Clustering computer mouse tracking data with informed hierarchical shrinkage partition priors.

ABSTRACT: Mouse-tracking data, which record computer mouse trajectories while participants perform an experimental task, provide valuable insights into subjects' underlying cognitive processes. Neuroscientists are interested in clustering the subjects' responses during computer mouse-tracking tasks to reveal patterns of individual decision-making behaviors and identify population subgroups with similar neurobehavioral responses. These data can be combined with neuroimaging data to provide additional information for personalized interventions. In this article, we develop a novel hierarchical shrinkage partition (HSP) prior for clustering summary statistics derived from the trajectories of mouse-tracking data. The HSP model defines a subjects' cluster as a set of subjects that gives rise to more simil

SUBMITTER: Song Z 

PROVIDER: S-EPMC11523067 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature