JOINT MODELING FOR LEARNING DECISION-MAKING DYNAMICS IN BEHAVIORAL EXPERIMENTS.

ABSTRACT: Major depressive disorder (MDD), a leading cause of disability and mortality, is associated with reward-processing abnormalities and concentration issues. Motivated by the probabilistic reward task from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, we propose a novel framework that integrates the reinforcement learning (RL) model and drift-diffusion model (DDM) to jointly analyze reward-based decision-making with response times. To account for emerging evidence suggesting that decision-making may alternate between multiple interleaved strategies, we model latent state switching using a hidden Markov model (HMM). In the engaged state, decisions follow an RL-DDM, simultaneously capturing reward processing, decision dynamics, and tem

SUBMITTER: Bian Y 

PROVIDER: S-EPMC12814034 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature