John Pearson

Assistant Professor of Biostatistics & Bioinformatics

My research focuses on the application of machine learning methods to the analysis of brain data and behavior. I have a special interest in the neurobiology of reward and decision-making, particularly issues surrounding foraging, impulsivity, and self-control. More generally, I am interested in computational principles underlying brain organization at the mesoscale, and work in my lab studies phenomena that range from complex social behaviors to coding principles of the retina.

Appointments and Affiliations

  • Assistant Professor of Biostatistics & Bioinformatics
  • Assistant Professor in the Department of Electrical and Computer Engineering
  • Assistant Research Professor in Neurobiology
  • Member of the Center for Cognitive Neuroscience

Contact Information

  • Office Location: Levine Science Research Center, B255, Durham, NC 27710
  • Office Phone: (919) 613-8338
  • Email Address: john.pearson@duke.edu
  • Websites:

Education

  • B.S. University of Kentucky, 1999
  • Ph.D. Princeton University, 2004

Awards, Honors, and Distinctions

  • Gordon G. Hammes Faculty Teaching Award. Duke University School of Medicine. 2020

Courses Taught

  • EGR 491: Projects in Engineering
  • NEUROBIO 393: Research Independent Study
  • NEUROBIO 735: Quantitative Approaches in Neurobiology
  • NEUROBIO 793: Research in Neurobiology
  • NEUROSCI 493: Research Independent Study 1
  • NEUROSCI 494: Research Independent Study 2
  • NEUROSCI 755: Interdisciplinary Program in Cognitive Neuroscience (IPCN) Independent Research Rotation

In the News

Representative Publications

  • O’Neill, Kevin, Paul Henne, Paul Bello, John Pearson, and Felipe De Brigard. “Confidence and gradation in causal judgment.” Cognition 223 (June 2022): 105036. https://doi.org/10.1016/j.cognition.2022.105036.
  • Martinez, Miles, and John Pearson. “Reproducible, incremental representation learning with Rosetta VAE,” January 13, 2022.
  • Hsiung, Abigail, John Pearson, Jia-Hou Poh, Shabnam Hakimi, Alison Adcock, and Scott Huettel. “Between heuristics and optimality: Flexible integration of cost and evidence during information sampling.” BioRxiv, 2022. https://doi.org/10.1101/2022.05.17.492355.
  • Subramanian, Divya, John Pearson, and Marc Sommer. “Contributions of Bayesian and Discriminative Models to Active Visual Perception across Saccades.” BioRxiv, 2022. https://doi.org/10.1101/2022.06.22.497244.
  • Jun, Na Young, Greg Field, and John Pearson. “Efficient coding, channel capacity and the emergence of retinal mosaics.” BioRxiv, 2022. https://doi.org/10.1101/2022.08.29.505726.