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    Metadata details:
    filename: spm_SEM_demo.m
    path of file: ./repos/spm12/toolbox/SPEM_and_DCM
    
    the code of the file until where you have to start completion: % DCM for SPEM: Demo routine for meta-Bayesian inference
%--------------------------------------------------------------------------
% This routine is a demonstration routine illustrating the procedure for
% inverting dynamic causal models of eye tracking data, acquired under
% different experimental conditions. In what follows, we load the data
% structure and assemble the slow (pursuit) eye movement trajectories that
% will be subsequently fitted using a (Bayes optimal) model of oculomotor
% control. This is an example of meta-modelling - in the sense that the
% Bayesian modelling of empirical eye tracking assumes that the behaviour
% remodelled was generated by a Bayesian observer. There are four
% conditions (slow and fast target velocity under smooth and noisy target
% motion). These data are first conditioned by normalising the (sinusoidal)
% trajectory to a unit cycle. This means that the (target) trajectory comes
% a function of phase during the
    
function of phase during the cycle with an amplitude of one;
% irrespective of the actual amplitude and frequency of target motion. We
% then illustrate the difference between observed trajectories and those
% predicted under prior expectations about model parameters. Finally, the
% DCM is inverted using bespoke routines (in this toolbox) to provide
% posterior densities over model parameters. In this instance, these
% parameters pertain to the subject expectations about oculomotor kinetics
% and the precision of their random fluctuations.
%
% See also: spm_dcm_spem; spm_dcm_spem_data; spm_dcm_spem_results
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging

% Karl Friston
% $Id: spm_SEM_demo.m 6501 2015-07-17 14:32:09Z spm $


% load and create data structure
%==========================================================================
clear
load DATA
 

% data structure
%--------------------------------------------------------------------------
xY.Y{1} = allsubj.SS.one;        % eye position    (condition 1)       
xY.Y{2} = allsubj.SN.one;        % eye position    (condition 2)  
xY.C{1} = allsubj.SS.target;     % target position (condition 1)
xY.C{2} = allsubj.SS.target;     % target position (condition 2)

% data structure
%--------------------------------------------------------------------------
xY.Y{3} = allsubj.FS.one;        % eye position    (condition 3)       
xY.Y{4} = allsubj.FN.one;        % eye position    (condition 4)  
xY.C{3} = allsubj.FS.target;     % target position (condition 3)
xY.C{4} = allsubj.FS.target;     % target position (condition 4)
 
xY.DT   = 0.1;                   % time bin (ms)
xY.occ  = @(x) x > 0 | x < -0.8; % occluder function of normalised x
 
% decimate and normalise
%--------------------------------------------------------------------------
DCM.xY  = spm_dcm_spem_data(xY);
 
 
% condition specific effects (in the form of a design matrix)
%==========================================================================
DCM.xU  = [-1  1 -1  1;          % smooth vs noisy condotions
           -1 -1  1  1];         % slow vs. fast stimulus speed
       
                                 % = 2 effects x 4 conditions
 
 
% model specification - in terms of priors
%==========================================================================
 
% Parameters (expectations of parameters and precisions)
%--------------------------------------------------------------------------
P.k = sparse(1,6);               % parameters of motion
P.h = sparse(1,3);               % parameters of precision
P.u = sparse(1,2);               % parameters of priors
 
% condition specific effects
%--------------------------------------------------------------------------
nu  = size(DCM.xU,1);            % number of (2) experimental effects
B   = cell(1,nu);
for i = 1:nu
    B{i} = P;
end