Organization of the summer school
In general, our goal is that the trainees acquire both solid conceptual knowledge and practical skills that will allow them to pursue a complete modeling approach in autonomy when they come back to their lab.
Each topic will be covered based on a mixture of: an introductory lecture; tutorials where trainees apply the method to behavioral datasets with the help of teaching assistants; group project sessions devoted to the application of new techniques to experimental data. In addition, we will have two keynote research talks.
The school will be structured around the following topics (the ordering of the days may change):
Day 1: Basics of behavioral modeling: model comparison and validation, parameter fitting and recovery
Day 2: Process models of decision making: drift-diffusion models
Day 3: Modeling sequential effects and reinforcement learning
Day 4: Modeling behavior with artificial neural networks
Day 5: “Day off”: social activities and group projects
Day 6: Bayesian models of decision-making
Day 7: Expectation maximization, mixture models, hidden Markov models
Day 8: Group project work
Day 9: Project presentations
We will begin each day with a lecture that introduces the topic, delivered by one of our faculty members. Lectures cover relevant background and the state of the art for the respective set of techniques. Our focus is not on teaching the mathematical derivations; instead, we will teach formal underpinnings where they are critical for providing a conceptual understanding of the approaches described.
We will devote extensive time (around 4 hours on a typical a day) to implementing analyses on real-world behavioral datasets. Experienced Teaching Assistants (TAs) will guide the trainees during all these tutorials.
Participants will work in teams of three on a project involving open-access, published behavioral datasets. They will think about how to choose the most relevant of the techniques presented and to best apply them to these data, under supervision by TAs and faculty members. We expect the trainees to implement a (likely partial, yet sound) modeling analysis of these data during the course.
Two keynote research talks will focus on behavioral and/or neuroscience topics where cutting-edge modeling techniques have been successfully applied. Speakers will put emphasis on how mechanistic models of behavior can illuminate cognitive and neuroscience experiments.
One-to-one meetings with faculty members
We plan one-to-one sessions between trainees and faculty that will provide the opportunity to talk in depth about their work, career plans, etc.