دورية أكاديمية
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A step-by-step tutorial on active inference and its application to empirical data.

التفاصيل البيبلوغرافية
العنوان: A step-by-step tutorial on active inference and its application to empirical data.
المؤلفون: Smith R; Laureate Institute for Brain Research, Tulsa, OK, USA., Friston KJ; Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, WC1N 3AR, UK., Whyte CJ; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
المصدر: Journal of mathematical psychology [J Math Psychol] 2022 Apr; Vol. 107. Date of Electronic Publication: 2022 Feb 04.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Academic Press Country of Publication: United States NLM ID: 2985082R Publication Model: Print-Electronic Cited Medium: Internet ISSN: 0022-2496 (Print) Linking ISSN: 00222496 NLM ISO Abbreviation: J Math Psychol Subsets: PubMed not MEDLINE
أسماء مطبوعة: Publication: New York, NY : Academic Press
Original Publication: New York.
مستخلص: 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.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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معلومات مُعتمدة: MC_UU_00005/17 United Kingdom MRC_ Medical Research Council; P20 GM121312 United States GM NIGMS NIH HHS
فهرسة مساهمة: Keywords: Active inference; Bayesian inference; Computational neuroscience; Decision-making; Learning; Machine learning
تواريخ الأحداث: Date Created: 20220328 Latest Revision: 20220716
رمز التحديث: 20221213
مُعرف محوري في PubMed: PMC8956124
DOI: 10.1016/j.jmp.2021.102632
PMID: 35340847
قاعدة البيانات: MEDLINE
الوصف
تدمد:0022-2496
DOI:10.1016/j.jmp.2021.102632