Reinforcement Learning with Hidden Markov Models for Discovering
  Decision-Making Dynamics
Xingche Guo Donglin Zeng Yuanjia Wang
Major depressive disorder (MDD) presents challenges in diagnosis and
treatment due to its complex and heterogeneous nature. Emerging evidence
indicates that reward processing abnormalities may serve as a behavioral marker
for MDD. To measure reward processing, patients perform computer-based
behavioral tasks that involve making choices or responding to stimulants that
are associated with different outcomes. Reinforcement learning (RL) models are
fitted to extract parameters that measure various aspects of reward processing
to characterize how patients make decisions in behavioral tasks. Recent
findings suggest the inadequacy of characterizing reward learning solely based
on a single RL model; instead, there may be a switching of decision-making
processes between multiple strategies. An important scientific question is how
the dynamics of learning strategies in decision-making affect the reward
learning ability of individuals with MDD. Motivated by the probabilistic reward
task (PRT) within the EMBARC study, we propose a novel RL-HMM framework for
analyzing reward-based decision-making. Our model accommodates learning
strategy switching between two distinct approaches under a hidden Markov model
(HMM): subjects making decisions based on the RL model or opting for random
choices. We account for continuous RL state space and allow time-varying
transition probabilities in the HMM. We introduce a computationally efficient
EM algorithm for parameter estimation and employ a nonparametric bootstrap for
inference. We apply our approach to the EMBARC study to show that MDD patients
are less engaged in RL compared to the healthy controls, and engagement is
associated with brain activities in the negative affect circuitry during an
emotional conflict task.