HGF Toolbox V4.10

Version 4.10 of the HGF Toolbox has been released.

The HGF Toolbox implements many variants of the hierarchical Gaussian filter (HGF) and many other models used in time-series modeling, such as hidden Markov models, hierarchical hidden Markov Models, Rescorla-Wagner, etc.

The main highlights of this release are

- addition of the hgf_categorical model for categorical outcomes,

- addition of the hgf_binary_pu model for binary outcomes with perceptual uncertainty (pu), and

- addition of the Pearce-Hall reinforcement learning model (ph_binary)

Many additional improvements have been made, and the full release notes are below.

The HGF is a generic Bayesian hierarchical model for inference on a changing environment based on sequential input. This makes it a general model of learning in discrete time. It was introduced in

Mathys C, Daunizeau J, Friston KJ, Stephan KE (2011). A Bayesian foundation for individual learning under uncertainty. Frontiers in Human Neuroscience. 5:39. doi:10.3389/fnhum.2011.00039

and is explained in more detail in

Mathys C, Lomakina EI, Daunizeau J, Iglesias S, Brodersen KH, Friston KJ, & Stephan KE (2014). Uncertainty in perception and the Hierarchical Gaussian Filter. Frontiers in Human Neuroscience, 8:825. doi:10.3389/fnhum.2014.00825

After downloading, unzip the toolbox and read the Manual.pdf file.

 

Release Notes
———————–
v4.10
~~~~~
- Added hgf_categorical_norm
- Added Boltzmann distribution (i.e., softmax normalization) as tapas_boltzmann()

v4.9
~~~
- Set implied learning rate at first level to 0 if update is zero

v4.8
~~~
- Give choice of using predictions or posteriors with softmax_binary

v4.7
~~~
- Added cdfgaussian_obs model
- Added hgf_binary_pu (perceptual uncertainty) model
- Improvements for beta_obs with hgf_whichworld

v4.6
~~~
- Adapted beta_obs to deal with ph_binary
- Added Pearce-Hall in ph_binary
- Clarified the role of default settings in comments of fitModel
- Brought softmax_binary_sim up to date

v4.5
~~~
- Improved comments in softmax_binary_sim
- Improved comments in tapas_beta_obs.m
- Added tapas_beta_obs_{sim,namep}.m

v4.4
~~~
- Added tapas_hgf_ar1_binary_namep.m
- Improved rw_binary

v4.3
~~~
- Added bayes_optimal_categorical
- Improved hgf_categorical_plotTraj

v4.2
~~~
- Adapted softmax_sim to hgf_categorical
- Added hgf_categorical
- Added datagen_categorical and categorical data example

v4.1
~~~
- Improved hgf_jget