The neural computations supporting hierarchical reinforcement learning

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The neural computations supporting hierarchical reinforcement learning - Project Summary. This project explores how humans learn at multiple hierarchical levels in parallel, and how this supports human intelligence. Human decisions are typically hierarchically structured: we make high-level decisions (making a cup of coffee), which constrain lower level decisions (grinding coffee beans, boiling water, etc.), which themselves constrain simpler and simpler decisions and motor actions. This hierarchy in decisions is paralleled by a hierarchy in our representation of our environment: some sensory signals trigger simple decisions (a red light signals a stop), while other signal a broader, more abstract behavioral change (rain signals a set of adaptations when driving). Thus, complex hierarchical structure underlies the way we respond to our environment in seemingly simple, everyday tasks. This ability is supported by the prefrontal cortex, which represents states and decisions at multiple degrees of hierarchical abstraction. My previous work shows that hierarchical representations support transfer and generalization while learning, an ability that artificial agents still struggle to match human performance in. However, how we learn to form these hierarchical representations is poorly understood, despite how crucial it is for human intelligence. The proposed work will examine how multiple, parallel hierarchical loops between prefrontal cortex and the basal ganglia support reinforcement learning at multiple hierarchical levels in parallel, and how this promotes flexible behavior. To this end, we will address three aims: 1. We will show that the same reinforcement learning computations happen in parallel at multiple levels of abstraction, as hypothesized by our computational model of prefrontal- subcortical networks. 2. We will demonstrate that humans partition learning problems into multiple sequential subgoals so they can learn multiple simple strategies instead of one complex strategy, and that reusing these simple strategies promotes fast exploration and learning. 3. We will show that hierarchical learning does not rely exclusively on rewards, but that novelty signals are crucial for identifying subgoals and learning through curiosity. Across all three aims, we will use behavioral experiments in conjunction with computational modeling to characterize how humans learn hierarchically. In addition, we will use EEG and fMRI to identify the neural computations underlying the cognitive systems inferred from behavior and modeling. This project will provide new insights into the computational mechanisms that give rise to learning, and thus provide a better handle on the sources of learning dysfunction observed in many psychiatric diseases, including schizophrenia, depression, anxiety, ADHD, and OCD. Additionally, it will provide new tools, in the form of experimental protocols and precise computational models, for studying learning across populations and species.