Merge branch 'development' into 'master'

Development

See merge request !18

CHANGELOG.md

@@ -0,0 +1,28 @@
+# Changelog
+TAPAS toolbox 
+
+
+
+## [2.7.3] 2017-08-17
+
+## Added
+- Added condhalluc_obs and condhalluc_obs2 models.
+
+## Changed
+- Introduced kappa1 in all binary HGF models.
+
+## [2.7.2] 2017-08-17
+## Added
+- New PhysIO CHANGELOG.md specific file.
+
+## Changed
+- Specified in PhysIO/CHANGELOG.md.
+
+## [2.7.1] 2017-08-17
+
+### Added
+- Added a CHANGELOG.md file
+
+### Changed
+- Now the README.txt file is in markdown format.
+- The documentation is integrated with the wiki in github.

HGF/README.md

@@ -46,6 +46,10 @@ hgf_demo.pdf.
 
 ## Release notes
 
+### v5.1
+- Added condhalluc_obs and condhalluc_obs2 models
+- Introduced kappa1 in all binary HGF models
+
 ### v5.0
 - Ported interactive demo to Matlab LiveScript
 - Various additional small improvements

HGF/hgf_demo.m

@@ -78,7 +78,7 @@
 %%
 sim = tapas_simModel(u,...
                      'tapas_hgf_binary',...
-                     [NaN 0 1 NaN 1 1 NaN 0 0 NaN 1 NaN -2.5 -6],...
+                     [NaN 0 1 NaN 1 1 NaN 0 0 1 1 NaN -2.5 -6],...
                      'tapas_unitsq_sgm',...
                      5);
 %% 

HGF/tapas_condhalluc_obs.m

@@ -0,0 +1,49 @@
+function [logp, yhat, res] = tapas_condhalluc_obs(r, infStates, ptrans)
+% Calculates the log-probability of response y=1 under the unit-square sigmoid model
+%
+% --------------------------------------------------------------------------------------------------
+% Copyright (C) 2015-2016 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 beta to its native space
+be = exp(ptrans(1));
+
+% Initialize returned log-probabilities as NaNs so that NaN is
+% returned for all irregualar trials
+n = size(infStates,1);
+logp = NaN(n,1);
+yhat = NaN(n,1);
+res  = NaN(n,1);
+
+% Check input format
+if size(r.u,2) ~= 2
+    error('tapas:hgf:CondHalluc:InputsIncompatible', 'Inputs incompatible with condhalluc_obs observation model. See tapas_condhalluc_obs_config.m.')
+end
+
+% Get true-positive rate corresponding to stimuli
+tp = r.u(:,2);
+
+% Weed irregular trials out
+mu1hat = infStates(:,1,1);
+mu1hat(r.irr) = [];
+y = r.y(:,1);
+y(r.irr) = [];
+tp(r.irr) = [];
+
+% 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));
+
+% Calculate log-probabilities for non-irregular trials
+reg = ~ismember(1:n,r.irr);
+logp(reg) = -log(1+exp(-be.*(2.*x-1).*(2.*y-1)));
+yhat(reg) = x;
+res(reg) = (y-x)./sqrt(x.*(1-x));
+
+return;

HGF/tapas_condhalluc_obs2.m

@@ -0,0 +1,48 @@
+function [logp, yhat, res] = tapas_condhalluc_obs2(r, infStates, ptrans)
+% Calculates the log-probability of response y=1 under the unit-square sigmoid model
+%
+% --------------------------------------------------------------------------------------------------
+% Copyright (C) 2016 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 alpha and beta to their native spaces
+be = exp(ptrans(1));
+nu = exp(ptrans(2));
+
+% Initialize returned log-probabilities as NaNs so that NaN is
+% returned for all irregualar trials
+n = size(infStates,1);
+logp = NaN(n,1);
+yhat = NaN(n,1);
+res  = NaN(n,1);
+
+% Check input format
+if size(r.u,2) ~= 2
+    error('tapas:hgf:CondHalluc:InputsIncompatible', 'Inputs incompatible with condhalluc_obs observation model. See tapas_condhalluc_obs_config.m.')
+end
+
+% Get true-positive rate corresponding to stimuli
+tp = r.u(:,2);
+
+% Weed irregular trials out
+mu1hat = infStates(:,1,1);
+mu1hat(r.irr) = [];
+y = r.y(:,1);
+y(r.irr) = [];
+tp(r.irr) = [];
+
+% Update belief using precision-weighted prediction error
+% with nu the generalized precision
+x = mu1hat + 1/(1 + nu)*(tp - mu1hat);
+
+% Calculate log-probabilities for non-irregular trials
+reg = ~ismember(1:n,r.irr);
+logp(reg) = -log(1+exp(-be.*(2.*x-1).*(2.*y-1)));
+yhat(reg) = x;
+res(reg) = (y-x)./sqrt(x.*(1-x));
+
+return;

HGF/tapas_condhalluc_obs2_config.m

@@ -0,0 +1,55 @@
+function c = tapas_condhalluc_obs2_config
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% Contains the configuration for the response model used to analyze data from conditioned
+% hallucination paradigm by Powers & Corlett
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% The rationale for this model is as follows:
+%
+% TO BE DESCRIBED...
+%
+% --------------------------------------------------------------------------------------------------
+% Copyright (C) 2016 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/>.
+
+% Config structure
+c = struct;
+
+% Model name
+c.model = 'tapas_condhalluc_obs2';
+
+% Sufficient statistics of Gaussian parameter priors
+
+% Beta
+c.logbemu = log(48);
+c.logbesa = 1;
+
+% Nu
+c.lognumu = log(1);
+c.lognusa = 1;
+
+% Gather prior settings in vectors
+c.priormus = [
+    c.logbemu,...
+    c.lognumu,...
+         ];
+
+c.priorsas = [
+    c.logbesa,...
+    c.lognusa,...
+         ];
+
+% Model filehandle
+c.obs_fun = @tapas_condhalluc_obs2;
+
+% Handle to function that transforms observation parameters to their native space
+% from the space they are estimated in
+c.transp_obs_fun = @tapas_condhalluc_obs2_transp;
+
+return;

HGF/tapas_condhalluc_obs2_namep.m

@@ -0,0 +1,15 @@
+function pstruct = tapas_condhalluc_obs2_namep(pvec)
+% --------------------------------------------------------------------------------------------------
+% Copyright (C) 2016 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/>.
+
+pstruct = struct;
+
+pstruct.be = pvec(1);
+pstruct.nu = pvec(2);
+
+return;

HGF/tapas_condhalluc_obs2_sim.m

@@ -0,0 +1,35 @@
+function y = tapas_condhalluc_obs2_sim(r, infStates, p)
+% Simulates responses according to the condhalluc_obs model
+%
+% --------------------------------------------------------------------------------------------------
+% Copyright (C) 2016 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/>.
+
+% Get parameters
+be = p(1);
+nu = p(2);
+
+% Prediction trajectory
+mu1hat = infStates(:,1,1);
+
+% Get true-positive rate corresponding to stimuli
+tp = r.u(:,2);
+
+% Update belief using precision-weighted prediction error
+% with nu the generalized precision
+x = mu1hat + 1/(1 + nu)*(tp - mu1hat);
+
+% Apply the logistic sigmoid to the inferred beliefs
+prob = tapas_sgm(be.*(2.*x-1),1);
+
+% Initialize random number generator
+rng('shuffle');
+
+% Simulate
+y = binornd(1, prob);
+
+return;

HGF/tapas_condhalluc_obs2_transp.m

@@ -0,0 +1,19 @@
+function [pvec, pstruct] = tapas_condhalluc_obs2_transp(r, ptrans)
+% --------------------------------------------------------------------------------------------------
+% Copyright (C) 2016 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/>.
+
+pvec    = NaN(1,length(ptrans));
+pstruct = struct;
+
+pvec(1)    = exp(ptrans(1));         % be
+pstruct.be = pvec(1);
+
+pvec(2)    = exp(ptrans(2));         % nu
+pstruct.nu = pvec(2);
+
+return;

HGF/tapas_condhalluc_obs_config.m

@@ -0,0 +1,94 @@
+function c = tapas_condhalluc_obs_config
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% Contains the configuration for the response model used to analyze data from conditioned
+% hallucination paradigm by Powers & Corlett
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% The rationale for this model is as follows:
+%
+% We apply decision noise (i.e., a logistic sigmoid, see below) to the probability that the subject
+% says “yes” on a given trial:
+%
+% p( yes | belief ) = sigmoid( belief ),
+%
+% where belief = p( tone | percept, light ),
+%
+% and where in turn “percept” is the subjective experience of hearing (or not hearing) a tone, while
+% “tone” is the objective presentation of a tone, and “light” is the presentation of a light.
+%
+% In trial where there is a tone, we may use Bayes’ theorem to get the belief:
+%
+% belief = p( tone | percept, light ) = p( percept | tone )*p( tone | light ) / (p( percept |
+%                 tone )*p( tone | light ) + p( percept | no tone )*p( no tone | light ))
+%
+% Unpacking the various ingredients, we have
+%
+% - p( percept | tone ) is given by experimental design: the true positive rate of the tone
+%   presented without light at each trial - 1/4, 1/2, or 3/4
+%
+% - p( tone | light ) is the prior from learning using the HGF: mu1hat
+%
+% - p( percept | no tone ) is the false positive rate, which we can take to be 1 - mu1hat
+%
+% - p( no tone | light ) is the other half of the prior: 1 - mu1hat
+%
+% In trials where there is no tone, the belief is mu1hat.
+%
+% The logistic sigmoid is
+%
+% f(x) = 1/(1+exp(-beta*(v1-v0))),
+%
+% where v1 and v0 are the values of options 1 and 0, respectively, and beta > 0 is a parameter that
+% determines the slope of the sigmoid. Beta is sometimes referred to as the (inverse) decision
+% temperature. In the formulation above, it represents the probability of choosing option 1.
+% Reversing the roles of v1 and v0 yields the probability of choosing option 0.
+%
+% Beta can be interpreted as inverse decision noise. To have a shrinkage prior on this, choose a
+% high value. It is estimated log-space since it has a natural lower bound at zero. In general, v1
+% and v0 can be any real numbers.
+%
+% This observation model expects the first column of the input matrix to contain the outcomes
+% (corresponding in this case to the choices of the subject): 1 or 0 for each trial. The SECOND
+% COLUMN of the input matrix is expected to contain the true-positive rate of the stimulus for
+% each trial. The response matrix only contains one column consisting of the choices of the
+% subject. This means that it will be identical to the first column of the input matrix.
+%
+% --------------------------------------------------------------------------------------------------
+% Copyright (C) 2015 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/>.
+
+% Config structure
+c = struct;
+
+% Model name
+c.model = 'tapas_condhalluc_obs';
+
+% Sufficient statistics of Gaussian parameter priors
+
+% Beta
+c.logbemu = log(48);
+c.logbesa = 1;
+
+% Gather prior settings in vectors
+c.priormus = [
+    c.logbemu,...
+         ];
+
+c.priorsas = [
+    c.logbesa,...
+         ];
+
+% Model filehandle
+c.obs_fun = @tapas_condhalluc_obs;
+
+% Handle to function that transforms observation parameters to their native space
+% from the space they are estimated in
+c.transp_obs_fun = @tapas_condhalluc_obs_transp;
+
+return;

HGF/tapas_condhalluc_obs_namep.m

@@ -0,0 +1,14 @@
+function pstruct = tapas_condhalluc_obs_namep(pvec)
+% --------------------------------------------------------------------------------------------------
+% Copyright (C) 2016 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/>.
+
+pstruct = struct;
+
+pstruct.be = pvec(1);
+
+return;