Tentative Program

Day 1: Introduction to model-based neuroscience
Lecture 1: Introduction to model-based cognitive neuroscience (Birte Forstmann/Leendert van Maanen)
Lecture 2 / Practical 1: Introducing evidence accumulation models, including simulating and exploring the DDM, LBA & LNR (Andrew Heathcote)
15:00 Poster session (all lecturers present)

Day 2: Introduction to model-based neuroscience cont’d
Lecture 3: TBA
13:00 Practical 2: Bayesian estimation of evidence accumulation models, including introduction to Bayes theorem, priors and posteriors, DE-MCMC, and sampling single subjects (Dora Matzke, Brandon Turner)

Day 3: Fitting choice models
Practical 3: Bayesian estimation of evidence accumulation models continued, including more on sampling single subjects, model selection, and introduction to hierarchical models (Andrew Heathcote, 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
Lecture 4: Introduction to model-based EEG (Bernadette van Wijk)
11:00 Lecture 5: Introduction to model-based functional MRI (Steven Miletic)
13:00 Practical 5: Using the Python framework for model-based functional MRI analysis (Steven Miletic)

Day 5: Joint modelling of brain and behavior
Lecture 7: Bayesian joint modeling of brain and behavior (Brandon Turner)
13:00 Lecture 8: Capturing trial-by-trial variability in cognitive models to inform neuroimaging analyses (Sebastian Gluth)
15:00 Lecture 9: Using spike trains to drive accumulator models (Gordon Logan)
17:00 Closing remarks