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

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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]
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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 ;
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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 [traj, infStates] = tapas_rw_binary_dual(r, p, varargin)
% Calculates the trajectories of v under the Rescorla-Wagner learning model for dual updates.
%
% This function can be called in two ways:
% 
% (1) tapas_rw_binary_dual(r, p)
%   
%     where r is the structure generated by tapas_fitModel and p is the parameter vector in native space;
%
% (2) tapas_rw_binary_dual(r, ptrans, 'trans')
% 
%     where r is the structure generated by tapas_fitModel, ptrans is the parameter vector in
%     transformed space, and 'trans' is a flag indicating this.
%
% --------------------------------------------------------------------------------------------------
% 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 <http://www.gnu.org/licenses/>.

% Transform paramaters back to their native space if needed
if ~isempty(varargin) && strcmp(varargin{1},'trans');
    p = tapas_rw_binary_dual_transp(r, p);
end

% Unpack parameters
v_0 = p(1:2);
al  = p(3);
ka  = p(4);

% Add dummy "zeroth" trial
u = [0; r.u(:,1)];
y = [0; r.y(:,1)];
n = length(u);

% Initialize updated quantity: value
v  = NaN(n,2);
da = NaN(n,2);

% Prior
v(1,:) = v_0;

% Pass through value update loop
for k = 2:1:n
    if not(ismember(k, r.ign))
        
        %%%%%%%%%%%%%%%%%%%%%%
        % Effect of input u(k)
        %%%%%%%%%%%%%%%%%%%%%%
        
        % Prediction error
        if u(k)==1
            da(k,y(k))   = 1 -v(k-1,y(k));
            da(k,3-y(k)) = 0 -v(k-1,3-y(k));
        elseif u(k)==0
            da(k,y(k))   = 0 -v(k-1,y(k));
            da(k,3-y(k)) = 1 -v(k-1,3-y(k));
        end
        
        % Value
        v(k,y(k))   = v(k-1,y(k))      +al*da(k,y(k));
        v(k,3-y(k)) = v(k-1,3-y(k)) +ka*al*da(k,3-y(k));
    else
        da(k,:) = [0, 0];
        v(k,:)  = v(k-1,:);
    end
end

% Predicted value
vhat = v;
vhat(end,:) = [];

% Remove representation priors
v(1,:)  = [];
da(1,:) = [];

% Create result data structure
traj = struct;

traj.v     = v;
traj.vhat  = vhat;
traj.da    = da;

% Create matrix (in this case: vector) needed by observation model
infstates = NaN(n-1,1,2,1,1);
infStates(:,1,1,1,1) = vhat(:,1);
infStates(:,1,2,1,1) = vhat(:,2);

return;