Summary of Hmm For Discovering Decision-making Dynamics Using Reinforcement Learning Experiments, by Xingche Guo et al.
HMM for Discovering Decision-Making Dynamics Using Reinforcement Learning Experiments
by Xingche Guo, Donglin Zeng, Yuanjia Wang
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP); Methodology (stat.ME); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper investigates the neural mechanisms underlying reward processing in major depressive disorder (MDD). The authors propose a novel reinforcement learning-hidden Markov model (RL-HMM) framework to analyze decision-making processes in patients with MDD. The framework accommodates switching between two distinct approaches: making decisions based on reinforcement learning or opting for random choices. The researchers apply their approach to the EMBARC study and find that MDD patients are less engaged in reinforcement learning compared to healthy controls, which is associated with brain activities in the negative affect circuitry during an emotional conflict task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how people with depression make decisions based on rewards. It uses special computer programs to understand how their brains work differently from those who don’t have depression. The scientists found that people with depression are less good at learning and making choices when it comes to rewards, which is connected to how their brains process emotions. |
Keywords
* Artificial intelligence * Hidden markov model * Reinforcement learning
