Decoding Human Gaze Behavior in Stroop Tasks Using Inverse Reinforcement Learning from EEG Data Understanding human gaze behavior during cognitively demanding tasks offers valuable insights into underlying cognitive processes and potential clinical applications. This dissertation presents a novel framework for reconstructing human gaze behavior in the Stroop test by integrating electroencephalography (EEG) data with inverse reinforcement learning (IRL). We employed Generative Adversarial Imitation Learning (GAIL) combined with Proximal Policy Optimization (PPO) to model gaze trajectories, using EEG signals to guide the learning process. Data were collected from ten adult participants performing the Stroop test while their eye movements and EEG signals were recorded.