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 us attain those goals. How is it that we are able to flexibly process information and direct our actions to attain these specific goals? How do we integrate diverse sources subjective motivational and affective signals to modulate effort strategies during value-based decisions? I am interested in the neurocomputational mechanisms that underlie how the brain integrates diverse types of motivational signals (e.g., appetitive vs. aversive, primary vs. secondary) interact with cognitive control to drive the optimal allocation of effort to support goal-directed behavior. To accomplish this goal, I apply multi-modal methods (e.g., fMRI, pharmacology) and computational modeling (e.g., drift diffusion models) to investigate how monoaminergic systems (e.g., dopamine, serotonin) moderate the relationship between neural activity and motivated behavior.

Motivation-Cognition Interactions
Across the Adult Lifespan

An orthogonal line of research relates to understanding how motivational and cognitive changes (and their interaction) are impacted by healthy human aging. Decision-making processes fluctuate across the human adult lifespan, but the how these cognitive, affective, and motivational processes interact and are altered across development remains an open question. How does aging influence the valuation of diverse motivational incentives (e.g., what are the more important and relevant incentives?) and how do these motivational signals drive distinct strategies for allocating effort during cognitive control tasks. I leverage neuroscience techniques (e.g., fMRI, pharmacology) and innovative task paradigms to identify age-related biological changes that may serve as the locus of these cognitive, affective, and motivational changes. Such work will shed insight into how motivation-cognition interactions change across the lifespan, and potentially the neurobiological mechanisms may go awry in pathological aging (e.g., Alzheimer’s).

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Computational Psychiatry:
Harnessing basic mechanisms to understand variability in motivation and cognition in psychopathology

Motivational and cognitive control have been implicated as transdiagnostic processes underlying psychiatric symptoms (e.g., anhedonia, apathy, anxiety), yet a significant challenge is that extant neurocomputational frameworks are currently limited in their ability to inform psychiatric disorders. I apply a computational psychiatry approach to and use theoretically driven computational models to address psychiatric questions. Specifically, I use multivariate approaches (e.g., multivariate pattern analysis, representational similarity analysis), reinforcement learning, and drift diffusion modeling to characterize the mechanistic basis of motivation and cognition, and harness this knowledge to understand how variability in these processes can give rise to mood and affective disorders (e.g., depression, anxiety, OCD). Clarifying these precise mechanisms provides an important intermediary step for translating this knowledge to better understand the pervasive motivational and cognitive impairments often observed in psychopathology.

Linking Across Neural and Computational Levels of Motivation, Affect, and Decision-Making

I strive to adopt an interdisciplinary approach that combines computation, cognitive and affective neuroscience, systems neuroscience, and psychiatry to develop a clear mechanistic understanding of motivation, affect, and cognition. By leveraging perspectives and tools across these diverse fields and working with individuals with different scientific backgrounds (e.g., psychologists, neuroscientists, computer scientists, engineers, psychiatrists, clinicians), I believe we will make significant progress toward understanding the key factors that contribute to the motivational, affective, and cognitive impairments in mental illness across the adult lifespan.