Unlocking New Horizons in Cognitive Modeling with Simulation-Based Inference
Application deadline: April 30, 2026 (AoE) — Summer School: July 27–31, 2026 — Apply now
At a Glance
Modern simulation-based inference (SBI) represents a merger of deep learning and computational modeling, transforming how we think about, develop, fit, validate, and compare cognitive models. This summer school will introduce the basics of computational cognitive modeling and guide participants through the latest deep learning methods for supercharging their workflows. Participants will:
- Learn the basics of computational cognitive modeling
- Explore deep learning for cognitive science
- Apply modern SBI tools to real data
- Participate in hands-on demonstrations
- Discover new models using unsupervised learning
- Develop your own research project
Summer School Themes
Learn how computational models are used to explain cognitive processes. Understand the principles behind model construction and validation.
Explore Bayesian methods for parameter estimation and model comparison. Learn how to quantify, interpret, and embrace uncertainty.
Discover how SBI leverages simulations and deep learning to fit complex models that are otherwise intractable. Apply SBI to model real behavioral data.
Integrate multiple data sources or behavioral tasks into unified models. Joint modeling enables richer inferences and more robust predictions.
Model cognitive processes that unfold over time. Dynamic models capture the complex temporal structure of behavior and cognition.
Leverage unsupervised learning to extract new models directly from data. Compare general neural models to task-performing models.
Sponsors
This summer school is generously sponsored by the William K. and Katherine W. Estes Fund overseen by the Psychonomic Society and the Association for Psychological Science. The organizers acknowledge support from the National Science Foundation and The Ohio State College of Arts & Sciences.