Day 1: Introduction to model-based neuroscience
11:00 Lecture 1: Introduction to model-based cognitive neuroscience (Birte Forstmann)
13:00 Lecture 2 / Practical 1: Introducing evidence-accumulation models, including simulating and exploring the diffusion decision model (DDM), the linear ballistic accumulator (LBA), and the lognormal race model (Andrew Heathcote)
15:00 Mini-talks (all lecturers present): We invite all participants to give a short (max 3 min & max 3 slides) talk about their recent work or about research questions to which they would like to apply model-based neuroscience techniques.
Day 2: Introduction to model-based neuroscience continued
10:00 Lecture 3: Approaches to analyses in model-based cognitive neuroscience (Birte Forstmann)
13:00 Practical 2: Bayesian estimation of evidence-accumulation models, including Bayes theorem, priors and posteriors, and sampling single subjects (Dora Matzke and Andrew Heathcote)
Day 3: Fitting evidence-accumulation models
10:00 Practical 3: Bayesian estimation of evidence-accumulation models continued, including more on sampling single subjects, model selection, and introduction to hierarchical models (Andrew Heathcote and Dora Matzke)
13:00 Practical 4: Bayesian estimation of evidence accumulation models continued, including sampling hierarchical models, model selection, and plausible values (Andrew Heathcote)
Day 4: Introduction to functional neuroanatomy/neuroscientific methods
10:00 Lecture 4: Introduction to model-based EEG (Bernadette van Wijk)
11:00 Lecture 5: Introduction to model-based functional MRI (Steven Miletić)
13:00 Practical 5: Using the Python framework for model-based functional MRI analysis (Steven Miletić)
Day 5: Joint modeling of learning, brain, and behavior
10:00 Lecture 6: Bayesian joint modeling of brain and behavior (Brandon Turner)
13:00 Lecture 7: Introduction to reinforcement learning (Steven Miletić)
14:00 Practical 6: Introduction to fitting joint learning and evidence-accumulation models (Andrew Heathcote)
15:00 Lecture 8: Capturing trial-by-trial variability in cognitive models to inform neuroimaging analyses (Sebastian Gluth)
17:00 Closing remarks
