How the brain forms, tunes, and uses predictive models that specify the causal links between stimuli in the environment, our choices, and their outcomes is a fundamental question in Psychology and Neuroscience. In order to make good predictions in a complex world, the brain needs to attribute good and bad outcomes to their most likely causes, a problem known as "credit assignment". There is presently limited understanding of how people attribute outcomes to their most likely causes in real-world environments. Recent evidence suggests that humans can use an understanding of the environment's causal structure to attribute outcomes to their most likely causes ("model-based credit assignment"), rather than only attributing them to the most recently made choices ("model-free credit assignment"), as standard models posit. Dr. Boorman's research will develop the first neurally-inspired theory of model-based credit assignment. The insights from this basic science research have the potential to inform (1) theories about human cognition more broadly; (2) targeted education programs that leverages a better understanding of how learning works in the human brain; (3) mechanistic predictions for application to Artificial Intelligence research; and (4) principled investigations into potential biomarkers and treatment targets for psychiatric disorders such as Schizophrenia and PTSD, with deficits that include abnormal credit assignment.

The overarching goal of this proposal is to elucidate how the human brain attributes reinforcement outcomes to their likely causes, or "credit assignment". Recent evidence suggests the brain can harness a model of the environment or task structure to assign credit for outcomes adaptively ("model-based credit assignment"), in addition to the most recently made choices ("model-free credit assignment"). Dr. Boorman's proposed research will integrate multivariate analysis methods with Bayesian models that formalize updating and inference, and apply them to fMRI, EEG, and TMS data. Insights from this fundamental research will inform mechanistic models of human learning, theories of cognition more broadly, reinforcement learning in Artificial Intelligence research, and investigations into psychiatric disorders with deficits that include abnormal credit assignment, such as Schizophrenia and PTSD.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Behavioral and Cognitive Sciences (BCS)
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Soo-Siang Lim
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University of California Davis
United States
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