How prefrontal cortex augments reinforcement learning

Brown University, Providence RI

Investigators

Abstract

This research project investigates the nature of interactions between different brain regions involved in how people learn and control their actions. Theoretical models and empirical data suggest that the prefrontal cortex (PFC) and basal ganglia (BG) interact in these types of motivated behavior, and that the neurochemical dopamine plays a central role in both of these brain regions. However, it has been difficult to isolate the separable contributions of these different brain systems to learning. Dr. Frank and colleagues will investigate how PFC augments human reinforcement learning by leveraging its well-studied roles in working memory, cognitive control, abstraction, and rule representation. This work has potential to substantially advance our understanding of how humans are able to regulate their behaviors as a function of motivation and cognitive control. Learning impairments are prevalent in many psychiatric disorders. While in some cases, such as Parkinson's disease, the mechanisms involved are relatively well understood, in others, such as schizophrenia, the underlying deficits are poorly characterized. It is crucial to properly isolate different components contributing to learning, so as to appropriately relate them to separable mechanisms giving rise to those deficits. If learning is considered as a unitary system, learning deficits that arise due to impairments in one process and brain system may be mistakenly attributed to the other system and lead to erroneous conclusions. Following the same approach will shed light on the actual cause of learning impairments. As such, this research has the potential to identify mechanisms that explain how disruption of such brain circuitry leads to disorders in motivated behavior, cognitive control, and impulsivity. Similarly, developmental learning disabilities may involve a deficit in abstraction and generalization. The aim is to better understand the underlying mechanisms and computations needed for such functions. Drs. Frank and Collins will also provide mentoring on computational and data analytic methods for under-represented women in the STEM disciplines. Computerized experimental tasks will manipulate factors thought to depend on PFC function, including working memory load and the degree to which the discovery of coherent rules can be used to speed learning. Dr. Frank and colleagues use mathematical modeling to separately estimate the contributions of PFC function from that of basic BG processes. Using electroencephalography (EEG), the investigators will measure human participants' brain waves associated with PFC activity while they perform these tasks. Brain wave activity is expected to predict cognitive performance on these tasks. Critically, this brain-behavior relationship is expected to differ as a function of genetic variants that reflect differences in dopamine function in PFC and BG. Another study will directly manipulate dopamine (pharmacologically) in order to determine how these brain and behavior relationships are causally altered by dopamine levels. In all of these studies the investigators use detailed mathematical models to isolate specific brain-behavior relationships guided by contemporary theory. It is expected that genetic variants and pharmacological manipulations will affect the way that the brain learns from decision outcomes and controls actions.