BioLLMAgent: A Hybrid Framework with Enhanced Structural Interpretability for Simulating Human Decision-Making in Computational Psychiatry

Researchers introduce BioLLMAgent, a hybrid framework combining reinforcement learning models with large language models to simulate human decision-making in computational psychiatry. The framework demonstrates strong interpretability while accurately reproducing human behavioral patterns and successfully simulating cognitive behavioral therapy principles.