Release ID:
Copyright (C) 2012-2022 Christoph Mathys chmathys@ethz.ch Translational Neuromodeling Unit (TNU) University of Zurich and ETH Zurich
The HGF toolbox is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program (see the file COPYING). If not, see http://www.gnu.org/licenses/.
Please cite the following paper when using the HGF Toolbox:
Frässle, S., et al. (2021). TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry. Frontiers in Psychiatry, 12:680811. https://doi.org/10.3389/fpsyt.2021.680811
Move this folder to a location of your choice and add it to your Matlab path.
Documentation can be found in the manual contained in the Manual.pdf file. This will point you to the relevant configuration files. Further documentation is available throughout the source code.
There is a Matlab LiveScript tutorial demo that can be launched by opening hgf_demo.mlx in Matlab. A PDF version is available in hgf_demo.pdf.
- Add ehgf_ar1_plotTraj
- Bugfix
- Numerical stability improvements to optimization
- Combined response models (more than one observation per trial)
- Various bugfixes and minor improvements
- Improved functionality of beta_obs response model
- Included reference to TAPAS paper
- Introduced first eHGF models (ehgf, ehgf_binary, ehgf_jget)
- Enabled calling fitModel with config structures as arguments
- Enabled changing config structures on the fly
- Introduced sampleModel for sampling from the prior
- Various other additions, improvements, and bugfixes
- Enabled setting and storing of seed for random number generator in simulations
- Debugged reading of response model configuration in simModel
- Reduced default maxStep from 2 to 1 in quasinewton_oqptim_config
- Improved readability of siem files for unitsq_sgm and softmax_binary
- Added simulation capability for softmax_wld and softmax_mu3_wld
- Added softmax_wld response model
- Improved readability of softmax_mu3_wld code
- Improved readability of softmax and softmax_mu3 code
- Brought hgf_demo.pdf up to date
- Added gaussian_obs_offset response model
- Brought example in simModel up to date
- Added sim and namep files for unitsq_sgm_mu3
- Fixed typo in softmax_mu3_wld
- Introduced softmax_mu3_wld decision model
- Estimate mu0_2 by default in hgf_ar1_binary_mab
- Improved comment in softmax_mu3_config
- Change to pi_2 update in hgf_ar1_binary_mab
- Enabled simulation for hgf_ar1_binary_mab
- Added softmax_mu3
- Added hgf_ar1_binary_mab
- Fixed automatic detection of number of levels in hgf_ar1_binary
- Fixed documentation of hgf_ar1_binary
- Fixed hgf_binary_mab_plotTraj
- Fixed trajectory calculations in hgf_binary_mab
- Adapted riddersgradient and riddershessian to new Matlab versions
- Quashed bug in rw_binary_dual found by gbelluc@gmail.com
- Added condhalluc_obs and condhalluc_obs2 models
- Introduced kappa1 in all binary HGF models
- Ported interactive demo to Matlab LiveScript
- Various additional small improvements
- Updated manual
- Updated and renamed README to README.md
- Improvements to logrt_linear_binary_minimal
- Added the binary HGF with trial-by-trial perceptual uncertainty as hgf_binary_pu_tbt
- Added the Kalman filter as kf
- Improved the beta_obs model
- Improved calculation of implied 1st-level learning rate
- Corrected sign of update trajectories
- Added option to base responses on predictions or posteriors in the beta_obs model
- Added tapas_autocorr.m
- Predictions and residuals returned by all observations models
- Added tapas_fit_plotResidualDiagnostics()
- Added hgf_categorical_norm
- Added Boltzmann distribution (i.e., softmax normalization) as tapas_boltzmann()
- Set implied learning rate at first level to 0 if update is zero
- Give choice of using predictions or posteriors with softmax_binary
- Added cdfgaussian_obs model
- Added hgf_binary_pu (perceptual uncertainty) model
- Improvements for beta_obs with hgf_whichworld
- 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
- Improved comments in softmax_binary_sim
- Improved comments in tapas_beta_obs.m
- Added tapas_beta_obs_{sim,namep}.m
- Added tapas_hgf_ar1_binary_namep.m
- Improved rw_binary
- Added bayes_optimal_categorical
- Improved hgf_categorical_plotTraj
- Adapted softmax_sim to hgf_categorical
- Added hgf_categorical
- Added datagen_categorical and categorical data example
- Improved hgf_jget
- Added PDF manual
- Added interactive demo in hgf_demo
- Added file of raw commands from hgf_demo in hgf_demo_commands
- Adapted fitModel to calculate AIC and BIC
- Renamed F (negative variational free energy) to LME (log-model evidence, to which it is an approximation)
- Calculate accuracy and complexity in fitModel
- Save everything relating to model quality under r.optim
- Improved output of fitModel
- Added hierarchical hidden Markov model (hhmm)
- Added hidden Markov model (hmm)
- Added WhatWorld (hgf_whatworld) model
- Added linear log-reaction time (logrt_linear_whatworld) model for WhatWorld
- Added WhichWorld (hgf_whichworld) model
- Added AR(1) model for binary outcomes (hgf_ar1_binary)
- Added Jumping Gaussian Estimation (hgf_jget) model
- Added unitsq_sgm_mu3 decision model
- Added binary multi-armed bandit model hgf_binary_mab
- Added beta_obs observation model for decision noise on the unit interval
- Added softmax decision model with different inverse temperature for each kind of binary decision (softmax_2beta)
- Added logrt_linear_binary decision model
- Added Rescorla-Wagner model with different learning rate for each kind of binary outcome (rw_binary_dual)
- Included additional trajectories in output of hgf, hgf_ar1, hgf_ar1_mab, hgf_binary, hgf_ar1_binary, hgf_binary_mab, hgf_whichworld, and hgf_whatworld
- Made infStates more consistent across models
- Removed deprecated hgf_binary3l
- Made fitModel explicitly return negative log-joint probability and negative log-likelihood
- Modified simModel to read configuration files of perceptual and observation models
- Abolished theta in hgf, hgf_binary, hgf_ar1, hgf_ar1_mab, hgf_ar1_binary, hgf_binary_mab, and hgf_jget
- Moved kappa estimation from logit-space to log-space for hgf, hgf_binary, hgf_ar1, hgf_ar1_mab, hgf_ar1_binary, hgf_binary_mab, and hgf_jget
- Introduced checking for implausible jumps in trajectories for hgf, hgf_binary, hgf_ar1, hgf_ar1_mab, hgf_ar1_binary, hgf_binary_mab, and hgf_jget
- Adapted fitModel to deal with cases the <prc_model>_transp() function performs operations important to the () function
- Introduced multinomial softmax decision model
- Improved documentation for hgf_ar1_mab model
- Added error IDs for all errors
- Improved error handling in tapas_fitModel()
- Prefixed all function names with “tapas_”
- Added rs_precision
- Added rs_belief
- Added rs_surprise
- Added sutton_k1
- Added hgf_ar1_mab
- Added softmax for continuous responses
- Improved checking of trajectory validity in HGF models
- Debugged input handling in softmax_binary
- Introduced Bayesian parameter averaging
- Amended calculation of log-priors in fitModel.m
- Debugged construction of time axis in hgf_plotTraj
- Debugged removal of placeholder field in estimate structure
- Estimation of Bayes optimal parameters added
- infStates the same 3-dim array in hgf_binary as in hgf
- Changes to softmax_binary: trial-by-trial rewards read from input matrix
- hgf_binary generalized to n levels
- Old hgf_binary lives on as hgf_binary3l
- Input at irregular intervals enabled in hgf and hgf_binary
- Support for constant drift in hgf and hgf_binary
- Introduced use of placeholders in config files
- quasinewton_optim: increased default maximum number of regularizations to 16
- Automatic detection of upper bound on theta for hgf
- Improved input checks
- Support for AR(1) processes in new hgf_ar1
- quasinewton_optim: improved resetting after exhaustion of regularizations
- Original release