Jialin Li
I'm interested in the cognitive and neural principle of the brain that support human adaptive decision-making and learning. By integrating approaches from psychology, machine learning, neuroscience, and behavioral economics, I build computational models to study how the brain constructs its mental representation of the structure of the world that supports such intelligent behavior. Currently, I work with Prof. Marcelo Mattar on using recurrent neural networks to study the sampling-based computation and inference in artificial and biological agents. Previously, I completed my B.S. in Psychology at Peking University. Outside of academia, I enjoy playing music, taking photos, and exploring new places (see my Portfolio).
News
Education
New York University (NYU)
Peking University (PKU)
Selected Research
Optimal Stopping Rule in Sequential Sampling for Decision-making
We trained reinforcement learning agent to study the optimal stopping rule in heterogeneous sequential sampling for decision-making.
Planning in Decision Tree
We designed a decision tree task and build computational model to study human planning through reaction time and eye-tracking data,.
Description-Experience Gap in Exploration-Exploitation
We designed a minimalistic reinforcement learning task to study the context-dependent decision-making in exploration-exploitation task.