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This summary is machine-generated.

This tutorial introduces active inference using partially observable Markov decision processes (POMDPs) for modeling neurocognitive processes. It provides practical guidance for building, simulating, and fitting these models, making active inference research more accessible.

Keywords:
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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Machine Learning

Background:

  • Active inference is a popular framework for modeling neurocognitive processes.
  • Recent formulations utilize partially observable Markov decision processes (POMDPs).
  • Existing resources often assume advanced technical backgrounds, limiting accessibility.

Purpose of the Study:

  • To provide a step-by-step tutorial on building and running active inference models using POMDPs.
  • To lower the barrier to entry for researchers interested in active inference.
  • To equip readers with practical tools for theoretical and empirical studies.

Main Methods:

  • A tutorial approach is used, assuming minimal programming and mathematical background.
  • Detailed explanations of equations and exemplar MATLAB scripts are provided.
  • The tutorial covers model building, simulation, and fitting to empirical data.

Main Results:

  • The paper offers a practical guide to implementing active inference models.
  • Exemplar scripts and explanations facilitate customization for various research needs.
  • Optional technical sections and appendices provide further depth.

Conclusions:

  • This tutorial empowers researchers to apply active inference in their work.
  • It facilitates the use of POMDPs for modeling cognitive processes.
  • The resource aims to support engagement with emerging advances in active inference research.