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rd_plotTemporalAttentionAdjustErrorsGroup.m
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rd_plotTemporalAttentionAdjustErrorsGroup.m
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% rd_plotTemporalAttentionAdjustErrorsGroup.m
%% setup
subjectIDs = {'bl','rd','id','ec','ld','en','sj','ml','ca','jl','ew','jx'};
nSubjects = numel(subjectIDs);
run = 9;
plotIndivFigs = 0;
analyzeProbe = 0;
%% get data
for iSubject = 1:nSubjects
subjectID = subjectIDs{iSubject};
[groupData0(iSubject).errors, ...
groupData0(iSubject).targetOrients, ...
groupData0(iSubject).nonTargetOrients, ...
groupData0(iSubject).targetOrientDiff, ...
groupData0(iSubject).probeOrients, ...
groupData0(iSubject).probeOrientDiff, ...
groupData0(iSubject).responses, ...
groupData0(iSubject).targetOrientDiffSmooth] = ...
rd_plotTemporalAttentionAdjustErrors(subjectID, run, plotIndivFigs);
end
if analyzeProbe==0
groupData0 = rmfield(groupData0,'probeOrients');
groupData0 = rmfield(groupData0,'probeOrientDiff');
end
%% organize data
fNames = fieldnames(groupData0(1));
for iSubject = 1:nSubjects
for iF = 1:numel(fNames)
for iRI = 1:2
for iCV = 1:3
fName = fNames{iF};
switch fName
case 'targetOrientDiffSmooth'
groupData.(fName)(:,:,:,iSubject) = groupData0(iSubject).(fName);
otherwise
groupData.(fName){iCV,iRI}(:,iSubject) = ...
groupData0(iSubject).(fName){iCV,iRI};
end
end
end
end
end
%% descriptive statistics
% test circ stats
% sd = 100;
% theta = vonmisesrnd(0, deg2k(sd), [10000 1]);
% figure
% hist(theta)
% [ang_rad sd_rad] = circ_std(theta/180*pi);
% sd_est = sd_rad*180/pi;
% mean_est = circ_mean(theta/180*pi)*180/pi;
% fprintf('sd: %.2f, mean: %.2f\n', sd_est, mean_est)
descripData = [];
for iRI = 1:2
for iCV = 1:3
theta = groupData.errors{iCV,iRI};
[ang_rad sd_rad] = circ_std(theta/180*pi);
descripData.sd(:,iCV,iRI) = sd_rad*180/pi;
descripData.mean(:,iCV,iRI) = circ_mean(theta/180*pi)*180/pi;
end
end
descripData.absMean = abs(descripData.mean);
fieldNames = fields(descripData);
for iField = 1:numel(fieldNames)
fieldName = fieldNames{iField};
descripMean.(fieldName) = squeeze(mean(descripData.(fieldName),1));
descripSte.(fieldName) = squeeze(std(descripData.(fieldName),0,1))./sqrt(nSubjects);
end
%% points analyses
% calculate mean errors for each x-axis value
for iRI = 1:2
for iCV = 1:3
allErrors = groupData.errors{iCV,iRI}(:);
allTargetOrients = groupData.targetOrients{iCV,iRI}(:);
allNonTargetOrients = groupData.nonTargetOrients{iCV,iRI}(:);
allTargetOrientDiff = groupData.targetOrientDiff{iCV,iRI}(:);
if analyzeProbe
allProbeOrients = groupData.probeOrients{iCV,iRI}(:);
allProbeOrientDiff = groupData.probeOrientDiff{iCV,iRI}(:);
end
ods = unique(allTargetOrientDiff);
for iOD = 1:numel(ods)
odIdx = allTargetOrientDiff==ods(iOD);
errorByOD{iCV,iRI}(iOD) = mean(allErrors(odIdx));
errorByODStd{iCV,iRI}(iOD) = std(allErrors(odIdx));
errorByODAbs{iCV,iRI}(iOD) = mean(abs(allErrors(odIdx)));
end
tos = unique(allTargetOrients);
for iTO = 1:numel(tos)
toIdx = allTargetOrients==tos(iTO);
errorByTO{iCV,iRI}(iTO) = mean(allErrors(toIdx));
end
ntos = unique(allNonTargetOrients);
for iNTO = 1:numel(ntos)
ntoIdx = allNonTargetOrients==ntos(iNTO);
errorByNTO{iCV,iRI}(iNTO) = mean(allErrors(ntoIdx));
end
% joint of target and non-target
binSize = 10;
orients = 0:binSize:179;
for iTO = 1:numel(orients)
to = orients(iTO);
for iNTO = 1:numel(orients)
nto = orients(iNTO);
idxL = allTargetOrients>=to & allNonTargetOrients>=nto;
idxU = allTargetOrients<to+binSize & allNonTargetOrients<nto+binSize;
idx = idxL & idxU;
if nnz(idx)==0
val = NaN;
else
val = mean(allErrors(idx));
end
errorByTNTO{iCV,iRI}(iTO,iNTO) = val;
end
end
if analyzeProbe
pos = unique(allProbeOrients);
for iPO = 1:numel(pos)
poIdx = allProbeOrients==pos(iPO);
errorByPO{iCV,iRI}(iPO) = mean(allErrors(poIdx));
end
pds = unique(allProbeOrientDiff);
for iPD = 1:numel(pds)
pdIdx = allProbeOrientDiff==pds(iPD);
errorByPD{iCV,iRI}(iPD) = mean(allErrors(pdIdx));
end
else
pos = NaN;
pds = NaN;
end
odx{iCV,iRI} = ods;
tox{iCV,iRI} = tos;
ntox{iCV,iRI} = ntos;
pox{iCV,iRI} = pos;
pdx{iCV,iRI} = pds;
end
end
% weighted average of target x non-target error matrices
allErrorByTNTO = (errorByTNTO{1,1} + errorByTNTO{1,2}).*0.6 + ...
(errorByTNTO{2,1} + errorByTNTO{2,2}).*0.2 + ...
(errorByTNTO{3,1} + errorByTNTO{3,2}).*0.2;
%% smoothed data
groupMean.targetOrientDiffSmooth = nanmean(groupData.targetOrientDiffSmooth, 4);
groupSte.targetOrientDiffSmooth = nanstd(groupData.targetOrientDiffSmooth, 0, 4)./sqrt(nSubjects);
winSize = 29; % check in rd_plotTemporalAttentionAdjustErrors.m
steps = -90+floor(winSize/2):90-floor(winSize/2);
%% plot figures
% setup plots
targetNames = {'T1','T2'};
colors = {'b','g','r'};
errorLims = [-100 100];
orientationLims = [-10 190];
errorXTicks = [-90 -45 0 45 90];
orientationXTicks = [0 45 90 135 180];
smoothSize = 10; % 5
b = (1/smoothSize)*ones(1,smoothSize);
a = 1;
validityNames = {'valid','invalid','neutral'};
validityOrder = [1 3 2];
fieldNames = fields(descripMean);
groupFigTitle = [sprintf('%s ',subjectIDs{:}) sprintf('(N=%d), run %d', nSubjects, run)];
f = [];
%% descriptive stats
% indiv subjects
ylims = [];
ylims.absMean = [-1 8];
ylims.mean = [-8 8];
ylims.sd = [0 30];
for iField = 1:numel(fieldNames)
fieldName = fieldNames{iField};
% figNames{end+1} = [fieldName 'Indiv'];
f(end+1) = figure;
for iRI = 1:2
subplot(1,2,iRI)
bar(descripData.(fieldName)(:,validityOrder,iRI))
set(gca,'XTickLabel',subjectIDs)
colormap(flag(3))
xlim([0 nSubjects+1])
ylim(ylims.(fieldName))
if iRI==1
ylabel(fieldName)
legend(validityNames(validityOrder))
end
title(targetNames{iRI})
end
rd_supertitle(groupFigTitle);
rd_raiseAxis(gca);
end
% scatter
fieldName = 'absMean';
conds = [1 3];
f(end+1) = figure;
for iRI = 1:2
subplot(1,2,iRI)
plot(descripData.(fieldName)(:,conds(1),iRI),descripData.(fieldName)(:,conds(2),iRI),'.')
hold on
plot(ylims.(fieldName),ylims.(fieldName),'k')
xlim(ylims.(fieldName))
ylim(ylims.(fieldName))
axis square
title(targetNames{iRI})
if iRI==1
xlabel(sprintf('%s %s', validityNames{conds(1)}, fieldName))
ylabel(sprintf('%s %s', validityNames{conds(2)}, fieldName))
end
end
% group
ylims.absMu = [-1 4];
ylims.mu = [-4 4];
ylims.sd = [0 25];
for iField = 1:numel(fieldNames)
fieldName = fieldNames{iField};
% figNames{end+1} = [fieldName 'Group'];
f(end+1) = figure;
for iRI = 1:2
subplot(1,2,iRI)
hold on
b1 = bar(1:3, descripMean.(fieldName)(validityOrder,iRI),'FaceColor',[.5 .5 .5]);
p1 = errorbar(1:3, descripMean.(fieldName)(validityOrder,iRI)', ...
descripSte.(fieldName)(validityOrder,iRI)','k','LineStyle','none');
ylim(ylims.(fieldName))
ylabel(fieldName)
set(gca,'XTick',1:3)
set(gca,'XTickLabel', validityNames(validityOrder))
title(targetNames{iRI})
end
rd_supertitle(groupFigTitle);
rd_raiseAxis(gca);
end
%% descriptive stats
for iRI = 1:2
fprintf('\n%s\n',targetNames{iRI})
vals = descripData.sd(:,:,iRI);
[h pVI ci statVI] = ttest(vals(:,1), vals(:,2));
[h pVN ci statVN] = ttest(vals(:,1), vals(:,3));
[h pNI ci statNI] = ttest(vals(:,3), vals(:,2));
fprintf('valid vs. invalid: t(%d) = %1.5f, p = %1.5f\n', ...
statVI.df, statVI.tstat, pVI)
fprintf('valid vs. neutral: t(%d) = %1.5f, p = %1.5f\n', ...
statVN.df, statVN.tstat, pVN)
fprintf('neutral vs. invalid: t(%d) = %1.5f, p = %1.5f\n', ...
statNI.df, statNI.tstat, pNI)
end
%% dots
% target orientation
figure
for iRI = 1:2
subplot(2,1,iRI)
hold on
plot(orientationLims,[0 0], 'k')
for iCV = 1:3
% w = groupData.targetOrients{iCV,iRI}>90;
w = logical(ones(size(groupData.targetOrients{iCV,iRI})));
plot(groupData.targetOrients{iCV,iRI}(w), groupData.errors{iCV,iRI}(w), '.', 'Color', colors{iCV})
end
for iCV = 1:3
smoothError = filter(b,a,errorByTO{iCV,iRI});
plot(tox{iCV,iRI}, smoothError,'Color', colors{iCV}, 'LineWidth',2)
end
set(gca,'XTick', orientationXTicks)
xlim(orientationLims)
ylim(errorLims)
ylabel('error')
title(targetNames{iRI})
end
xlabel('target orientation')
rd_supertitle(sprintf('%s ', subjectIDs{:}));
rd_raiseAxis(gca);
% flip flip
figure
for iRI = 1:2
subplot(2,1,iRI)
hold on
for iCV = 1:3
to = groupData.targetOrients{iCV,iRI};
e = groupData.errors{iCV,iRI};
w = to>90;
targetOrientsFF{iCV,iRI} = to;
targetOrientsFF{iCV,iRI}(w) = 180 - to(w);
errorsFF{iCV,iRI} = e;
errorsFF{iCV,iRI}(w) = -e(w);
eff = errorsFF{iCV,iRI}(:);
toff = targetOrientsFF{iCV,iRI}(:);
plot(toff, eff, '.', 'Color', colors{iCV})
end
plot([0 90], [0 0], 'k', 'LineWidth', 2)
xlim([0 90])
xlabel('flipflip target orientation')
ylabel('flipflip error')
end
toErrorsFF = errorsFF;
% non-target orientation
figure
for iRI = 1:2
subplot(2,1,iRI)
hold on
plot(orientationLims,[0 0], 'k')
for iCV = 1:3
plot(groupData.nonTargetOrients{iCV,iRI}, groupData.errors{iCV,iRI}, '.', 'Color', colors{iCV})
end
set(gca,'XTick', orientationXTicks)
xlim(orientationLims)
ylim(errorLims)
ylabel('error')
title(targetNames{iRI})
end
xlabel('non-target orientation')
rd_supertitle(sprintf('%s ', subjectIDs{:}));
rd_raiseAxis(gca);
% orientation difference between targets
figure
for iRI = 1:2
subplot(2,1,iRI)
hold on
plot(errorLims,[0 0], 'k')
for iCV = 1:3
plot(groupData.targetOrientDiff{iCV,iRI}, groupData.errors{iCV,iRI}, '.', 'Color', colors{iCV})
end
for iCV = 1:3
smoothError = filter(b,a,errorByODAbs{iCV,iRI});
plot(odx{iCV,iRI}, smoothError,'Color', colors{iCV}, 'LineWidth',2)
end
set(gca,'XTick', errorXTicks)
xlim(errorLims)
ylim(errorLims)
ylabel('error')
title(targetNames{iRI})
end
xlabel('non-target - target orientation difference')
rd_supertitle(sprintf('%s ', subjectIDs{:}));
rd_raiseAxis(gca);
% flip flip
binEdgesFF = -90:10:0;
effVar = [];
figure
for iRI = 1:2
subplot(2,1,iRI)
hold on
for iCV = 1:3
tod = groupData.targetOrientDiff{iCV,iRI};
e = groupData.errors{iCV,iRI};
w = tod>0;
targetOrientDiffFF{iCV,iRI} = tod;
targetOrientDiffFF{iCV,iRI}(w) = -tod(w);
errorsFF{iCV,iRI} = e;
errorsFF{iCV,iRI}(w) = -e(w);
eff = errorsFF{iCV,iRI}(:);
todff = targetOrientDiffFF{iCV,iRI}(:);
plot(todff, abs(eff), '.', 'Color', colors{iCV})
% bin
for iBin = 1:numel(binEdgesFF)-1
edges = binEdgesFF(iBin:iBin+1);
effVar(iBin,iCV,iRI) = var(eff(todff>edges(1) & todff<=edges(2)));
effMean(iBin,iCV,iRI) = mean(abs(eff(todff>edges(1) & todff<=edges(2))));
end
end
plot([-90 0], [0 0], 'k', 'LineWidth', 2)
xlim([-90 0])
xlabel('flipflip non-target - target orientation difference')
ylabel('flipflip error')
end
todErrorsFF = errorsFF;
figure
for iRI = 1:2
subplot(1,2,iRI)
plot(effVar(:,:,iRI))
xlabel('flipflip non-target - target bin')
ylabel('error variance')
title(targetNames{iRI})
set(gca,'XTick',1:2:10)
set(gca,'XTickLabel',binEdgesFF(1:2:end))
end
legend('valid','invalid','neutral')
figure
for iRI = 1:2
subplot(1,2,iRI)
plot(effMean(:,:,iRI))
xlabel('flipflip non-target - target bin')
ylabel('abs error mean')
title(targetNames{iRI})
set(gca,'XTick',1:2:10)
set(gca,'XTickLabel',binEdgesFF(1:2:end))
end
legend('valid','invalid','neutral')
% target x non-target orientation
validityNames = {'valid','invalid','neutral'};
validityOrder = [1 3 2];
figure
for iRI = 1:2
for iCV = 1:3
subplot(3,2,(validityOrder(iCV)-1)*2 + iRI)
e = errorByTNTO{iCV,iRI};
e(isnan(e)) = 0;
sm = smooth2a(e,3,3);
imagesc(sm')
colormap(othercolor('PRGn5',256));
set(gca,'clim',[-8 8])
axis square
title(validityNames{iCV})
end
end
figure
e = allErrorByTNTO;
e(isnan(e)) = 0;
sm = smooth2a(e,3,3);
imagesc(sm')
colormap(othercolor('PRGn5',256));
set(gca,'clim',[-3.5 3.5])
set(gca,'XTickLabel',[])
set(gca,'YTickLabel',[])
axis square
colorbar
xlabel('target orientation')
ylabel('non-target orientation')
title('all trials')
if analyzeProbe
% probe orientation
figure
for iRI = 1:2
subplot(2,1,iRI)
hold on
plot(orientationLims,[0 0], 'k')
for iCV = 1:3
plot(groupData.probeOrients{iCV,iRI}, groupData.errors{iCV,iRI}, '.', 'Color', colors{iCV})
end
for iCV = 1:3
smoothError = filter(b,a,errorByPO{iCV,iRI});
plot(pox{iCV,iRI}, smoothError,'Color', colors{iCV}, 'LineWidth',2)
end
set(gca,'XTick', orientationXTicks)
xlim(orientationLims)
ylim(errorLims)
ylabel('error')
title(targetNames{iRI})
end
xlabel('probe orientation')
rd_supertitle(sprintf('%s ', subjectIDs{:}));
% orientation difference between probe and target
figure
for iRI = 1:2
subplot(2,1,iRI)
hold on
plot(errorLims,[0 0], 'k')
for iCV = 1:3
plot(groupData.probeOrientDiff{iCV,iRI}, groupData.errors{iCV,iRI}, '.', 'Color', colors{iCV})
end
for iCV = 1:3
smoothError = filter(b,a,errorByPD{iCV,iRI});
plot(pdx{iCV,iRI}, smoothError,'Color', colors{iCV}, 'LineWidth',2)
end
set(gca,'XTick', errorXTicks)
xlim(errorLims)
ylim(errorLims)
ylabel('error')
title(targetNames{iRI})
end
xlabel('probe - target orientation difference')
rd_supertitle(sprintf('%s ', subjectIDs{:}));
rd_raiseAxis(gca);
end
% smoothed difference between target and non-target orientation
colors = {'b','g','r'};
figure
for iRI = 1:2
subplot(2,1,iRI)
hold on
for iCV = 1:3
shadedErrorBar(steps, ...
groupMean.targetOrientDiffSmooth(:,iRI,iCV), ...
groupSte.targetOrientDiffSmooth(:,iRI,iCV), ...
colors{iCV},1)
end
plot([-90 90],[0 0],'k')
xlim([steps(1) steps(end)])
ylim([-15 15])
ylabel(sprintf('error (sliding window average, size=%d)', winSize))
if iRI==1
% legend('valid','invalid','neutral')
end
end
xlabel('non-target - target orientation difference')
rd_supertitle(sprintf('%s ', subjectIDs{:}));
rd_raiseAxis(gca);
%% Fit lines to data - target orientation
xgridFF = 0:90;
for iSubject = 1:nSubjects
figure
hold on
for iRI = 1:2
for iCV = 1:3
eFF = toErrorsFF{iCV,iRI}(:,iSubject);
toFF = targetOrientsFF{iCV,iRI}(:,iSubject);
p = polyfit(toFF, eFF, 1);
subplot(3,2,(validityOrder(iCV)-1)*2 + iRI)
hold on
plot(toFF,eFF,'.')
plot(xgridFF,polyval(p,xgridFF),'r')
title(validityNames{iCV})
params{iCV,iRI}(iSubject,:) = p;
end
end
rd_supertitle(subjectIDs{iSubject})
end
for iRI = 1:2
for iCV = 1:3
paramsMean(iCV,iRI,:) = mean(params{iCV,iRI},1);
paramsSte(iCV,iRI,:) = std(params{iCV,iRI},0,1)./sqrt(nSubjects);
end
end
% plot bars
paramNames = {'slope','intercept'};
ylims = [-.5 .5; -10 25];
for iP = 1:numel(p)
figure
for iRI = 1:2
for iCV = 1:3
subplot(3,2,(validityOrder(iCV)-1)*2 + iRI)
bar(params{iCV,iRI}(:,iP))
title(validityNames{iCV})
ylim(ylims(iP,:))
end
end
rd_supertitle(paramNames{iP})
end
for iP = 1:numel(p)
figure
barweb(paramsMean(validityOrder,:,iP)',paramsSte(validityOrder,:,iP)', ...
[], targetNames, [], [], [], gray)
legend(validityNames{validityOrder})
ylabel(paramNames{iP})
end
fit.to.params = params;
fit.to.paramsMean = paramsMean;
fit.to.paramsSte = paramsSte;
fit.to.paramNames = paramNames;
%% Fit lines to data - orientation difference between targets
xgridFF = -90:0;
for iSubject = 1:nSubjects
figure
hold on
for iRI = 1:2
for iCV = 1:3
eFF = todErrorsFF{iCV,iRI}(:,iSubject);
todFF = targetOrientDiffFF{iCV,iRI}(:,iSubject);
p = polyfit(todFF, eFF, 1);
subplot(3,2,(validityOrder(iCV)-1)*2 + iRI)
hold on
plot(todFF,eFF,'.')
plot(xgridFF,polyval(p,xgridFF),'r')
title(validityNames{iCV})
params{iCV,iRI}(iSubject,:) = p;
end
end
rd_supertitle(subjectIDs{iSubject})
end
for iRI = 1:2
for iCV = 1:3
paramsMean(iCV,iRI,:) = mean(params{iCV,iRI},1);
paramsSte(iCV,iRI,:) = std(params{iCV,iRI},0,1)./sqrt(nSubjects);
end
end
% plot bars
paramNames = {'slope','intercept'};
ylims = [-.5 .5; -10 25];
for iP = 1:numel(p)
figure
for iRI = 1:2
for iCV = 1:3
subplot(3,2,(validityOrder(iCV)-1)*2 + iRI)
bar(params{iCV,iRI}(:,iP))
title(validityNames{iCV})
ylim(ylims(iP,:))
end
end
rd_supertitle(paramNames{iP})
end
for iP = 1:numel(p)
figure
barweb(paramsMean(validityOrder,:,iP)',paramsSte(validityOrder,:,iP)', ...
[], targetNames, [], [], [], gray)
legend(validityNames{validityOrder})
ylabel(paramNames{iP})
end
fit.tod.params = params;
fit.tod.paramsMean = paramsMean;
fit.tod.paramsSte = paramsSte;
fit.tod.paramNames = paramNames;
%% Fit lines to data - orientation difference between targets, absolute
%% value of errors
xgridFF = -90:0;
for iSubject = 1:nSubjects
figure
hold on
for iRI = 1:2
for iCV = 1:3
eFF = abs(todErrorsFF{iCV,iRI}(:,iSubject));
todFF = targetOrientDiffFF{iCV,iRI}(:,iSubject);
p = polyfit(todFF, eFF, 1);
subplot(3,2,(validityOrder(iCV)-1)*2 + iRI)
hold on
plot(todFF,eFF,'.')
plot(xgridFF,polyval(p,xgridFF),'r')
title(validityNames{iCV})
params{iCV,iRI}(iSubject,:) = p;
end
end
rd_supertitle(subjectIDs{iSubject})
end
for iRI = 1:2
for iCV = 1:3
paramsMean(iCV,iRI,:) = mean(params{iCV,iRI},1);
paramsSte(iCV,iRI,:) = std(params{iCV,iRI},0,1)./sqrt(nSubjects);
end
end
% plot bars
paramNames = {'slope','intercept'};
ylims = [-.5 .5; -10 25];
for iP = 1:numel(p)
figure
for iRI = 1:2
for iCV = 1:3
subplot(3,2,(validityOrder(iCV)-1)*2 + iRI)
bar(params{iCV,iRI}(:,iP))
title(validityNames{iCV})
ylim(ylims(iP,:))
end
end
rd_supertitle(paramNames{iP})
end
for iP = 1:numel(p)
figure
barweb(paramsMean(validityOrder,:,iP)',paramsSte(validityOrder,:,iP)', ...
[], targetNames, [], [], [], gray)
legend(validityNames{validityOrder})
ylabel(paramNames{iP})
end
fit.todAbsError.params = params;
fit.todAbsError.paramsMean = paramsMean;
fit.todAbsError.paramsSte = paramsSte;
fit.todAbsError.paramNames = paramNames;
%% Set figure properties
% set font size of titles, axis labels, and legends
% set(findall(gcf,'type','text'),'FontSize',14)