mpib_memoreye/Figure_2_and_stats.m

clear all

   mainfolder='';

addpath(genpath([mainfolder, 'subfunctions/']))
addpath(genpath(['subfunctions/rm_anova2']))

%% Uncomment to load the RSA data and perform the cluster perm


% folder{1}=[mainfolder,'results/mean_recentered_th100/euclideanmodels/single_trial_RSA_first_check_object_1_cued_Pearson/']
% folder{2}=[mainfolder,'results/mean_recentered_th100/euclideanmodels/single_trial_RSA_first_check_object_1_uncued_Pearson/']
% folder{3}=[mainfolder,'results/mean_recentered_th100/euclideanmodels/single_trial_RSA_first_check_object_2_cued_Pearson/']
% folder{4}=[mainfolder,'results/mean_recentered_th100/euclideanmodels/single_trial_RSA_first_check_object_2_uncued_Pearson/']
% 
% load([mainfolder, 'good_partis.mat']) %list of participant with a good performance
% partis=good_partis';
% 
% 
% for fff=1:size(folder,2)
%     
%     counter=1;
%     RHO_id=[];
%     
%     for ppp=1:length(partis)
%         
%         toload=[folder{fff}, partis{ppp,:},'.mat']
%         load(toload)
%         RHO_id(:,counter)=nanmean(RSA_obj_rot.rho,1);
%         counter=counter+1;
%         
%     end
%     RHO_all{fff}=RHO_id
% end
% 
% time=RSA_obj_rot.time;
% 
% 
% %% Cluster analysis for each line
% 
% pv_cluster=0.0125; % p-vals for clust perm test
% az=zeros(size(RHO_all{1})); % matrix to be compare (in this case against 0);
% 
% timesignclust=allclustperm(RHO_all,time,az,pv_cluster) % output variable (rows -> lines; columns -> start and end (ms))
% 
% mean([timesignclust(1,1) timesignclust(2,1) timesignclust(3,1)-1000 timesignclust(4,1)-1000]) %average of "starting" point;
% 
% 
% %% Detecting peaks during encoding
% 
% maxstim1cue=maxintime(RHO_all{1},time,0,1000)
% maxstim1uncue=maxintime(RHO_all{2},time,0,1000)
% maxstim2cue=maxintime(RHO_all{3},time,1000,2000)-1000
% maxstim2uncue=maxintime(RHO_all{4},time,1000,2000)-1000
% 
% mean([maxstim1cue maxstim1uncue maxstim2cue maxstim2uncue]) %returning average from all 4 lines

%% Optional -> Loading the data ready to be plotted

load('data_for_plots/Figure_2/delay1_data_n41_2022.mat');

%% And plot

colores1=(colormap(flip(autumn(7))));
colores2=(colormap(flip(summer(7))));
colores=[colores2(7,:);colores1(7,:);colores2(5,:);colores1(4,:)];

figure('Position',([200 900 760 360])) %alt ([200 900 860 360]))
leng1='cued 1st'
leng2='uncued'
leng3='cued 1st'
leng4='uncued'
titu=[];%'RSA - Correlation with continuos model - single trial'
xl=[-500 5850];
yl=[-0.05 0.17];
aga=0;
pval=0.01
spa=-0.004
data=[];
data{1}=RHO_all{1};
data{2}=RHO_all{2};
data{3}=RHO_all{3};
data{4}=RHO_all{4};

% plot4lines_fdr(time,data,colores,xl,yl,leng1,leng2,leng3,leng4,titu,aga,pval,spa,1)
plot4lines(time,data,colores,xl,yl,leng1,leng2,leng3,leng4,titu,1,1,1,timesignclust,colores)

% fill([2000 2350 2000 2350],[20 20 0 0],'k','LineStyle','none','FaceAlpha',0.20);

 set(gca,'FontSize',22);
 
 %% 2nd delay

clear all

%% Uncomment to load the RSA data and perform the cluster perm

% 
% folder{1}=[mainfolder,'results/mean_recentered_th100/euclideanmodels/2nd_cue/single_trial_RSA_first_check_object_1_cued_Pearson/']
% folder{2}=[mainfolder,'results/mean_recentered_th100/euclideanmodels/2nd_cue/single_trial_RSA_first_check_object_1_uncued_Pearson/']
% folder{3}=[mainfolder,'results/mean_recentered_th100/euclideanmodels/2nd_cue/single_trial_RSA_first_check_object_2_cued_Pearson/']
% folder{4}=[mainfolder,'results/mean_recentered_th100/euclideanmodels/2nd_cue/single_trial_RSA_first_check_object_2_uncued_Pearson/']
% 
% load([mainfolder, 'good_partis.mat']) %list of participant with a good performance
% partis=good_partis';
% 
% for fff=1:size(folder,2)
%     
%     counter=1;
%     RHO_id=[];
%     
%     for ppp=1:length(partis)
%         
%         toload=[folder{fff}, partis{ppp,:},'.mat']
%         load(toload)
%         RHO_id(:,counter)=nanmean(RSA_obj_rot.rho,1);
%         counter=counter+1;
%         
%     end
%     RHO_all{fff}=RHO_id
% end
% 
% time=RSA_obj_rot.time;
% 
% %% Cluster analysis for each line
% 
% pv_cluster=0.0125; % p-vals for clust perm test
% az=zeros(size(RHO_all{1})); % matrix to be compare (in this case against 0);
% 
% timesignclust=allclustperm(RHO_all,time,az,pv_cluster) % output variable (rows -> lines; columns -> start and end (ms))
% 
% % mean([timesignclust(1,1) timesignclust(2,1) timesignclust(3,1)-1000 timesignclust(4,1)-1000]) %average of "starting" point;


%% And plot

%% Optional -> Loading the data ready to be plotted

load('data_for_plots/Figure_2/delay2_data_n41_2022.mat');

%% mode 6
colores1=(colormap(flip(autumn(7))));
colores2=(colormap(flip(summer(7))));
colores=[colores2(7,:);colores1(7,:);colores2(5,:);colores1(4,:)];

figure('Position',([200 900 420 360])) % alt. [200 900 440 360]
leng1='cued 1st'
leng2='cued 2nd'
leng3='cued 1st'
leng4='cued 2nd'
titu=[];%'RSA - Correlation with continuos model - single trial'
xl=[-300 2850];
yl=[-0.05 0.17];
aga=0;
pval=0.01
spa=-0.004
data=[];
data{1}=RHO_all{1};
data{2}=RHO_all{2};
data{3}=RHO_all{3};
data{4}=RHO_all{4};

% plot4lines_fdr_2nd_2(time,data,colores,xl,yl,leng1,leng2,leng3,leng4,titu,aga,pval,spa,1)
plot4lines_2nd(time,data,colores,xl,yl,leng1,leng2,leng3,leng4,titu,1,1,1,timesignclust,colores)

 set(gca,'FontSize',22);
 

%% Subfunctions

%Times for encoding of stimulus 1
function output = maxintime(data,time,toi1,toi2)
t1=find(time==toi1);
t2=find(time==toi2);
time2=time(t1:t2);
[B I]=sort(mean(data(t1:t2,:),2),'descend');
output=time2(I(1));
end

function timesignclust=allclustperm(RHO_all,time,az,pv_cluster)
timesignclust=nan(4,2); % output variable (rows -> lines; columns -> start and end (ms))
for r=1:4;      
    [clusters, p_values, t_sums, permutation_distribution] =permutest(RHO_all{r},az,1,0.05,5000)
    if p_values(1)<pv_cluster
        
        timesignclust(r,1)=time(clusters{1}(1))
        timesignclust(r,2)=time(clusters{1}(end))
        
    end
end
end