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rd_plotTemporalAttentionAdjustErrors.m
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rd_plotTemporalAttentionAdjustErrors.m
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function [errors, targetOrients, nonTargetOrients, targetOrientDiff, probeOrients, probeOrientDiff, responses, targetOrientDiffSmooth] = ...
rd_plotTemporalAttentionAdjustErrors(subjectID, run, plotFigs)
%% setup
% subjectID = 'rd';
subject = sprintf('%s_a1_tc100_soa1000-1250', subjectID);
% run = 9;
if nargin<3
plotFigs = 1;
end
expName = 'E3_adjust';
% dataDir = 'data';
% figDir = 'figures';
dataDir = pathToExpt('data');
figDir = pathToExpt('figures');
dataDir = sprintf('%s/%s/%s', dataDir, expName, subject(1:2));
figDir = sprintf('%s/%s/%s', figDir, expName, subject(1:2));
%% load data
dataFile = dir(sprintf('%s/%s_run%02d*', dataDir, subject, run));
load(sprintf('%s/%s', dataDir, dataFile(1).name))
%% get trials info
p = expt.p;
trials = expt.trials;
trials_headers = expt.trials_headers;
targetRotations = expt.targetRotations;
nTrials = size(trials,1);
targetContrastIdx = strcmp(trials_headers,'targetContrast');
respIntervalIdx = strcmp(trials_headers,'respInterval');
cueValidityIdx = strcmp(trials_headers,'cueValidity');
responseErrorIdx = strcmp(trials_headers,'responseError');
responseIdx = strcmp(trials_headers,'response');
%% get probe orientations in the order of trials
if isfield(expt.trialsPresented(1).vals(1),'probeStartAngle')
analyzeProbe = 1;
else
analyzeProbe = 0;
probeOrients = NaN;
probeOrientDiff = NaN;
end
if analyzeProbe
trialsPresented = expt.trialsPresented;
nRuns = numel(trialsPresented);
probeRotations = zeros(nTrials/nRuns, nRuns);
for iRun = 1:nRuns
vals = trialsPresented(iRun).vals;
for iTrial = 1:numel(vals)
if isempty(vals(iTrial).probeStartAngle)
probeRotations(vals(iTrial).trialIdx,iRun) = NaN;
else
probeRotations(vals(iTrial).trialIdx,iRun) = vals(iTrial).probeStartAngle;
end
end
end
probeRotations = reshape(probeRotations,nTrials,1);
end
%% group trials and target rotations by validity
for iRI = 1:numel(p.respInterval)
for iCV = 1:numel(p.cueValidity)
for iTC = 1:numel(p.targetContrasts)
w = trials(:,respIntervalIdx)==iRI & trials(:,cueValidityIdx)==iCV & trials(:,targetContrastIdx)==iTC;
totals.all{iCV,iRI}(:,:,iTC) = trials(w,:);
totals.targetRot{iCV,iRI}(:,:,iTC) = targetRotations(w,:);
if analyzeProbe
totals.probeRot{iCV,iRI}(:,:,iTC) = probeRotations(w,:);
end
end
end
end
%% analyze how errors depend on the non-post-cued target
if analyzeProbe
probeOrients = totals.probeRot;
end
for iRI = 1:numel(p.respInterval)
for iCV = 1:numel(p.cueValidity)
rotations = totals.targetRot{iCV,iRI};
targetIdx = totals.all{iCV,iRI}(1,respIntervalIdx); % taking advantage of the previous grouping
targetOrients{iCV,iRI} = rotations(:,targetIdx);
nonTargetOrients{iCV,iRI} = rotations(:,3-targetIdx);
errors{iCV,iRI} = totals.all{iCV,iRI}(:,responseErrorIdx);
responses{iCV,iRI} = totals.all{iCV,iRI}(:,responseIdx);
tod = nonTargetOrients{iCV,iRI} - targetOrients{iCV,iRI};
tod(tod>90) = tod(tod>90) - 180;
tod(tod<-90) = tod(tod<-90) + 180;
targetOrientDiff{iCV,iRI} = tod;
if analyzeProbe
pod = probeOrients{iCV,iRI} - targetOrients{iCV,iRI};
pod(pod>90) = pod(pod>90) - 180;
pod(pod<-90) = pod(pod<-90) + 180;
probeOrientDiff{iCV,iRI} = pod;
end
end
end
%% smooth
winSize = 29;
valsRange = [-90 90];
targetOrientDiffSmooth = [];
for iRI = 1:numel(p.respInterval)
for iCV = 1:numel(p.cueValidity)
vals = targetOrientDiff{iCV,iRI};
err = errors{iCV,iRI};
[errSmoothed, steps] = ...
rd_slidingWindow(vals, err, winSize, valsRange);
targetOrientDiffSmooth(:,iRI,iCV) = errSmoothed;
end
end
%% plot figs
if plotFigs
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];
% target orientation
figure
for iRI = 1:numel(p.respInterval)
subplot(numel(p.respInterval),1,iRI)
hold on
plot(orientationLims,[0 0], 'k')
for iCV = 1:numel(p.cueValidity)
plot(targetOrients{iCV,iRI}, errors{iCV,iRI}, '.', 'Color', colors{iCV})
end
set(gca,'XTick', orientationXTicks)
xlim(orientationLims)
ylim(errorLims)
ylabel('error')
title(targetNames{iRI})
end
xlabel('target orientation')
rd_supertitle(sprintf('%s run %d', subjectID, run));
rd_raiseAxis(gca);
% orientation difference between targets
figure
for iRI = 1:numel(p.respInterval)
subplot(numel(p.respInterval),1,iRI)
hold on
plot(errorLims,[0 0], 'k')
for iCV = 1:numel(p.cueValidity)
plot(targetOrientDiff{iCV,iRI}, errors{iCV,iRI}, '.', 'Color', colors{iCV})
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 run %d', subjectID, run));
rd_raiseAxis(gca);
if analyzeProbe
% probe orientation
figure
for iRI = 1:numel(p.respInterval)
subplot(numel(p.respInterval),1,iRI)
hold on
plot(orientationLims,[0 0], 'k')
for iCV = 1:numel(p.cueValidity)
plot(probeOrients{iCV,iRI}, errors{iCV,iRI}, '.', 'Color', colors{iCV})
end
set(gca,'XTick', orientationXTicks)
xlim(orientationLims)
ylim(errorLims)
ylabel('error')
title(targetNames{iRI})
end
xlabel('probe orientation')
rd_supertitle(sprintf('%s run %d', subjectID, run));
rd_raiseAxis(gca);
% orientation difference between probe and target
figure
for iRI = 1:numel(p.respInterval)
subplot(numel(p.respInterval),1,iRI)
hold on
plot(errorLims,[0 0], 'k')
for iCV = 1:numel(p.cueValidity)
plot(probeOrientDiff{iCV,iRI}, errors{iCV,iRI}, '.', 'Color', colors{iCV})
end
set(gca,'XTick', errorXTicks)
xlim(errorLims)
ylim(errorLims)
ylabel('error')
title(targetNames{iRI})
end
xlabel('probe - target orientation difference')
rd_supertitle(sprintf('%s run %d', subjectID, run));
rd_raiseAxis(gca);
end
% smoothed orientation difference between targets
colors = {'b','g','r'};
figure
hold on
for iRI = 1:numel(p.respInterval)
for iCV = 1:numel(p.cueValidity)
plot(steps, targetOrientDiffSmooth(:,iRI,iCV), colors{iCV})
end
end
plot(valsRange,[0 0],'k')
xlim(valsRange)
end