Ph.D., University of Pittsburgh
B.S., Cornell University
etricomi (at) psychology.rutgers.edu
Broadly speaking, my research focuses on the influences of affective information on cognitive processing in the brain. The affective qualities of our experience not only produce subjective feelings that may be positive or negative, but also provide information that allows us to shape future behavior. To understand how the consequences of ones decisions can be used to determine future actions, I use functional magnetic resonance imaging (fMRI) to investigate the role of the brains reward processing system in feedback-based learning. My work examines contextual influences on learning and decision making, and the neural systems that underlie these processes. For example, my research indicates that the sensitivity of the striatum, a region in the basal ganglia, to reward-related information depends on factors such as whether one feels a sense of agency in producing an outcome or whether a habit has been formed after extensive experience. This research has important implications for understanding how cognitive processes such as learning and decision making are carried out in the normal brain, as well as for understanding how impairments of the brains reward processing system may give rise to disorders such as addiction and other compulsive behaviors.
Graduate Student, Fifth Year
P.h.D. Candidate - Rutgers University
M.S., Seton Hall University
B.A., Seton Hall University
My broad research interests include the neurobehavioral mechanisms by which humans learn from outcome-based information related to their cognitive performance. Receipt of feedback signaling either adequate performance (positive feedback), or the need to improve performance (negative feedback), increases our chances of effective performance and learning. Feedback-based learning typically comprises three components – value-based decision-making that determines whether performance feedback is worth pursuing; processing of information within acquired feedback; and execution of that feedback information through performance. My research examines each of these components in the multiple sclerosis (MS) population, which frequently experiences debilitating cognitive fatigue. Within the brains of affected individuals, this fatigue targets corticostriatal networks seeded within the caudate nucleus that have also been shown to process performance feedback in neurologically healthy individuals. This intriguing connection provides a basis for my current research, in which I integrate neuroimaging, behavioral, neuroeconomic, and intervention-based methodologies to examine the mechanisms of feedback-based learning within the MS population and their differences from those of neurologically healthy individuals. Furthermore, I am also testing novel experimental paradigms designed to enhance the value of feedback information and motivate feedback-seeking behavior in neurotypical individuals – with the future goal of extending this work to the MS population.
Graduate Student, Second Year
Graduate Student - Rutgers University
B.S., University of Vermont
Broadly, I am interested in the processes by which humans learn and make decisions, the influences of context, feedback, incentive structures, and cognitive biases, and the reasons that individuals or groups behave in ways that are counter to their goals. Using methods combining psychology, neuroscience, and behavioral economics, I hope to expand on what we know about these counterproductive human behaviors. More specifically, I am interested in applying this research to study and nudge behavior in many areas including personal goals, health, resource management, climate-conscious actions, and more.
Lab Manager / Research Assistant
B.A., Rutgers University, New Brunswick
My scientific interests exist at the intersection of neuroscience and education. Specifically, I am interested in the neural substrates of feedback-seeking behavior and feedback-based learning in the presence of negative feedback. Generally, negative feedback is aversive and is sometimes avoided despite its utility to improve individual work performance. How can we encourage students and those in the work force to seek out negative feedback? Here, I would like to investigate manipulations in feedback timing (e.g. increasing delay between performance and feedback) to encourage feedback-seeking behaviors/feedback-based learning in negative feedback conditions.
Research Assistants as of Spring 2021
Post-baccalaureate Research Assistant
Working with: Chris Cagna