Daeyeol Lee

Daeyeol Lee

Bloomberg Distinguished Professor of Neuroscience and Psychological and Brain Sciences

Contact Information

Research Interests: Neural mechanisms of decision making, planning, and numerical cognition.

Education: Ph.D. University of Illinois at Urbana-Champaign

Dr. Daeyeol Lee is a Bloomberg Distinguished Professor of Neuroscience and Psychological and Brain Sciences at Johns Hopkins University.  He received his bachelor’s degree in Economics from Seoul National University in Korea and his PhD in Neuroscience from the University of Illinois at Urbana-Champaign. He then received a postdoctoral training in neurophysiology at the University of Minnesota. His current research focuses on the brain mechanisms of decision making, including the role of the prefrontal cortex and basal ganglia in reinforcement learning and economic choices. His laboratory also investigates how timing and numerical information is represented and transformed in the brain. His research employs diverse methods developed in economics, psychology, and neuroscience. He is also an expert in statistical modeling of behavioral and neurophysiological data. He has published over 90 original research articles. He was the recipient of the Fellowship for Prominent Collegians from Korea Foundation for Advanced Studies, University Fellowship from the University of Illinois, and the James S. McDonnell Foundation Cognitive Neuroscience Grant.

Our lab studies the brain mechanisms of decision making and reinforcement learning. We are particularly interested in how the brain flexibly switches among different decision-making strategies. For example, we can choose our response by incrementally adjusting the estimates of expected outcomes through experience, or by relying on our memory of specific events we experienced in the past. We seek to understand the mechanisms that enable us to decide which strategy would work best. In addition, we are also interested in how the brain exploits temporal regularities in the environment, and how different types of numerical information, such as probably and magnitude, are represented and transformed to guide willful and intelligent behaviors.  For more information, visit Research page in Lee Lab.


See Publications at the Lee Lab Website