Smith, R;
Friston, KJ;
Whyte, CJ;
(2022)
A step-by-step tutorial on active inference and its application to empirical data.
Journal of Mathematical Psychology
, 107
, Article 102632. 10.1016/j.jmp.2021.102632.
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Abstract
The active inference framework, and in particular its recent formulation as a partially observable Markov decision process (POMDP), has gained increasing popularity in recent years as a useful approach for modeling neurocognitive processes. This framework is highly general and flexible in its ability to be customized to model any cognitive process, as well as simulate predicted neuronal responses based on its accompanying neural process theory. It also affords both simulation experiments for proof of principle and behavioral modeling for empirical studies. However, there are limited resources that explain how to build and run these models in practice, which limits their widespread use. Most introductions assume a technical background in programming, mathematics, and machine learning. In this paper we offer a step-by-step tutorial on how to build POMDPs, run simulations using standard MATLAB routines, and fit these models to empirical data. We assume a minimal background in programming and mathematics, thoroughly explain all equations, and provide exemplar scripts that can be customized for both theoretical and empirical studies. Our goal is to provide the reader with the requisite background knowledge and practical tools to apply active inference to their own research. We also provide optional technical sections and multiple appendices, which offer the interested reader additional technical details. This tutorial should provide the reader with all the tools necessary to use these models and to follow emerging advances in active inference research.
| Type: | Article |
|---|---|
| Title: | A step-by-step tutorial on active inference and its application to empirical data |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.jmp.2021.102632 |
| Publisher version: | https://doi.org/10.1016/j.jmp.2021.102632 |
| Language: | English |
| Additional information: | © 2021 The Author(s). Published by Elsevier Inc. under a Creative Commons license (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
| Keywords: | Active inference, Computational neuroscience, Bayesian inference, Learning, Decision-making, Machine learning |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10143770 |
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