Organization of the summer school
Covering the core topics in computational modelling of decision-making, including fundamentals of Bayesian analysis (model fitting and comparison, GLMs, latent models, hierarchical models, etc.); standard models of decision-making (optimal observer, drift-diffusion model, actor-critic model, etc.), as well as integrating neural data into behavioral models. An emphasis will be given to sound statistical interpretation of modelling analysis. Our focus is not on teaching the mathematical derivations; instead we will teach formal underpinnings only where they are critical for providing a conceptual understanding of the approaches described.
We will devote extensive time (around 4 hours a day) to implementing the analyses using Matlab/Python with actual behavioral data. Technical Assistants (TAs) will guide the trainees during all these tutorials.
One speaker giving an in-depth presentation of a research question, starting from presentation of classical models to current research projects. Speakers will put emphasis on modelling challenges and how they were overcome, and how models of behavior illuminate neuroscience experiments.
Trainees will pair up to conduct a personal project during off-hours, based on the research project of either trainee. Trainees should arrive with behavioral data from their homelab research that they wish to analyze (synthetic datasets will also be available where appropriate). The trainees will apply the presented techniques to their own dataset, under supervision by TAs. We expect the trainees to complete within the week a (likely partial yet) sound modelling analysis of their dataset.