This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. The ability to learn from and remember salient information in the environment is essential for an individual to survive and thrive throughout the lifespan. During the transition to adulthood, adolescents navigate a dynamic environment filled with new and often emotional experiences. Actions in emotional situations, during this time of heightened emotional sensitivity, can result in poor decision-making and negative outcomes, including increased incidence of psychopathology and preventable death. Normative developmental changes in emotional learning and the emotional facilitation of episodic memory may contribute to adolescents' unique behavior in emotionally charged contexts. A rich literature examining classical conditioning has highlighted how the emotional significance of stimuli fluctuates as the statistics of the environment change. Recent work suggests that qualitative differences in the cognitive representations formed during dynamic emotional learning may modulate the persistence of emotional memories. Still, little is known about how this variation emerges, in both behavior and brain, across development or how these changes may influence episodic memory. The proposed research uses a computational model of classical conditioning to examine individual differences in emotional learning and episodic memory across development. By leveraging computational, neuroimaging, and behavioral approaches to better understand developmental changes in how individuals learn and remember the structure of the emotional environment, this research may shed light on the neurocognitive processes that promote healthy development of motivated behaviors. The insights from the proposed research will have implications for affective, developmental, and computational cognitive neuroscience each independently, and for bridging the three research fields.
The proposed research uses a formalized computational model of classical conditioning to examine individual differences in emotional learning and episodic memory across development. This project will expand on and integrate several lines of research by investigating: 1) developmental trajectories of individual differences in emotional learning, 2) the utility of a computational model of conditioning for making predictions about engagement of neural circuitry underlying emotional learning, and 3) how individual differences in emotional learning may influence episodic memory for category-specific items presented during learning. We will implement computational modeling of psychophysiological emotional learning data, analyze neural activity and functional connectivity during emotional learning, and examine how emotional learning influences episodic memory across development. We hypothesize that the increase in emotionally driven behaviors observed during adolescence may be reflective of normative developmental changes in the cognitive structure of emotional learning and memory processes. This research program will provide an important contribution to the sparse literature examining emotional learning and underlying neural circuitry in humans from childhood to adulthood, modeling age continuously in order to determine distinct patterns of age-related change. These data will also be the first to characterize the neural correlates of a computational model of threat learning across development in humans and to extend the model predictions to the effects of emotional learning on episodic memory, also across development. This project builds on prior work examining negative emotion, but we predict results will be similar examining positive emotion. This work will serve as a foundation for future studies using computational modeling approaches to study individual differences in the cognitive structure of learning and memory across development. These data may ultimately help identify ways to take advantage of individual differences and developmental changes in emotional learning and memory to help optimize learning and bolster healthy development.
