function [loglik] = model_RL_8states_v2(parameters,subj)
num_state = 8;
num_action = 2;
nd_alpha = parameters(1); % normally-distributed alpha
alpha = 1/(1+exp(-nd_alpha)); % alpha (transformed to be between zero and one)
nd_beta = parameters(2);
beta = exp(nd_beta);
% unpack data
actions = subj.actions+1; % adding 1 such thact actions are {1, 2}
outcome = subj.Outcome;
color = subj.Color ;
orientation = subj.Orientation ;
mouth_dir = subj.Mouth_dir ;
% counter over all trials
ctr = 1;
% calculating numTrails
U = unique(subj.block());
numTrails = zeros(1, length(U));
for i=1:length(U)
ind=find(subj.block()==U(i));
numTrails(i)=length(ind);
end
for t1=1:size(numTrails, 2)
% number of trials
T = numTrails(t1);
% Q-value for each action
q = .5 * ones(num_state, num_action); % Q-value for both actions initialized at 0
% to save probability of choice. Currently NaNs, will be filled below
p = []; %nan(T,1);
for t=1:T
if num_state==8
state= mouth_dir(ctr)*2*2 + orientation(ctr)*2 + color(ctr)+1 ;
elseif num_state==4
state= subj.States4(ctr)+1 ;
elseif num_state==2
state= subj.States2(ctr)+1 ;
end
% probability of action 1
% this is equivalant to the softmax function, but overcomes the problem
% of overflow when q-values or beta are large.
p1 = 1./(1+exp(-beta*(q(state, 1)-q(state, 2))));
% probability of action 2
p2 = 1-p1;
% read info for the current trial
a = actions(ctr); % action on this trial
o = outcome(ctr); % outcome on this trial
% store probability of the chosen action
if a==1
p(ctr) = p1;
elseif a==2
p(ctr) = p2;
end
delta1 = o - q(state, a); % prediction error
q(state, a) = q(state, a) + (alpha*delta1);
a2 = mod(a, 2)+1 ;
delta2 = 1- o - q(state, a2); % prediction error
q(state, a2) = q(state, a2) + (alpha*delta2);
ctr = ctr+1;
end
end
% log-likelihood is defined as the sum of log-probability of choice data
% (given the parameters).
loglik = sum(log(p+eps));
% Note that eps is a very small number in matlab (type eps in the command
% window to see how small it is), which does not have any effect in practice,
% but it overcomes the problem of underflow when p is very very small
% (effectively 0).
end
function [loglik] = model_RL_8states_v2(parameters,subj)
num_state = 8; num_action = 2;
nd_alpha = parameters(1); % normally-distributed alpha alpha = 1/(1+exp(-nd_alpha)); % alpha (transformed to be between zero and one)
nd_beta = parameters(2); beta = exp(nd_beta);
% unpack data actions = subj.actions+1; % adding 1 such thact actions are {1, 2} outcome = subj.Outcome; color = subj.Color ; orientation = subj.Orientation ; mouth_dir = subj.Mouth_dir ;
% counter over all trials ctr = 1; % calculating numTrails U = unique(subj.block()); numTrails = zeros(1, length(U)); for i=1:length(U) ind=find(subj.block()==U(i)); numTrails(i)=length(ind); end
for t1=1:size(numTrails, 2) % number of trials T = numTrails(t1);
end
% log-likelihood is defined as the sum of log-probability of choice data % (given the parameters). loglik = sum(log(p+eps)); % Note that eps is a very small number in matlab (type eps in the command % window to see how small it is), which does not have any effect in practice, % but it overcomes the problem of underflow when p is very very small % (effectively 0). end