Hua-Dong Xiong

The paper uses population-level individual differences to discover shared computational components in perceptual decision-making, revealing a six-dimensional structure across datasets.

This paper introduces DynamicRL, a method to estimate how humans adapt their reinforcement learning strategies over time, showing parameter updates resemble policy gradient ascent.

This study compares parameter estimation methods in reinforcement learning models, finding that deep learning outperforms traditional optimization in reliability and generalizability.