Clustering Computer Mouse Tracking Data with Informed Hierarchical Shrinkage Partition Priors

TL;DR

This paper introduces a hierarchical shrinkage partition prior for clustering mouse-tracking data, enabling the identification of behavioral patterns and subgroups with flexible nested partitioning, which enhances analysis of cognitive decision-making.

Contribution

The paper develops a novel hierarchical shrinkage partition (HSP) model that incorporates prior information and allows deviations in nested partitions, advancing clustering methods for mouse-tracking data.

Findings

Effective clustering of mouse-tracking data revealed behavioral patterns.

Model successfully identified subgroups with similar decision-making behaviors.

Demonstrated advantages over existing bi-clustering and nested clustering methods.

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 neuro-imaging 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 similar (rather than identical) nested partitions of the conditions. The proposed model can incorporate prior information about the partitioning of either subjects or conditions to facilitate clustering, and it allows for deviations of the nested partitions within each subject group. These features distinguish the HSP model from other bi-clustering methods that typically create identical nested partitions of conditions within a subject group. Furthermore, it differs from existing nested clustering methods, which define clusters based on common parameters in the sampling model and identify subject groups by different distributions. We illustrate the unique features of the HSP model on a mouse tracking dataset from a pilot study and in simulation studies. Our results show the ability and effectiveness of the proposed exploratory framework in clustering and revealing possible different behavioral patterns across subject groups.