This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program and is supported by both the Perception, Action and Cognition and the Cognitive Neuroscience programs in SBE. 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. Under the sponsorship of Dr. Daniel Schacter at Harvard University, this postdoctoral fellowship award supports an early career scientist studying how remembering shapes problem solving. Converging evidence suggests that people rely on past experiences when faced with a difficult problem. Groups with known memory deficits (e.g., older adults, patients with hippocampal lesions) struggle to solve problems. Brief training in recollecting details of a recent event helps people to solve social means-end and personally worrisome problems. This behavioral work suggests that episodic retrieval contributes to problem solving, but cannot speak to the neural mechanisms underlying this relationship. Meanwhile, emerging research in cognitive neuroscience shows that internally directed cognition (e.g., future thinking, creativity) requires coordinated activity in large-scale networks that usually oppose each other. The proposed research investigates the network dynamics underlying both the benefits and costs of remembering a recently encountered solution.
This project examines how episodic retrieval improves and constrains solutions to worrisome problems. For the first time, the means-end problem solving task will be modified for functional magnetic resonance imaging (fMRI). Participants will either generate steps to solve problems or retrieve them from memory. Functional connectivity analyses will measure dynamic coupling between the executive control network (ECN), which typically comes on line during abstract problem solving, and memory regions in the default mode network (DMN). While episodic retrieval clearly supports problem solving in many contexts, recent experiences can also interfere with finding solutions. Old, inappropriate ideas can lead to fixation, preventing the generation of new ideas. Participants will also generate novel steps in the presence or absence of interference from previously encoded steps. Again, functional connectivity analyses will assess how interference changes DMN-ECN coupling, as recalled steps are actively inhibited. The results will inform information-processing theories of problem solving, as well as our understanding of when and how the ECN and DMN cooperate to modulate self-generated information.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
