% 
% Skeleton solution for the stimulus sampling theory solution based on
% Estes & Burke.
%
% You need to change the bits that say INSERT CODE HERE.
% Feel free to use as is or start from scratch.
% 
% ----------------------------------------------------------
%
% Modeling Behavior
% Psychology 891C
% Fall 2004
%
% Andrew L. Cohen
% 9/23/04
%
% ---------------------------------------------------------
%

% First set up some constants --------------------

% The number of elements in sets 1 and 2
N1 = 100;
N2 = 100;

% The total number of elements
N = N1 + N2;

% The sampling probabilities for sets 1 and 2
theta1 = .3;
theta2 = .1;

% The average sampling probability over all sets
theta_bar = (theta1 + theta2)/2;

% The range of trials to plot
nRange = 0:50;

% Calculate analytic solution ---------------------
k = 1;
for n = nRange
    p_analytical(k) = % INSERT CODE HERE 
        
    k = k + 1;
end

% Plot analytic solution
plot(nRange, p_analytical);
hold on

% Calculate simulated solution --------------------

% SX_associated has 1's where the elements from Set X are associated with A 
% and 0's where the elements are not associated with A. Start off with
% nothing associated with A.
S1_associated = zeros(1, N1);
S2_associated = zeros(1, N2);

k = 1;
for n = nRange
    
    % SX_activated has 1's where the elements from Set X are activated on a
    % particular trial and 0's where the elements are not activated
    % (sampled). Remember that this should be independent of past trials.
    % Check out what rand(1, 10) < .3 does. ones(1, 10) might also be helpful.
    S1_activated = % INSERT CODE HERE 
    S2_activated = % INSERT CODE HERE
    
    % Combines the 2 sets of elements into one vector
    activated = [S1_activated S2_activated];
    associated = [S1_associated S2_associated];
    
    % forA is a 1xN vector that has 1's where items are activated AND
    % associated with A and 0 otherwise. Hint: check out what .* does to
    % two vectors of 0's and 1's.
    forA = % INSERT CODE HERE
    
    % Given that we now know which elements are activated and which are 
    % associated with A, determines the probability of an A response.
    p_simulated(k) = % INSERT CODE HERE
    
    % Associates all of the activated elements with A. 1's where they are
    % associated and 0's where they are not. Hint: check out what | does to
    % two vectors of 0's and 1's.
    S1_associated = % INSERT CODE HERE
    S2_associated = % INSERT CODE HERE

    k = k + 1;
end

% Plot simulated solution
plot(nRange, p_simulated, 'r--');
hold off