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
loglik = 0; % Initialize log-likelihood
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 episode data
episode_states = zeros(T, 1);
episode_actions = zeros(T, 1);
episode_rewards = zeros(T, 1);
p = zeros(T, 1); % to save probability of choice
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 equivalent 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(t) = p1;
elseif a == 2
p(t) = p2;
end
% Save the episode data
episode_states(t) = state;
episode_actions(t) = a;
episode_rewards(t) = o;
ctr = ctr + 1;
end
% Monte Carlo update after the episode ends
returns = zeros(T, 1);
G = 0;
for t = T:-1:1
G = episode_rewards(t) + G;
returns(t) = G;
end
for t = 1:T
state = episode_states(t);
a = episode_actions(t);
G = returns(t);
q(state, a) = q(state, a) + alpha * (G - q(state, a));
a2 = mod(a, 2) + 1;
q(state, a2) = q(state, a2) + alpha * ((1 - G) - q(state, a2));
end
% Accumulate log-likelihood
loglik = loglik + sum(log(p + eps));
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
loglik = 0; % Initialize log-likelihood
for t1 = 1:size(numTrails, 2) % number of trials T = numTrails(t1);
end end