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| # HGF Toolbox | ||
| Release ID: $Format:%h %d$ | ||
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| --- | ||
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| Copyright (C) 2012-2022 Christoph Mathys <chmathys@ethz.ch> | ||
| Translational Neuromodeling Unit (TNU) | ||
| University of Zurich and ETH Zurich | ||
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| --- | ||
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| 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. | ||
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| 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. | ||
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| 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/>. | ||
|
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| --- | ||
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| ## How to cite the HGF Toolbox | ||
| Please cite the following paper when using the HGF Toolbox: | ||
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| 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 | ||
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| ## Installation | ||
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| Move this folder to a location of your choice and add it to your Matlab | ||
| path. | ||
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| ## Documentation and configuration | ||
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| 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. | ||
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| ## Tutorial demo | ||
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| 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. | ||
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| ## Release notes | ||
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| ### v7.1 | ||
| - Add ehgf_ar1_plotTraj | ||
| - Bugfix | ||
| ### v7.0 | ||
| - Numerical stability improvements to optimization | ||
| - Combined response models (more than one observation per trial) | ||
| - Various bugfixes and minor improvements | ||
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| ### v6.1 | ||
| - Improved functionality of beta_obs response model | ||
| - Included reference to TAPAS paper | ||
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| ### v6.0 | ||
| - 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 | ||
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| ### v5.3 | ||
| - 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 | ||
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| ### v5.2 | ||
| - 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 | ||
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| ### v5.1 | ||
| - Added condhalluc_obs and condhalluc_obs2 models | ||
| - Introduced kappa1 in all binary HGF models | ||
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| ### v5.0 | ||
| - Ported interactive demo to Matlab LiveScript | ||
| - Various additional small improvements | ||
| - Updated manual | ||
| - Updated and renamed README to README.md | ||
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| ### v4.17 | ||
| - Improvements to logrt_linear_binary_minimal | ||
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| ### v4.16 | ||
| - Added the binary HGF with trial-by-trial perceptual uncertainty as hgf_binary_pu_tbt | ||
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| ### v4.15 | ||
| - Added the Kalman filter as kf | ||
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| ### v4.14 | ||
| - Improved the beta_obs model | ||
| - Improved calculation of implied 1st-level learning rate | ||
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| ### v4.13 | ||
| - Corrected sign of update trajectories | ||
| - Added option to base responses on predictions or posteriors in | ||
| the beta_obs model | ||
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| ### v4.12 | ||
| - Added tapas_autocorr.m | ||
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| ### v4.11 | ||
| - Predictions and residuals returned by all observations models | ||
| - Added tapas_fit_plotResidualDiagnostics() | ||
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| ### v4.10 | ||
| - Added hgf_categorical_norm | ||
| - Added Boltzmann distribution (i.e., softmax normalization) as tapas_boltzmann() | ||
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| ### v4.9 | ||
| - Set implied learning rate at first level to 0 if update is zero | ||
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| ### v4.8 | ||
| - Give choice of using predictions or posteriors with softmax_binary | ||
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| ### v4.7 | ||
| - Added cdfgaussian_obs model | ||
| - Added hgf_binary_pu (perceptual uncertainty) model | ||
| - Improvements for beta_obs with hgf_whichworld | ||
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| ### 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 | ||
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| ### v4.5 | ||
| - Improved comments in softmax_binary_sim | ||
| - Improved comments in tapas_beta_obs.m | ||
| - Added tapas_beta_obs_{sim,namep}.m | ||
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| ### v4.4 | ||
| - Added tapas_hgf_ar1_binary_namep.m | ||
| - Improved rw_binary | ||
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| ### v4.3 | ||
| - Added bayes_optimal_categorical | ||
| - Improved hgf_categorical_plotTraj | ||
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| ### v4.2 | ||
| - Adapted softmax_sim to hgf_categorical | ||
| - Added hgf_categorical | ||
| - Added datagen_categorical and categorical data example | ||
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| ### v4.1 | ||
| - Improved hgf_jget | ||
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| ### v4.0 | ||
| - 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 <model>() function | ||
| - Introduced multinomial softmax decision model | ||
| - Improved documentation for hgf_ar1_mab model | ||
| - Added error IDs for all errors | ||
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| ### v3.0 | ||
| - 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 | ||
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| ### v2.1 | ||
| - 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 | ||
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| ### v2.0 | ||
| - 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 | ||
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| ### v1.0 | ||
| - Original release |
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