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Indiana University Bloomington

Joshua Brown

Dr. Joshua Brown



jwmbrown [at]

office: PY 336 | (812)855-9282

lab: Cognitive Control Lab
   PY A216 | (812)856-1846


Develop computational models of brain circuitry involved in cognitive control; test computational model predictions with fMRI; investigate the neural bases of cognitive impairment in psychopathology, especially substance abuse and schizophrenia, using fMRI and computational modeling

Educational Background

  • 1990-1996 - B.S. Mechanical Engineering summa cum laude, Revelle College, University of California,
    San Diego
  • 1996-2000 - Ph.D. Cognitive & Neural Systems, Boston University
  • 2000-2001 - Postdoctoral fellow, systems neurophysiology, Vanderbilt University
  • 2001-2005 - Postdoctoral fellow, fMRI and computational neural modeling, Washington University in St. Louis

Areas of Study

  • Cognitive Science
  • Neuroscience

Research Topics

  • Develop computational models of brain circuitry involved in cognitive control.
  • Test computational model predictions with fMRI.
  • Investigate the neural bases of cognitive impairment clinical populations, using fMRI and computational modeling.

Research Summary:

My interests are wide-ranging but focus on the frontal lobes. How do people and animals learn, optimize, and control goal-directed behavior in complex and changing environments? These abilities entail reinforcement learning, planning, prediction, expectation, evaluation, and sequential ordering of movements, in addition to complex sensory processing. Currently I have three main research thrusts:

1) Develop computational models of brain circuitry involved in cognitive control. My earlier model of the Anterior Cingulate Cortex, or ACC (Alexander & Brown, 2011, Nature Neuroscience), suggests that ACC is critically involved in predicting and evaluating outcomes. We are currently developing computational neural models of goal-directed planning and decision-making, involving the interaction of the hippocampus, ventral prefrontal cortex, parietal cortex, and visual cortex.

2) Test computational model predictions with fMRI. Computational modeling often provides counter-intuitive, non-trivial predictions that strongly guide empirical investigations. We are exploring the neural mechanisms of planning and decision-making across the brain, using newer fMRI methods such as quantitative computational neural model regressors for fMRI analysis.

3) Investigate the neural bases of cognitive impairment in clinical populations using fMRI and computational modeling, especially addiction. We are interested in how impairments in decision making play out in addiction, and how methods such as neurostimulation can be used to treat clinical disorders. We have developed novel methods for administering operant drug reward such as nicotine from an e-cigarette to humans in the fMRI scanner, to understand real-time addiction processes. Computational modeling provides a framework for understanding the nature of information processing in both normal and pathological human brains.

Representative Publications

  1. Alexander WH, Brown JW (in press) The Role of the Anterior Cingulate Cortex in Prediction Error and Signaling Surprise. Topics in Cogn. Sci.

  2. Brown JW, Alexander WH (2017) Foraging value, risk avoidance, and multiple control signals: How the anterior cingulate cortex controls value-based decision-making. J. Cogn. Neurosci. 29(10):1656-73.

  3. Forster SE, Finn PR, Brown JW (2017) Neural responses to negative outcomes predict success in community based substance use treatment. Addiction. 112(5):884-96.

  4. Jahn A, Nee DE, Alexander W, Brown JW (2016) Distinct regions within medial prefrontal cortex process pain and cognition. J. Neurosci. 36(49):12385-12392.

  5. Forster SE, Finn PR, Brown JW (2016) A preliminary study of longitudinal neuroadaptation associated with recovery from addiction. Drug and Alcohol Dependence 168:52-60.

  6. Kini P, McInnis S, Gabana N, Wong J, Brown JW (2016) The effects of gratitude expression on neural activity. NeuroImage 128:1-10.

  7. Zarr N, Brown JW (2016) Hierarchical error representations in medial prefrontal cortex. NeuroImage 124:238-47.

  8. Alexander WH, Brown JW (2015) Hierarchical error representation: A computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Comput.27(11):2354-2410

  9. Alexander WH, Fukunaga R, Finn PR, Brown JW (2015) Reward salience and risk aversion underlies differential ACC activity in substance dependence. NeuroImage: Clinical 8:59-71

  10. Alexander WH, Brown JW (2015) Reciprocal interactions of computational modeling and empirical investigation. In Forstmann and Wagenmakers (eds.) An introduction to model-based cognitive neuroscience. New York: Springer. Pp. 321-338

  11. Vélez de Mendizábal N, Jones DR, Jahn A, Bies RR, Brown JW (2014) Nicotine and Cotinine Exposure From Electronic Cigarettes: A Population Approach. Clinical Pharmacokinetics 54(6):615-26. DOI 10.1007/s40262-014-0221-7

  12. Brown JW (2014) The tale of the neuroscientists and the computer: why mechanistic theory matters. Frontiers in Neuroscience 8:349.

  13. Jahn A, Nee DE, Alexander WH, Brown JW (2014) Anterior cingulate cortex activity signals multiple predicted outcomes of actions. NeuroImage 95:80-89.

  14. Nee DE, Jahn A, Brown JW (2014) Prefrontal cortex organization: Dissociating effects of temporal abstraction, relational abstraction, and integration with fMRI. Cerebral Cortex 24:2377-87. doi: 10.1093/cercor/bht091

  15. Brown JW (2013) Beyond conflict monitoring: Cognitive control and the neural basis of thinking before you act. Current Directions in Psychological Science. 22(3):179-185.

  16. Fukunaga R, Bogg T, Finn P, Brown JW (2013) Decisions during negatively-framed messages yield smaller risk-aversion-related brain activation in substance-dependent individuals. Psychology of Addictive Behaviors. 27(4):1141-1152 DOI: 10.1037/a0030633

  17. Nee D, Brown JW, Askren M, Berman M, Demiralp E, Krawitz A, Jonides J (2013) A meta-analysis of executive components of working memory. Cerebral Cortex 23(2):264-282. doi:10.1093/cercor/bhs007.

  18. Nee DE, Brown JW (2012) Rostral‐Caudal Gradients of Abstraction Revealed by Multi‐Variate Pattern Analysis of Working Memory. NeuroImage 63(3):1285-94.

  19. Fukunaga R, Brown JW, Bogg T (2012) Decision Making in the Balloon Analogue Risk Task (BART): Anterior Cingulate Cortex Signals Loss-Aversion but not the Infrequency of Risky Choices. Cogn. Aff. Behav. Neurosci.12:479-90.

  20. Bogg T, Fukunaga R, Finn P, Brown JW (2012) Cognitive Control Links Alcohol Use, Trait Disinhibition, and Reduced Cognitive Capacity: Evidence for Medial Prefrontal Cortex Dysregulation during Reward-Seeking Behavior. Drug and Alcohol Dependence. 122:112-8

  21. Alexander WH, Brown JW (2011) Medial prefrontal cortex as an action-outcome predictor. Nature Neurosci. 14(10):1338-44. doi: 10.1038/nn.2921

  22. Brown JW, Braver TS (2005) Learned predictions of error likelihood in the anterior cingulate cortex Science 307(5712) 1118-1121

  23. Brown JW, Bullock D, Grossberg S (2004) How laminar frontal cortex and basal ganglia circuits Interact to control planned and reactive saccades. Neural Networks 17(4):471-510.

  24. Ito S, Stuphorn V, Brown JW, Schall JD (2003) Performance monitoring by anterior cingulate cortex during saccade countermanding. Science 302(5642):120-2.

Professional Memberships

  • Society for Neuroscience
  • Cognitive Neuroscience Society
  • Association for Psychological Science