and Brain Sciences
Dr. Joshua Brown
jwmbrown [at] indiana.edu
office: PY 336 | (812)855-9282
lab: Cognitive Control Lab
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
- 1990-1996 - B.S. Mechanical Engineering summa cum laude, Revelle College,
University of California,
- 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
- 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.
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 recent model of the Anterior Cingulate Cortex, or ACC (Brown & Braver, 2005, Science), suggests that ACC is critically involved in predicting the likelihood of making a mistake. Current simulations further predict that ACC activity also depends on the predicted severity of the consequences of a mistake, should one occur.
2) Test computational model predictions with fMRI. Computational modeling often provides counter-intuitive, non-trivial predictions that strongly guide empirical investigations. We are beginning to test whether ACC activity in healthy individuals reflects perceived behavioral risk, as predicted by the computational modeling work.
3) Investigate the neural bases of cognitive impairment in clinical populations using fMRI and computational modeling. We are interested in how impairments in working memory interact with possible impairments in an individual’s ability to monitor their own behavior. Computational modeling provides a framework for understanding the nature of information processing in both normal and pathological human brains.
Krawitz A, Fukunaga R, Brown JW (In Press) Anterior insula activity predicts the influence of gain framed messages on risky decision-making. Cogn. Aff. Behav. Neurosci.
Ahn WY, Krawitz A, Busemeyer JR, Kim W, Brown JW (in press) Neural Correlates of Subjective Outcome Evaluation in an Experience-Based Decision-Making Task. J. Neurosci. Psychol. Econ.
Alexander WH, Brown JW (In Press) Computational models of performance monitoring and cognitive control. TopiCS
Alexander WH, Brown JW (2010) Hyperbolically discounted temporal difference learning. Neural Computation 22(6):1511-1527.
Jessup RK, Busemeyer JR, Brown JW (2010) Error effects in anterior cingulate cortex reverse when error likelihood is high. J. Neurosci. 30(9):3467-3472. Stuphorn V, Brown JW, Schall JD (2010) Relationship of supplementary eye field to saccade initiation during a countermanding task: executive not immediate control. J. Neurophysiol. 103(2):801-16 doi:10.1152/jn.00221.2009
Alexander WH, Brown JW (2010) Competition between learned reward and error outcome predictions in anterior cingulate cortex. NeuroImage 49:3210-3218. doi:10.1016/j.neuroimage.2009.11.065
Brown JW (2009) Conflict effects without conflict in medial prefrontal cortex: multiple response effects and context specific representations. NeuroImage 47:334-341
Brown JW (2009) Multiple cognitive control effects of error likelihood and conflict. Psychological Research. 73:744-750. DOI 10.1007/s00426-008-0198-7
Brown JW (2008) Multiple cognitive control effects of error likelihood and conflict. Psychological Research. DOI 10.1007/s00426-008-0198-7
Brown JW, Hanes DP, Ruch K, Schall JD, Stuphorn V (2008) Relation of frontal eye field activity to saccade initiation during a countermanding task. Exp. Brain Res. 190:135-151.
Emeric EE, Brown JW, Leslie M, Pouget P, Stuphorn V, Schall JD (2008) Error-Related Local Field Potentials in the Medial Frontal Cortex of Primates. J. Neurophysiol. 99(2):759-72.
Brown JW, Braver TS (2008) A computational model of risk, conflict, and individual difference effects in the anterior cingulate cortex. Brain Research. 1202:99-108.
Brown JW, Braver TS (2007) Risk Prediction and Aversion by Anterior Cingulate Cortex. Cogn. Aff. Behav. Neurosci. 7(4):266-277.
Brown JW, Reynolds JR, Braver TS (2007) A computational model of fractionated conflict-control mechanisms in task switching. Cognitive Psychology. 55:37-85.
Reynolds JR, Braver TS, Brown JW, Stigchel S (2006) Computational and neural mechanisms of task-switching. Neurocomputing 69:1332-6.
Brown JW, Braver TS. (2005) Learned predictions of error likelihood in the anterior cingulate cortex. Science 307 (5712) 1118-1121.
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.
Ito S, Stuphorn V, Brown JW, Schall JD. (2003) Performance monitoring by anterior cingulate cortex during saccade countermanding. Science 302(5642):120-2.
Schall JD, Stuphorn V, Brown JW. (2002) Monitoring and control of action by the frontal lobes. Neuron 36:309-322.
Cohen JD, Braver TS, Brown JW. (2002) Computational perspectives on dopamine function in prefrontal cortex. Curr. Op. Neurobiol., 12:223-229.
- Society for Neuroscience
- Cognitive Neuroscience Society
- Cognitive Science Homepage
- Dr. Brown's Laboratory Web Site
- Dr. Brown's Cognitive Science Web Page
- Program in Neuroscience Homepage
- IU Imaging Research Facility