Neural Mechanisms of Rule-Based Behavior

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PROJECT SUMMARY/ABSTRACT Over our lifetime, we learn hundreds of ‘rules’ that define how we should act in a given situation. For example, when in a restaurant, we follow a set of rules that guide the way we order, eat, and pay for a meal. By learning and using rules, we can optimize our behavior and maximize social and physical rewards. Disrupting one’s ability to learn and follow rules can be pathological. Such disruptions are associated with many neuropsychiatric and neurodegenerative disorders, such as schizophrenia and dementia, where they carry high social and economic costs. To develop novel, mechanistically-informed, treatments for these diseases, we must first develop a detailed understanding of the neural mechanisms that support rules. Here, we aim to understand how the brain flexibly learns, follows, and switches between several different rules. Combining large-scale, multi-region electrophysiology with novel behavioral paradigms in monkeys, we will study two aspects of flexible rule-based behavior: First, one must be able to discover which rule to follow in a new situation. This requires integrating information from the world to decide which rule, from a set of known rules, is the correct one for the situation. Our first aim will leverage our large-scale recording techniques to distinguish hypotheses about the relative role of prefrontal cortex, parietal cortex, and basal ganglia in integrating feedback and deciding which rule to follow. Second, we aim to understand how multiple rules are learned, represented, and executed. Specifically, we will test hypotheses that the representation of rules is structured: computationally similar rules use similar neural mechanisms. Such structure is theorized to allow us to rapidly learn new rules in new situations. To this end, monkeys will learn and perform multiple, computationally-related, rules. In our second aim, we will use a combination of chronic and acute electrophysiology to track the neural representation of a rule through learning. This will distinguish hypotheses about how the neural representation of a rule is structured, and how it relates to other, similar, rules. In parallel, our third aim will use the same recordings to understand how rules act on stimulus representations to transform them into rule-appropriate responses. We will test three theories of cognitive control, including a novel dynamic model that hypothesizes rules act by dynamically transforming neural representations between subspaces of neural activity. While our proposed research is basic in nature, we believe it is an important first step in a mechanistic understanding of the core cognitive deficits of several mental illnesses, including schizophrenia. We believe this understanding will improve mental health by leading to new diagnostics and treatments for cognitive disorders. In particular, we hope to use our results to develop physiological markers that will improve detection, allow for earlier intervention, and guide targeted treatments.