Frontostriatal Computations in Learning and Choice: From Motor to Cognitive Decisions

Abstract: The basal ganglia and dopaminergic (DA) systems are well studied for their roles in reinforcement learning, but the underlying architecture is notoriously complex. First, I will present a computational account of how this complexity is optimized to provide robust advantages over traditional reinforcement learning models over a range of environments, and suggest that empirical observations of altered learning and decision making in patient populations reflect a byproduct of an otherwise normative mechanism. Second, I will show how this system, when interacting with prefrontal cortex, can learn to influence cognitive actions such as working memory updating and "chunking" strategies that are adapted as function of task demands, mimicking human performance and normative models.

Brief Bio: Michael J. Frank is Edgar L Marston Professor of Cognitive and Psychological Sciences at Brown University. He directs the Center for Computational Brain Science within the Carney Institute for Brain Science. He received his PhD in Neuroscience and Psychology in 2004 at the University of Colorado, following undergraduate and master's degrees in electrical and bioengineering. Frank’s work focuses primarily on theoretical models of frontostriatal circuits and their modulation by dopamine, especially their cognitive functions and implications for neurological and psychiatric disorders. The models are tested and refined with experiments across species, neural recording methods, and neuromodulation. Honors include the Troland Research Award from the National Academy of Sciences (2021), Kavli Fellow (2016), the Cognitive Neuroscience Society Young Investigator Award (2011), and the Janet T Spence Award for early career transformative contributions (Association for Psychological Science, 2010). Dr Frank is a senior editor for eLife.