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]
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 [df, err] = tapas_riddersdiff(f, x, varargin)
% Differentiates the function f at point x according to Ridders' method:
%
% Ridders, CJF. (1982). Accurate computation of F'(x) and F'(x) F''(x). Advances in Engineering
%     Software, 4(2), 75-6.
%
% INPUT:
%    f             Function handle of a scalar real function of one real variable
%    x             Point at which to differentiate f
%
% OUTPUT:
%    df            First derivative of f at x
%    err           Error estimate
%
% OPTIONS:
%    Optionally, the third argument of the function can be a structure containing further
%    settings for Ridder's method.
%
%    varargin{1}.init_h      Initial finite difference (default: 1)
%    varargin{1}.div         Divisor used to reduce h on each step (default: 1.2)
%    varargin{1}.min_steps   Minimum number of steps in h (default: 3)
%    varargin{1}.max_steps   Maximum number of steps in h (default: 100)
%    varargin{1}.tf          Terminate if last step worse than preceding by a factor of tf
%                            (default: 2)
%
% --------------------------------------------------------------------------------------------------
% Copyright (C) 2012-2013 Christoph Mathys, TNU, UZH & ETHZ
%
% This file 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/>.
    
    % Defaults
    init_h     = 1;
    div        = 1.2;
    min_steps  = 3;
    max_steps  = 100;
    tf         = 2;
    df         = NaN;
    err        = realmax;
    
    % Overrides
    if nargin > 2
        options = varargin{1};

        if isfield(options,'init_h')
            init_h = options.init_h;
        end
        
        if isfield(options,'div')
            div = options.div;
        end
        
        if isfield(options,'min_steps')
            min_steps = options.min_steps;
        end
        
        if isfield(options,'max_steps')
            max_steps = options.max_steps;
        end
        
        if isfield(options,'tf')
            tf = options.tf;
        end
    end
    
    % Initialize matrix of polynomial interpolation values
    P = NaN(max_steps);
    
    % Initialize finite difference step
    h = init_h;
        
    % Approximate derivative at initial step
    P(1,1) = (f(x+h)-f(x-h))/(2*h);
    
    % Loop through rows of P (i.e., steps of h)
    for i = 2:max_steps
        
        % New step size
        h = h/div;
        
        % Approximate derivative at this step
        P(i,1) = (f(x+h)-f(x-h))/(2*h);
        
        % Use square of div for extrapolation because errors increase
        % quadratically with h (here, of course, they decrease quadratically
        % because we're reducing h...)
        divsq = div^2;
        t = divsq;
        
        % Fill the current row using Richardson extrapolation
        for j = 2:i
            
            % Richardson
            P(i,j) = (t*P(i,j-1)-P(i-1,j-1))/(t-1);
            
            % Increment extrapolation factor
            t = t*divsq;
            
            % Error on this trial is defined as the maximum absolute difference
            % to the extrapolation parents
            currerr = max(abs(P(i,j)-P(i,j-1)),abs(P(i,j)-P(i-1,j-1)));
            
            if currerr < err
                err = currerr;
                df = P(i,j);
            end
        end

        % Stop if errors start increasing (to be expected for very small
        % values of h)
        if i > min_steps && abs(P(i,i)-P(i-1,i-1)) > tf*err
            return
        end
    end
end

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