Hierarchical Gaussian Filter (HGF) model of conditioned hallucinations task (Powers et al 2017)

 Download zip file 
Help downloading and running models
Accession:229278
This is an instantiation of the Hierarchical Gaussian Filter (HGF) model for use with the Conditioned Hallucinations Task.
Reference:
1 . Powers AR, Mathys C, Corlett PR (2017) Pavlovian conditioning-induced hallucinations result from overweighting of perceptual priors. Science 357:596-600 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Hallucinations;
Implementer(s): Powers, Al [albert.powers at yale.edu]; Mathys, Chris H ;
/
HGF
analysis
hgfToolBox_condhalluc1.4
README
COPYING *
example_binary_input.txt
example_categorical_input.mat
example_usdchf.txt
Manual.pdf
tapas_autocorr.m
tapas_bayes_optimal.m
tapas_bayes_optimal_binary.m
tapas_bayes_optimal_binary_config.m
tapas_bayes_optimal_binary_transp.m
tapas_bayes_optimal_categorical.m
tapas_bayes_optimal_categorical_config.m
tapas_bayes_optimal_categorical_transp.m
tapas_bayes_optimal_config.m
tapas_bayes_optimal_transp.m
tapas_bayes_optimal_whatworld.m
tapas_bayes_optimal_whatworld_config.m
tapas_bayes_optimal_whatworld_transp.m
tapas_bayes_optimal_whichworld.m
tapas_bayes_optimal_whichworld_config.m
tapas_bayes_optimal_whichworld_transp.m
tapas_bayesian_parameter_average.m
tapas_beta_obs.m
tapas_beta_obs_config.m
tapas_beta_obs_namep.m
tapas_beta_obs_sim.m
tapas_beta_obs_transp.m
tapas_boltzmann.m
tapas_cdfgaussian_obs.m
tapas_cdfgaussian_obs_config.m
tapas_cdfgaussian_obs_transp.m
tapas_condhalluc_obs.m
tapas_condhalluc_obs_config.m
tapas_condhalluc_obs_namep.m
tapas_condhalluc_obs_sim.m
tapas_condhalluc_obs_transp.m
tapas_condhalluc_obs2.m
tapas_condhalluc_obs2_config.m
tapas_condhalluc_obs2_namep.m
tapas_condhalluc_obs2_sim.m
tapas_condhalluc_obs2_transp.m
tapas_Cov2Corr.m
tapas_datagen_categorical.m
tapas_fit_plotCorr.m
tapas_fit_plotResidualDiagnostics.m
tapas_fitModel.m
tapas_gaussian_obs.m
tapas_gaussian_obs_config.m
tapas_gaussian_obs_namep.m
tapas_gaussian_obs_sim.m
tapas_gaussian_obs_transp.m
tapas_hgf.m
tapas_hgf_ar1.m
tapas_hgf_ar1_binary.m
tapas_hgf_ar1_binary_config.m
tapas_hgf_ar1_binary_namep.m
tapas_hgf_ar1_binary_plotTraj.m
tapas_hgf_ar1_binary_transp.m
tapas_hgf_ar1_config.m
tapas_hgf_ar1_mab.m
tapas_hgf_ar1_mab_config.m
tapas_hgf_ar1_mab_plotTraj.m
tapas_hgf_ar1_mab_transp.m
tapas_hgf_ar1_namep.m
tapas_hgf_ar1_plotTraj.m
tapas_hgf_ar1_transp.m
tapas_hgf_binary.m
tapas_hgf_binary_condhalluc_plotTraj.m
tapas_hgf_binary_config.m
tapas_hgf_binary_config_startpoints.m
tapas_hgf_binary_mab.m
tapas_hgf_binary_mab_config.m
tapas_hgf_binary_mab_plotTraj.m
tapas_hgf_binary_mab_transp.m
tapas_hgf_binary_namep.m
tapas_hgf_binary_plotTraj.m
tapas_hgf_binary_pu.m
tapas_hgf_binary_pu_config.m
tapas_hgf_binary_pu_namep.m
tapas_hgf_binary_pu_tbt.m
tapas_hgf_binary_pu_tbt_config.m
tapas_hgf_binary_pu_tbt_namep.m
tapas_hgf_binary_pu_tbt_transp.m
tapas_hgf_binary_pu_transp.m
tapas_hgf_binary_transp.m
tapas_hgf_categorical.m
tapas_hgf_categorical_config.m
tapas_hgf_categorical_namep.m
tapas_hgf_categorical_norm.m
tapas_hgf_categorical_norm_config.m
tapas_hgf_categorical_norm_transp.m
tapas_hgf_categorical_plotTraj.m
tapas_hgf_categorical_transp.m
tapas_hgf_config.m
tapas_hgf_demo.m
tapas_hgf_demo_commands.m
tapas_hgf_jget.m
tapas_hgf_jget_config.m
tapas_hgf_jget_plotTraj.m
tapas_hgf_jget_transp.m
tapas_hgf_namep.m
tapas_hgf_plotTraj.m
tapas_hgf_transp.m
tapas_hgf_whatworld.m
tapas_hgf_whatworld_config.m
tapas_hgf_whatworld_namep.m
tapas_hgf_whatworld_plotTraj.m
tapas_hgf_whatworld_transp.m
tapas_hgf_whichworld.m
tapas_hgf_whichworld_config.m
tapas_hgf_whichworld_namep.m
tapas_hgf_whichworld_plotTraj.m
tapas_hgf_whichworld_transp.m
tapas_hhmm.m
tapas_hhmm_binary_displayResults.m
tapas_hhmm_config.m
tapas_hhmm_transp.m
tapas_hmm.m
tapas_hmm_binary_displayResults.m
tapas_hmm_config.m
tapas_hmm_transp.m
tapas_kf.m
tapas_kf_config.m
tapas_kf_namep.m
tapas_kf_plotTraj.m
tapas_kf_transp.m
tapas_logit.m
tapas_logrt_linear_binary.m
tapas_logrt_linear_binary_config.m
tapas_logrt_linear_binary_minimal.m
tapas_logrt_linear_binary_minimal_config.m
tapas_logrt_linear_binary_minimal_transp.m
tapas_logrt_linear_binary_namep.m
tapas_logrt_linear_binary_sim.m
tapas_logrt_linear_binary_transp.m
tapas_logrt_linear_whatworld.m
tapas_logrt_linear_whatworld_config.m
tapas_logrt_linear_whatworld_transp.m
tapas_ph_binary.m
tapas_ph_binary_config.m
tapas_ph_binary_namep.m
tapas_ph_binary_plotTraj.m
tapas_ph_binary_transp.m
tapas_quasinewton_optim.m
tapas_quasinewton_optim_config.m
tapas_riddersdiff.m
tapas_riddersdiff2.m
tapas_riddersdiffcross.m
tapas_riddersgradient.m
tapas_riddershessian.m
tapas_rs_belief.m
tapas_rs_belief_config.m
tapas_rs_precision.m
tapas_rs_precision_config.m
tapas_rs_precision_whatworld.m
tapas_rs_precision_whatworld_config.m
tapas_rs_surprise.m
tapas_rs_surprise_config.m
tapas_rs_transp.m
tapas_rs_whatworld_transp.m
tapas_rw_binary.m
tapas_rw_binary_config.m
tapas_rw_binary_dual.m
tapas_rw_binary_dual_config.m
tapas_rw_binary_dual_plotTraj.m
tapas_rw_binary_dual_transp.m
tapas_rw_binary_namep.m
tapas_rw_binary_plotTraj.m
tapas_rw_binary_transp.m
tapas_sgm.m
tapas_simModel.m
tapas_softmax.m
tapas_softmax_2beta.m
tapas_softmax_2beta_config.m
tapas_softmax_2beta_transp.m
tapas_softmax_binary.m
tapas_softmax_binary_config.m
tapas_softmax_binary_namep.m
tapas_softmax_binary_sim.m
tapas_softmax_binary_transp.m
tapas_softmax_config.m
tapas_softmax_namep.m
tapas_softmax_sim.m
tapas_softmax_transp.m
tapas_squared_pe.m
tapas_squared_pe_config.m
tapas_squared_pe_transp.m
tapas_sutton_k1_binary.m
tapas_sutton_k1_binary_config.m
tapas_sutton_k1_binary_plotTraj.m
tapas_sutton_k1_binary_transp.m
tapas_unitsq_sgm.m
tapas_unitsq_sgm_config.m
tapas_unitsq_sgm_mu3.m
tapas_unitsq_sgm_mu3_config.m
tapas_unitsq_sgm_mu3_transp.m
tapas_unitsq_sgm_namep.m
tapas_unitsq_sgm_sim.m
tapas_unitsq_sgm_transp.m
                            
function c = tapas_hgf_whichworld_config
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Contains the configuration for the Hierarchical Gaussian Filter (HGF) for binary inputs restricted
% to 3 levels, no drift, and no inputs at irregular intervals, in the absence of perceptual
% uncertainty.
%
% This model deals with the situation where an agent has to determine in which of several
% possible worlds, each characterized by a different Bernoulli distribution of binary outcomes,
% he is currently living in. The probabilities of the different possible distributions are
% assumed to perform Gaussian random walks in logit space. The volatilities of all of these walks
% are determined by the same higher-level state x_3 in standard HGF fashion.
%
% The HGF is the model introduced in 
%
% Mathys C, Daunizeau J, Friston, KJ, and Stephan KE. (2011). A Bayesian foundation
% for individual learning under uncertainty. Frontiers in Human Neuroscience, 5:39.
%
% This file refers to BINARY inputs (Eqs 1-3 in Mathys et al., (2011));
% for continuous inputs, refer to hgf_config.
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% The HGF configuration consists of the priors of parameters and initial values. All priors are
% Gaussian in the space where the quantity they refer to is estimated. They are specified by their
% sufficient statistics: mean and variance (NOT standard deviation).
% 
% Quantities are estimated in their native space if they are unbounded (e.g., omega). They are
% estimated in log-space if they have a natural lower bound at zero (e.g., sigma2).
% 
% Kappa and theta are estimated in 'logit-space' because bounding them above (in addition to
% their natural lower bound at zero) is an effective means of preventing the exploration of
% parameter regions where the assumptions underlying the variational inversion (cf. Mathys et
% al., 2011) no longer hold.
% 
% 'Logit-space' is a logistic sigmoid transformation of native space with a variable upper bound
% a>0:
% 
% logit(x) = ln(x/(a-x)); x = a/(1+exp(-logit(x)))
%
% Parameters can be fixed (i.e., set to a fixed value) by setting the variance of their prior to
% zero. Aside from being useful for model comparison, the need for this arises whenever the scale
% and origin of x3 are arbitrary. This is the case if the observation model does not contain the
% representations mu3 and sigma3 from the third level. A choice of scale and origin is then
% implied by fixing the initial value mu3_0 of mu3 and either kappa or omega.
%
% Kappa and theta can be fixed to an arbitrary value by setting the upper bound to twice that
% value and the mean as well as the variance of the prior to zero (this follows immediately from
% the logit transform above).
% 
% Fitted trajectories can be plotted by using the command
%
% >> tapas_hgf_whichworld_plotTraj(est)
% 
% where est is the stucture returned by fitModel. This structure contains the estimated
% perceptual parameters in est.p_prc and the estimated trajectories of the agent's
% representations (cf. Mathys et al., 2011). Their meanings are:
%              
%         est.p_prc.mu2_0      initial values of the mu2s
%         est.p_prc.sa2_0      initial values of the sigma2s
%         est.p_prc.mu3_0      initial value of mu3
%         est.p_prc.sa3_0      initial value of sigma3
%         est.p_prc.ka         kappa
%         est.p_prc.om         omega
%         est.p_prc.th         theta
%
%         est.traj.mu          mu
%         est.traj.sa          sigma
%         est.traj.muhat       prediction mean
%         est.traj.sahat       prediction variance
%         est.traj.v           inferred variances of random walks
%         est.traj.w           weighting factor of informational and environmental uncertainty at the 2nd level
%         est.traj.da          prediction errors
%         est.traj.ud          updates with respect to prediction
%         est.traj.psi         precision weights on prediction errors
%         est.traj.epsi        precision-weighted prediction errors
%         est.traj.wt          full weights on prediction errors (at the first level,
%                                  this is the learning rate)
%
% Tips:
% - When analyzing a new dataset, take your inputs u and use
%
%   >> est = tapas_fitModel([], u, 'tapas_hgf_whichworld_config', 'tapas_bayes_optimal_whichworld_config');
%
%   to determine the Bayes optimal perceptual parameters (given your current priors as defined in
%   this file here, so choose them wide and loose to let the inputs influence the result). You can
%   then use the optimal parameters as your new prior means for the perceptual parameters.
%
% - If you get an error saying that the prior means are in a region where model assumptions are
%   violated, lower the prior means of the omegas, starting with the highest level and proceeding
%   downwards.
%
% - Alternatives are lowering the prior mean of kappa, if they are not fixed, or adjusting
%   the values of the kappas or omegas, if any of them are fixed.
%
% - If the log-model evidence cannot be calculated because the Hessian poses problems, look at
%   est.optim.H and fix the parameters that lead to NaNs.
%
% - 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) 2013-2014 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 <http://www.gnu.org/licenses/>.


% Config structure
c = struct;

% Model name
c.model = 'hgf_whichworld';

% Number of worlds
c.nw = 2;

% Upper bound for kappa and theta (lower bound is always zero)
c.kaub = 2;
c.thub = 2;

% Sufficient statistics of Gaussian parameter priors

% Initial mu2
c.mu2_0mu = [tapas_logit(1/2,1), tapas_logit(1/2,1)];
c.mu2_0sa = [           0,            0];

% Initial sigma2
c.logsa2_0mu = [log(1), log(1)];
c.logsa2_0sa = [     1,      1];

% Initial mu3
% Usually best kept fixed to 1 (determines origin on x3-scale).
c.mu3_0mu = 1;
c.mu3_0sa = 0;

% Initial sigma3
c.logsa3_0mu = log(0.1);
c.logsa3_0sa = 1;

% Kappa
% This should be fixed (preferably to 1) if the observation model
% does not use mu3 (kappa then determines the scaling of x3).
c.logitkamu = 0;
c.logitkasa = 0;

% Omega
c.ommu = 0;
c.omsa = 5^2;

% Theta
c.logitthmu = 0;
c.logitthsa = 2;

% m
c.mmu = 0;
c.msa = 0;

% Phi
c.logitphimu = tapas_logit(0.1,1);
c.logitphisa = 2;

% Gather prior settings in vectors
c.priormus = [
    c.mu2_0mu,...
    c.logsa2_0mu,...
    c.mu3_0mu,...
    c.logsa3_0mu,...
    c.logitkamu,...
    c.ommu,...
    c.logitthmu,...
    c.mmu,...
    c.logitphimu,...
         ];

c.priorsas = [
    c.mu2_0sa,...
    c.logsa2_0sa,...
    c.mu3_0sa,...
    c.logsa3_0sa,...
    c.logitkasa,...
    c.omsa,...
    c.logitthsa,...
    c.msa,...
    c.logitphisa,...
         ];

% Model function handle
c.prc_fun = @tapas_hgf_whichworld;

% Handle to function that transforms perceptual parameters to their native space
% from the space they are estimated in
c.transp_prc_fun = @tapas_hgf_whichworld_transp;

return;

Loading data, please wait...