NCS-FO: Uncovering the cognitive and neural fingerprints that make each of us unique

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

Efforts to understand the brain systems that support mental abilities often assume that all people share a common cognitive and neural “template” that differs only subtly and randomly from person to person. Yet many classic studies have shown substantial variation in both mind and brain across different groups. Language relies on different brain systems for left handers compared to right handers. Students who struggle with reading show different patterns of neural connectivity than do fluent readers. Math experts see different kinds of relationships amongst even simple integers than do college undergraduates. This project will combine new computational insights, functional brain imaging, and brain stimulation to characterize variability in the mental and neural structures that support perception, language, social cognition, and high-order thought. This work has potential to impact human health and patterns of recovery from brain damage, approaches to education for typical and atypical populations, and other fields of science that can benefit from discovery of patterned variation. The scientific goals involve developing new theory and algorithms for signal discovery in behavioral and neural data, and using these methods to identify commonalities and differences across individuals in the structure of their mental representations and the corresponding neural underpinnings. For both brain and behavior, structure estimation requires a lot of data, and data generated by a single participant is typically noisy. To address this, classic approaches treat data from each participant as a sample drawn from an underlying system common to all members of a population. In effect, individual differences are treated as noise. Our approach instead assumes that individuals can differ significantly in the mental and neural structures that generate data, but that such variation is structured in ways that can aid in signal-discovery. This project will formalize these ideas in a computational approach to structure estimation, establish bounds on sample complexity for the model, and develop practical algorithms for applying the approach to behavioral and brain imaging data. In parallel with this computational stream, the investigators will collect behavioral data, functional brain images, and brain-stimulation data for several tasks tapping different aspects of cognition. These experiments, combined with the new computational methods, will experimentally determine how people differ in the structure of their mental and neural representations—potentially changing our basic understanding of the different ways brains can give rise to minds. This project is funded by the Integrated Strategies for Understanding Neural and Cognitive Systems (NCS) program, which is jointly supported by the Directorates for Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE). 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.