cond 4:
[p,h] = signrank(mus_psych(:,4), mu_cf_fit_all(:,4)); % 0.306
[effectSize,pValue,CI,bootstrapCI] = BCa_CI_Sp(mus_psych(:,4),mu_cf_fit_all(:,4),0.05,100000)
[p,h] = signrank(mus_psych(:,4), mu_rt_fit_all(:,4)); % 0.92
[effectSize,pValue,CI,bootstrapCI] = BCa_CI_Sp(mus_psych(:,4),mu_rt_fit_all(:,4),0.05,100000)
for ci = 1:4
[p_mus_ci_psych_cf(ci),h] = signrank(mus_psych(:,ci), mu_cf_fit_all(:,ci));%signrank(mus_psych(:,ci), mu_cf_fit_all(:,ci));
[p_mus_ci_psych_rt(ci),h] = signrank(mus_psych(:,ci), mu_rt_fit_all(:,ci));%signrank(mus_psych(:,ci), mu_rt_fit_all(:,ci));
end
%% confidence and RT track with psychometric curves: confidence curves paper
%close all;
%hf2 = figure(2)
clear h;
figure(4)
set(gcf, 'Position', [100 100 600 800])
linewi = 1;%1.1;%0.9;
sz_dot = 2;
dashed_linez = 0;
nboot = 5000;
if exp_i == 1
tight_subplot(4,3,1,1, guttera, marginsa)
for ci = 1:Ncond
h(ci) = plot(mean(squeeze(binz_pos(indi_sel,ci,:)),1), nanmean(squeeze(prop_cw_all(indi_sel,ci,:)),1),'o','MarkerSize', msz_all(ci),'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor', colorz(ci,:)); hold on;
errorbar(mean(squeeze(binz_pos(indi_sel,ci,:)),1), nanmean(squeeze(prop_cw_all(indi_sel,ci,:)),1), nanstd(squeeze(prop_cw_all(indi_sel,ci,:)),1)/sqrt(length(indi_sel)), 'Color', colorz(ci,:), 'Linewidth', linewi, 'CapSize',0); hold on;
if dashed_linez
plot(nanmean(params_psych_m2_all(indi_sel,ci,1)) *ones(1,10), linspace(0,1,10),'--', 'Color', colorz(ci,:),'Linewidth', linewi ); hold on;
plot(linspace(-0.5,nanmean(params_psych_m2_all(indi_sel,ci,1)),10), 0.5*ones(1,10),'--', 'Color', colorz(ci,:),'Linewidth',linewi ); hold on;
end
end
box off
if exp_i == 1
xlim_min = -0.5; xlim_max = 0.5;
elseif exp_i == 2
xlim_min = -0.3; xlim_max = 0.3;
end
ylim_min = 0;
ylim_max = 1.01;
xlim([xlim_min xlim_max])
ylim([ylim_min ylim_max])
plot(zeros(1,nbinz), linspace(0,1,nbinz), '--k'); hold on;
box off
set(gca, 'xtick',[-0.5:0.1:0.5])
set(gca, 'ytick', [0:0.25:1])
set(gca, 'tickdir', 'out')
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',[tlen1 tlen2])
%xlabel('test stimulus speed clockwise (a.u.)') % degrees of visual angle (dva)
ylabel('proportion response clockwise')
title('Exp 1: group')
tight_subplot(4,3,2,1,guttera, marginsa)
for ci = 1:Ncond
h(ci) = plot(mean(squeeze(binz_pos(indi_sel,ci,:)),1), nanmean(squeeze(prop_cf_high_all(indi_sel,ci,:)),1),'rs','MarkerSize', msz_all(ci),'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor', colorz(ci,:)); hold on;
errorbar(mean(squeeze(binz_pos(indi_sel,ci,:)),1), nanmean(squeeze(prop_cf_high_all(indi_sel,ci,:)),1), nanstd(squeeze(prop_cf_high_all(indi_sel,ci,:)),1)/sqrt(length(indi_sel)), 'Color', colorz(ci,:), 'Linewidth', linewi, 'CapSize',0); hold on;
if dashed_linez
plot(nanmean(params_psych_m2_all(indi_sel,ci,1)) *ones(1,10), linspace(0,1,10),'--', 'Color', colorz(ci,:),'Linewidth',linewi ); hold on;
end
end
box off
xlim([xlim_min xlim_max])
ylim([ylim_min ylim_max])
plot(zeros(1,nbinz), linspace(0,1,nbinz), '--k'); hold on;
box off
set(gca, 'xtick',[-0.5:0.1:0.5])
set(gca, 'ytick', [0:0.25:1])
set(gca, 'tickdir', 'out')
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',[tlen1 tlen2])
% xlabel('test stimulus speed clockwise (a.u.)') % degrees of visual angle (dva)
ylabel('proportion high confidence')
tight_subplot(4,3,3,1,guttera, marginsa)
for ci = 1:Ncond
h(ci) = plot(mean(squeeze(binz_pos(indi_sel,ci,:)),1), nanmean(squeeze(rt_all(indi_sel,ci,:)),1),'^','MarkerSize', msz_all(ci),'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor', colorz(ci,:)); hold on;
errorbar(mean(squeeze(binz_pos(indi_sel,ci,:)),1), nanmean(squeeze(rt_all(indi_sel,ci,:)),1), nanstd(squeeze(rt_all(indi_sel,ci,:)),1)/sqrt(length(indi_sel)), 'Color', colorz(ci,:), 'Linewidth', linewi, 'CapSize',0); hold on;
if dashed_linez
plot(nanmean(params_psych_m2_all(indi_sel,ci,1)) *ones(1,10), linspace(0,2.25*ylim_max,10),'--', 'Color', colorz(ci,:),'Linewidth',linewi ); hold on;
end
end
box off
xlim([xlim_min xlim_max])
ylim([1 2.25*ylim_max])
plot(zeros(1,nbinz), linspace(0,3,nbinz), '--k'); hold on;
box off
set(gca, 'xtick',[-0.5:0.1:0.5])
set(gca, 'tickdir', 'out')
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',[tlen1 tlen2])
xlabel('test stimulus speed clockwise (a.u.)')
ylabel('reaction times (s)')
tight_subplot(4,3,4,1,guttera, marginsa)
for ci = 1: Ncond
pgr_psych(ci) = plot(nanmean(params_psych_m2_all(indi_sel,ci,1)) , 20-5*(ci-1), 'o', 'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor',colorz(ci,:)); hold on;
pgr_cf(ci) = plot(nanmean(mu_estim_cf(indi_sel,ci,1)), 19-5*(ci-1),'rs','MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor',colorz(ci,:)); hold on;
pgr_rt(ci) = plot(nanmean(mu_estim_rt(indi_sel,ci,1)), 18-5*(ci-1), '^','MarkerFaceColor', colorz(ci,:),'MarkerEdgeColor',colorz(ci,:)); hold on;
errorbar(nanmean(params_psych_m2_all(indi_sel,ci,1)) , 20-5*(ci-1), nanstd(params_psych_m2_all(indi_sel,ci,1)), 'horizontal','Color', colorz(ci,:), 'Capsize', 0,'Linewidth',linewi); hold on;
errorbar(nanmean(mu_estim_cf(indi_sel,ci,1)) , 19-5*(ci-1), nanstd(mu_estim_cf(indi_sel,ci)), 'horizontal','Color', colorz(ci,:),'Capsize', 0,'Linewidth',linewi); hold on;
errorbar(nanmean(mu_estim_rt(indi_sel,ci,1)) , 18-5*(ci-1), nanstd(mu_estim_rt(indi_sel,ci)), 'horizontal','Color', colorz(ci,:),'Capsize', 0,'Linewidth',linewi); hold on;
end
plot(zeros(1,nbinz), linspace(0,22,nbinz), '--k'); hold on;
ylim([0 22])
box off
set(gca, 'tickdir', 'out')
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',[tlen1 tlen2])
elseif exp_i == 2
% DECIDE ON A PARTICIPANT
sii = 13;
tight_subplot(4,3,1,2, guttera, marginsa)
for ci = 1:Ncond
h(ci) = plot(squeeze(binz_pos(sii,ci,:)), squeeze(prop_cw_all(sii,ci,:)),'o','MarkerSize', msz_all(ci),'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor', colorz(ci,:)); hold on;
h(ci) = plot(squeeze(binz_pos(sii,ci,:)), squeeze(prop_cw_pr_all(sii,ci,:)),'Color', colorz(ci,:), 'Linewidth', linewi); hold on;
if dashed_linez
if ci>=3
plot(params_psych_m2_all(sii,ci,1) *ones(1,10), linspace(0,1,10),'--', 'Color', colorz(ci,:),'Linewidth',linewi ); hold on;
plot(linspace(-0.3,params_psych_m2_all(sii,ci,1),10), 0.5*ones(1,10),'--', 'Color', colorz(ci,:),'Linewidth',linewi ); hold on;
end
end
end
if exp_i == 1
xlim_min = -0.5; xlim_max = 0.5;
elseif exp_i == 2
xlim_min = -0.3; xlim_max = 0.3;
end
ylim_min = 0;
ylim_max = 1.01;
xlim([xlim_min xlim_max])
ylim([ylim_min ylim_max])
plot(zeros(1,nbinz), linspace(0,1,nbinz), '--k'); hold on;
box off
set(gca, 'xtick',[-0.3:0.1:0.3])
set(gca, 'ytick', [0:0.25:1])
set(gca, 'tickdir', 'out')
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',[tlen1 tlen2])
%xlabel('test stimulus speed clockwise (a.u.)') % degrees of visual angle (dva)
ylabel('prop response clockwise')
title('Exp 2: participant 13')
tight_subplot(4,3,2,2,guttera, marginsa)
for ci = 1:Ncond
h(ci) = plot(squeeze(binz_pos(sii,ci,:)), squeeze(squeeze(prop_cf_high_all(sii,ci,:))),'rs','MarkerSize', msz_all(ci),'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor', colorz(ci,:)); hold on;
hp(ci) = plot(squeeze(binz_pos(sii,ci,:)), squeeze(squeeze(prop_cf_high_all(sii,ci,:))),'Color', colorz(ci,:), 'Linewidth', linewi); hold on;
if dashed_linez
plot(squeeze(params_psych_m2_all(sii,ci,1)) *ones(1,10), linspace(0,1,10),'--', 'Color', colorz(ci,:),'Linewidth',linewi ); hold on;
end
end
box off
xlim([xlim_min xlim_max])
ylim([ylim_min ylim_max])
plot(zeros(1,nbinz), linspace(0,1,nbinz), '--k'); hold on;
box off
set(gca, 'xtick',[-0.3:0.1:0.3])
set(gca, 'ytick', [0:0.25:1])
set(gca, 'tickdir', 'out')
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',[tlen1 tlen2])
%xlabel('test stimulus speed clockwise (a.u.)') % degrees of visual angle (dva)
ylabel('proportion high confidence')
tight_subplot(4,3,3,2,guttera, marginsa)
for ci = 1:Ncond
h(ci) = plot(squeeze(binz_pos(sii,ci,:)), squeeze(squeeze(rt_all(sii,ci,:))),'^','MarkerSize', msz_all(ci),'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor', colorz(ci,:)); hold on;
hp(ci) = plot(squeeze(binz_pos(sii,ci,:)), squeeze(squeeze(rt_all(sii,ci,:))),'Color', colorz(ci,:), 'Linewidth', linewi); hold on;
if dashed_linez
plot(squeeze(params_psych_m2_all(sii,ci,1))*ones(1,10), linspace(0,2.25*ylim_max,10),'--', 'Color', colorz(ci,:),'Linewidth',linewi ); hold on;
end
end
box off
xlim([xlim_min xlim_max])
ylim([1 2.25*ylim_max])
plot(zeros(1,nbinz), linspace(0,3,nbinz), '--k'); hold on;
box off
set(gca, 'xtick',[-0.3:0.1:0.3])
set(gca, 'tickdir', 'out')
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',[tlen1 tlen2])
xlabel('test stimulus speed clockwise (a.u.)')
ylabel('reaction times (s)')
% AGGREGATE DATA FROM EXP 2
tight_subplot(4,3,1,3, guttera, marginsa)
for ci = 1:Ncond
h(ci) = plot(mean(squeeze(binz_pos(indi_sel,ci,:)),1), nanmean(squeeze(prop_cw_all(indi_sel,ci,:)),1),'o','MarkerSize', msz_all(ci),'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor', colorz(ci,:) ); hold on;
errorbar(mean(squeeze(binz_pos(indi_sel,ci,:)),1), nanmean(squeeze(prop_cw_all(indi_sel,ci,:)),1), nanstd(squeeze(prop_cw_all(indi_sel,ci,:)),1)/sqrt(length(indi_sel)), 'Color', colorz(ci,:), 'Linewidth', linewi, 'CapSize',0); hold on;
% nan is needed to accomodate for exp 1
if dashed_linez
plot(nanmean(params_psych_m2_all(indi_sel,ci,1)) *ones(1,10), linspace(0,1,10),'--', 'Color', colorz(ci,:),'Linewidth',linewi ); hold on;
plot(linspace(-0.3,nanmean(params_psych_m2_all(indi_sel,ci,1)),10), 0.5*ones(1,10),'--', 'Color', colorz(ci,:),'Linewidth',linewi ); hold on;
end
end
box off
if exp_i == 1
xlim_min = -0.5; xlim_max = 0.5;
elseif exp_i == 2
xlim_min = -0.3; xlim_max = 0.3;
end
ylim_min = 0;
ylim_max = 1.01;
xlim([xlim_min xlim_max])
ylim([ylim_min ylim_max])
plot(zeros(1,nbinz), linspace(0,1,nbinz), '--k'); hold on;
box off
set(gca, 'xtick',[-0.3:0.1:0.3])
set(gca, 'ytick', [0:0.25:1])
set(gca, 'tickdir', 'out')
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',[tlen1 tlen2])
%xlabel('test stimulus speed clockwise (a.u.)') % degrees of visual angle (dva)
%ylabel('proportion response clockwise')
title('Exp 2: group')
tight_subplot(4,3,2,3,guttera, marginsa)
for ci = 1:Ncond
h(ci) = plot(mean(squeeze(binz_pos(indi_sel,ci,:)),1), nanmean(squeeze(prop_cf_high_all(indi_sel,ci,:)),1),'rs','MarkerSize', msz_all(ci),'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor', colorz(ci,:) ); hold on;
errorbar(mean(squeeze(binz_pos(indi_sel,ci,:)),1), nanmean(squeeze(prop_cf_high_all(indi_sel,ci,:)),1), nanstd(squeeze(prop_cf_high_all(indi_sel,ci,:)),1)/sqrt(length(indi_sel)), 'Color', colorz(ci,:), 'Linewidth', linewi, 'CapSize',0); hold on;
if dashed_linez
plot(nanmean(params_psych_m2_all(indi_sel,ci,1)) *ones(1,10), linspace(0,1,10),'--', 'Color', colorz(ci,:),'Linewidth',linewi ); hold on;
end
end
box off
xlim([xlim_min xlim_max])
ylim([ylim_min ylim_max])
plot(zeros(1,nbinz), linspace(0,3,nbinz), '--k'); hold on;
box off
set(gca, 'xtick',[-0.3:0.1:0.3])
set(gca, 'ytick', [0:0.25:1])
set(gca, 'tickdir', 'out')
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',[tlen1 tlen2])
%xlabel('test stimulus speed clockwise (a.u.)') % degrees of visual angle (dva)
%ylabel('proportion high confidence')
tight_subplot(4,3,3,3,guttera, marginsa)
for ci = 1:Ncond
h(ci) = plot(mean(squeeze(binz_pos(indi_sel,ci,:)),1), nanmean(squeeze(rt_all(indi_sel,ci,:)),1),'^','MarkerSize', msz_all(ci),'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor', colorz(ci,:) ); hold on;
errorbar(mean(squeeze(binz_pos(indi_sel,ci,:)),1), nanmean(squeeze(rt_all(indi_sel,ci,:)),1), nanstd(squeeze(rt_all(indi_sel,ci,:)),1)/sqrt(length(indi_sel)), 'Color', colorz(ci,:), 'Linewidth', linewi, 'CapSize',0); hold on;
if dashed_linez
plot(nanmean(params_psych_m2_all(indi_sel,ci,1)) *ones(1,10), linspace(0,2.25*ylim_max,10),'--', 'Color', colorz(ci,:),'Linewidth',linewi ); hold on;
end
end
box off
plot(zeros(1,nbinz), linspace(0,3,nbinz), '--k'); hold on;
xlim([xlim_min xlim_max])
ylim([1 2.25*ylim_max])
box off
set(gca, 'xtick',[-0.3:0.1:0.3])
set(gca, 'tickdir', 'out')
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',[tlen1 tlen2])
xlabel('test stimulus speed clockwise (a.u.)')
%ylabel('reaction times (s)')
tight_subplot(4,3,4,3,guttera, marginsa)
for ci = 1: Ncond
vala_psych = squeeze(params_psych_m2_all(indi_sel,ci,1));
vala_cf = squeeze(mu_estim_cf(indi_sel,ci,1));
vala_rt = squeeze(mu_estim_rt(indi_sel,ci,1));
clear psych_samples; clear cf_samples; clear rt_samples;
for kk = 1:nboot
sample_psych = randsample(vala_psych,length(indi_sel),1);
psych_samples(kk) = nanmedian(sample_psych);
sample_cf = randsample(vala_cf,length(indi_sel),1);
cf_samples(kk) = nanmedian(sample_cf);
sample_rt = randsample(vala_rt,length(indi_sel),1);
rt_samples(kk) = nanmedian(sample_rt);
end
bci_psych = [quantile(psych_samples,ci_bnd_low); quantile(psych_samples,ci_bnd_high)];
bci_cf = [quantile(cf_samples,ci_bnd_low); quantile(cf_samples,ci_bnd_high)];
bci_rt = [quantile(rt_samples,ci_bnd_low); quantile(rt_samples,ci_bnd_high)];
pgr_psych(ci) = plot(nanmedian(vala_psych) , 20-5*(ci-1), 'o', 'MarkerSize', msz_all(ci),'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor',colorz(ci,:)); hold on;
pgr_cf(ci) = plot(nanmedian(vala_cf), 19-5*(ci-1),'rs','MarkerSize', msz_all(ci),'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor',colorz(ci,:)); hold on;
pgr_rt(ci) = plot(nanmedian(vala_rt), 18-5*(ci-1), '^','MarkerSize', msz_all(ci),'MarkerFaceColor', colorz(ci,:),'MarkerEdgeColor',colorz(ci,:)); hold on;
errorbar(nanmedian(vala_psych) , 20-5*(ci-1),bci_psych(1),bci_psych(2), 'horizontal','Color', colorz(ci,:), 'Capsize', 0,'Linewidth',linewi); hold on;
errorbar(nanmedian(vala_cf) , 19-5*(ci-1), bci_cf(1),bci_cf(2), 'horizontal','Color', colorz(ci,:),'Capsize', 0,'Linewidth',linewi); hold on;
errorbar(nanmedian(vala_rt) , 18-5*(ci-1), bci_rt(1),bci_rt(2), 'horizontal','Color', colorz(ci,:),'Capsize', 0,'Linewidth',linewi); hold on;
end
plot(zeros(1,nbinz), linspace(0,22,nbinz), '--k'); hold on;
ylim([0 22])
xlim([-0.3 0.3])
box off
set(gca, 'tickdir', 'out')
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',[tlen1 tlen2])
l_pgr = legend([pgr_psych(1) pgr_cf(1) pgr_rt(1)], {'Psychometric', 'Confidence', 'RT'}, 'FontName', 'Helvetica', 'FontSize', fontsz)
set(l_pgr, 'Position', [0.4309 0.1779 0.1908 0.0634])
end
%%
psname = ['Fig4_Psych_confidence_RT_Means_exp_',num2str(exp_i), '.pdf']
%print_pdf(psname)
%%
hf2 = figure(13)
set(gcf, 'Position', [100 100 600 700])
linewi = 1.1;
dashed_linez = 0;
if exp_i == 2
si_vec = [ 2 8 20]; % pick 3 participants from exp 2
for sii_ind = 1:3
sii = si_vec(sii_ind);
tight_subplot(3,3,1,sii_ind, guttera, marginsa)
for ci = 1:Ncond
h(ci) = plot(squeeze(binz_pos(sii,ci,:)), squeeze(prop_cw_all(sii,ci,:)),'o','MarkerSize', msz_all(ci),'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor', colorz(ci,:)); hold on;
h(ci) = plot(squeeze(binz_pos(sii,ci,:)), squeeze(prop_cw_pr_all(sii,ci,:)),'Color', colorz(ci,:), 'Linewidth', linewi); hold on;
if dashed_linez
if ci>=3
plot(params_psych_m2_all(sii,ci,1) *ones(1,10), linspace(0,1,10),'--', 'Color', colorz(ci,:),'Linewidth',linewi ); hold on;
plot(linspace(-0.3,params_psych_m2_all(sii,ci,1),10), 0.5*ones(1,10),'--', 'Color', colorz(ci,:),'Linewidth',linewi ); hold on;
end
end
end
if exp_i == 1
xlim_min = -0.5; xlim_max = 0.5;
elseif exp_i == 2
xlim_min = -0.3; xlim_max = 0.3;
end
ylim_min = 0;
ylim_max = 1.01;
xlim([xlim_min xlim_max])
ylim([ylim_min ylim_max])
plot(zeros(1,nbinz), linspace(0,1,nbinz), '--k'); hold on;
box off
set(gca, 'xtick',[-0.3:0.1:0.3])
set(gca, 'ytick', [0:0.25:1])
set(gca, 'tickdir', 'out')
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',[tlen1 tlen2])
%xlabel('test stimulus speed clockwise (a.u.)') % degrees of visual angle (dva)
if sii_ind == 1
ylabel('proportion response clockwise')
end
title(['Exp 2: participant ', num2str(sii)])
tight_subplot(3,3,2,sii_ind,guttera, marginsa)
for ci = 1:Ncond
h(ci) = plot(squeeze(binz_pos(sii,ci,:)), squeeze(squeeze(prop_cf_high_all(sii,ci,:))),'o','MarkerSize', msz_all(ci),'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor', colorz(ci,:)); hold on;
hp(ci) = plot(squeeze(binz_pos(sii,ci,:)), squeeze(squeeze(prop_cf_high_all(sii,ci,:))),'Color', colorz(ci,:), 'Linewidth', linewi); hold on;
if dashed_linez
plot(squeeze(params_psych_m2_all(sii,ci,1)) *ones(1,10), linspace(0,1,10),'--', 'Color', colorz(ci,:),'Linewidth',linewi ); hold on;
end
end
box off
xlim([xlim_min xlim_max])
ylim([ylim_min ylim_max])
plot(zeros(1,nbinz), linspace(0,1,nbinz), '--k'); hold on;
box off
set(gca, 'xtick',[-0.3:0.1:0.3])
set(gca, 'ytick', [0:0.25:1])
set(gca, 'tickdir', 'out')
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',[tlen1 tlen2])
%xlabel('test stimulus speed clockwise (a.u.)') % degrees of visual angle (dva)
if sii_ind == 1
ylabel('proportion high confidence')
end
tight_subplot(3,3,3,sii_ind,guttera, marginsa)
for ci = 1:Ncond
h(ci) = plot(squeeze(binz_pos(sii,ci,:)), squeeze(squeeze(rt_all(sii,ci,:))),'o','MarkerSize', msz_all(ci),'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor', colorz(ci,:)); hold on;
hp(ci) = plot(squeeze(binz_pos(sii,ci,:)), squeeze(squeeze(rt_all(sii,ci,:))),'Color', colorz(ci,:), 'Linewidth', linewi); hold on;
if dashed_linez
plot(squeeze(params_psych_m2_all(sii,ci,1)) *ones(1,10), linspace(0,2.25*ylim_max,10),'--', 'Color', colorz(ci,:),'Linewidth',linewi ); hold on;
end
end
box off
xlim([xlim_min xlim_max])
ylim([1 2.25*ylim_max])
plot(zeros(1,nbinz), linspace(0,3,nbinz), '--k'); hold on;
box off
set(gca, 'xtick',[-0.3:0.1:0.3])
set(gca, 'tickdir', 'out')
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',[tlen1 tlen2])
if sii_ind == 2
xlabel('test stimulus speed clockwise (a.u.)')
end
if sii_ind == 1
ylabel('reaction times (s)')
end
end
end
%%
psname = 'Fig3_Supp_Psych_confidence_RT_paper.pdf'
%print_pdf(psname)
%% MODEL FIGURE
%close all;
figure(5)
set(gcf, 'Position', [100 100 740 700])
indi_sel = 1:1:Nsubj;
xlim_min = -0.3;%min(min(min(binz_pos)));
xlim_max = 0.3;%max(max(max(binz_pos)));
tight_subplot(5,4,3,1,guttera, marginsa)
for ci = 1:Ncond
h(ci) = plot(mean(squeeze(binz_pos(indi_sel,ci,:)),1), nanmean(squeeze(rt_all(indi_sel,ci,:)),1),'o','MarkerSize', msz_all(ci),'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor', colorz(ci,:)); hold on;
errorbar(mean(squeeze(binz_pos(indi_sel,ci,:)),1), nanmean(squeeze(rt_all(indi_sel,ci,:)),1), nanstd(squeeze(rt_all(indi_sel,ci,:)),1)/sqrt(length(indi_sel)), 'Color', colorz(ci,:), 'LineStyle', 'none','CapSize',0, 'Linewidth', linewi); hold on;
end
box off
xlim([xlim_min xlim_max])
ylim([1 2])
plot(zeros(1,nbinz), linspace(0,4,nbinz), '--k'); hold on;
box off
set(gca, 'xtick',[-0.3:0.1:0.3])
set(gca, 'tickdir', 'out')
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',[tlen1 tlen2])
ylabel('reaction times (s)')
% decision variable
if exp_i == 2
tight_subplot(5,4,3,2,guttera, marginsa)
for ci = 1:Ncond
% decision variable
if exp_i == 2
tight_subplot(5,4,3,2,guttera, marginsa)
for ci = 1:Ncond
h(ci) = plot(mean(squeeze(binz_pos(indi_sel,ci,:)),1), -nanmean(log(abs(squeeze(dv_val_binz(indi_sel,ci,:)))),1),'o','MarkerSize', msz_all(ci),'MarkerFaceColor', colorz(ci,:), 'MarkerEdgeColor', colorz(ci,:)); hold on;
errorbar(mean(squeeze(binz_pos(indi_sel,ci,:)),1), -nanmean(log(abs(squeeze(dv_val_binz(indi_sel,ci,:)))),1), nanstd(squeeze(log(abs(dv_val_binz(indi_sel,ci,:)))),1)/sqrt(length(indi_sel)), 'Color', colorz(ci,:),'CapSize',0, 'Linewidth', linewi); hold on;
end
box off
xlim([xlim_min xlim_max])
plot(zeros(1,nbinz), linspace(-5,1,nbinz), '--k'); hold on;
box off
set(gca, 'xtick',[-0.3:0.1:0.3])
set(gca, 'tickdir', 'out')
set(gca, 'FontSize', fontsz)
set(gca, 'ticklength',[tlen1 tlen2])
set(gca, 'tickdir', 'out')
%xlabel('test stim')
%ylabel('-decision variable')