Neural Mechanisms of Rule-Based Behavior

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.

Grant Number: 5R01MH129492-04
NIH Institute/Center: NIH
Principal Investigator: Timothy Buschman