Title: Automated discovery of interpretable cognitive models
Abstract:
Much research in human and animal decision making uses hand-designed reinforcement learning models to capture trial-by-trial choice behavior and associated neural signals during learning. Despite a long literature and many refinements, these theories represent a relatively narrow class of models restricted by core assumptions that are not well justified and remain controversial. Recent work using data-driven methods show that additional structure remains to be captured in the data, but offer little insight or interpretability about it. I present two projects that adopt techniques from modern AI to discover new models automatically. In particular, we scale up datasets and computation, and leverage more flexible model classes to discover more accurate theories across several human and animal datasets. The resulting models fit comparably well as those from black-box methods, but use novel approaches --- first, hybrid neuro-symbolic networks and second, LLM-driven program synthesis --- to preserve interpretability, by explicitly manipulating the tradeoff between interpretability and fidelity. The discovered theories offer a new perspective on learning in classic laboratory tasks and on the promises and limitations of AI-assisted scientific discovery.