The goal of this project, led by a team of researchers at Stanford University, is to develop novel neurocognitive models that integrate behavioral and neural (fMRI, functional magnetic resonance imaging) data to understand the computational, cognitive, and brain mechanisms underlying individual differences in mathematical problem solving and strategy use, the effects of training, and longitudinal development. Understanding how symbols are processed in the brain has direct implications for education and the remediation of cognitive difficulties. The researchers will perform sophisticated computational analyses on a dataset derived from children 7 to 12 years of age in an fMRI study, a tutoring study, and a longitudinal study. The findings of this study will provide a basis for customized training and will provide novel platforms for diagnostic and intervention procedures for learning difficulties. This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NCS), a multidisciplinary program 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 research will leverage extensive behavioral data, cognitive assessments, as well as brain imaging data, to model moment-by-moment changes in latent cognitive dynamics associated with mathematical problem solving. The multidisciplinary approach described here seeks to develop unsupervised computational models to infer differences in the latent cognitive strategies used by an individual on a trial-by-trial basis. Specifically, this project aims to (1) develop computational cognitive models for inferring latent problem-solving dynamics and strategy use, (2) develop novel integrated neurocognitive models to identify distinct and overlapping brain circuits underlying latent problem-solving dynamics and strategy use, and (3) determine integrated cognitive and neural mechanisms underlying the impact of cognitive tutoring on changes in latent problem-solving dynamics and strategy use. The planned studies will help in dissociating mathematical problem solving into multiple cognitive sub-processes, characterizing sources of individual differences across these sub-processes, identifying how each of these might relate to difficulties in problem solving, and evaluate whether these cognitive sub-processes may be remediated by cognitive tutoring programs. Ultimately, this research will enhance our understanding of dynamic cognitive processes and problem-solving strategies in children?s numerical cognition and provide new insights into latent behavioral dynamics associated with typical and atypical math abilities in children.

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.

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Stanford University
United States
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