John Michael Pearson
Assistant Professor of Biostatistics and 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 and Bioinformatics
- Assistant Professor in the Department of Electrical and Computer Engineering
- Assistant Research Professor in Neurobiology
- Assistant Research Professor in the Department of Psychology and Neuroscience
- Member of the Center for Cognitive Neuroscience
- Office Location: Levine Science Research Center, B255, Durham, NC 27710
- Office Phone: (919) 613-8338
- Email Address: firstname.lastname@example.org
- Ph.D. Princeton University, 2004
- B.S. University of Kentucky at Lexington, 1999
- NEUROBIO 735: Quantitative Approaches in Neurobiology
- NEUROBIO 793: Research in Neurobiology
- NEUROSCI 150: Research Practicum
- NEUROSCI 493: Research Independent Study 1
- NEUROSCI 494: Research Independent Study 2
- NEUROSCI 495: Research Independent Study 3
- NEUROSCI 755: Interdisciplinary Program in Cognitive Neuroscience (IPCN) Independent Research Rotation
- PSY 755: Research Practicum
- PSY 756: Research Practicum
In the News
- Why More Severe Crimes Are More Likely to Lead to Wrongful Convictions (Jul 24, 2019 | Duke Research Blog)
- John Pearson: Data Explorer Inside the Brain (Oct 29, 2015)
- McDonald, KR; Pearson, JM, Cognitive bots and algorithmic humans: toward a shared understanding of social intelligence, Current Opinion in Behavioral Sciences, vol 29 (2019), pp. 55-62 [10.1016/j.cobeha.2019.04.013] [abs].
- McDonald, KR; Broderick, WF; Huettel, SA; Pearson, JM, Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game., Nature Communications, vol 10 no. 1 (2019) [10.1038/s41467-019-09789-4] [abs].
- Iqbal, SN; Yin, L; Drucker, CB; Kuang, Q; Gariépy, J-F; Platt, ML; Pearson, JM, Latent goal models for dynamic strategic interaction., Plos Computational Biology, vol 15 no. 3 (2019) [10.1371/journal.pcbi.1006895] [abs].
- Pearson, JM; Law, JR; Skene, JAG; Beskind, DH; Vidmar, N; Ball, DA; Malekpour, A; Carter, RM; Skene, JHP, Modelling the effects of crime type and evidence on judgments about guilt., Nature Human Behaviour, vol 2 no. 11 (2018), pp. 856-866 [abs].
- Lighthall, NR; Pearson, JM; Huettel, SA; Cabeza, R, Feedback-Based Learning in Aging: Contributions and Trajectories of Change in Striatal and Hippocampal Systems., Journal of Neuroscience, vol 38 no. 39 (2018), pp. 8453-8462 [10.1523/JNEUROSCI.0769-18.2018] [abs].