My research aims to understand how richly structured knowledge about the environment is acquired, and how this knowledge aids adaptive behavior. I use a combination of behavioral, neuroimaging and computational techniques to pursue these questions.
One prong of this research focuses on how humans and animals discover the hidden states underlying their observations, and how they represent these states. In some cases, these states correspond to complex data structures, like graphs, grammars or programs. These data structures strongly constrain how agents infer which actions will lead to reward. A second prong of my research is teasing apart the interactions between different learning systems. Evidence suggests the existence of at least two systems: a "goal-directed" system that builds an explicit model of the environment, and a "habitual" system that learns state-action response rules. These two systems are subserved by separate neural pathways that compete for control of behavior, but the systems may also cooperate with one another.
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