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
% to save probability of choice. Currently NaNs, will be filled below
p = nan(1, sum(numTrails));
for t1 = 1:length(numTrails)
% number of trials
T = numTrails(t1);
% Q-value for each action
q = 0.5 * ones(num_state, num_action); % Q-value for both actions initialized at 0
for t = 1:T
% determine state
state = subj.States4(ctr) + 1;
% Ensure state is within bounds
if state > num_state || state < 1
error('State index out of bounds: %d', state);
end
% probability of action 1
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
% SARSA
if rand < p(ctr) % epsilon-greedy exploration
a_next = a;
else
a_next = mod(a, num_action) + 1; % Choose the other action with 1 - epsilon probability
end
delta = o - q(state, a); % prediction error
q(state, a) = q(state, a) + (alpha * delta);
delta_next = 1 - o - q(state, a_next); % prediction error
q(state, a_next) = q(state, a_next) + (alpha * delta_next);
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
% to save probability of choice. Currently NaNs, will be filled below p = nan(1, sum(numTrails));
for t1 = 1:length(numTrails) % 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