In this tutorial, we will recap the theory behind the Hierarchical Gaussian Filter (HGF) and introduce the model in an accessible way. We will then discuss practical issues when fitting computational models to behavioral data in general and specific to the HGF. We will work through exercises to learn how to analyze data with the HGF using the HGF-toolbox (in Matlab).

This tutorial provides a practical guide on developing computational models. Students will be taught to build simple simulations using Matlab, and will use these to explore the behavioural 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, participants 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 be also provided. Participants will also learn important steps and issues to check when reporting modeling results in publications.

The tutorial will provide a practical introduction to analyzing decision making data with the drift diffusion model (DDM) using the open source python toolbox HDDM. We will go through practical steps of applying the models to data, from importing data to running and validating models. We will also briefly go over extensions to the DDM, including the reinforcement learning drift diffusion model (RLDDM).

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 (eg. Q-learning, DCM). No previous experience with modelling is required, but basic knowledge of Matlab is recommended.

Would you like to learn more about modeling individual differences and heterogeneity in psychiatry? In this tutorial, we will abandon the classical patient vs. healthy control framework. You will be guided through how to run an analysis using normative modeling implemented in the PCNtoolkit.

This tutorial will examine specific features of EEG data that can be used to optimize a cell and receptor specific model of brain connectivity. EEG data acquired from event related (ERP) and temporally extended / resting state studies (Spectral responses) will be examined. The neural mass models - their assumptions and parametrizations will be compared. Students will learn to use the SPM graphical user interface and to write batch code in MATLAB to perform Dynamic Causal Modeling of EEG.

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.

In this tutorial, we will recap the theory underlying the hMeta-d model for quantifying metacognitive efficiency, our ability to monitor and evaluate our own decisions. We will introduce the model in an accessible way, then discuss practical issues when fitting computational models to behavioral data and go through code examples using the hMeta-d toolbox.

In this tutorial, you will learn how to use the regression dynamic causal modeling (rDCM) toolbox to perform effective (directed) connectivity analyses in whole-brain networks from functional magnetic resonance imaging (fMRI) data. We will provide you with the necessary theoretical background of the rDCM approach and detail practical aspects that are relevant for whole-brain connectivity analyses. After having laid the foundation, a hands-on part will allow you to obtain a better feeling for the behavior of the model as well as provide you with in-depth experience on how to apply the model to empirical fMRI data. The goal of this tutorial is to familiarize you with the theoretical and practical aspects of rDCM, which will allow you to seamlessly integrate the approach into your own research. We will provide clear instructions on how to perform the analyses. However, experience with the analysis of fMRI data (already some experience with classical DCM for fMRI would be ideal) as well as experience with MATLAB are beneficial.