HMM For Discovering Decision-making Dynamics Using Reinforcement Learning Experiments

Abstract

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) wi

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyguo2024hmm

Related papers

• Joint Modeling For Learning Decision-making Dynamics In Behavioral Experiments (2025)0.00
• Unified Models Of Human Behavioral Agents In Bandits, Contextual Bandits And RL (2020)8.35
• Burning RED: Unlocking Subtask-driven Reinforcement Learning And Risk-awareness In Average-reward Markov Decision Processes (2024)0.00
• Reinforcement Learning In Dynamic Treatment Regimes Needs Critical Reexamination (2024)2.35
• A Story Of Two Streams: Reinforcement Learning Models From Human Behavior And Neuropsychiatry (2019)0.00
• Reinforcement Learning With Human Feedback: Learning Dynamic Choices Via Pessimism (2023)0.00
• Automated Reinforcement Learning: An Overview (2022)0.00
• Implications Of Human Irrationality For Reinforcement Learning (2020)0.00