Indiana University Bloomington

Neuroscience
Neuroscience
Joshua W. Brown

Joshua W. Brown

B.S., University of California, San Diego, 1996
Ph.D., Boston University, 2001
Postdoctoral Fellow, Vanderbilt University, 2001
Postdoctoral Fellow, Washington University, St. Louis, 2001-2005

Email address: jwmbrown(at)indiana.edu

Research Interests

My research focuses on the neural mechanisms of cognitive control, namely how humans monitor and flexibly direct their own behavior to achieve complex goals. I have used a variety of methodologies, including single-unit neurophysiology in awake behaving primates, studies of behavior and individual differences, functional neuroimaging, and computational neural modeling to provide unified accounts of neurophysiology, fMRI, and behavioral data. My current focus is on the neural mechanisms of error likelihood prediction and risk perception in decision-making, using combined fMRI and computational neural 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 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.

(See also: Brown Lab Website - Cognitive Control Lab) www.indiana.edu/~cclab/

Representative Publications

Ahn, W.Y., Krawitz, A., Busemeyer, J.R., Kim, W., and Brown, J.W. (2011). A model-based fMRI analysis with hierarchical bayesian parameter estimation. Journal of Neuroscience, Psychology, and Economics 4(2): 95-110.

Forster, S.E., and Brown, J.W. (2011). Medial prefrontal cortex learns to predict the timing of action outcomes. NeuroImage 55(1): 253-265.

Krawitz, A., Braver, T.S., Barch, D.M., and Brown, J.W. (2011). Impaired Error-Likelihood Prediction and Evaluation in Anterior Cingulate Cortex in Schizophrenia. NeuroImage 54(2): 1506-17.

Nee, D.E., Kastner, S., and Brown, J.W. (2011). Functional heterogeneity of conflict, error, and task switching effects within medial prefrontal cortex. NeuroImage 54: 528-540.

Alexander, W.H., and Brown, J.W. (2010). Computational models of performance monitoring and cognitive control. TopiCS 2(4): 658-677.

Krawitz, A., Fukunaga, R., and Brown, J.W. (2010). Anterior insula activity predicts the influence of gain framed messages on risky decision-making. Cognitive, Affective & Behavioral Neuroscience 10(3): 392-405.

Alexander, W.H., and Brown, J.W. (2010). Hyperbolically discounted temporal difference learning. Neural Computation 22(6): 1511-1527.

Jessup, R.K., Busemeyer, J.R., and Brown, J.W. (2010). Error effects in anterior cingulate cortex reverse when error likelihood is high. Journal of Neuroscience 30(9): 3467-3472.

Alexander, W.H., and Brown, J.W. (2010). Competition between learned reward and error outcome predictions in anterior cingulate cortex. NeuroImage 49: 3210-3218.

Brown, J.W. (2009). Conflict effects without conflict in medial prefrontal cortex: multiple response effects and context specific representations. NeuroImage 47: 334-341.

Brown, J.W. (2009). Multiple cognitive control effects of error likelihood and conflict. Psychological Research 73: 744-750.

Brown, J.W., and Braver, T.S. (2008). A computational model of risk, conflict, and individual difference effects in the anterior cingulate cortex. Brain Research 1202: 99-108.

Brown, J.W., and Braver, T.S. (2007). Risk prediction and aversion by anterior cingulate cortex. Cognitive, Affective & Behavioral Neuroscience 7(4): 266-277.

Brown, J.W., Reynolds, J.R., and Braver, T.S. (2007). A computational model of fractionated conflict-control mechanisms in task switching. Cognitive Psychology 55: 37-85.

Brown, J.W., and Braver, T.S. (2005). Learned predictions of error likelihood in the anterior cingulate cortex. Science 307(5712): 1118-1121.

Brown, J.W., 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, J.W., and Schall, J.D. (2003). Performance monitoring by anterior cingulate cortex during saccade countermanding. Science 302(5642): 120-122.