Commit d3e22813 authored by Laurence Hunt's avatar Laurence Hunt
Browse files

add session 3

parent 49c30e1e
File added
function [e] = RLModel(p,subjN,data,plot_fits)
% In case we ever want to fit two different learning rates 'alpha' and one
% or two different inverse temperatures 'beta', make the script flexible
% and duplicate if only one was specified
if length(p)==2
learningRate = [p(1) p(1)]; % learning rate
inverseT = [p(2) p(2)]; % inverse temparature
elseif length(p)==3
learningRate = [p(1) p(2)]; % two learning rates
inverseT = [p(3) p(3)]; % one temperature
elseif length(p)==4
learningRate = [p(1) p(2)]; % two learning rates
inverseT = [p(3) p(4)]; % two temperatures
% check that parameters are within legal constraints
if any(learningRate<0) || any(inverseT<0) || any(learningRate>1)
e = 10000; % give back a large error term (i.e. 'bad fit')
% Define variables for this subject
opt1Rewarded = data.opt1Rewarded(:,subjN); % whether (on each trial), there was an outcome behind option 1 (='1') or behind option 2 (='0')
magOpt1 = data.magOpt1(:,subjN); % the magnitude of option 1 shown at the time of choice
magOpt2 = data.magOpt2(:,subjN);
opt1chosen = data.opt1Chosen(:,subjN);
trueProbability = data.trueProbability(:,subjN);
isStable = 2-data.isStableBlock(:,subjN); % turns 1=stable/0=volatile into: 1=stable; 2=volatile
numtrials= size(opt1Rewarded,1); % number of trials in the experiment
% The below lines of code were taken from RL section of session 2
% ====================================================================== %
% Pre-define the variables that we want to compute:
% In Matlab it is useful to define how many entries variables will have
% before e.g. filling them using a 'for loop' (see below). The reason for
% this is that makes the code faster, but more importantly, it also allows
% you to check whether you have coded everything correctly by checking that
% each entry has gotten filled and that the size of the variables after a
% loop is the same as before
predictionOpt1 = nan(numtrials,1); % Predictions about whether there will be a reward if option 1 is selected
rpe = nan(numtrials,1); % Prediction errors on each trial
% The learning model will now applying the two learning equations, i.e.
% compute the prediction error ([1] in handout) and update the predictions
% for the next trial ([2] in handout) for each trial of the experiment.
% This is done using a 'for loop' that counts from the first trial until
% the last trial (i.e. 'numtrials')
% On the first trial, we assume that the model thinks that the reward could
% be behind either option. In other words, that the probability of getting
% a reward if choosing option 1 is 50%
predictionOpt1(1) = 0.5;
for t = 1:numtrials-1 % The for-loop is one shorter than the experiment, because we compute the prediction for the next trial
% #2: Complete the equation to compute prediction error on trial t
rpe(t) = opt1Rewarded(t)-predictionOpt1(t);
% #3: Complete the equation to compute the new prediction for the next
% trial (t+1)
predictionOpt1(t+1) = predictionOpt1(t) + learningRate(isStable(t))*rpe(t);
% As we know that that for every trial only one option is rewarded, we
% also know that the reward expectation for option 1 needs to be the
% opposite than for option two and both need to add up to one.
% This is only the case in our experiment to make learning simpler, as it would
% theoretically be possible for a learner to learn about two options independently
% holding two separate expectations in mind, if option probabilities were indeed
% independent from each other.
% This should be switched off when fitting many participants, but for
% evaluating a single participant, it can be handy to plot the important
% variables
if plot_fits
plot(trueProbability,'k'); hold on;
ylabel('Reward probability');
ylim([-0.1 1.1]);
% The below lines of code were taken from section on softmax from session 2
% ====================================================================== %
% Decision variable
DV = utility2-utility1;
% for all trials, calculate the two choice probabilities, then save the one
% of the chosen option as 'probChoice'
for t=1:numtrials
if opt1chosen(t)==1
probChoice(t) = ChoiceProbability1(t);
probChoice(t) = ChoiceProbability2(t);
if plot_fits
legend('True Probability','Outcomes received','Choice','PredictionOpt1','ChoiceProbOpt1');
% Add up the error terms (->log likelihood) for the trials used for fitting
TrialsusedForFitting = intersect([1:numtrials],[11:100 111:200]);
e= -sum(log(probChoice(TrialsusedForFitting))); %if probChoice==0 then log(0)=-Inf i.e. penalizes being confident at predicting wrong choice
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