JA_TMS_2022/Pilot_Psychotoolbox functions for Stimuli Presentation/JA_CFs_quickness_games_distribution.m

function [GD] = JA_CFs_quickness_games_distribution(relationship)

% the function spits 4 variables

% 1. games_distribution
% it spits out 13 numbers. From 2:13 they represent 
% how many SLOW responses will be provided by CF within each game
% the first game is always the wash-out game (coded as -1)

% fast games
% 1 game: 2 out of 9 (non catch-trials) 
% 2 games: 3/9
% 3 games: 4/9

% slow games
% 1 game: 7 out of 9 (non catch-trials) 
% 2 games: 6/9
% 3 games: 5/9


% 2. CF_slow_out_of_bounds
% spits out the games in which, in a slow trial CF will be too slow (4 games)

% 3. CF_fast_out_of_bounds
% spits out the games in which, in a fast trial CF will be too fast (4 games)


% 4. CF_catch_failing_game
% spits out the game in which CF fails the catch trial (1 game)


%% games distribution

% repmat(number of slow responses, [times,1])
games_distribution_tmp = cat(1,repmat(2,[1,1]),...
repmat(3,[2,1]),...
repmat(4,[3,1]),...
repmat(5,[3,1]),...
repmat(6,[2,1]),...
repmat(7,[1,1]));


% randomize order of the indices going from 1 to number of 'real' games(12)
idx = randperm(length(games_distribution_tmp));

% apply the new order to the new variable games_distribution
games_distribution = games_distribution_tmp(idx);

% add the washout game as the first game to the game_distribution variable with the 0 code
games_distribution = cat(1,-1, games_distribution);

%%
fast_games = intersect(find(games_distribution > 0),find(games_distribution < 5));
slow_games = intersect(find(games_distribution > 0),find(games_distribution >= 5));
wash_out_games = games_distribution < 0;

games_distribution_names = cell(length(games_distribution),1);

games_distribution_names(fast_games) = {'fast'};
games_distribution_names(slow_games) = {'slow'};
games_distribution_names(wash_out_games) = {'wash_out'};

%% CF_slow_out_of_bounds

tmp = randperm(length(games_distribution_tmp))+1;

CF_slow_out_of_bounds_games = tmp(1:4);

%% CF_fast_out_of_bounds

CF_fast_out_of_bounds_games = tmp(5:8);

%% CF_catch_game_failing

CF_catch_failing_game = tmp(9);

%% outcome feedback information (to be used into JA_outcome_feedback)

outcome_feedback_relative2SBJ = size(games_distribution_names,1);
SBJ_wins_distribution = zeros(size(games_distribution_names,1),1);


switch relationship
    
    case 'parallel'
        % note that washout game is always zero
        % permute randomly the order of wins and lost games
        SBJ_wins_idx_tmp = randperm(outcome_feedback_relative2SBJ-1)+1;
        SBJ_wins_idx = SBJ_wins_idx_tmp(1:(outcome_feedback_relative2SBJ-1)/2);
        SBJ_wins_distribution(SBJ_wins_idx) = 1;
        
    case 'competitive'
        SBJ_wins_idx = strcmp(games_distribution_names, 'slow');
        SBJ_wins_distribution(SBJ_wins_idx) = 1;
        
    case 'joint'
        SBJ_wins_idx = strcmp(games_distribution_names, 'fast');
        SBJ_wins_distribution(SBJ_wins_idx) = 1;
        
    case 'imagery'
        SBJ_wins_idx_tmp = randperm(outcome_feedback_relative2SBJ-1)+1;
        SBJ_wins_idx = SBJ_wins_idx_tmp(1:(outcome_feedback_relative2SBJ-1)/2);
        SBJ_wins_distribution(SBJ_wins_idx) = 1;
        
    case 'warming_up'
     
    case 'training'
       
end
%%
GD.games_distribution = games_distribution;
GD.games_distribution_names = games_distribution_names;
GD.CF_slow_out_of_bounds_games = CF_slow_out_of_bounds_games;
GD.CF_fast_out_of_bounds_games = CF_fast_out_of_bounds_games;
GD.CF_catch_failing_game = CF_catch_failing_game;
GD.SBJ_wins_distribution = SBJ_wins_distribution;

end