Research

I study motivated behavior change. My approach is interdisciplinary and multimodal: I use diverse methods (e.g., genomics, pharmacological manipulation, neuroimaging, eye tracking, mobile experience sampling and physiological monitoring) to resolve and integrate the many signals that describe individual behavior. By characterizing the neural and psychological mechanisms supporting learning, cognitive control, and decision making, I develop robust models of behavior that can inform more effective behavior change interventions.

My current research focuses on three questions:

• 1. How does motivational context shape learning and decision making?

  2. What drives individual differences in goal-directed decision making?

  3. Can computational cognitive neuroscience approaches identify novel, neurally-informed targets for more precise, personalized behavioral change intervention?

Selected works in progress

• Using attention-motivation interactions to predict therapeutic response in ADHD (with Alison Adcock, Scott Kollins, and Ian Krajbich)

• Modeling learning from 'cognitive neurostimulation' using real-time neurofeedback from the midbrain (with Alison Adcock, Jeff MacInnes, and Katie Dickerson)

• Investigating feedback learning and its relationship to perfectionism in young adults (with Alison Adcock and Nancy Zucker)

• Decomposing risk representation in parietal cortex using Bayesian analytical approaches (with McKell Carter and Scott Huettel)

©2024 Shabnam Hakimi