Asymmetric Reinforcement Learning Explains Human Choice Patterns in Decision-making Under Risk
This study demonstrates that an asymmetric Risk Sensitive reinforcement learning model, which differentially weights rewards and losses, provides a superior explanation for human choice patterns and response times in decision-making under risk compared to symmetric learning approaches.