Neural Mechanisms of Motivation and Cognitive Control
When we set a challenging or or long-term goal for ourselves, it is unequivocal that motivation is required to help attain those goals. How is it that we are able to monitor and flexibly direct and update our actions to attain specific goals or outcomes? I am particularly interested in understanding the neuromodulatory mechanisms (e.g., dopaminergic, serotonergic) that underlie how appetitive and aversive motivational processes interact with cognitive control to influence goal-directed behavior. To accomplish this goal, I am applying multi-modal imaging techniques (e.g., PET, fMRI) to investigate how the dopaminergic system mediates relationships between neural activations and motivated behaviors.
Motivation-Cognition Interactions in Aging and Development
An orthogonal line of research questions relate to understanding how these motivation-cognition interactions are impacted by healthy human aging. There has been a long history of research in how decision-making is impacted by aging, but a majority of the extant research have focused on unitary psychological dimensions. I am interested in understanding how aging and development influences the interaction of motivation and cognition. To investigate these questions, I am currently applying a novel experimental task paradigm (Yee et al., 2016) that examines motivation and cognition in older adults and adolescents, in order to develop a broader understanding of how motivation-cognition interactions change across the lifespan.
Incentive Integration and Subjective Value
A fundamental open question in motivation and decision-making is how the human brain computes value. While it is intuitive that decisions we make are driven by value, which is subjective, the neural mechanisms that underlie the formation of this subjective value computation is not well understood. Thus, a major focus in my research is examining and quantifying how individuals combine diverse incentives into subjective value computations, which will be a central contribution towards advancing current understanding of how subjective value biases motivated actions and cognitive processing.
Computational Approaches of Motivation and Cognitive Control
As it is becoming evident that higher-order cognitive behaviors are quite intricate and complex, there is an increased need for more nuanced computational approaches towards understanding the biological bases of motivation-cognition interactions. To this end, I aim to use machine learning (e.g., multivariate pattern analyses, graph theoretic, and reinforcement learning approaches) to accomplish enhance investigations of psychological and neural mechanisms that underlie motivation and cognitive control.