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+clear all
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+
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+ mainfolder='';
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+
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+addpath(genpath([mainfolder 'subfunctions/']))
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+addpath(genpath('subfunctions/rm_anova2'))
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+
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+%% Uncomment to load the RSA data and perform the cluster perm
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+
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+% folder{1}=[mainfolder, 'results/mean_recentered_th100/euclideanmodels/single_trial_with_between_RSA_object_1_cued_cont_Pearson/']
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+% folder{2}=[mainfolder, 'results/mean_recentered_th100/euclideanmodels/single_trial_with_between_RSA_object_1_uncued_cont_Pearson/']
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+% folder{3}=[mainfolder, 'eyedata/results/mean_recentered_th100/euclideanmodels/single_trial_with_between_RSA_object_2_cued_cont_Pearson/']
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+% folder{4}=[mainfolder, 'eyedata/results/mean_recentered_th100/euclideanmodels/single_trial_with_between_RSA_object_2_uncued_cont_Pearson/']
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+%
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+% % %2nd cue
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+%
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+% folder2{1}=[mainfolder, 'results/mean_recentered_th100/euclideanmodels/2nd_cue/single_trial_with_between_RSA_object_1_cued_cont_Pearson/']
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+% folder2{2}=[mainfolder, 'results/mean_recentered_th100/euclideanmodels/2nd_cue/single_trial_with_between_RSA_object_1_uncued_cont_Pearson/']
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+% folder2{3}=[mainfolder, 'results/mean_recentered_th100/euclideanmodels/2nd_cue/single_trial_with_between_RSA_object_2_cued_cont_Pearson/']
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+% folder2{4}=[mainfolder, 'results/mean_recentered_th100/euclideanmodels/2nd_cue/single_trial_with_between_RSA_object_2_uncued_cont_Pearson/']
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+%
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+% load([mainfolder, 'good_partis.mat']) %list of participant with a good performance
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+% %
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+% partis=good_partis'
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+%
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+% %% Loading with the script
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+% [RHO_id_group_1, time_1]=loadwithbet(folder, partis)
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+% [RHO_id_group_2, time_2]=loadwithbet(folder2, partis)
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+%
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+% %% Ordering variables
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+%
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+% a1_1=[];
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+% a2_1=[];
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+% a3_1=[];
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+%
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+% a1_1{1}=RHO_id_group_1{1};
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+% a1_1{2}=RHO_id_group_1{2};
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+% a1_1{3}=RHO_id_group_1{4};
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+% a1_1{4}=RHO_id_group_1{5};
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+%
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+% a2_1{1}=RHO_id_group_1{7};
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+% a2_1{2}=RHO_id_group_1{8};
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+% a2_1{3}=RHO_id_group_1{10};
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+% a2_1{4}=RHO_id_group_1{11};
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+%
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+% a3_1{1}=RHO_id_group_1{3};
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+% a3_1{2}=RHO_id_group_1{6};
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+% a3_1{3}=RHO_id_group_1{9};
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+% a3_1{4}=RHO_id_group_1{12};
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+%
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+% a1_2=[];
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+% a2_2=[];
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+% a3_2=[];
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+%
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+% a1_2{1}=RHO_id_group_2{1};
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+% a1_2{2}=RHO_id_group_2{2};
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+% a1_2{3}=RHO_id_group_2{4};
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+% a1_2{4}=RHO_id_group_2{5};
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+%
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+% a2_2{1}=RHO_id_group_2{7};
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+% a2_2{2}=RHO_id_group_2{8};
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+% a2_2{3}=RHO_id_group_2{10};
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+% a2_2{4}=RHO_id_group_2{11};
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+%
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+% a3_2{1}=RHO_id_group_2{3};
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+% a3_2{2}=RHO_id_group_2{6};
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+% a3_2{3}=RHO_id_group_2{9};
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+% a3_2{4}=RHO_id_group_2{12};
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+%
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+% %% Cluster analysis of the difference within-between (delay 1)
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+%
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+% %Setting times (from -100 stim onset until the end of the delay period)
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+% t1=find(time_1==-100)
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+% t2=find(time_1==5850)
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+% t1_2=find(time_1==900)
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+% [timesignclust cluspval] = cluster_dif(a1_1,a2_1,time_1,t1,t2,t1_2)
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+%
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+% % mean([timesignclust(1,1) timesignclust(2,1)]) %average of "starting" points;
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+%
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+% % Creating 2 variables to use it for the plots and respect the colours
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+% timesignclust_1 = nan(4,2);
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+% timesignclust_2 = nan(4,2);
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+% timesignclust_1([1 3],:)=timesignclust([1 3],:);
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+% timesignclust_2([1 3],:)=timesignclust([2 4],:);
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+%
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+% %% Cluster analysis of the difference within-between (delay 2)
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+%
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+% %Setting times (from -100 stim onset until the end of the delay period)
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+% t1=find(time_2==-100);
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+% t2=find(time_2==2850);
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+% t1_2=find(time_2==-100);
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+% [timesignclust2 cluspva2] = cluster_dif(a1_2,a2_2,time_2,t1,t2,t1_2)
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+%
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+% % mean([timesignclust(1,1) timesignclust(2,1)]) %average of "starting" point;
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+%
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+% % Creating 2 variables to use it for the plots and respect the colours
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+% timesignclust_1_2 = nan(4,2);
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+% timesignclust_2_2 = nan(4,2);
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+% timesignclust_1_2([1 3],:)=timesignclust2([1 3],:);
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+% timesignclust_2_2([1 3],:)=timesignclust2([2 4],:);
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+
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+%% Optional -> Loading the data ready to be plotted
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+
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+load('data_for_plots/Figure_3/data_n41_2022.mat');
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+
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+%% And plot
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+
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+%Preparing colors
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+colores1=(colormap((winter(4))));
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+colores2=(colormap((cool(4))));
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+colores=[colores2(4,:);colores2(3,:);colores1(4,:);colores1(3,:)];
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+
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+colores3=(colormap((winter(8))));
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+colores4=(colormap((cool(8))));
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+coloresclus=[colores4(7,:);colores4(7,:);colores3(7,:);colores3(7,:)];
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+
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+figure('Position',([200 800 750 700]))
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+subplot(2,1,1)
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+
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+leng1='cued within'
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+leng2='cued between'
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+leng3='uncued within'
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+leng4='uncued between'
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+titu=''
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+xl=[-500 5850];
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+yl=[-0.05 0.17];
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+plot4lines_errorback(time_1,a1_1,colores,xl,yl,leng1,leng2,leng3,leng4,titu,1,0,1,timesignclust_1,coloresclus)
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+set(gca,'FontSize',22);
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+
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+subplot(2,1,2)
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+
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+titu=''
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+plot4lines_errorback(time_1,a2_1,colores,xl,yl,leng1,leng2,leng3,leng4,titu,1,0,1,timesignclust_2,coloresclus)
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+set(gca,'FontSize',22);
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+
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+
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+ %% Plotting the 2nd delay
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+
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+figure('Position',([200 800 420 700]))
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+
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+subplot(2,1,1)
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+
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+leng1='cued 1st within'
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+leng2='cued 1st between'
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+leng3='cued 2nd within'
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+leng4='cued 2nd between'
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+titu=''
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+xl=[-300 2850];
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+yl=[-0.05 0.17];
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+plot4lines_2nd_errorback(time_2,a1_2,colores,xl,yl,leng1,leng2,leng3,leng4,titu,1,0,1,timesignclust_1_2,coloresclus)
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+set(gca,'FontSize',22);
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+
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+subplot(2,1,2)
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+
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+titu=''
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+
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+plot4lines_2nd_errorback(time_2,a2_2,colores,xl,yl,leng1,leng2,leng3,leng4,titu,1,0,1,timesignclust_2_2,coloresclus)
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+set(gca,'FontSize',22);
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+
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+
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+%% STATS ANOVA
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+% Encoding
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+
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+ toi1ori1 = [0 1000];%time of interest for ori 1
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+ toi1ori2 = [1000 2000];%time of interest for ori 1
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+
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+% toi1 = [0 500];%time of interest for ori 1
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+
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+t1_1=find(time_1==toi1ori1(1));
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+t2_1=find(time_1==toi1ori1(2));
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+
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+t1_1ori2=find(time_1==toi1ori2(1));
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+t2_1ori2=find(time_1==toi1ori2(2));
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+
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+%ori1 cued
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+av_a1cued1 =nanmean(a1_1{1}(t1_1:t2_1,:),1); % with 1
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+av_a1cued2 =nanmean(a1_1{2}(t1_1:t2_1,:),1); % bet 1
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+
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+%ori2 cued
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+av_a2cued1 =nanmean(a2_1{1}(t1_1ori2:t2_1ori2,:),1); % w 2
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+av_a2cued2 =nanmean(a2_1{2}(t1_1ori2:t2_1ori2,:),1); % betwen 2
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+
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+% addpath(genpath(['/rm_anova2']))
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+
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+aa= cat(1,av_a1cued1,av_a1cued2,av_a2cued1,av_a2cued2)'
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+p=size(av_a1cued1,2);
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+
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+S=[1:p,1:p,1:p,1:p];
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+F1=[ones(p,1);ones(p,1)*2;ones(p,1);ones(p,1)*2]'
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+F2=[ones(p*2,1);ones(p*2,1)*2]'
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+FACTNAMES={'between-within', 'item order'}
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+
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+stats = rm_anova2(aa,S,F1,F2,FACTNAMES)
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+
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+totalSS=sum([stats{2,2},stats{3,2},stats{4,2},stats{5,2},stats{6,2},stats{7,2}]);
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+etaS=[stats{2,2}/totalSS,stats{3,2}/totalSS,stats{4,2}/totalSS]
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+
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+%% STATS ANOVA
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+% 1st delay
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+
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+ toi1 = [2350 5850];%time of interest for ori 1
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+% toi1 = [0 500];%time of interest for ori 1
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+% toi1 = [3350 5850];%time of interest for delay 1 1s_after_cueonset
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+
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+
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+t1_1=find(time_1==toi1(1));
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+t2_1=find(time_1==toi1(2));
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+
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+%ori1 cued
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+av_a1cued1 =nanmean(a1_1{1}(t1_1:t2_1,:),1); % with 1
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+av_a1cued2 =nanmean(a1_1{2}(t1_1:t2_1,:),1); % bet 1
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+
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+%ori2 cued
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+av_a2cued1 =nanmean(a2_1{1}(t1_1:t2_1,:),1); % w 2
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+av_a2cued2 =nanmean(a2_1{2}(t1_1:t2_1,:),1); % betwen 2
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+
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+addpath(genpath(['/rm_anova2']))
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+
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+aa= cat(1,av_a1cued1,av_a1cued2,av_a2cued1,av_a2cued2)'
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+p=size(av_a1cued1,2);
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+
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+S=[1:p,1:p,1:p,1:p];
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+F1=[ones(p,1);ones(p,1)*2;ones(p,1);ones(p,1)*2]'
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+F2=[ones(p*2,1);ones(p*2,1)*2]'
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+FACTNAMES={'between-within', 'item order'}
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+
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+stats = rm_anova2(aa,S,F1,F2,FACTNAMES)
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+
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+totalSS=sum([stats{2,2},stats{3,2},stats{4,2},stats{5,2},stats{6,2},stats{7,2}]);
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+etaS=[stats{2,2}/totalSS,stats{3,2}/totalSS,stats{4,2}/totalSS]
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+
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+%% T-test (delay1)
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+
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+[h,p,ci,stats]= ttest(av_a2cued1,av_a2cued2) % stim 2 cued - withing vs between
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+[h,p,ci,stats]= ttest(av_a1cued1,av_a1cued2) % stim 2 cued - withing vs between
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+
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+
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+%% 2nd delay
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+
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+% toi12 = [350 2850];%time of interest for ori 1
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+ toil2 = [1350 2850];%time of interest for delay 1 1s_after_cueonset
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+
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+
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+t1_2=find(time_2==toi12(1));
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+t2_2=find(time_2==toi12(2));
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+
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+%ori1 cued
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+av_a1cued1 =nanmean(a1_2{3}(t1_2:t2_2,:),1); % with 1
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+av_a1cued2 =nanmean(a1_2{4}(t1_2:t2_2,:),1); % bet 1
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+
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+%ori2 cued
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+av_a2cued1 =nanmean(a2_2{3}(t1_2:t2_2,:),1); % w 2
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+av_a2cued2 =nanmean(a2_2{4}(t1_2:t2_2,:),1); % betwen 2
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+
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+addpath(genpath(['/rm_anova2']))
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+
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+aa= cat(1,av_a1cued1,av_a1cued2,av_a2cued1,av_a2cued2)'
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+p=size(av_a1cued1,2);
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+
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+S=[1:p,1:p,1:p,1:p];
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+F1=[ones(p,1);ones(p,1)*2;ones(p,1);ones(p,1)*2]'
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+F2=[ones(p*2,1);ones(p*2,1)*2]'
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+FACTNAMES={'between-within', 'item order'}
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+
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+stats = rm_anova2(aa,S,F1,F2,FACTNAMES)
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+
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+%%
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+%% T-test (delay2)
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+
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+[h,p,ci,stats]= ttest(av_a2cued1,av_a2cued2) % stim 2 cued - withing vs between
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+[h,p,ci,stats]= ttest(av_a1cued1,av_a1cued2) % stim 1 cued - withing vs between
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+
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+%% On the DIF
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+
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+toi11 = [2350 5850];%time of interest for delay 1 1s_after_cueonset
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+
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+t1_1=find(time_1==toi11(1));
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+t2_1=find(time_1==toi11(2));
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+
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+toil2 = [350 2850];%time of interest for delay 1 1s_after_cueonset
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+
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+t1_2=find(time_2==toil2(1));
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+t2_2=find(time_2==toil2(2));
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+
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+av_a1cued1=nanmean(a1_1{1}(t1_1:t2_1,:),1)-nanmean(a1_1{2}(t1_1:t2_1,:),1); %dif item 1, cue 1 b-w 1st delay
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+av_a2cued1=nanmean(a2_1{1}(t1_1:t2_1,:),1)-nanmean(a2_1{2}(t1_1:t2_1,:),1); %dif item 2, cue 1 b-w 1st delay
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+
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+av_a1cued2=nanmean(a1_2{3}(t1_2:t2_2,:),1)-nanmean(a1_2{4}(t1_2:t2_2,:),1); %dif item 1, cue 1 b-w 1st delay
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+av_a2cued2=nanmean(a2_2{3}(t1_2:t2_2,:),1)-nanmean(a2_2{4}(t1_2:t2_2,:),1); %dif item 2, cue 1 b-w 1st delay
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+
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+aa= cat(1,av_a1cued1,av_a1cued2,av_a2cued1,av_a2cued2)'
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+p=size(av_a1cued1,2);
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+
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+S=[1:p,1:p,1:p,1:p];
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+F1=[ones(p,1);ones(p,1)*2;ones(p,1);ones(p,1)*2]'
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+F2=[ones(p*2,1);ones(p*2,1)*2]'
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+FACTNAMES={'delay 1st or 2nd', 'item order'}
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+
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+stats = rm_anova2(aa,S,F1,F2,FACTNAMES)
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+
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+totalSS=sum([stats{2,2},stats{3,2},stats{4,2},stats{5,2},stats{6,2},stats{7,2}]);
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+etaS=[stats{2,2}/totalSS,stats{3,2}/totalSS,stats{4,2}/totalSS]
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+
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+%%
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+eye.all{1}(:,1)=av_a1cued1
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+eye.all{1}(:,2)=av_a2cued1
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+eye.all{1}(:,3)=av_a1cued2
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+eye.all{1}(:,4)=av_a2cued2
|
|
|
+
|
|
|
+
|
|
|
+%% eye plot
|
|
|
+
|
|
|
+labels={'stim 1/delay 1','stim 2/delay 1','stim 1/delay 2','stim 2/delay 2'};
|
|
|
+
|
|
|
+figure
|
|
|
+barplotbias(eye.all{1},[-.075,.2],' ','diff within - between',labels)
|
|
|
+
|
|
|
+%% Linear trend regarding distance
|
|
|
+
|
|
|
+%from closest to longest disntace
|
|
|
+
|
|
|
+distances(:,1)=av_a2cued1 %stim 2 cued 1st
|
|
|
+distances(:,2)=av_a1cued1 %stim 1 cued 1st
|
|
|
+distances(:,3)=av_a2cued2 %stim 2 cued 2nd
|
|
|
+distances(:,4)=av_a1cued2 %stim 1 cued 2nd
|
|
|
+
|
|
|
+
|
|
|
+%% Check linear trend
|
|
|
+
|
|
|
+ball=[];
|
|
|
+yfit=[];
|
|
|
+for ppp = 1:size(distances,1)
|
|
|
+
|
|
|
+ tof=[];
|
|
|
+ for c=1:size(distances,2);
|
|
|
+ tof=[tof; distances(ppp,c)];
|
|
|
+ end
|
|
|
+
|
|
|
+ [b,dev,stats] = glmfit(1:size(distances,2),tof,'normal');
|
|
|
+ yfit(ppp,:) = polyval([b(2,1),b(1,1)],[1,2,3,4]);
|
|
|
+
|
|
|
+
|
|
|
+ ball(ppp) = b(2);
|
|
|
+end
|
|
|
+
|
|
|
+
|
|
|
+[h,p,ci,stats] = ttest(ball,0)
|
|
|
+[p,h,stats] = signrank(ball,0)
|
|
|
+
|
|
|
+
|
|
|
+ %%
|
|
|
+ all{1}= distances
|
|
|
+
|
|
|
+ labels={'Stim 2','Stim 1','Stim 2','Stim 1'};
|
|
|
+
|
|
|
+figure
|
|
|
+
|
|
|
+plot(yfit','Color',[0 0 0 0.25],'LineWidth',1);hold on;
|
|
|
+
|
|
|
+plot(mean(yfit,1)','Color',[0 0 0],'LineWidth',4);hold on;
|
|
|
+barplotbias(all{1},[-.07,.19],' ','\Delta (rho) within - between',labels)
|
|
|
+
|
|
|
+
|
|
|
+%% subfuctions
|
|
|
+
|
|
|
+function barplotbias(data,yl,tito,yla,labels)
|
|
|
+
|
|
|
+ax=notBoxPlot(data,'style','sdline')
|
|
|
+
|
|
|
+line([0,5], [0,0], 'Color', 'k','LineStyle',':','LineWidth',2);hold on;
|
|
|
+line([2.5,2.5], [-10,10], 'Color', 'k','LineStyle','--','LineWidth',2);hold on;
|
|
|
+
|
|
|
+
|
|
|
+for i=1:4
|
|
|
+ ax(i).semPtch.FaceColor = [0.75 0.75 0.75];
|
|
|
+ ax(i).semPtch.EdgeColor = [0.75 0.75 0.75];
|
|
|
+% ax(i).semPtch.LineWidth = 10;
|
|
|
+ ax(i).mu.LineWidth = 5;
|
|
|
+
|
|
|
+ ax(i).data.MarkerSize = 8;
|
|
|
+ ax(i).mu.Color = [0 0 0];
|
|
|
+ ax(i).sd.Color = [0 0 0];
|
|
|
+end
|
|
|
+
|
|
|
+xticks([1:4])
|
|
|
+xticklabels(labels)
|
|
|
+% xtickangle(45)
|
|
|
+ylim(yl)
|
|
|
+ylabel(yla)
|
|
|
+xlim([0.5 4.5])
|
|
|
+
|
|
|
+sublabels{1}='Delay 1'
|
|
|
+sublabels{2}='Delay 2'
|
|
|
+xpos=0.15
|
|
|
+
|
|
|
+text(1.5,xpos,sublabels{1},'HorizontalAlignment','center','FontSize',25,'FontWeight','bold')
|
|
|
+text(3.5,xpos,sublabels{2},'HorizontalAlignment','center','FontSize',25,'FontWeight','bold')
|
|
|
+
|
|
|
+set(gca,'FontSize',25);
|
|
|
+
|
|
|
+title(tito)
|
|
|
+
|
|
|
+end
|
|
|
+
|
|
|
+function [timesignclust cluspv]=allclustperm2(RHO_all,time,az,pv_cluster)
|
|
|
+timesignclust=nan(size(RHO_all,2),2); % output variable (rows -> lines; columns -> start and end (ms))
|
|
|
+for r=1:size(RHO_all,2);
|
|
|
+ [clusters, p_values, t_sums, permutation_distribution] =permutest(RHO_all{r},az{r},1,0.05,20000)
|
|
|
+ if p_values(1)<pv_cluster
|
|
|
+
|
|
|
+ timesignclust(r,1)=time{r}(clusters{1}(1))
|
|
|
+ timesignclust(r,2)=time{r}(clusters{1}(end))
|
|
|
+
|
|
|
+ end
|
|
|
+
|
|
|
+ cluspv(r)=p_values(1);
|
|
|
+end
|
|
|
+end
|
|
|
+
|
|
|
+function [timesignclust cluspv] = cluster_dif(a1_1,a2_1,time_1,t1,t2,t1_2)
|
|
|
+
|
|
|
+%ori1 cued
|
|
|
+wi1 =a1_1{1}(t1:t2,:); % with 1
|
|
|
+bet1 =a1_1{2}(t1:t2,:); % bet 1
|
|
|
+%ori1 uncued
|
|
|
+wi1u =a1_1{3}(t1:t2,:); % with 1
|
|
|
+bet1u =a1_1{4}(t1:t2,:); % bet 1
|
|
|
+%ori2 cued
|
|
|
+wi2 =a2_1{1}(t1_2:t2,:); % w 2
|
|
|
+bet2 =a2_1{2}(t1_2:t2,:); % betwen 2
|
|
|
+%ori2 uncued
|
|
|
+wi2u =a2_1{3}(t1_2:t2,:); % w 2
|
|
|
+bet2u =a2_1{4}(t1_2:t2,:); % betwen 2
|
|
|
+
|
|
|
+RHOD_wi{1}=wi1();
|
|
|
+RHOD_wi{2}=wi2();
|
|
|
+RHOD_wi{3}=wi1u();
|
|
|
+RHOD_wi{4}=wi2u();
|
|
|
+
|
|
|
+RHOD_be{1}=bet1();
|
|
|
+RHOD_be{2}=bet2();
|
|
|
+RHOD_be{3}=bet1u();
|
|
|
+RHOD_be{4}=bet2u();
|
|
|
+
|
|
|
+pv_cluster=0.0125; % p-vals for clust perm test
|
|
|
+% az=zeros(size(RHOD_diff{1})); % matrix to be compare (in this case against 0);
|
|
|
+
|
|
|
+timesall{1}=time_1(t1:t2);
|
|
|
+timesall{2}=time_1(t1_2:t2);
|
|
|
+timesall{3}=time_1(t1:t2);
|
|
|
+timesall{4}=time_1(t1_2:t2);
|
|
|
+
|
|
|
+[timesignclust cluspv]=allclustperm2(RHOD_wi,timesall,RHOD_be,pv_cluster) % output variable (rows -> lines; columns -> start and end (ms))
|
|
|
+
|
|
|
+end
|