This award was provided as part of NSF's Social, Behavioral and Economic Sciences (SBE) Postdoctoral Research Fellowships (SPRF) program and SBE's Science of Learning and Augmented Intelligence 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. Under the sponsorship of Dr. Franco Pestilli at Indiana University, this postdoctoral fellowship award supports an early career scientist investigating the role of white matter communication pathways in the human brain in learning and generalization. Prior work has related individual differences in human white matter to current abilities as a measurement of past learning; the proposed work will, instead, use individual differences in human white matter to predict future learning. The hypothesis addressed in the proposed work is that sensorimotor training changes white matter communication pathways in ways that allow for generalization to untrained behaviors. The investigators will test this hypothesis by using machine-learning methods and implementing an explicit model testing approach. This research will provide the field with important information concerning learning-related changes in the brain that will be applicable to educational and neuro-rehabilitation practices.
This project integrates cutting-edge measurements of white matter communication pathways in the brain with novel behavioral assessments. The proposed work builds from a well-documented and repeatable finding: sensorimotor learning leads to learning that generalizes (e.g., handwriting increases letter recognition). The project has three goals. The first goal is to demonstrate that training on a sensorimotor task (i.e., drawing novel symbols) leads to task-specific changes in the tissue properties of white matter communication pathways. We will employ a between-participants training manipulation and assess differences in learning-related white matter microstructure among training groups. The second goal is to demonstrate that the white matter changes associated with sensorimotor learning support generalization to an untrained behavior. We will use machine-learning to build a model of the relationship between learning-related changes in white matter tissue microstructure and sensorimotor learning. We will then quantify how well that model predicts visual recognition learning (i.e., learning to recognize the novel symbols). The expectation is that individual variability in global white matter tissue properties will predict sensorimotor learning and generalization. The final goal of the work is to leverage the cloud computing platform–brainlife.io–to deliver open-science and reproducible methods as well as publicly available analyses and services. Data, analyses, and results will be shared on brainlife.io with the potential to impact multiple communities of scientists interested in learning: behavioral scientists, computer scientists, and neuroscientists.
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.
