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- load('SuppPOmData.mat')
- %%
- POm_cumvals = nan(150,size(POmCumVals,1));
- for i = 1:size(POmCumVals,1)
- cumvals = POmCumVals{i}';
- if size(cumvals,1) < 150
- POm_cumvals(1:size(cumvals,1),i) = cumvals;
- else
- POm_cumvals(:,i) = cumvals(1:150);
- end
- end
- %%
- POm_cumvals = POm_cumvals(1:100,:);
- %%
- figure
- errorbar(nanmean(Control_cumvals,2),nanstd(Control_cumvals,[],2)/sqrt(20),'k')
- hold on
- errorbar(nanmean(POm_cumvals,2),nanstd(POm_cumvals,[],2)/sqrt(7),'b')
- xlim([0 100])
- ylabel('Cummulative Sum')
- xlabel('trial #')
- %%
- % figure
- % boundedline(1:150, nanmean(Control_cumvals,2), nanstd(Control_cumvals,[],2)/sqrt(11),'k')
- % hold on
- % boundedline(1:150, nanmean(BacL1_cumvals,2),nanstd(BacL1_cumvals,[],2)/sqrt(12),'r')
- % hold on
- % boundedline(1:150, nanmean(POm_cumvals,2),nanstd(POm_cumvals,[],2)/sqrt(5),'b')
- %
- % ylabel('Cummulative Sum')
- % xlabel('trial #')
- %%
- for i =1:size(Control_cumvals,2)
- plot(Control_cumvals(:,i),'k')
- hold on
- end
- for i =1:size(POm_cumvals,2)
- plot(POm_cumvals(:,i),'b')
- hold on
- end
- legend({'Control (n=20)', 'POm (n=7)'})
- ylim([-50 100])
- %%
- figure
- PlotAllDataPoints(Ctrl, POm)
- set(gca, 'XTick',1:2, 'XTickLabel', {'Ctrl', 'POm'})
- ylabel('Normalized learning score')
- xlim([0.5 2.5])
- [p h] = ranksum(Ctrl, POm)
- text(1.8, 0.7, ['p = ', num2str(p)])
- ylim([-1 1])
- set(gca,'YTick', [-1:0.5:1])
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