Preliminary Program

Day 1: Introduction to model-based neuroscience

11:00 Lecture 1: Introduction to model-based cognitive neuroscience (Birte Forstmann)
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 Poster session (all lecturers present): We invite all participants to present a poster (1m height x 1.20m width) with recent work or with 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 (Brandon Turner)
13:00 Practical 2: Bayesian estimation of evidence-accumulation models, including Bayes theorem, priors and posteriors, DE-MCMC, and sampling single subjects (Dora Matzke and Brandon Turner)

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 Miletic)
13:00 Practical 5: Using the Python framework for model-based functional MRI analysis (Steven Miletic)

Day 5: Joint modeling of brain and behavior

10:00 Lecture 6: Bayesian joint modeling of brain and behavior (Brandon Turner)
13:00 Lecture 7: Capturing trial-by-trial variability in cognitive models to inform neuroimaging analyses (Sebastian Gluth)
15:00 Lecture 8: TBA (Gordon Logan)
17:00 Closing remarks

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