Workshop: Reinforcement Learning Modeling For Human Choice Behavior

Reinforcement learning (RL) algorithms have proven to be exceptionally effective in modeling human value-based learning and decision-making behaviors. This workshop offers an in-depth introduction to RL algorithms and their application in modeling human decision-making behaviors. Starting with the fundamentals, participants will learn model-free RL algorithms applied to a multi-armed bandit task. The workshop will then advance to explore two key extensions; (1) hierarchical RL modeling, where a sequence of action is required to complete a goal, and (2) model-based RL modeling where action-values are computed based on mental simulation of possible state transitions. Throughout the workshop, participants will engage in practical, hands-on coding exercises. These will include Bayesian parameter recovery to identify RL agents' parameters using Stan. Additionally, attendees will experience parameter estimation applied to existing human empirical data. The workshop is tailored for a wide audience from those with a basic understanding of programming and statistics to experienced researchers in cognitive modeling seeking to deepen their understanding of basic RL modeling techniques. By the end of the session, participants will have acquired basic theoretical knowledge and practical skills in implementing RL models, setting the stage for further exploration and application of RL in various domains of cognitive science and beyond.