In this tutorial, we will recap the theory underlying the HGF (a Hierarchical Bayesian Inference Model) and introduce the model in an accessible way. We will then discuss practical issues when fitting computational models to behavioral data and go through code examples using the HGF Toolbox.

This tutorial provides a practical guide on developing computational models. You will be taught to build simple simulations using Matlab, and will use these to explore the behavioral and electrophysiological manifestations of active inference. We aim to develop an intuitive understanding of the theoretical principles, without requiring detailed technical knowledge.

In this tutorial, you will learn how to use a Bayesian package called hBayesDM for modeling various reinforcement learning and decision making (RLDM) tasks. A short overview of (hierarchical) Bayesian modeling will also be provided. You will also learn the most important things you need to check when reporting modeling results in publications.

This hands-on tutorial is a crash course on practical computational modelling. You will build a simple model (delay discounting) and learn how to apply it on empirical data to perform parameter estimation and model selection. We will use the VBA-toolbox which contains all the tools to simulate, estimate, and diagnose your models, as well as a collection of ready-to-use models (e.g. Q-learning, DCM). No previous experience with modelling is required, but basic knowledge of Matlab is recommended.

Would you like to learn more about normative modelling in psychiatry? In this tutorial you will learn the “why” and the “how” of simple normative models. You will take home a better understanding of this technique, machine learning, and some transferable Python programming skills.

In this tutorial you will learn how to use the SPM software to perform a dynamic causal modeling (DCM) analysis in Matlab. We will first guide you through all steps of a basic DCM analysis of a single subject: Data extraction, Model setup, Model inversion and, finally, inspection of results. We will then proceed to look at a group of subjects. Here, we will focus on model comparison and inspection of model parameters. We will provide a point-by-point recipe on how to perform the analysis. However, it is of advantage if you have worked with neuroimaging (fMRI) data and Matlab before.

Regression dynamic causal modeling (rDCM) represents a novel variant of DCM for fMRI that allows inferring effective (directed) connectivity in whole-brain networks with high computational efficiency. In this tutorial, you will familiarize yourself with the rDCM toolbox, get insights into the theoretical concepts and practical applications of the model, and learn how to set-up your own whole-brain effective connectivity analyses. Experience with (standard) DCM and Matlab programming is beneficial for this tutorial.

In this tutorial, you will learn how to use the HUGE-toolbox to perform model-based group-level analysis on task fMRI data. HUGE is a hierarchical extension of dynamic causal model (DCM) for fMRI which provides a unified framework for performing fitting of DCM and group-level analysis, such as empirical Bayesian analysis or stratification, i.e. the identification of subgroups, of heterogeneous cohorts. For this tutorial, we expect that you have experience with classical fMRI analysis and classical DCM, and a basic level of familiarity with the Matlab programming language.