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    filename: ALAP_demo_attenuation.m
    path of file: ./repos/bspm/thirdparty/spm12/toolbox/DEM
    
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% This demonstration illustrates context or state-dependent precision (i.e.
% attention), which is necessary to disambiguate between sensations
% caused exogenously and self-generated sensations. In brief, it is
% necessary to attend away from the sensory consequences of action to
% preclude sensory evidence overriding the prior beliefs that cause
% movement. This necessarily reduced the confidence in self-generated
% sensations and provides a simple (Bayes-optimal) explanation for sensory
% attenuation - in terms of the attention of sensory precision. We

    
function ALAP_demo_attenuation
% This demonstration illustrates context or state-dependent precision (i.e.
% attention), which is necessary to disambiguate between sensations
% caused exogenously and self-generated sensations. In brief, it is
% necessary to attend away from the sensory consequences of action to
% preclude sensory evidence overriding the prior beliefs that cause
% movement. This necessarily reduced the confidence in self-generated
% sensations and provides a simple (Bayes-optimal) explanation for sensory
% attenuation - in terms of the attention of sensory precision. We
% illustrate this in the setting of the force matching illusion and go on
% to show that increasing the conviction in (precision of) prior beliefs
% abolishes sensory attenuation at the expense of false (delusional) 
% posterior beliefs about antagonistic external forces.
%__________________________________________________________________________
% Copyright (C) 2012 Wellcome Trust Centre for Neuroimaging
 
% Karl Friston
% $Id: ALAP_demo_attenuation.m 4826 2012-08-03 16:45:09Z karl $
 
% process (G) and model (M)
%==========================================================================
 
% set dimensions for generalised coordinates
%--------------------------------------------------------------------------
G(1).E.d        = 2;                   % approximation order
G(1).E.n        = 4;                   % embedding order
 
M(1).E.d        = 2;                   % approximation order
M(1).E.n        = 4;                   % embedding order
M(1).E.s        = 1/2;                 % embedding order
 
M(1).E.method.x = 1;                   % state-dependent noise
M(1).E.method.v = 1;                   % state-dependent noise
M(1).E.method.h = 0;                   % suppress optimisation
M(1).E.method.g = 0;                   % suppress optimisation
 
G(1).f  = inline('tanh(a) - x/4','x','v','a','P');
G(1).g  = inline('[x; v + x]','x','v','a','P');
G(1).x  = 0;                           % hidden state
G(1).v  = [0; 0];                      % hidden cause (sensory data)
G(1).V  = exp(8);                      % precision (noise)
G(1).W  = exp(8);                      % precision (states)
G(1).U  = [exp(0) 0];                  % precision (action)
 
 
% level 2; causes
%--------------------------------------------------------------------------
G(2).v  = 0;                           % exogenous  cause
G(2).a  = 0;                           % end