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Commit 625ca4c0 authored by laurencehunt's avatar laurencehunt
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add session 4

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Session3/run_session3.m
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......@@ -15,7 +15,7 @@ clear all;close all; % close any open figures and delete variables
% load it in the same way as we did for the simulated data in week 2.
% Adjust the basepath to your directory
basepath='/Users/laurence/Dropbox/Private/teaching/WIN_Grad_Programme/2019/win-computational-models-2019/Session3/';
basepath='/Users/lhunt/Dropbox/Private/teaching/WIN_Grad_Programme/2019/win-computational-models-2019/Session3/';
load(fullfile(basepath,'data.mat'));
warning off;
......@@ -119,7 +119,7 @@ data.discardSubj = sum([abs(AccZscored) abs(meanRTZscored)' abs(stdRTZscored)']>
% Add one line of code here to check how many subjects were excluded with the threshold we used
% the command 'find' might be useful which finds any element of a vector that
% is not zero
keyboard %LINE TO BE ADDED BY STUDENT
find(data.discardSubj) %LINE TO BE ADDED BY STUDENT
% This plots a red circle on top of the mean accuracy IF there is a
% subject that is an outlier
......@@ -280,7 +280,7 @@ end
% Please insert here one line that saves the mean of the best parameter
% estimates across subjects in a variable called meanBestParam; we can
% then use that mean to initialize fminsearch in the next cell
meanBestParam = []; %LINE TO BE COMPLETED BY STUDENT
meanBestParam = mean(bestParams); %LINE TO BE COMPLETED BY STUDENT
%% 3.3 MODEL FITTING USING FMINSEARCH
%% 3.3.1 Fitting two learning rates and one beta
......
Session4/ModelBasedFMRI/Session4.m 0 → 100755
View file @ 625ca4c0
%% Section 1: Understanding haemodynamic convolution (hrf)
% Make the hrf
window = 14; % how much time (in s) to display
TR = 1; % what the time between FMRI volumes was
sigma1 = 3; % parameter for the shape of the hrf
my1 = 7; % parameter for hrf shape
alpha1=my1^2/sigma1^2;
beta1=my1/sigma1^2;
% Code the HRF
t=0:(TR):(window); % create the time window (from 0 s to 14s) for which to show the hrf
hrf = gammapdf(alpha1,beta1,t); % create the hrf
% Plot the hrf shape
figure('name','Convolution','color',[1 1 1]);
subplot(2,1,1);hold on;
plot(hrf,'Linewidth',3);set(gca,'Fontsize',16);title('HRF function')
% Create an example set of events
event_times = [50 100 150 160 170 180 190 200 303 306 309 312 315 330]; % onsets of the events
event_duration= [0 30 0 0 0 0 0 0 0 0 0 0 0 0]; % duration of events
event_values = [1 1 2 1 1 1 1.5 2 1 1 1 1 1 -1]; % event values
% Create a vector that has for every time point whether there is an event
% or not (and the size and direction of the event)
nT = 400; %total number of timepoints in our fake FMRI data
regressor = zeros(nT,1); % the vector to be filled
for i = 1:length(event_times)
regressor(event_times(i):event_times(i)+event_duration(i)) = event_values(i);
end
% Convolve the vector with the hrf
convolved_regressor = conv(regressor,hrf);
% Plot the resulting vector
subplot(2,1,2)
plot(regressor,'k','Linewidth',3);hold on;plot(convolved_regressor,'Linewidth',3)
legend('unconvolved regressor','convolved regressor');set(gca,'Fontsize',16);title('Convolved and unconvolved regressors')
\ No newline at end of file
Session4/ModelBasedFMRI/exampleSub/DecPhase 0 → 100755
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Session4/ModelBasedFMRI/exampleSub/LastWinloMag 0 → 100755
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