Neural mechanisms of cognitive control: interactions of goal-directed and habit systems

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The overall goal of my research is to understand how the brain adapts choices in complex and dynamic environments to achieve long-term goals. The mammalian brain has at least two distinct systems that can control choice: a `goal directed' system that uses reward outcomes to adapt choices quickly; and a `habit' system involving sensorimotor processing that adapts choices slowly. My NSERC-funded work led to the important discovery that the habit system alone can drive rapid adaptation of choice following omission of reward. This contradicts most prevailing theories of choice, and indicates that major revisions are needed. Moreover, this finding reveals a novel method to study how the goal-directed system can suppress the habit system. Such `cognitive control' is important for achieving long-term goals by suppressing actions for short-term gain, such as foregoing dessert to lose weight. The near-term objectives of my research program over the next 5 years are to capitalize on these discoveries to advance our understanding of the neural mechanisms by which goal-directed systems can regulate habits, and to identify why this is often different among sexes during task uncertainty. Specifically, we will (1) use neural recordings in rats to identify neural encoding of reward, uncertainty, and response inhibition in habit and goal systems during choice, (2) examine how promoting goal-directed control by increasing task uncertainty affects neural encoding and interactions of these systems, (3) investigate the effects of biological sex on these processes, and (4) investigate the computational advantages of uncertainty-based competition between systems. This is essential information needed for advancing our knowledge of several prevalent behavioural phenomena, including trial-and-error learning, cognitive control of choice, and sex differences in choice paradigms involving uncertain outcomes (e.g. gambling). We will take advantage of the fact that rats and humans perform very similarly on our novel choice task, which allows us to translate neural recordings in behaving rodents to choice performance in humans. We will use computational techniques and analytics to estimate treatment effects on hidden variables (e.g. learning rates) and test the computational properties of algorithms inspired by the experimental results. This program of research supports my long-term objective of developing a mechanistic theory for how the brain uses mental models of the environment (beliefs) in goal-directed systems to disrupt habitual responses, which is of fundamental importance for adapting behaviour in many real-world situation. My multidisciplinary research program integrates work in animals, humans, and computational models, and provides exceptional training for HQP. As far as I am aware, this research program is highly original and is poised for transformative advances in our theory of decision-making in the brain and how we might engineer intelligent artificial systems.