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rd_plotTemporalAttentionMultiSOACRFGroup.m
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rd_plotTemporalAttentionMultiSOACRFGroup.m
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% rd_plotTemporalAttentionMultiSOAGroup.m
%% Setup
% subjectInits = {'rd','vp','hl','ho'};
subjectInits = {'dg','sl','mr','ly','pv','ek','gk','md','ax'}; % 'ek'
% subjectInits = {'sl','pv','gk'}; % 100
% subjectInits = {'mr','md','ax'}; % 300
% subjectInits = {'dg','ly','ek'}; % 800
% subjectInits = {'dg','sl','mr','ax'}; % rd ran (full or partial)
% subjectInits = {'ly','pv','ek','gk','md'}; % wd ran
% subjectInits = {'ax'};
nSubjects = numel(subjectInits);
groupStr = sprintf('N=%d', nSubjects);
expName = 'E4'; % 'E2','E4'
T1T2Axis = ''; % 'same','diff'
extraSelection = ''; % 'sameOrient','diffOrient','sameContrastOneBack','diffContrastOneBack'
cleanRT = 1;
switch extraSelection
case 'sameOrient'
analStr2 = 'SO';
case 'diffOrient'
analStr2 = 'DO';
case 'sameContrastOneBack'
analStr2 = 'SC1';
case 'diffContrastOneBack'
analStr2 = 'DC1';
otherwise
analStr2 = '';
end
switch T1T2Axis
case 'same'
analStr3 = 'SA';
case 'diff'
analStr3 = 'DA';
otherwise
analStr3 = '';
end
analStr = sprintf('%s%s', analStr2, analStr3);
if ~isempty(analStr)
if strcmp(analStr(end),'_')
analStr = analStr(1:end-1);
end
end
if cleanRT
rtStr = '_RTx';
else
rtStr = '';
end
figPrefix = sprintf('g%s_N%d_%s%s', expName, nSubjects, analStr, rtStr);
normalizeData = 0;
saveFigs = 0;
%% Get indiv subject data
for iSubject = 1:nSubjects
subjectInit = subjectInits{iSubject};
[acc rt t1t2soa p dp eff] = ...
rd_plotTemporalAttentionMultiSOACRF(subjectInit, T1T2Axis, extraSelection, cleanRT);
for iEL = 1:numel(acc)
accData{iEL}(:,:,:,iSubject) = acc{iEL};
rtData{iEL}(:,:,:,iSubject) = rt{iEL};
% dpData{iEL}(:,:,iSubject) = dp{iEL};
% effData{iEL}(:,:,iSubject) = eff{iEL};
end
end
% %% Normalize data
% if normalizeData
% for iEL = 1:numel(accData)
% accData{iEL} = normalizeDC(accData{iEL});
% rtData{iEL} = normalizeDC(rtData{iEL});
% dpData{iEL} = normalizeDC(dpData{iEL});
% effData{iEL} = normalizeDC(effData{iEL});
% end
% end
%
% %% Cuing effect and average across cue validities
% for iEL = 1:numel(accData)
% accDataCueEff{iEL} = squeeze(accData{iEL}(1,:,:) - accData{iEL}(2,:,:));
% accDataCueAve{iEL} = squeeze(mean(accData{iEL},1));
% end
%
% %% Cuing effect valid/invalid vs. neutral
% for iEL = 1:numel(accData)
% accDataCueEffN{iEL}(:,:,1) = squeeze(accData{iEL}(1,:,:) - accData{iEL}(3,:,:));
% accDataCueEffN{iEL}(:,:,2) = squeeze(accData{iEL}(3,:,:) - accData{iEL}(2,:,:));
% end
%
% %% Group summary stats
% for iEL = 1:numel(accData)
% accMean{iEL} = mean(accData{iEL},3);
% rtMean{iEL} = mean(rtData{iEL},3);
% dpMean{iEL} = mean(dpData{iEL},3);
% effMean{iEL} = mean(effData{iEL},3);
%
% accSte{iEL} = std(accData{iEL},0,3)./sqrt(nSubjects);
% rtSte{iEL} = std(rtData{iEL},0,3)./sqrt(nSubjects);
% dpSte{iEL} = std(dpData{iEL},0,3)./sqrt(nSubjects);
% effSte{iEL} = std(effData{iEL},0,3)./sqrt(nSubjects);
% end
%
% %% Plot figs
% intervalNames = {'T1','T2'};
% cueNames = {'valid','invalid','neutral'};
% accLims = [0.2 1];
% rtLims = [0.2 1.6];
% dpLims = [-0.5 2.7];
% effLims = [0 3.5];
% accDiffLims = [-0.15 0.25];
% accCueEffLims = [-0.3 0.35];
% soaLims = [t1t2soa(1)-100 t1t2soa(end)+100];
% colors = get(0,'DefaultAxesColorOrder');
% axTitle = '';
%
% % subjectColHSV = rgb2hsv(varycolor(nSubjects));
% % subjectColors = hsv2rgb([subjectColHSV(:,1:2) subjectColHSV(:,3)*0.8]);
% subjectColors = varycolor(nSubjects)*.9;
% % subjectColors = colors((1:nSubjects)+2,:);
%
% fig(1) = figure;
% for iRI = 1:numel(p.respInterval)
% subplot(1,numel(p.respInterval),iRI)
% hold on
% plot(soaLims, [0.5 0.5], '--k');
%
% p1 = plot(repmat(t1t2soa',1,numel(p.cueValidity)),...
% accMean{iRI}', '-', 'LineWidth', 1.5);
% e1 = errorbar(repmat(t1t2soa',1,numel(p.cueValidity)),...
% accMean{iRI}', accSte{iRI}', '.', 'MarkerSize', 20, ...
% 'LineWidth', 1);
%
% xlabel('soa')
% ylabel('acc')
% title(intervalNames{iRI})
% xlim(soaLims)
% ylim(accLims)
% legend(p1, cueNames,'location','best')
% rd_supertitle(subjectInits);
% rd_raiseAxis(gca);
% rd_supertitle(axTitle);
% end
%
% fig(2) = figure;
% for iRI = 1:numel(p.respInterval)
% subplot(1,numel(p.respInterval),iRI)
% hold on
%
% p1 = plot(repmat(t1t2soa',1,numel(p.cueValidity)),...
% rtMean{iRI}', '-', 'LineWidth', 1.5);
% e1 = errorbar(repmat(t1t2soa',1,numel(p.cueValidity)),...
% rtMean{iRI}', rtSte{iRI}', '.', 'MarkerSize', 20, ...
% 'LineWidth', 1);
%
% xlabel('soa')
% ylabel('rt')
% title(intervalNames{iRI})
% xlim(soaLims)
% ylim(rtLims)
% legend(p1, cueNames,'location','best')
% box off
% rd_supertitle(subjectInits);
% rd_raiseAxis(gca);
% rd_supertitle(axTitle);
% end
%
% fig(3) = figure;
% for iRI = 1:numel(p.respInterval)
% subplot(1,numel(p.respInterval),iRI)
% hold on
% plot(soaLims, [0 0], '--k');
%
% p1 = plot(repmat(t1t2soa',1,numel(p.cueValidity)),...
% dpMean{iRI}', '-','LineWidth', 1.5);
% e1 = errorbar(repmat(t1t2soa',1,numel(p.cueValidity)),...
% dpMean{iRI}', dpSte{iRI}', '.', 'MarkerSize', 20, ...
% 'LineWidth', 1);
%
% xlabel('soa')
% ylabel('dprime')
% title(intervalNames{iRI})
% xlim(soaLims)
% ylim(dpLims)
% legend(p1, cueNames,'location','best')
% rd_supertitle(subjectInits);
% rd_raiseAxis(gca);
% rd_supertitle(axTitle);
% end
%
% fig(4) = figure;
% for iRI = 1:numel(p.respInterval)
% subplot(1,numel(p.respInterval),iRI)
% hold on
%
% p1 = plot(repmat(t1t2soa',1,numel(p.cueValidity)),...
% effMean{iRI}', '-', 'LineWidth', 1.5);
% e1 = errorbar(repmat(t1t2soa',1,numel(p.cueValidity)),...
% effMean{iRI}', effSte{iRI}', '.', 'MarkerSize', 20, ...
% 'LineWidth', 1);
% set(p1(2),'LineWidth',1.5)
%
% xlabel('soa')
% ylabel('efficiency (dprime/rt)')
% title(intervalNames{iRI})
% xlim(soaLims)
% ylim(effLims)
% legend(p1, cueNames,'location','best')
% rd_supertitle(subjectInits);
% rd_raiseAxis(gca);
% rd_supertitle(axTitle);
% end
%
% fig(5) = figure;
% for iRI = 1:numel(p.respInterval)
% subplot(1,numel(p.respInterval),iRI)
% set(gca,'ColorOrder',subjectColors)
% hold all
% plot(soaLims, [0 0], '--k');
%
% p1 = plot(t1t2soa, accDataCueEff{iRI},'LineWidth',1.5);
% plot(t1t2soa, mean(accDataCueEff{iRI},2),'k','LineWidth',2.5)
%
% xlabel('soa')
% ylabel('cuing effect (accuracy valid-invalid)')
% title(intervalNames{iRI})
% xlim(soaLims)
% ylim(accDiffLims)
% rd_supertitle(subjectInits);
% rd_raiseAxis(gca);
% rd_supertitle(axTitle);
% end
%
% fig(6) = figure;
% for iRI = 1:numel(p.respInterval)
% subplot(1,numel(p.respInterval),iRI)
% set(gca,'ColorOrder',subjectColors)
% hold all
% plot(soaLims, [0 0], '--k');
%
% plot(t1t2soa, accDataCueAve{iRI},'LineWidth',1.5)
% plot(t1t2soa, mean(accDataCueAve{iRI},2),'k','LineWidth',2.5)
%
% xlabel('soa')
% ylabel('acc average')
% title(intervalNames{iRI})
% xlim(soaLims)
% ylim(accLims)
% rd_supertitle(subjectInits);
% rd_raiseAxis(gca);
% rd_supertitle(axTitle);
% end
%
%
% fig(7) = figure;
% hold on
% plot(soaLims, [0 0], '--k')
% p1 = [];
% p1(1) = plot(t1t2soa, mean(accDataCueEff{1},2),'-','LineWidth',2);
% p1(2) = plot(t1t2soa, mean(accDataCueEff{2},2),'r-','LineWidth',2);
%
% e1(1) = errorbar(t1t2soa, mean(accDataCueEff{1},2), ...
% std(accDataCueEff{1},0,2)./sqrt(nSubjects),...
% '.','MarkerSize', 20,'LineWidth', 1);
% e1(2) = errorbar(t1t2soa, mean(accDataCueEff{2},2), ...
% std(accDataCueEff{2},0,2)./sqrt(nSubjects),...
% '.r','MarkerSize', 20,'LineWidth', 1);
%
% plot(t1t2soa, mean(accDataCueEff{1},2),'o','LineWidth',1,'MarkerSize',8,'MarkerFaceColor','w')
% plot(t1t2soa, mean(accDataCueEff{2},2),'rs','LineWidth',1,'MarkerSize',8,'MarkerFaceColor','w')
%
% legend(p1, intervalNames)
% xlabel('soa')
% ylabel('cuing effect (accuracy valid-invalid)')
% xlim(soaLims)
% ylim(accCueEffLims)
%
%
% fig(8) = figure;
% hold on
% plot(soaLims, [0.5 0.5], '--k');
% p1 = [];
% p1(1) = plot(t1t2soa, mean(accDataCueAve{1},2),'-','LineWidth',2);
% p1(2) = plot(t1t2soa, mean(accDataCueAve{2},2),'r-','LineWidth',2);
%
% e1(1) = errorbar(t1t2soa, mean(accDataCueAve{1},2), ...
% std(accDataCueAve{1},0,2)./sqrt(nSubjects),...
% '.','MarkerSize', 20,'LineWidth', 1);
% e1(2) = errorbar(t1t2soa, mean(accDataCueAve{2},2), ...
% std(accDataCueAve{2},0,2)./sqrt(nSubjects),...
% '.r','MarkerSize', 20,'LineWidth', 1);
%
% plot(t1t2soa, mean(accDataCueAve{1},2),'o','LineWidth',1,'MarkerSize',8,'MarkerFaceColor','w')
% plot(t1t2soa, mean(accDataCueAve{2},2),'rs','LineWidth',1,'MarkerSize',8,'MarkerFaceColor','w')
%
% legend(p1, intervalNames)
% xlabel('soa')
% ylabel('acc average')
% xlim(soaLims)
% ylim(accLims)
%
% viNames = {'valid - neutral','neutral - invalid'};
% fig(9) = figure;
% for iVI = 1:2
% subplot(2,1,iVI)
% hold on
% plot(soaLims, [0 0], '--k')
% p1 = [];
% p1(1,:) = plot(t1t2soa, squeeze(mean(accDataCueEffN{1}(:,:,iVI),2)),'-','LineWidth',2);
% p1(2,:) = plot(t1t2soa, squeeze(mean(accDataCueEffN{2}(:,:,iVI),2)),'-r','LineWidth',2);
%
% e1 = [];
% e1(1,:) = errorbar(t1t2soa, squeeze(mean(accDataCueEffN{1}(:,:,iVI),2)), ...
% squeeze(std(accDataCueEffN{1}(:,:,iVI),0,2))./sqrt(nSubjects),...
% '.','MarkerSize', 20,'LineWidth', 1);
% e1(2,:) = errorbar(t1t2soa, squeeze(mean(accDataCueEffN{2}(:,:,iVI),2)), ...
% squeeze(std(accDataCueEffN{2}(:,:,iVI),0,2))./sqrt(nSubjects),...
% '.r','MarkerSize', 20,'LineWidth', 1);
%
% plot(t1t2soa, squeeze(mean(accDataCueEffN{1}(:,:,iVI),2)),'o','LineWidth',1,'MarkerSize',8,'MarkerFaceColor','w')
% plot(t1t2soa, squeeze(mean(accDataCueEffN{2}(:,:,iVI),2)),'rs','LineWidth',1,'MarkerSize',8,'MarkerFaceColor','w')
%
% legend(p1(:,1), intervalNames)
% xlabel('soa')
% ylabel('cuing effect (accuracy)')
% xlim(soaLims)
% title(viNames{iVI})
% end
%
% %% set figure properties
% for iF = 1:numel(fig)
% % set font size of titles, axis labels, and legends
% % see also: http://stackoverflow.com/questions/8934468/changing-fonts-in-matlab-plots
% set(findall(fig(iF),'type','text'),'FontSize',14)
% end
%
% %% Save figs
% % figPrefix = [figPrefix '_sameAxDiffOrient'];
% if saveFigs
% figNames = {'acc','rt','dprime','eff','accCueEffect','accCueAve','accCueEffectOverlay','accCueAveOverlay','accCueEffectNOverlay'};
% rd_saveAllFigs(fig, figNames, figPrefix, [])
% end