Current Studies

Computational Phenotyping of Negative Affectivity: Reinforcement Learning Mechanisms of Trait Negative Affectivity

Trait negative affectivity has been linked to maladaptive responses to aversive environments, but competing theories differ on whether these responses arise from perceptions of controllability, alterations in aversive learning, or Pavlovian predominance over instrumental behavior in aversive contexts. Our goal is to adjudicate among the three most common competing accounts of trait negative affectivity. Our hope is that testing each of these accounts will help identify the computational parameters that are sensitive and specific to negative affectivity.

In this study, participants complete a series of computers games that assess different forms of learning. We use computational modeling to characterizing learning and decision processes that underlie behavior on the tasks. We then link computational parameters with personality traits.

Role of Sensitivity to Neuroendocrine Systems in Social Decisions

Dr. Alison Schreiber serves as a co-investigator of the ROSES Study, which is conducted at the University of North Carolina at Chapel Hill under the direction of Dr. Michael Hallquist of the Developmental Personality Neuroscience Lab. 

Emotions have a potent effect on the types of decisions we make (e.g., we may hug a friend if we’re feeling caring). Yet even as this phenomenon is widely discussed in popular culture, the exact ways in which emotions affect decision making are not well understood. ROSES is interested in understanding the precise ways in which emotions, particularly stress, alter different types of decisions. We’ve developed a novel task that allows us to observe some of these types of decisions so that we can begin to understand this phenomenon.

In this study, participants complete a stress task and are asked to complete a computer game in which they’re tasked with navigating a space (e.g., a fictional town) and engaging in actions (e.g., buying a book at the bookstore) in order to achieve a goal (e.g., delivering a book to a friend). We use computational modeling of participants’ task behavior to quantify how these task manipulations alter different decision processes.