mpib_memoreye/behavioural_modeling/findAbehavAllConditions.m

clear all

load('behavdata/dat.mat')

aCue{1}=squeeze(accrot_cu_uncue(:,1,:)); %Accuracy 1st Test
bCue{1}=squeeze(bias_cue(:,1,:)); %Bias 1st Test

aCue{2}=squeeze(accrot_cu_uncue(:,2,:)); %Accuracy 2st Test
bCue{2}=squeeze(bias_uncue(:,1,:)); %Bias 2st Test

%Averaging between 1st and 2nd test 
aCueg = ((aCue{1}+aCue{2})./2)
bCueg = ((bCue{1}+bCue{2})./2)

%% order the data in standard presentation

ao=deg2rad(accrot_cu_uncue(:,3,1))
ao2=abs(2*pi-ao) %move anticlockwise
ao3=(angdiff(-ao2,-(ones(16,1).*pi*1.5)))+pi

[ao2,ao3]

[bb ind]=sort(ao3)

aCueo=aCueg(ind,:)
bCueo=bCueg(ind,:)

%%

% aCueGo = ((aCue1o+aCue2o)./2)
% bCueGo = ((bCue1o+bCue2o)./2)


%% With general accuracy (1st and 2nd test combined)

% Keep C=1
% 
% (i) SSQ based on ave between acuSSQ and biasSSQ
% (ii) SSQ based only biasSSQ
% (iii) SSQ based on ave between acuSSQ and biasSSQ (but acuSSQ is calculated on general accu)

%%
BestParams=nan(size(accrot_cu_uncue,3),4);

parfor ppp =1:size(accrot_cu_uncue,3)
    
        
        aaCue=(aCueo(:,ppp)');
        bbCue=(bCueo(:,ppp)');
        
        CvetIn=0:0.1:1;%vector grid search of C
        
        BestParams(ppp,:)=gridsearch_jld(aaCue,bbCue,0,2,CvetIn,0.1); % This fuction is the the same as original, but we can add some input (ssqmat only based on accu,granularity for the grid search)
          
end

%%

all=BestParams
%%

        s=BestParams(1,1);     % noise (in memory/decision-making)
        A=BestParams(1,2);      % Key Parameter (squircle) 0: all circle; 1: all square
        C=BestParams(1,3);      % Reduce noise near cardinal by this factor (1: off)
        makefig=0;
        
        behB=bCueo(:,ppp)'; %BehaviouralBias
        
        figure;
        [yourAccu(:,ppp) yourBias(:,ppp)]=squircleBehave2compB(s,A,C,behB,makefig);

%% Changing format

Params{1}= squeeze(BestParams(:,:,1));% info about test 1
Params{2}= squeeze(BestParams(:,:,2));% info about test 1

%% Anova 

%%

%% and Group plot
figure;

for test=1:0
oi=bCueo{test};

yourBias=[];
yourAccu=[];
for ppp=1:size(oi,2)
    behB=oi(:,ppp)'; %BehaviouralBias
    
    s=Params{test}(ppp,1);     % noise (in memory/decision-making)
    A=Params{test}(ppp,2);      % Key Parameter (squircle) 0: all circle; 1: all square
    C=Params{test}(ppp,3);      % Reduce noise near cardinal by this factor (1: off)
    makefig=0;
    [yourAccu(:,ppp) yourBias(:,ppp)]=squircleBehave2compB(s,A,C,behB,makefig);
    
end

subplot(1,2,test)
    quickplotcompB(bb',mean(yourBias,2)',mean(oi,2)',['Group plot Bias - test- ' num2str(test)]);
end

%%
for ig=1:2;
    
    if ig==1
        p=1:15;
    else
        p=16:29;
    end
    
figure;
counter=1;
for ppp =p
    behB=bCue1o(:,ppp)'; %BehaviouralBias
    
    s=BestParams(ppp,1);     % noise (in memory/decision-making)
    A=BestParams(ppp,2);      % Key Parameter (squircle) 0: all circle; 1: all square
    C=BestParams(ppp,3);      % Reduce noise near cardinal by this factor (1: off)
    makefig=0;
    subplot(4,4,counter)
    [yourAccu yourBias]=squircleBehave2compB(s,A,C,behB,makefig);
    rlim([0 1]);
    counter=counter+1;
    
end

end
%%

colores=(colormap((winter(6))));
figure

%Cue Acc
for ppp=1:29
subplot(6,5,ppp)

accrottask_mod=accrot_cu_uncue;
accrottask_mod(size(accrottask_mod,1)+1,:,:)=accrot_cu_uncue(1,:,:);

dat=squeeze(accrottask_mod(:,1,ppp));
ap=(dat);
theta=deg2rad(accrottask_mod(:,3,1));
theta2=(max(theta)-theta);
theta3=abs(2*pi-theta);

chance=ap;
chance(:)=0.5;
polarplot(theta3,chance,'k-.','Color',colores(5,:),'LineWidth',1.5);hold on;
polarplot(theta3,ap,'-','Color',colores(3,:),'LineWidth',1.5);hold on;
r=polarplot(theta3,ap,'o','Color',colores(3,:),'MarkerFaceColor',colores(5,:),'LineWidth',2);

set(gca,'ThetaZeroLocation','top','FontSize',12);hold on;

title(['sigma = ' num2str(BestParams(ppp,1)) ' A= ' num2str(BestParams(ppp,2))])

end