Michael J Frank

Michael J Frank
Laboratory for Neural Computation and Cognition, Brown University
Providence, USA

Speaker of Workshop 2

Will talk about: Modeling decision making deficits in frontostriatal disorders

Bio sketch:

Link to lecture presentation slides

Michael J. Frank is Assistant Professor of Cognitive, Linguistic & Psychological Sciences and Psychiatry in the Brown Institute for Brain Science at Brown University. He directs the Laboratory for Neural Computation and Cognition. He received his PhD in Neuroscience and Psychology in 2004 at the University of Colorado.  His work focuses primarily on theoretical models of basal ganglia, frontal cortex, and dopamine function in cognition and their implication for Parkinson's Disease and related disorders. This research utilizes computer models linking brain to behavior which are then tested and refined with experiments using pharmacological manipulation, deep brain stimulation, electroencephalography, and genetics. Awards include the Janet T Spence Award for early career transformative contributions (Association for  Psychological Science, 2010) and the DG Marquis Behavioral Neuroscience award (2006). Dr Frank is  a member of Faculty of 1000 Biology (TheoreticalNeuroscience section), and serves on the editorial board of  the Journal of Mathematical Psychology, European Journal of Neuroscience, and Frontiers in Decision Neuroscience.

Talk abstract:

Computational models of adaptive action selection and reinforcement learning have been increasingly used in combination with neural data to understand their mechanisms, and how they go awry in disorders of fronto-striatal circuitry. Whereas the basal ganglia dopaminergic system is associated with learning the probabilities that a given action will lead to a positive or negative outcome, the prefrontal cortex supports executive control over learning, for example driving exploration as a function of the information that can be gained about reinforcement statistics. I will present evidence for these mechanisms using computational analysis of behavioral data in a reward-guided decision making task, together with their neural correlates using EEG, fMRI, neurogenetics and pharmacology. Finally, I will show that disorders of frontostriatal circuitry (Parkinson's, schizophrenia and obsessive compulsive disorder) are associated with differential modulation of distinct computational parameters in this framework.

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