function c = tapas_rw_binary_config %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % Contains the configuration for the Rescorla-Wagner (RW) learning model for binary inputs. % % The RW model was introduced in : % % Rescorla, R. A., and Wagner, A. R. (1972). "A theory of Pavlovian conditioning: % Variations in the effectiveness of reinforcement," in Classical Conditioning % II: Current Research and Theory, eds. A. H. Black and W. F. Prokasy (New % York: Appleton-Century-Crofts), 64-99. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % The RW configuration consists of the priors of the learning rate alpha and the initial value v_0 % of the value v. The priors are Gaussian in the space where the parameters they refer to are % estimated. They are specified by their sufficient statistics: mean and variance (NOT standard % deviation). % % Both alpha and v_0 are estimated in 'logit-space' because they are bounded inside the unit % interval. % % 'Logit-space' is a logistic sigmoid transformation of native space % % tapas_logit(x) = ln(x/(1-x)); x = 1/(1+exp(-tapas_logit(x))) % % Any of the parameters can be fixed (i.e., set to a fixed value) by setting the variance of their % prior to zero. To fix v_0 to 0.5 set the mean as well as the variance of the prior to zero. % % Fitted trajectories can be plotted by using the command % % >> tapas_rw_binary_plotTraj(est) % % where est is the stucture returned by tapas_fitModel. This structure contains the estimated % parameters alpha and v_0 in est.p_prc and the estimated trajectories of the agent's % representations: % % est.p_prc.v_0 initial value of v % est.p_prc.alpha alpha % % est.traj.v value: v % est.traj.da prediction error: delta % % Tips: % - Your guide to all these adjustments is the log-model evidence (LME). Whenever the LME increases % by at least 3 across datasets, the adjustment was a good idea and can be justified by just this: % the LME increased, so you had a better model. % % -------------------------------------------------------------------------------------------------- % Copyright (C) 2012-2013 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 . % Config structure c = struct; % Model name c.model = 'tapas_rw_binary'; % Initial v c.logitv_0mu = tapas_logit(0.5, 1); c.logitv_0sa = 0; % Alpha c.logitalmu = tapas_logit(0.5, 1); c.logitalsa = 1; % Gather prior settings in vectors c.priormus = [ c.logitv_0mu,... c.logitalmu,... ]; c.priorsas = [ c.logitv_0sa,... c.logitalsa,... ]; % Model function handle c.prc_fun = @tapas_rw_binary; % Handle to function that transforms perceptual parameters to their native space % from the space they are estimated in c.transp_prc_fun = @tapas_rw_binary_transp; return;