%Mat's code to analyze behavioural data from MDM Muse.
clear all; 
clc;

%Participant Number, Phase, ((Block)), Block, Trial, Total Trial, Reaction Time, Last Block Accuracy, Disease, Condition, Accuracy, etc
% 1,4,5,11,18,19,20,22,23,24,25,27,34,37,48: Non learners
filenames = dir(strcat('*.txt'));
filenames([4,9,11,18:22,24,26,30,33,40],:) = [];

%Participant loop 
Grand_Scatter = [];
for counter = 1:length(filenames)
    beh_data = [];
    beh_data = load(filenames(counter).name); %Load participant data
    if counter > 1
        beh_data(:,3) = [];
    end
    
    %Check if learner
    learner_check = find(beh_data(:,2)== 2);
    if isempty(learner_check) == 1
        learner(counter,1) = 0;
    elseif beh_data(learner_check(1,1),7) < 90
        learner(counter,1) = 0;
    else
        learner(counter,1) = 1;
    end

    %Find conflict and no conflict
    
    findc1 = find(beh_data(:,9)==4); %low conflict
    findc2 = find(beh_data(:,9)==3); %high conflict
    
    %Find RT for conflict conditions
    
    c1_Rt = beh_data(findc1,6);
    c2_Rt = beh_data(findc2,6);
    
    %Find accuracy for conflict conditions
    
    c1_ac = beh_data(findc1,10);
    c2_ac = beh_data(findc2,10);
    
    %Find confidence for conflict conditions
    
    c1_con = beh_data(findc1,11);
    c2_con = beh_data(findc2,11);
    
    %Mean Rt
    
    d_stats(counter,1) = mean(c1_Rt);
    d_stats(counter,2) = mean(c2_Rt);
    
    %Mean Accuracy
    
    d_stats(counter,3) = mean(c1_ac);
    d_stats(counter,4) = mean(c2_ac);
    
    %Mean Confidence
    
    d_stats(counter,5) = mean(c1_con);
    d_stats(counter,6) = mean(c2_con);
    
    %STD Rt
    
    d_stats(counter,7) = std(c1_Rt);
    d_stats(counter,8) = std(c2_Rt);
    
    %STD Accuracy
    
    d_stats(counter,9) = std(c1_ac);
    d_stats(counter,10) = std(c2_ac);
    
    %STD Confidence
    
    d_stats(counter,11) = std(c1_con);
    d_stats(counter,12) = std(c2_con);
    

% Find Stats for first block
d_stats(counter,13) = mean(beh_data(1:20,6)); %Early Rt
d_stats(counter,14) = mean(beh_data(1:20,10)); %Early Accuracy
d_stats(counter,15) = mean(beh_data(1:20,11)); % Early Confidence

% Find Stats for last block
lateSet = [];
learningP = [];

%Isolate Learning Phase
lateSet = (find(beh_data(:,2)==1));
learningP = beh_data(lateSet,:); 

d_stats(counter,16) = mean(learningP(end-19:end,6)); %Late RT
d_stats(counter,17) = mean(learningP(end-19:end,10)); %Late Accuracy
d_stats(counter,18) = mean(learningP(end-19:end,11)); %Late Confidence 

end

%Ensure data is learners only, switch to kept_stats
keep = find(learner==1);
kept_stats = d_stats(keep,:);

%Calcuate differences between groups of interest 
mDiff2RT = (kept_stats(:,2))-(kept_stats(:,1)); %Conflict RT - Nonconflict RT
mDiff2Acc = (kept_stats(:,4)*100)-(kept_stats(:,3)*100); %Conflict Acc - Nonconflict Acc
mDiff2Con = (kept_stats(:,6))-(kept_stats(:,5)); %Conflict Confidence - Nonconflict Confidence
mDiff1RT = (kept_stats(:,16)*1000)-(kept_stats(:,13)*1000); %Late RT - Early RT
mDiff1Acc = (kept_stats(:,17)*100)-(kept_stats(:,14)*100); %Late Acc - Early Acc
mDiff1Con = (kept_stats(:,18))-(kept_stats(:,15)); %Late Confidence - Early Confidence

%Make Confidence Intervals
CI(:,1) = tinv(.975,21) * (std(kept_stats(:,1)) / sqrt(length(keep))); %Non ConflictRT
CI(:,2) = tinv(.975,21) * (std(kept_stats(:,2)) / sqrt(length(keep))); %Conflict RT
CI(:,3) = tinv(.975,21) * (std(kept_stats(:,3)*100) / sqrt(length(keep))); %Non Conflict Acc
CI(:,4) = tinv(.975,21) * (std(kept_stats(:,4)*100) / sqrt(length(keep))); %Conflcit Acc
CI(:,5) = tinv(.975,21) * (std(kept_stats(:,5)) / sqrt(length(keep))); %Non Conflict Confidence
CI(:,6) = tinv(.975,21) * (std(kept_stats(:,6)) / sqrt(length(keep))); %Conflict Confidence
CI(:,7) = tinv(.975,21) * (std(kept_stats(:,13)) / sqrt(length(keep))); %Early RT
CI(:,8) = tinv(.975,21) * (std(kept_stats(:,16)) / sqrt(length(keep))); %Late RT
CI(:,9) = tinv(.975,21) * (std(kept_stats(:,14)*100) / sqrt(length(keep))); %Early Accuracy
CI(:,10) = tinv(.975,21) * (std(kept_stats(:,17)*100) / sqrt(length(keep))); %Late Accuracy
CI(:,11) = tinv(.975,21) * (std(kept_stats(:,15)) / sqrt(length(keep))); %Early Confidence
CI(:,12) = tinv(.975,21) * (std(kept_stats(:,18)) / sqrt(length(keep))); %Late Confidence
CI(:,13) = tinv(.975,21) * (std(mDiff2RT)) / sqrt(length(keep)); %ConflictRT difference
CI(:,14) = tinv(.975,21) * (std(mDiff2Acc)) / sqrt(length(keep)); %ConflictAcc difference
CI(:,15) = tinv(.975,21) * (std(mDiff2Con)) / sqrt(length(keep)); %ConflictConfidence difference
CI(:,16) = tinv(.975,21) * (std(mDiff2RT)) / sqrt(length(keep)); %EarlyLateRT difference
CI(:,17) = tinv(.975,21) * (std(mDiff2Acc)) / sqrt(length(keep)); %EarlyLateAcc difference
CI(:,18) = tinv(.975,21) * (std(mDiff2Con)) / sqrt(length(keep)); %EarlyLateConfidence difference

%manual p test, enter two variables of interest and specificy alpha value
[h,p,ci,stats] = ttest(kept_stats(:,15),kept_stats(:,18), 'Alpha', 0.00000001)  

%Make Figures
figure;

subplot(1,3,1);
d=bar(mean(kept_stats(:,3:4))*100); ylim([40 100]); ylabel('Accuracy (%)'); xticklabels({'No Conflict', 'Conflict'}); title('A');
hold on
d.FaceColor= 'flat';
d.CData(2,:) = [.9 .3 .3];
ax = gca;
set(ax,'box','off');
ax.FontName = 'Arial';
ax.FontSize = 12.00;
ax.TickLength = [0 0];
hold on
e = errorbar(1:2, mean(kept_stats(:,3:4))*100, CI(:,3:4), 'k', 'LineStyle', 'none');
e.LineWidth = 2;
hold off

subplot(1,3,2);
c=bar(mean(kept_stats(:,1:2))); ylim([3 6.5]); ylabel('Seconds (s)'); xticklabels({'No Conflict', 'Conflict'}); title('B');
hold on
c.FaceColor= 'flat';
c.CData(2,:) = [.9 .3 .3];
ax = gca;
set(ax,'box','off');
ax.FontName = 'Arial';
ax.FontSize = 12.00;
ax.TickLength = [0 0];
hold on
e = errorbar(1:2, mean(kept_stats(:,1:2)), CI(:,1:2), 'k', 'LineStyle', 'none');
e.LineWidth = 2;
hold off

subplot(1,3,3)
e=bar(mean(kept_stats(:,5:6))); ylim([7 10]); ylabel('Confidence Rating'); xticklabels({'No Conflict', 'Conflict'}); title('C');
hold on
e.FaceColor= 'flat';
e.CData(2,:) = [.9 .3 .3];
ax = gca;
set(ax,'box','off');
ax.FontName = 'Arial';
ax.FontSize = 12.00;
ax.TickLength = [0 0];
hold on
e = errorbar(1:2, mean(kept_stats(:,5:6)), CI(:,5:6), 'k', 'LineStyle', 'none');
e.LineWidth = 2;
hold off
    

figure; 

subplot(1,3,1)
d=bar(mean(kept_stats(:,[14,17]))*100); ylim([30 100]);ylabel('Accuracy (%)'); xticklabels({'Early','Late'}); title('A');
hold on
d.FaceColor= 'flat';
d.CData(2,:) = [.9 .3 .3];
ax = gca;
set(ax,'box','off');
ax.FontName = 'Arial';
ax.FontSize = 12.00;
ax.TickLength = [0 0];
hold on
e = errorbar(1:2, mean(kept_stats(:,[14,17]))*100, CI(:,9:10), 'k', 'LineStyle', 'none');
e.LineWidth = 2; 
hold off

subplot(1,3,2)
c=bar(mean(kept_stats(:,[13,16]))); ylim([3 17]);ylabel('Seconds (s)'); xticklabels({'Early','Late'}); title('B');
hold on
c.FaceColor= 'flat';
c.CData(2,:) = [.9 .3 .3];
ax = gca;
set(ax,'box','off');
ax.FontName = 'Arial';
ax.FontSize = 12.00;
ax.TickLength = [0 0];
hold on
e = errorbar(1:2, mean(kept_stats(:,[13,16])), CI(:,7:8), 'k', 'LineStyle', 'none');
e.LineWidth = 2;
hold off

subplot(1,3,3)
e=bar(mean(kept_stats(:,[15,18]))); ylim([0 10]); ylabel('Confidence Rating'); xticklabels({'Early','Late'}); title('C');
hold on
e.FaceColor= 'flat';
e.CData(2,:) = [.9 .3 .3];
ax = gca;
set(ax,'box','off');
ax.FontName = 'Arial';
ax.FontSize = 12.00;
ax.TickLength = [0 0];
hold on
e = errorbar(1:2, mean(kept_stats(:,[15,18])), CI(:,11:12), 'k', 'LineStyle', 'none');
e.LineWidth = 2; 
hold off