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BEH_ModelFits_OrientTask.m
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BEH_ModelFits_OrientTask.m
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% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
% INFORMATION
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
% Need the MemToolbox found here: http://visionlab.github.io/MemToolbox/
% Fitting to mixed model and goodness-of-fit are done with functions from
% the MemToolbox
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
%% Load settings
load('filt_byTargets_v4_Settings.mat');
%% General location of saved processed data
saveLocation = [exp.dataLocation '\ProcessData\'];
%% Load behavioral data and time & freq parameters
load([exp.dataLocation '\ProcessData\ALLEEG_' exp.settings '.mat'])
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
%% OR load data file with response errors for each subject (and other task data)
%this loads all behavioral data including trials that might be excluded due
%to bad EEG data
error_deg = cell(1,length(exp.participants)); %pre-allocate
for i_part = 1:length(exp.participants)
% Load each subject's data
load([exp.dataLocation '\RawData\BEH\' num2str(exp.participants{i_part}) '_Orient_v3.mat'])
% Remove practice trials (first 20 trials)
error_deg_tmp = data.errorDegrees(1,21:end);
targets = data.target(1,21:length(data.errorDegrees));
% Remove trials where no target appeared
error_deg{i_part} = error_deg_tmp(targets == 1);
clear error_deg_tmp targets data
end
clear i_part
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
%% Compare fits of errors to different working memory models
% Figure 3 used the information below, but was created in excel
% Akaike Information Criterion (AIC): + values evidence in favor of model 2, - in favor of model 1
% Bayesian Information Criterion (BIC): + values evidence in favor of model 2, - in favor of model 1
% Log likelihood: - values evidence in favor of model 2, + in favor of model 1
% Corrected Akaike Information (AICc): + values evidence in favor of model 2, - in favor of model 1
% Deviance Information Criterion (DIC): + values evidence in favor of model 2, - in favor of model 1
model1 = StandardMixtureModel(); %standard 2 parameter model
model2 = WithBias(StandardMixtureModel); % model with mu
% model3 = ContinuousResourceModel();
model3 = VariablePrecisionModel();
model4 = VariablePrecisionModel_GammaPrecision();
model_cmp = cell(1,length(exp.participants)); %pre-allocate
for ii = 1:length(exp.participants)
sprintf(['Subj ' num2str(ii)])
data.errors = resp_errdeg{ii}';
data.n = ones(length(data.errors),1);
model_cmp{ii} = MemFit(data,{model1,model2,model3,model4}); %comparing models
%Make differences for easy comparison
cmpDiff.AIC(ii,1) = model_cmp{ii}.AIC(1,1) - model_cmp{ii}.AIC(1,2);
cmpDiff.AIC(ii,2) = model_cmp{ii}.AIC(1,1) - model_cmp{ii}.AIC(1,3);
cmpDiff.AIC(ii,3) = model_cmp{ii}.AIC(1,1) - model_cmp{ii}.AIC(1,4);
cmpDiff.AIC(ii,4) = model_cmp{ii}.AIC(1,2) - model_cmp{ii}.AIC(1,3);
cmpDiff.AIC(ii,5) = model_cmp{ii}.AIC(1,2) - model_cmp{ii}.AIC(1,4);
cmpDiff.AIC(ii,6) = model_cmp{ii}.AIC(1,3) - model_cmp{ii}.AIC(1,4);
cmpDiff.BIC(ii,1) = model_cmp{ii}.BIC(1,1) - model_cmp{ii}.BIC(1,2);
cmpDiff.BIC(ii,2) = model_cmp{ii}.BIC(1,1) - model_cmp{ii}.BIC(1,3);
cmpDiff.BIC(ii,3) = model_cmp{ii}.BIC(1,1) - model_cmp{ii}.BIC(1,4);
cmpDiff.BIC(ii,4) = model_cmp{ii}.BIC(1,2) - model_cmp{ii}.BIC(1,3);
cmpDiff.BIC(ii,5) = model_cmp{ii}.BIC(1,2) - model_cmp{ii}.BIC(1,4);
cmpDiff.BIC(ii,6) = model_cmp{ii}.BIC(1,3) - model_cmp{ii}.BIC(1,4);
cmpDiff.logLike(ii,1) = model_cmp{ii}.logLike(1,1) - model_cmp{ii}.logLike(1,2);
cmpDiff.logLike(ii,2) = model_cmp{ii}.logLike(1,1) - model_cmp{ii}.logLike(1,3);
cmpDiff.logLike(ii,3) = model_cmp{ii}.logLike(1,1) - model_cmp{ii}.logLike(1,4);
cmpDiff.logLike(ii,4) = model_cmp{ii}.logLike(1,2) - model_cmp{ii}.logLike(1,3);
cmpDiff.logLike(ii,5) = model_cmp{ii}.logLike(1,2) - model_cmp{ii}.logLike(1,4);
cmpDiff.logLike(ii,6) = model_cmp{ii}.logLike(1,3) - model_cmp{ii}.logLike(1,4);
cmpDiff.AICc(ii,1) = model_cmp{ii}.AICc(1,1) - model_cmp{ii}.AICc(1,2);
cmpDiff.AICc(ii,2) = model_cmp{ii}.AICc(1,1) - model_cmp{ii}.AICc(1,3);
cmpDiff.AICc(ii,3) = model_cmp{ii}.AICc(1,1) - model_cmp{ii}.AICc(1,4);
cmpDiff.AICc(ii,4) = model_cmp{ii}.AICc(1,2) - model_cmp{ii}.AICc(1,3);
cmpDiff.AICc(ii,5) = model_cmp{ii}.AICc(1,2) - model_cmp{ii}.AICc(1,4);
cmpDiff.AICc(ii,6) = model_cmp{ii}.AICc(1,3) - model_cmp{ii}.AICc(1,4);
cmpDiff.DIC(ii,1) = model_cmp{ii}.DIC(1,1) - model_cmp{ii}.DIC(1,2);
cmpDiff.DIC(ii,2) = model_cmp{ii}.DIC(1,1) - model_cmp{ii}.DIC(1,3);
cmpDiff.DIC(ii,3) = model_cmp{ii}.DIC(1,1) - model_cmp{ii}.DIC(1,4);
cmpDiff.DIC(ii,4) = model_cmp{ii}.DIC(1,2) - model_cmp{ii}.DIC(1,3);
cmpDiff.DIC(ii,5) = model_cmp{ii}.DIC(1,2) - model_cmp{ii}.DIC(1,4);
cmpDiff.DIC(ii,6) = model_cmp{ii}.DIC(1,3) - model_cmp{ii}.DIC(1,4);
%Also put together table of goodness-of-fit values
gof.AIC(ii,1) = model_cmp{ii}.AIC(1,1);
gof.AIC(ii,2) = model_cmp{ii}.AIC(1,2);
gof.AIC(ii,3) = model_cmp{ii}.AIC(1,3);
gof.AIC(ii,4) = model_cmp{ii}.AIC(1,4);
gof.BIC(ii,1) = model_cmp{ii}.BIC(1,1);
gof.BIC(ii,2) = model_cmp{ii}.BIC(1,2);
gof.BIC(ii,3) = model_cmp{ii}.BIC(1,3);
gof.BIC(ii,4) = model_cmp{ii}.BIC(1,4);
gof.logLike(ii,1) = model_cmp{ii}.logLike(1,1);
gof.logLike(ii,2) = model_cmp{ii}.logLike(1,2);
gof.logLike(ii,3) = model_cmp{ii}.logLike(1,3);
gof.logLike(ii,4) = model_cmp{ii}.logLike(1,4);
gof.AICc(ii,1) = model_cmp{ii}.AICc(1,1);
gof.AICc(ii,2) = model_cmp{ii}.AICc(1,2);
gof.AICc(ii,3) = model_cmp{ii}.AICc(1,3);
gof.AICc(ii,4) = model_cmp{ii}.AICc(1,4);
gof.DIC(ii,1) = model_cmp{ii}.DIC(1,1);
gof.DIC(ii,2) = model_cmp{ii}.DIC(1,2);
gof.DIC(ii,3) = model_cmp{ii}.DIC(1,3);
gof.DIC(ii,4) = model_cmp{ii}.DIC(1,4);
clear data
end
clear ii error_deg model1 model2 model3 model4
T = struct2table(cmpDiff);
Tout = struct2table(gof);
% Save output to text file
writematrix(T,'model4_cmpDiff.txt','Delimiter','tab')
writematrix(Tout,'model4_gof.txt','Delimiter','tab')
% Plot fit differences
figure; boxplot(T.BIC(:,1:3),'Labels',{'Standard + Bias','Variable Percision',...
'Variable + GammaPrecision'},'Whisker',2)
ylim([-20 5])
ylabel('\Delta BIC from Standard Mixture Model','Interpreter','tex');
figure; boxplot(T.logLike(:,1:3),'Labels',{'Standard + Bias','Variable Percision',...
'Variable + GammaPrecision'},'Whisker',2)
ylabel('\Delta logLikelihood from Standard Mixture Model','Interpreter','tex');
clear cmpDiff gof T Tout
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
%% Compare fits of standard mix model & standard mix model + bias
% Akaike Information Criterion (AIC): + values evidence in favor of model 2, - in favor of model 1
% **Bayesian Information Criterion (BIC): + values evidence in favor of model 2, - in favor of model 1
% Log likelihood: - values evidence in favor of model 2, + in favor of model 1
% Corrected Akaike Information (AICc): + values evidence in favor of model 2, - in favor of model 1
% Deviance Information Criterion (DIC): + values evidence in favor of model 2, - in favor of model 1
model1 = StandardMixtureModel(); %standard 2 parameter model
model2 = WithBias(StandardMixtureModel); % model with mu
model_cmp = cell(1,length(exp.participants)); %pre-allocate
for ii = 1:length(exp.participants)
sprintf(['Subj ' num2str(ii)])
model_cmp{ii} = MemFit(resp_errdeg{ii},{model1,model2}); %comparing models
%Make differences for easy comparison
cmpDiff.AIC(ii,1) = model_cmp{ii}.AIC(1,1) - model_cmp{ii}.AIC(1,2);
cmpDiff.BIC(ii,1) = model_cmp{ii}.BIC(1,1) - model_cmp{ii}.BIC(1,2);
cmpDiff.logLike(ii,1) = model_cmp{ii}.logLike(1,1) - model_cmp{ii}.logLike(1,2);
cmpDiff.AICc(ii,1) = model_cmp{ii}.AICc(1,1) - model_cmp{ii}.AICc(1,2);
end
clear ii error_deg model1 model2
T = struct2table(cmpDiff);
% Plot fit differences
figure; boxplot([T.AIC,T.BIC,T.AICc],'Labels',{'Akaike Information Criterion (AIC)',...
'Bayesian Information Criterion (BIC)','Corrected Akaike Information (AICc)'},'Whisker',2.5)
ylim([-10 30])
ylabel(sprintf('Model Comparison Metric \n (Standard Mixture vs Standard Mixture w/Bias)'));
clear cmpDiff
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
%% Fit errors to standard mixed model with plotting
model1 = StandardMixtureModel(); %standard 2 parameter model
model2 = WithBias(StandardMixtureModel); % model with mu
model_out = cell(1,length(exp.participants)); %pre-allocate
for ii = 1:length(exp.participants)
if ii == 21 || ii == 22 %subjects had bias in mean errors
model_out{ii} = MemFit(resp_errdeg{ii},model2); %plots fits
% model_out{ii} = MLE(resp_errdeg{ii},model2); %fits without plotting - w/bias
model_out{ii} = model_out{ii}(2:3);
else
model_out{ii} = MemFit(resp_errdeg{ii},model1); %plots fits
% model_out{ii} = MLE(resp_errdeg{ii},model1); %fits without plotting
end
end
clear ii error_deg model model1 model2
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
%% Figure 2A in manuscript
% Very annoying to do...
% 1) Select one participants plot at a time
% 2) Run function below
fig=gco;
% 3) Save each participants' y-values in variable below. Change number in
% dataY_All(#,:) to correspond to the participant
dataY_All(26,:)=fig.YData;
% 4) Save the x-values once since they are the same for each participant
dataX_All(1,:)=fig.XData;
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
% Definitely want to save data so do not have to do again
save([saveLocation 'BEH_Fits_All_v4.mat'],'dataY_All','dataX_All')
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
% Boundline plot
figure('Color',[1 1 1]);
boundedline(dataX_All(1,:),squeeze(mean(dataY_All(:,:),1)),...
squeeze(std(dataY_All(:,:),[],1)./sqrt(26)),'k')
xlim([-180 180])
title(['Overall Model Fits (Mean +/- SEM)']);
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
% Put all the response errors across subjects into vector
resp_errdeg_cat = cat(2,resp_errdeg{1:end});
% Fit all errors to mixed model
model = StandardMixtureModel(); %standard 2 parameter model
% model = WithBias(StandardMixtureModel); %model with mu
model_out_cat = MemFit(resp_errdeg_cat,model); %fits with plotting
clear model
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
%% Figure 2B in manuscript
% Extract parameter values
g_out_cat = NaN(length(exp.participants),1);
sd_out_cat = NaN(length(exp.participants),1);
for i_part = 1:length(exp.participants)
g_out_cat(i_part) = model_out{1,i_part}(1);
sd_out_cat(i_part) = model_out{1,i_part}(2);
end
clear i_part
median(g_out_cat)
std(g_out_cat)
mean(g_out_cat)
median(sd_out_cat)
mean(sd_out_cat)
std(sd_out_cat)
% Boxplots
figure
boxplot(g_out_cat,'Labels',{'g'},'Whisker',2)
ylabel('Guess Rate');
ylim([0 0.8])
figure
boxplot(sd_out_cat,'Labels',{'SD'},'Whisker',2)
ylabel('Variability');
ylim([5 20])
clear g_out_cat sd_out_cat
% /////////////////////////////////////////////////////////////////////////
% -------------------------------------------------------------------------
% /////////////////////////////////////////////////////////////////////////
%% Compare fits of errors to different models
% Akaike Information Criterion (AIC): + values evidence in favor of model 2, - in favor of model 1
% Bayesian Information Criterion (BIC): + values evidence in favor of model 2, - in favor of model 1
% Log likelihood: - values evidence in favor of model 2, + in favor of model 1
% Corrected Akaike Information (AICc): + values evidence in favor of model 2, - in favor of model 1
% Deviance Information Criterion (DIC): + values evidence in favor of model 2, - in favor of model 1
% Note. This is set-up to run the DIC but that can take a long time. Can
% comment out the DIC code in the loop to skip that goodness-of-fit metric
model1 = StandardMixtureModel(); %standard 2 parameter model
model2 = WithBias(StandardMixtureModel); % model with mu
% model3 = ContinuousResourceModel();
model3 = VariablePrecisionModel();
model4 = VariablePrecisionModel_GammaPrecision();
model_cmp = cell(1,length(exp.participants)); %pre-allocate
for ii = 1:length(exp.participants)
sprintf(['Subj ' num2str(ii)])
data.errors = resp_errdeg{ii}';
data.n = ones(length(data.errors),1);
model_cmp{ii} = MemFit(data,{model1,model2,model3,model4}); %comparing models
%Make differences for easy comparison
cmpDiff.AIC(ii,1) = model_cmp{ii}.AIC(1,1) - model_cmp{ii}.AIC(1,2);
cmpDiff.AIC(ii,2) = model_cmp{ii}.AIC(1,1) - model_cmp{ii}.AIC(1,3);
cmpDiff.AIC(ii,3) = model_cmp{ii}.AIC(1,1) - model_cmp{ii}.AIC(1,4);
cmpDiff.AIC(ii,4) = model_cmp{ii}.AIC(1,2) - model_cmp{ii}.AIC(1,3);
cmpDiff.AIC(ii,5) = model_cmp{ii}.AIC(1,2) - model_cmp{ii}.AIC(1,4);
cmpDiff.AIC(ii,6) = model_cmp{ii}.AIC(1,3) - model_cmp{ii}.AIC(1,4);
cmpDiff.BIC(ii,1) = model_cmp{ii}.BIC(1,1) - model_cmp{ii}.BIC(1,2);
cmpDiff.BIC(ii,2) = model_cmp{ii}.BIC(1,1) - model_cmp{ii}.BIC(1,3);
cmpDiff.BIC(ii,3) = model_cmp{ii}.BIC(1,1) - model_cmp{ii}.BIC(1,4);
cmpDiff.BIC(ii,4) = model_cmp{ii}.BIC(1,2) - model_cmp{ii}.BIC(1,3);
cmpDiff.BIC(ii,5) = model_cmp{ii}.BIC(1,2) - model_cmp{ii}.BIC(1,4);
cmpDiff.BIC(ii,6) = model_cmp{ii}.BIC(1,3) - model_cmp{ii}.BIC(1,4);
cmpDiff.logLike(ii,1) = model_cmp{ii}.logLike(1,1) - model_cmp{ii}.logLike(1,2);
cmpDiff.logLike(ii,2) = model_cmp{ii}.logLike(1,1) - model_cmp{ii}.logLike(1,3);
cmpDiff.logLike(ii,3) = model_cmp{ii}.logLike(1,1) - model_cmp{ii}.logLike(1,4);
cmpDiff.logLike(ii,4) = model_cmp{ii}.logLike(1,2) - model_cmp{ii}.logLike(1,3);
cmpDiff.logLike(ii,5) = model_cmp{ii}.logLike(1,2) - model_cmp{ii}.logLike(1,4);
cmpDiff.logLike(ii,6) = model_cmp{ii}.logLike(1,3) - model_cmp{ii}.logLike(1,4);
cmpDiff.AICc(ii,1) = model_cmp{ii}.AICc(1,1) - model_cmp{ii}.AICc(1,2);
cmpDiff.AICc(ii,2) = model_cmp{ii}.AICc(1,1) - model_cmp{ii}.AICc(1,3);
cmpDiff.AICc(ii,3) = model_cmp{ii}.AICc(1,1) - model_cmp{ii}.AICc(1,4);
cmpDiff.AICc(ii,4) = model_cmp{ii}.AICc(1,2) - model_cmp{ii}.AICc(1,3);
cmpDiff.AICc(ii,5) = model_cmp{ii}.AICc(1,2) - model_cmp{ii}.AICc(1,4);
cmpDiff.AICc(ii,6) = model_cmp{ii}.AICc(1,3) - model_cmp{ii}.AICc(1,4);
cmpDiff.DIC(ii,1) = model_cmp{ii}.DIC(1,1) - model_cmp{ii}.DIC(1,2);
cmpDiff.DIC(ii,2) = model_cmp{ii}.DIC(1,1) - model_cmp{ii}.DIC(1,3);
cmpDiff.DIC(ii,3) = model_cmp{ii}.DIC(1,1) - model_cmp{ii}.DIC(1,4);
cmpDiff.DIC(ii,4) = model_cmp{ii}.DIC(1,2) - model_cmp{ii}.DIC(1,3);
cmpDiff.DIC(ii,5) = model_cmp{ii}.DIC(1,2) - model_cmp{ii}.DIC(1,4);
cmpDiff.DIC(ii,6) = model_cmp{ii}.DIC(1,3) - model_cmp{ii}.DIC(1,4);
%Also put together table of goodness-of-fit values
gof.AIC(ii,1) = model_cmp{ii}.AIC(1,1);
gof.AIC(ii,2) = model_cmp{ii}.AIC(1,2);
gof.AIC(ii,3) = model_cmp{ii}.AIC(1,3);
gof.AIC(ii,4) = model_cmp{ii}.AIC(1,4);
gof.BIC(ii,1) = model_cmp{ii}.BIC(1,1);
gof.BIC(ii,2) = model_cmp{ii}.BIC(1,2);
gof.BIC(ii,3) = model_cmp{ii}.BIC(1,3);
gof.BIC(ii,4) = model_cmp{ii}.BIC(1,4);
gof.logLike(ii,1) = model_cmp{ii}.logLike(1,1);
gof.logLike(ii,2) = model_cmp{ii}.logLike(1,2);
gof.logLike(ii,3) = model_cmp{ii}.logLike(1,3);
gof.logLike(ii,4) = model_cmp{ii}.logLike(1,4);
gof.AICc(ii,1) = model_cmp{ii}.AICc(1,1);
gof.AICc(ii,2) = model_cmp{ii}.AICc(1,2);
gof.AICc(ii,3) = model_cmp{ii}.AICc(1,3);
gof.AICc(ii,4) = model_cmp{ii}.AICc(1,4);
gof.DIC(ii,1) = model_cmp{ii}.DIC(1,1);
gof.DIC(ii,2) = model_cmp{ii}.DIC(1,2);
gof.DIC(ii,3) = model_cmp{ii}.DIC(1,3);
gof.DIC(ii,4) = model_cmp{ii}.DIC(1,4);
clear data
end
clear ii error_deg model1 model2 model3 model4
T = struct2table(cmpDiff);
Tout = struct2table(gof);
% *bar plots in manuscript were done in excel*
% Plot fit differences
figure; boxplot(T.BIC(:,1:3),'Labels',{'Standard Mixture + Bias',...
'Variable Percision','Variable + GammaPrecision'},'Whisker',2)
ylim([-20 5])
ylabel('\Delta BIC from Standard Mixture Model','Interpreter','tex');
figure; boxplot(T.logLike(:,1:3),'Labels',{'Standard Mixture + Bias',...
'Variable Percision','Variable + GammaPrecision'},'Whisker',2)
ylabel('\Delta logLikelihood from Standard Mixture Model','Interpreter','tex');
clear cmpDiff gof T Tout