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:
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Simulation Environment: MATLAB;
Model Concept(s): Hallucinations;
Implementer(s): Powers, Al [albert.powers at yale.edu]; Mathys, Chris H ;
function [data_structure] = ch_hgf_startpoints_calcx_startpoints(data_structure)

tp = data_structure.behavioral.hgf_startpoints.est.u(:,2);

mu1hat = data_structure.behavioral.hgf_startpoints.est.traj.muhat(:,1);

% Calculate belief x using Bayes' theorem
x = tp.*mu1hat./(tp.*mu1hat + (1-mu1hat).^2);

% Belief is mu1hat in trials where there is no tone
x(find(tp==0)) = mu1hat(find(tp==0));

data_structure.behavioral.hgf_startpoints.x = x;



data_structure.behavioral.summary.hgf_startpoints.x = mean(reshape(data_structure.behavioral.hgf_startpoints.x,30,12),1);



end