Characterizing Decision-Making Dynamics and Brain Activities

This project leverages statistical modeling, reinforcement learning (RL), and deep learning techniques to characterize human decision-making behavior and to learn robust, interpretable representations from electroencephalography and functional magnetic resonance imaging data.

We propose a novel framework that integrates RL with the drift-diffusion model to jointly analyze reward-based decisions and response times. Motivated by evidence that individuals may alternate between multiple decision-making strategies, we incorporate latent state switching using a hidden Markov model. Our goal is to characterize behavioral patterns, compare individuals with Major Depressive Disorder to healthy controls, and examine associations with neuroimaging markers of brain activity and clinical outcomes.

Currently, we are developing methods to integrate behavioral tasks and neuroimaging measures to uncover latent neural dynamics and elucidate brain–behavior relationships.