John Pearson | Scholars@Duke

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.

Current Appointments & Affiliations

Assistant Professor of Biostatistics & Bioinformatics, Biostatistics & Bioinformatics, Basic Science Departments 2021
Assistant Research Professor in Neurobiology, Neurobiology, Basic Science Departments 2018
Assistant Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2018
Member of the Center for Cognitive Neuroscience, Center for Cognitive Neuroscience, Duke Institute for Brain Sciences 2016

Contact Information

Duke Box 90999, Durham, NC 27708
Levine Science Research Center, B255, Durham, NC 27710
john.pearson@duke.edu (919) 613-8338
John's blog.
Lab Website

Background
Education, Training, & Certifications
- Ph.D., Princeton University 2004
- B.S., University of Kentucky 1999
Previous Appointments & Affiliations
- Assistant Research Professor in the Department of Psychology and Neuroscience, Psychology & Neuroscience, Trinity College of Arts & Sciences 2016 - 2021
- Assistant Professor of Biostatistics and Bioinformatics, Biostatistics & Bioinformatics, Basic Science Departments 2018 - 2020
- Assistant Research Professor in the Duke Institute for Brain Sciences, Duke Institute for Brain Sciences, University Institutes and Centers 2015 - 2018
- Faculty Network Member of the Duke Institute for Brain Sciences, Duke Institute for Brain Sciences, University Institutes and Centers 2015
Recognition
In the News
- APR 15, 2022 School of Medicine
  
  School of Medicine Celebrates 2022 Faculty Achievement Awards
- OCT 21, 2021
  
  Neuroscientists See How Practice Really Does Make Perfect
- SEP 28, 2021
  
  Living Retina Achieves Sensitivity and Efficiency Engineers Can Only Dream About
- JUL 24, 2019 Duke Research Blog
  
  Why More Severe Crimes Are More Likely to Lead to Wrongful Convictions
- OCT 29, 2015
  
  John Pearson: Data Explorer Inside the Brain
Awards & Honors
- Gordon G. Hammes Faculty Teaching Award. Duke University School of Medicine. 2020
Expertise
Subject Headings
Research
Selected Grants
- Medical Scientist Training Program awarded by National Institutes of Health 2022 - 2027
- Motor Modulation of Auditory Processing awarded by National Institutes of Health 2020 - 2025
- Neurobiology Training Program awarded by National Institutes of Health 2019 - 2024
- Neurocomputational Approaches to Emotion Representation awarded by National Institutes of Health 2020 - 2024
- Receptive field coordination across mosaics of diverse retinal ganglion cell types in the mammalian retina awarded by National Institutes of Health 2020 - 2024
- Corticostriatal contributions to motor exploration and reinforcement awarded by National Institutes of Health 2020 - 2023
- Adaptive Algorithms for Automated Circuit Dissection awarded by The Swartz Foundation 2020 - 2022
- Medical Scientist Training Program awarded by National Institutes of Health 1997 - 2022
- Real-time, all-optical interrogation of neural microcircuitry in the pretectum awarded by National Institutes of Health 2020 - 2022
- Neural Circuit Mechanisms Mediating TMS and Oxytocin Effects on Social Cognition awarded by University of Pennsylvania 2016 - 2022
- A Systems Biology Approach to HIV-associated Neurocognitive Impairment: Role of Drug Abuse and Neuroinflammation awarded by National Institutes of Health 2016 - 2021
- Computational Modeling of Decision Making by Prosecutors and Jurors in Criminal Justice awarded by National Science Foundation 2017 - 2021
- Nonparametric Bayes Methods for Big Data in Neuroscience awarded by National Institutes of Health 2014 - 2019
- Mechanisms of Parkinsonian Impulsivity in Human Subthalamic Nucleus awarded by National Institutes of Health 2014 - 2017
- Animal Model of Genetics and Social Behavior in Autism Spectrum Disorders awarded by National Institutes of Health 2012 - 2016
- Contributions of Areas LIP and VIP to Numerical Behavior awarded by National Institutes of Health 2009 - 2013
External Relationships
- National Institutes of Health
- University of Florida
Publications & Artistic Works
Selected Publications
- Academic Articles
  - 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.
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  - Vihani, Aashutosh, Maira Nagai, Conan Juan, Claire de March, Xiaoyang Hu, John Pearson, and Hiroaki Matsunami. “Encoding of odors by mammalian olfactory receptors,” December 28, 2021. https://doi.org/10.1101/2021.12.27.474279.
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  - Singh Alvarado, Jonnathan, Jack Goffinet, Valerie Michael, William Liberti, Jordan Hatfield, Timothy Gardner, John Pearson, and Richard Mooney. “Neural dynamics underlying birdsong practice and performance.” Nature 599, no. 7886 (November 2021): 635–39. https://doi.org/10.1038/s41586-021-04004-1.
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  - Jun, Na Young, Greg D. Field, and John Pearson. “Scene statistics and noise determine the relative arrangement of receptive field mosaics.” Proc Natl Acad Sci U S A 118, no. 39 (September 28, 2021). https://doi.org/10.1073/pnas.2105115118.
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  - Draelos, Anne, Pranjal Gupta, Na Young Jun, Chaichontat Sriworarat, and John Pearson. “Bubblewrap: Online tiling and real-time flow prediction on neural manifolds,” August 31, 2021.
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  - Goffinet, Jack, Samuel Brudner, Richard Mooney, and John Pearson. “Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires.” Elife 10 (May 14, 2021). https://doi.org/10.7554/eLife.67855.
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  - Albuquerque, Daniela de, Jack Goffinet, Rachael Wright, and John Pearson. “Deep Generative Analysis for Task-Based Functional MRI Experiments,” April 4, 2021. https://doi.org/10.1101/2021.04.04.438365.
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  - Roy, Suva, Na Young Jun, Emily L. Davis, John Pearson, and Greg D. Field. “Inter-mosaic coordination of retinal receptive fields.” Nature 592, no. 7854 (April 2021): 409–13. https://doi.org/10.1038/s41586-021-03317-5.
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  - Jun, Na Young, Greg Field, and John Pearson. “The optimal spatial arrangement of ON and OFF receptive fields,” March 11, 2021. https://doi.org/10.1101/2021.03.10.434612.
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  - Adams, Geoffrey K., Wei Song Ong, John M. Pearson, Karli K. Watson, and Michael L. Platt. “Neurons in primate prefrontal cortex signal valuable social information during natural viewing.” Philos Trans R Soc Lond B Biol Sci 376, no. 1819 (March 2021): 20190666. https://doi.org/10.1098/rstb.2019.0666.
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  - Yoo, Seng Bum Michael, Benjamin Yost Hayden, and John M. Pearson. “Continuous decisions.” Philos Trans R Soc Lond B Biol Sci 376, no. 1819 (March 2021): 20190664. https://doi.org/10.1098/rstb.2019.0664.
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  - Draelos, Anne, Maxim Nikitchenko, Chaichontat Sriworarat, Daniel Sprague, Matthew Loring, Eftychios Pnevmatikakis, Andrea Giovannucci, Eva Naumann, and John Pearson. “improv: A flexible software platform for adaptive neuroscience experiments,” February 23, 2021. https://doi.org/10.1101/2021.02.22.432006.
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  - Addicott, Merideth A., John M. Pearson, Julia C. Schechter, Jeffrey J. Sapyta, Margaret D. Weiss, and Scott H. Kollins. “Attention-deficit/hyperactivity disorder and the explore/exploit trade-off.” Neuropsychopharmacology 46, no. 3 (February 2021): 614–21. https://doi.org/10.1038/s41386-020-00881-8.
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  - Xu, Yunan, Yizi Lin, Ryan P. Bell, Sheri L. Towe, John M. Pearson, Tauseef Nadeem, Cliburn Chan, and Christina S. Meade. “Machine learning prediction of neurocognitive impairment among people with HIV using clinical and multimodal magnetic resonance imaging data.” J Neurovirol 27, no. 1 (February 2021): 1–11. https://doi.org/10.1007/s13365-020-00930-4.
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  - Besançon, M., T. Papamarkou, D. Anthoff, A. Arslan, S. Byrne, D. Lin, and J. Pearson. “Distributions.jl: Definition and modeling of probability distributions in the JuliaStats ecosystem.” Journal of Statistical Software 98 (January 1, 2021): 1–30. https://doi.org/10.18637/jss.v098.i16.
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  - Michael, Valerie, Jack Goffinet, John Pearson, Fan Wang, Katherine Tschida, and Richard Mooney. “Circuit and synaptic organization of forebrain-to-midbrain pathways that promote and suppress vocalization.” Elife 9 (December 29, 2020). https://doi.org/10.7554/eLife.63493.
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  - Mohl, Jeff T., John M. Pearson, and Jennifer M. Groh. “Monkeys and humans implement causal inference to simultaneously localize auditory and visual stimuli.” J Neurophysiol 124, no. 3 (September 1, 2020): 715–27. https://doi.org/10.1152/jn.00046.2020.
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  - Draelos, Anne, Eva A. Naumann, and John M. Pearson. “Online neural connectivity estimation with ensemble stimulation,” July 27, 2020.
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  - McDonald, Kelsey R., John M. Pearson, and Scott A. Huettel. “Dorsolateral and dorsomedial prefrontal cortex track distinct properties of dynamic social behavior.” Soc Cogn Affect Neurosci 15, no. 4 (June 23, 2020): 383–93. https://doi.org/10.1093/scan/nsaa053.
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  - Mohl, Jeff, John Pearson, and Jennifer Groh. “Monkeys and Humans Implement Causal Inference to Simultaneously Localize Auditory and Visual Stimuli,” October 29, 2019. https://doi.org/10.1101/823385.
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  - Goffinet, Jack, Samuel Brudner, Richard Mooney, and John Pearson. “Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires,” October 21, 2019. https://doi.org/10.1101/811661.
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  - McDonald, K. R., and J. M. Pearson. “Cognitive bots and algorithmic humans: toward a shared understanding of social intelligence.” Current Opinion in Behavioral Sciences 29 (October 1, 2019): 55–62. https://doi.org/10.1016/j.cobeha.2019.04.013.
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  - McDonald, Kelsey R., William F. Broderick, Scott A. Huettel, and John M. Pearson. “Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game.” Nat Commun 10, no. 1 (April 18, 2019): 1808. https://doi.org/10.1038/s41467-019-09789-4.
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  - Iqbal, Shariq N., Lun Yin, Caroline B. Drucker, Qian Kuang, Jean-François Gariépy, Michael L. Platt, and John M. Pearson. “Latent goal models for dynamic strategic interaction.” Plos Comput Biol 15, no. 3 (March 2019): e1006895. https://doi.org/10.1371/journal.pcbi.1006895.
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  - Pearson, John M., Jonathan R. Law, Jesse A. G. Skene, Donald H. Beskind, Neil Vidmar, David A. Ball, Artemis Malekpour, R McKell Carter, and JH Pate Skene. “Modelling the effects of crime type and evidence on judgments about guilt.” Nat Hum Behav 2, no. 11 (November 2018): 856–66.
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  - Lighthall, Nichole R., John M. Pearson, Scott A. Huettel, and Roberto Cabeza. “Feedback-Based Learning in Aging: Contributions and Trajectories of Change in Striatal and Hippocampal Systems.” J Neurosci 38, no. 39 (September 26, 2018): 8453–62. https://doi.org/10.1523/JNEUROSCI.0769-18.2018.
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  - Pearson, John. “Queuing cues in rapid cortical processing.” Nat Hum Behav 2, no. 9 (September 2018): 620–21. https://doi.org/10.1038/s41562-018-0427-z.
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  - Addicott, M. A., J. M. Pearson, M. M. Sweitzer, D. L. Barack, and M. L. Platt. “A Primer on Foraging and the Explore/Exploit Trade-Off for Psychiatry Research.” Neuropsychopharmacology 42, no. 10 (September 2017): 1931–39. https://doi.org/10.1038/npp.2017.108.
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  - Chen, Xin, Jeffrey M. Beck, and John M. Pearson. “Neuron's eye view: Inferring features of complex stimuli from neural responses.” Plos Comput Biol 13, no. 8 (August 2017): e1005645. https://doi.org/10.1371/journal.pcbi.1005645.
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  - Dowd, Emma Wu, John M. Pearson, and Tobias Egner. “Decoding working memory content from attentional biases.” Psychon Bull Rev 24, no. 4 (August 2017): 1252–60. https://doi.org/10.3758/s13423-016-1204-5.
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  - Iqbal, Shariq, and John Pearson. “A Goal-Based Movement Model for Continuous Multi-Agent Tasks,” February 23, 2017.
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  - Pearson, John M., Patrick T. Hickey, Shivanand P. Lad, Michael L. Platt, and Dennis A. Turner. “Local Fields in Human Subthalamic Nucleus Track the Lead-up to Impulsive Choices.” Front Neurosci 11 (2017): 646. https://doi.org/10.3389/fnins.2017.00646.
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  - San Martín, René, Youngbin Kwak, John M. Pearson, Marty G. Woldorff, and Scott A. Huettel. “Altruistic traits are predicted by neural responses to monetary outcomes for self vs charity.” Soc Cogn Affect Neurosci 11, no. 6 (June 2016): 863–76. https://doi.org/10.1093/scan/nsw026.
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  - Platt, Michael L., and John M. Pearson. “Dopamine: Context and counterfactuals.” Proc Natl Acad Sci U S A 113, no. 1 (January 5, 2016): 22–23. https://doi.org/10.1073/pnas.1522315113.
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  - Chang, Steve W. C., Nicholas A. Fagan, Koji Toda, Amanda V. Utevsky, John M. Pearson, and Michael L. Platt. “Neural mechanisms of social decision-making in the primate amygdala.” Proc Natl Acad Sci U S A 112, no. 52 (December 29, 2015): 16012–17. https://doi.org/10.1073/pnas.1514761112.
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  - Addicott, Merideth A., John M. Pearson, Nicole Kaiser, Michael L. Platt, and F Joseph McClernon. “Suboptimal foraging behavior: a new perspective on gambling.” Behav Neurosci 129, no. 5 (October 2015): 656–65. https://doi.org/10.1037/bne0000082.
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  - Dowd, E. W., J. M. Pearson, and T. Egner. “Mind-reading without the scanner: Behavioural decoding of working memory content.” Visual Cognition 23, no. 7 (August 9, 2015): 862–66. https://doi.org/10.1080/13506285.2015.1093244.
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  - Addicott, Merideth A., John M. Pearson, Brett Froeliger, Michael L. Platt, and F Joseph McClernon. “Smoking automaticity and tolerance moderate brain activation during explore-exploit behavior.” Psychiatry Res 224, no. 3 (December 30, 2014): 254–61. https://doi.org/10.1016/j.pscychresns.2014.10.014.
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  - Pearson, John M., Karli K. Watson, and Michael L. Platt. “Decision making: the neuroethological turn.” Neuron 82, no. 5 (June 4, 2014): 950–65. https://doi.org/10.1016/j.neuron.2014.04.037.
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  - Jones, Sarah M., John Pearson, Nicholas K. DeWind, David Paulsen, Ana-Maria Tenekedjieva, and Elizabeth M. Brannon. “Lemurs and macaques show similar numerical sensitivity.” Anim Cogn 17, no. 3 (May 2014): 503–15. https://doi.org/10.1007/s10071-013-0682-3.
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  - Ebitz, R Becket, John M. Pearson, and Michael L. Platt. “Pupil size and social vigilance in rhesus macaques.” Front Neurosci 8 (2014): 100. https://doi.org/10.3389/fnins.2014.00100.
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  - Kwak, Youngbin, John Pearson, and Scott A. Huettel. “Differential reward learning for self and others predicts self-reported altruism.” Plos One 9, no. 9 (2014): e107621. https://doi.org/10.1371/journal.pone.0107621.
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  - Yorzinski, J. L., G. L. Patricelli, J. S. Babcock, J. M. Pearson, and M. L. Platt. “Erratum: Through their eyes: Selective attention in peahens during courtship (Journal of Experimental Biology 216 (3035-3046)).” Journal of Experimental Biology 216, no. 22 (November 1, 2013): 4310. https://doi.org/10.1242/jeb.098392.
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  - Blanchard, Tommy C., John M. Pearson, and Benjamin Y. Hayden. “Postreward delays and systematic biases in measures of animal temporal discounting.” Proc Natl Acad Sci U S A 110, no. 38 (September 17, 2013): 15491–96. https://doi.org/10.1073/pnas.1310446110.
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  - Pearson, John M., and Michael L. Platt. “Dopamine: burning the candle at both ends.” Neuron 79, no. 5 (September 4, 2013): 831–33. https://doi.org/10.1016/j.neuron.2013.08.011.
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  - Yorzinski, Jessica L., Gail L. Patricelli, Jason S. Babcock, John M. Pearson, and Michael L. Platt. “Through their eyes: selective attention in peahens during courtship.” J Exp Biol 216, no. Pt 16 (August 15, 2013): 3035–46. https://doi.org/10.1242/jeb.087338.
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  - Chang, Steve W. C., Lauren J. N. Brent, Geoffrey K. Adams, Jeffrey T. Klein, John M. Pearson, Karli K. Watson, and Michael L. Platt. “Neuroethology of primate social behavior.” Proc Natl Acad Sci U S A 110 Suppl 2 (June 18, 2013): 10387–94. https://doi.org/10.1073/pnas.1301213110.
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  - San Martín, René, Lawrence G. Appelbaum, John M. Pearson, Scott A. Huettel, and Marty G. Woldorff. “Rapid brain responses independently predict gain maximization and loss minimization during economic decision making.” J Neurosci 33, no. 16 (April 17, 2013): 7011–19. https://doi.org/10.1523/JNEUROSCI.4242-12.2013.
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  - Addicott, Merideth A., John M. Pearson, Jessica Wilson, Michael L. Platt, and F Joseph McClernon. “Smoking and the bandit: a preliminary study of smoker and nonsmoker differences in exploratory behavior measured with a multiarmed bandit task.” Exp Clin Psychopharmacol 21, no. 1 (February 2013): 66–73. https://doi.org/10.1037/a0030843.
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  - Pearson, John M., Karli K. Watson, Jeffrey T. Klein, R Becket Ebitz, and Michael L. Platt. “Individual differences in social information gathering revealed through Bayesian hierarchical models.” Front Neurosci 7 (2013): 165. https://doi.org/10.3389/fnins.2013.00165.
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  - Pearson, John M., and Michael L. Platt. “Change detection, multiple controllers, and dynamic environments: insights from the brain.” J Exp Anal Behav 99, no. 1 (January 2013): 74–84. https://doi.org/10.1002/jeab.5.
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  - Adams, Geoffrey K., Karli K. Watson, John Pearson, and Michael L. Platt. “Neuroethology of decision-making.” Curr Opin Neurobiol 22, no. 6 (December 2012): 982–89. https://doi.org/10.1016/j.conb.2012.07.009.
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  - Pearson, John, and Michael L. Platt. “Dynamic decision making in the brain.” Nat Neurosci 15, no. 3 (February 24, 2012): 341–42. https://doi.org/10.1038/nn.3049.
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  - Hayden, Benjamin Y., John M. Pearson, and Michael L. Platt. “Neuronal basis of sequential foraging decisions in a patchy environment.” Nat Neurosci 14, no. 7 (June 5, 2011): 933–39. https://doi.org/10.1038/nn.2856.
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  - Pearson, John M., Sarah R. Heilbronner, David L. Barack, Benjamin Y. Hayden, and Michael L. Platt. “Posterior cingulate cortex: adapting behavior to a changing world.” Trends Cogn Sci 15, no. 4 (April 2011): 143–51. https://doi.org/10.1016/j.tics.2011.02.002.
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  - Hayden, Benjamin Y., Sarah R. Heilbronner, John M. Pearson, and Michael L. Platt. “Surprise signals in anterior cingulate cortex: neuronal encoding of unsigned reward prediction errors driving adjustment in behavior.” J Neurosci 31, no. 11 (March 16, 2011): 4178–87. https://doi.org/10.1523/JNEUROSCI.4652-10.2011.
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  - Pearson, John, J. D. Roitman, E. M. Brannon, M. L. Platt, and Sridhar Raghavachari. “A physiologically-inspired model of numerical classification based on graded stimulus coding.” Frontiers in Behavioral Neuroscience 4 (January 27, 2010): 1. https://doi.org/10.3389/neuro.08.001.2010.
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  - Pearson, John M., Benjamin Y. Hayden, and Michael L. Platt. “Explicit information reduces discounting behavior in monkeys.” Front Psychol 1 (2010): 237. https://doi.org/10.3389/fpsyg.2010.00237.
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  - Pearson, John M., Benjamin Y. Hayden, Sridhar Raghavachari, and Michael L. Platt. “Neurons in posterior cingulate cortex signal exploratory decisions in a dynamic multioption choice task.” Curr Biol 19, no. 18 (September 29, 2009): 1532–37. https://doi.org/10.1016/j.cub.2009.07.048.
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  - Pearson, John, and Michael L. Platt. “Confidence and corrections: how we make and un-make up our minds.” Neuron 63, no. 6 (September 24, 2009): 724–26. https://doi.org/10.1016/j.neuron.2009.09.011.
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  - Hayden, Benjamin Y., John M. Pearson, and Michael L. Platt. “Fictive reward signals in the anterior cingulate cortex.” Science 324, no. 5929 (May 15, 2009): 948–50. https://doi.org/10.1126/science.1168488.
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  - Watson, Karli K., and Michael L. Platt. “Neuroethology of reward and decision making.” Philos Trans R Soc Lond B Biol Sci 363, no. 1511 (December 12, 2008): 3825–35. https://doi.org/10.1098/rstb.2008.0159.
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  - Pearson, J., M. Spradlin, D. Vaman, H. Verlinde, and A. Volovich. “Tracing the string: BMN correspondence at finite J²/N.” Journal of High Energy Physics 7, no. 5 (May 1, 2003): 477–87. https://doi.org/10.1088/1126-6708/2003/05/022.
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  - Kachru, S., J. Pearson, and H. Verlinde. “Brane/flux annihilation and the string dual of a non-supersymmetric field theory.” Journal of High Energy Physics 6, no. 6 (June 1, 2002): 387–411. https://doi.org/10.1088/1126-6708/2002/06/021.
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  - Braaten, E., and J. Pearson. “Semiclassical corrections to the oscillation frequencies of a trapped bose-einstein condensate.” Physical Review Letters 82, no. 2 (January 1, 1999): 255–58. https://doi.org/10.1103/PhysRevLett.82.255.
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  - Kachru, Shamit, John Pearson, and Herman Verlinde. “Brane/flux annihilation and the string dual of a non-supersymmetric field theory.” Journal of High Energy Physics 2002, no. 06 (n.d.): 021–021. https://doi.org/10.1088/1126-6708/2002/06/021.
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  - McDonald, Kelsey R., William F. Broderick, Scott A. Huettel, and John M. Pearson. “Bayesian Nonparametric Models Characterize Instantaneous Strategies in a Competitive Dynamic Game,” n.d. https://doi.org/10.1101/385195.
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- Book Sections
  - Chang, S. W. C., L. J. N. Brent, G. K. Adams, J. T. Klein, J. M. Pearson, K. K. Watson, and M. L. Platt. “Neuroethology of primate social behavior.” In In the Light of Evolution, 7:115–34, 2014. https://doi.org/10.17226/18573.
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- Conference Papers
  - Draelos, A., P. Gupta, N. Y. Jun, C. Sriworarat, and J. Pearson. “Bubblewrap: Online tiling and real-time flow prediction on neural manifolds.” In Advances in Neural Information Processing Systems, 8:6062–74, 2021.
  - Draelos, A., and J. M. Pearson. “Online neural connectivity estimation with noisy group testing.” In Advances in Neural Information Processing Systems, Vol. 2020-December, 2020.
- Datasets
  - Goffinet, Jack, Richard Mooney, John Pearson, and Samuel Brudner. “Data from: Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires,” May 24, 2021. https://doi.org/10.7924/r4gq6zn8w.
    Data Access
- Preprints
  - Subramanian, Divya, John Pearson, and Marc Sommer. “Contributions of Bayesian and Discriminative Models to Active Visual Perception across Saccades.” BioRxiv, June 26, 2022. https://doi.org/10.1101/2022.06.22.497244.
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  - 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, May 19, 2022. https://doi.org/10.1101/2022.05.17.492355.
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  - Martinez, Miles, and John Pearson. “Reproducible, incremental representation learning with Rosetta VAE,” January 13, 2022.
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Teaching & Mentoring
Recent Courses
Advising & Mentoring
- We are looking for students with strong quantitative skills (statistics, computer science, engineering) who want to apply what they know to challenging problems in neuroscience. Come help us design the next generation of brain analysis tools!
Available to mentor:
- Undergraduate
Scholarly, Clinical, & Service Activities
Service to the Profession
- Participant. Faculty Curriculum on Anti-Racism. Duke Office of Faculty Advancement. January 11, 2021 - January 14, 2021 2021

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Education, Training, & Certifications

Previous Appointments & Affiliations

In the News

Awards & Honors

Subject Headings

Selected Grants

External Relationships

Selected Publications

Academic Articles

Book Sections

Conference Papers

Datasets

Preprints

Recent Courses

Advising & Mentoring

Available to mentor:

Service to the Profession