eLife-2017-data-and-analysis-scripts/crossings.asv at master · albey-code/eLife-2017-data-and-analysis-scripts · GitHub

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%% Figure 3E
%
% parameter estimates extracted from ROI 3
% data format: subjects x runs x object trial t-1 x object trial t
%
% (c) M.M. Garvert 2017

clear c

% Y_true = Y;
Y_true = mdscale(to_mds,2);

[n1,n2] = find(triu(dist)==1);
hold on
figure;

Y_ix = perms(1:7);
% Y_ix = Y_red;
% Y = Y_true([1 4 3 2 5 6 7],:)

c = zeros(5040,42);

for h = 1:5040
    if mod(h,100)==0
        disp(h)
    end

    Y = Y_true(Y_ix(h,:),:);
    c(h,1) = 0;
    counter = 0;

    for i = 1:7
        for j = 1:7
            if i ~=j
                counter = counter+1;

%                 line1
                x1  = [Y(n1(i),1) Y(n2(i),1)];
                y1  = [Y(n1(i),2) Y(n2(i),2)];
%                 line(x1, y1)

%                 line2
                x2  = [Y(n1(j),1) Y(n2(j),1)];
                y2  = [Y(n1(j),2) Y(n2(j),2)];
%                 line(x2, y2)

                [xi,yi] = polyxpoly(x1,y1,x2,y2);

                if ~isempty([xi,yi])
                    if ~([xi,yi] == [Y(n1(i),1) Y(n2(i),1)] | [xi,yi] == [Y(n1(i),2) Y(n2(i),2)])
                        c(h,counter) = 1;
                    end
                end


            end
        end
    end
    distances = squareform(pdist(Y));

    r(h) = corr(dist(eye(7)~=1),distances(eye(7)~=1));


%     if sum(c(h,:)) == 0
%         figure;
%                      scatter(Y(:,1),Y(:,2),'k','filled')
%                      text(0.05+Y(:,1),0.05+Y(:,2),num2str((1:7)'))   % label objects
%                      set(gcf,'color','w')
%                      set(findobj(gcf, 'type','axes'), 'Visible','off')
%
%                      % Connect the objects that were experienced during training
%                      [i,j] = find(dist==1);
%                      l=line([Y(i,1),Y(j,1)]',[Y(i,2),Y(j,2)]');
%                      set(l,'color','k');
%                      axis equal
%                      pause
%                      close all
%     end
end

c_plot = sum(c,2)/2;
figure;
subplot(1,2,1)
hold on
for i = 0:10
    bar(i,sum(c_plot==i))
end
subplot(1,2,2)
hist(r,20)

Y_true = mdscale(to_mds,2);
distances = squareform(pdist(Y_true));
r_true = corr(dist(eye(7)~=1),distances(eye(7)~=1));

figure; scatter(c_plot,r);
hold on
scatter(0,r_true,'r')
xlabel('Crossings')
xlabel('Correlation with link distance')
xlim([-1 8])
ylim([ -1 1])

for h = 1:5040

    if sum(c(h,:)) == 0
        Y = Y_true(Y_ix(h,:),:);

        figure;
        scatter(Y(:,1),Y(:,2),'k','filled')
        text(0.05+Y(:,1),0.05+Y(:,2),num2str((1:7)'))   % label objects
        set(gcf,'color','w')
        set(findobj(gcf, 'type','axes'), 'Visible','off')

        % Connect the objects that were experienced during training
        [i,j] = find(dist==1);
        l=line([Y(i,1),Y(j,1)]',[Y(i,2),Y(j,2)]');
        set(l,'color','k');
        axis equal
        pause
        close all
    end
end