Date of Award

Spring 5-20-2022

Author's School

McKelvey School of Engineering

Author's Department

Biomedical Engineering

Degree Name

Master of Science (MS)

Degree Type



Inferences about executive functions are commonly drawn through serial administration of various individual assessments that often take a long time to complete and cannot capture complex trends across multiple variables. In an attempt to improve upon current methods used to estimate latent brain constructs, this thesis makes two primary contributions to the field of behavioral modeling. First, it brings attention to sequential designs for more efficient diagnostic testing of fluctuations in executive functions with respect to a baseline level. It was shown that a sequential framework was successfully capable of detecting significant differences in cognitive performance more rapidly than conventional fixed approaches. Second, it introduces a scalable Gaussian Process estimator that can build individual psychometric models of task performance without requiring prohibitive amounts of data. This probabilistic machine learning classifier was capable of obtaining fully predictive models of working memory capacity person by person with high confidence.


English (en)


Dennis Barbour

Committee Members

Dennis Barbour, Jacob Gardner, Jason Hassenstab