function [loglik] = model_RL_4states_v2(parameters, subj)
num_state = 4;
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 that 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 (two Q tables for Double Q-learning)
Q1 = .5 * ones(num_state, num_action);
Q2 = .5 * ones(num_state, num_action);
% to save probability of choice. Currently NaNs, will be filled below
p = [];
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
% Calculate combined Q-value
Q_combined = Q1 + Q2;
% probability of action 1 using combined Q-value
p1 = 1 / (1 + exp(-beta * (Q_combined(state, 1) - Q_combined(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
% Randomly update one of the Q-tables
if rand < 0.5
% Update Q1
delta1 = o - Q1(state, a); % prediction error
Q1(state, a) = Q1(state, a) + (alpha * delta1);
a2 = mod(a, 2) + 1;
delta2 = 1 - o - Q1(state, a2); % prediction error for the non-chosen action
Q1(state, a2) = Q1(state, a2) + (alpha * delta2);
else
% Update Q2
delta1 = o - Q2(state, a); % prediction error
Q2(state, a) = Q2(state, a) + (alpha * delta1);
a2 = mod(a, 2) + 1;
delta2 = 1 - o - Q2(state, a2); % prediction error for the non-chosen action
Q2(state, a2) = Q2(state, a2) + (alpha * delta2);
end
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_4states_v2(parameters, subj)
num_state = 4; 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 that 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