The Representation, Learning and Decision-Making Lab, led by Dr. Seongmin A. Park, investigates the neural mechanisms underlying learning, decision-making, and social interactions in human brains. Our interdisciplinary approach combines behavioral experiments, neuroimaging techniques such as fMRI, MEG, and EEG, computational models, and neural networks. By drawing on concepts and tools from psychology, neuroscience, behavioral economics, and machine learning, we strive to answer fundamental questions about human intelligence.

• One area of our research focuses on abstraction, aiming to unravel how the brain represents the most relevant properties of the world. We investigate how the brain extracts key features from complex experiences and constructs meaningful representations.
• Another aspect of our research centers on planning. We seek to understand how the brain forms relevant sets of behavioral sequences. By investigating the neural mechanisms involved in planning, we aim to uncover the processes underlying the organization and execution of goal-directed decision-making and problem-solving.
• Generalization is another key focus of our lab. We aim to explore how the brain utilizes previous experiences to make better decisions in the future. By studying the neural mechanisms of generalization, we investigate how the brain extracts commonalities across different experiences and uses them to guide future decision-making processes.