2024_Hasegawa_Huang_et_al_CODE/Fig1/Fig1D.m

%% Figure 1D - Task performance
%
clear all
close all
%%
%Inital learning
Hit_P     = [0.7619 0.9762 0.9762 1 1 1]; %Initial Hit 
FA_P      = [0.9184 1 0.8367 0.1633 0.04 0.0408]; %Initial FA
CatchFA_P = [0.8163 1 0.6735 0.2041 0.0612 0.0816]; %Initial catchFA

%Reversal learning
Rev_Hit_P     = [0.0476 0.9762 1 1 1]; %Reversal Hit 
Rev_FA_P      = [0.3265 1 0.7188 0.3673 0.4082]; %Reversal FA
Rev_CatchFA_P = [0.0408 0.9796 0.7000 0.3673 0.6531]; %Reversal catchFA

for iLearning = 1:2
    %1 = Initial, 2 = Reversal
    switch iLearning
        case 1
            Hit     = Hit_P;
            FA      = FA_P;
            CatchFA = CatchFA_P;
            figure('Name', ['Initial_TaskPerfrmance'])
            set(gcf,'Position',[170 250 220 320])
            Position = 'southwest';
            FigTitle = 'Initial';
        case 2
            Hit     = Rev_Hit_P;
            FA      = Rev_FA_P;
            CatchFA = Rev_CatchFA_P;
            figure('Name', ['Reversal_TaskPerfrmance'])
            set(gcf,'Position',[170 250 220 320])
            Position = 'southeast';
            FigTitle = 'Reversal';
    end
    
    NofSession = size(Hit,2);
    plot(Hit', '-o', 'color',[0.25 0.85 0.25],'MarkerFaceColor',[0.25 0.85 0.25]);
    hold on;
    plot(FA', '-o', 'color','r','MarkerFaceColor','r');
    plot(CatchFA', '-o', 'color', [0.5 0.5 0.5],'MarkerFaceColor', [0.5 0.5 0.5]);
    title(FigTitle);
    
    xlim([0 NofSession+1]);
    ylim ([0 1.1]);
    
    NofXticks = 1:1:NofSession;
    xticks(NofXticks);
    yticks([0 1.0]);
    
    xlabel('Session')
    ylabel('Response ratio')
    box off
    
    %legend
    legend('Hit', 'FA', 'CatchFA', 'location', Position);
    legend box off
      
end %for iLearning