معرفی
John Pearson is an Associate Professor of Neurobiology at Duke University, with affiliations in the Basic Science Departments, Pratt School of Engineering, and the Duke Institute for Brain Sciences. His research focuses on understanding how the brain enables complex behaviors such as decision-making, learning, and perception through computational and experimental approaches. Pearson holds a Ph.D. in Physics from Princeton University and a B.S. in Physics and Mathematics from the University of Kentucky. His academic roles include leadership in the Center for Cognitive Neuroscience and multiple interdisciplinary collaborations.
Education:
- Ph.D., Physics, Princeton University, 2004
- B.S., Physics and Mathematics, University of Kentucky, 1999
Research Interests: Pearson investigates cognitive stability and flexibility, neural mechanisms of decision-making, and the application of machine learning to neuroscience. His lab studies how brain circuits adapt to dynamic environments, with a focus on birdsong learning, social decision-making, and visual perception. Recent work emphasizes Bayesian models and generative approaches to understand neural coding and behavior.
Publications: Pearson’s recent work spans cognitive control, neuromodulatory dynamics, and computational neuroscience. Key themes include task-switching mechanisms, anti-Bayesian judgments, and the neural basis of juror decisions. His methods combine experimental neuroscience with advanced computational modeling.
Recognition: His contributions to neuroscience have been highlighted in media, including a 2022 Faculty Achievement Award from Duke’s School of Medicine.
Advising & Grants: Pearson mentors a diverse team of graduate students and postdocs, overseeing grants such as the NIH-funded Neurobiology Training Program and the Duke Psychiatry Physician-Scientist Residency Program. His lab supports interdisciplinary training in neurobiology and computational methods.
Labs & Teams: Pearson leads the Pearson Lab, which integrates experimental and theoretical approaches to study neural systems. The lab’s work bridges neurobiology, psychology, and engineering, with ongoing projects on vocal learning, social cognition, and real-time neural analysis tools like improv.
