Steven Culpepper, an associate professor of statistics, will collaborate with Aron Barbey and members of the Beckman Institute’s Decision Neuroscience Lab on research funded by a three-year $350,000 grant from the National Science Foundation. The project advances statistical models and machine learning algorithms used to describe the structure underlying human response data.

The project, “Bayesian estimation of restricted latent class models for ordinal and nominal response data,” was funded through NSF’s Methodology, Measurement and Statistics Program.

"We develop technology to provide researchers with greater precision on measurements of human performance, decision-making, and behavior to advance development and cognitive and health interventions," said Culpepper, the project’s PI.

Decision makers and researchers in industry and the social, behavioral, and health professions collect and use large-scale datasets to understand human behavior and outcomes related to knowledge and skills, physical and mental health status, psychological constructs, and consumer preferences. The project considers a program of research that will enable precise, powerful modeling and testing of hypotheses about systematic patterns in human response data with the development and application of novel statistical models that uncover common patterns and profiles.

The Decision Neuroscience Lab is led by Barbey, a professor of psychology.