function [logp, yhat, res] = tapas_bayes_optimal_whatworld(r, infStates, ptrans) % Calculates the log-probability of the inputs given the current predictions % % -------------------------------------------------------------------------------------------------- % Copyright (C) 2013 Christoph Mathys, TNU, UZH & ETHZ % % This file is part of the HGF toolbox, which is released under the terms of the GNU General Public % Licence (GPL), version 3. You can redistribute it and/or modify it under the terms of the GPL % (either version 3 or, at your option, any later version). For further details, see the file % COPYING or . % Initialize returned log-probabilities as NaNs so that NaN is % returned for all irregualar trials n = size(infStates,1); logp = NaN(n,1); yhat = NaN(n,1); res = NaN(n,1); % Number of states whose contingencies have to be learned ns = r.c_prc.n_states; % Number of elements of the transition matrix ntr = ns^2; % Weed irregular trials out from predictions and inputs % States pred = squeeze(infStates(:,1,:,:,1,1)); pred(r.irr,:) = []; % Inputs u = r.u(:,1); % Transitions: first column - to; second column - from; ufrom = [1; u]; ufrom(end) = []; tr = [u ufrom]; % Weed transitions from irregular trials out tr(r.irr,:) = []; % Calculate probabilities of transitions for k = 1:length(u) p(k) = pred(k,tr(k,1),tr(k,2)); end % Calculate log-probabilities for non-irregular trials reg = ~ismember(1:n,r.irr); logp(reg) = log(p); yhat(reg) = p; res(reg) = -log(p); return;