Modeling and mapping multiple computational processes in human reinforcement learning

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Project Summary Learning from rewards is one of the fundamental roles of the nervous system, allowing for beneficial behaviors to be repeated, and detrimental behaviors to be avoided. It has recently become clear that when humans learn from rewards in the environment, they rely on multiple neural systems that work in tandem. What are the psychological and biological constraints of these systems, and how do they interact during learning? The proposed experiments are designed to answer these questions by developing precise computational models of human instrumental learning, as well as investigating the neural dynamics of, and interactions between, individual learning processes. Aim 1 will focus on isolating individual learning processes, further developing a novel model of human instrumental learning that highlights contributions to learning from both a flexible executive working memory module and an incremental reinforcement learning module. Behavioral experiments and computational modeling will be used to better characterize these two learning processes, with a focus on how they interact instantaneously and over time. Aim 2 will use a combination of brain stimulation and neuroimaging to better characterize the neural systems supporting each learning process, as well as the putative interactions between these neural circuits. These results will constrain our understanding of the neural mechanisms that drive human instrumental learning. The knowledge gained by this project will provide a vital framework for clinical applications, for instance, in understanding and treating working memory related learning deficits in schizophrenia, and reinforcement learning deficits in Parkinson's disease. Critically, a more precise model of individual learning processes could guide the development of clinical protocols that leverage intact learning systems when other learning systems are compromised. Finally, an enhanced understanding of human reward-based learning could improve theories of habit formation, which may further inform psychological and neurophysiological models of addiction.