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.

翻译：鼠标追踪数据记录了参与者在执行实验任务时的计算机鼠标轨迹，为理解受试者的潜在认知过程提供了宝贵洞见。神经科学家关注于对计算机鼠标追踪任务中受试者反应进行聚类，以揭示个体决策行为模式并识别具有相似神经行为反应的群体亚组。这些数据可与神经影像数据结合，为个性化干预提供额外信息。本文针对从鼠标追踪数据轨迹中提取的汇总统计量，提出了一种新颖的分层收缩划分（HSP）先验用于聚类。HSP模型将受试者聚类定义为能产生更相似（而非完全相同）条件嵌套划分的受试者集合。该模型可融入关于受试者或条件划分的先验信息以辅助聚类，并允许各受试者组内嵌套划分存在偏差。这些特性使HSP模型区别于通常在同一受试者组内创建完全相同条件嵌套划分的双向聚类方法。此外，该模型也不同于现有嵌套聚类方法——后者基于抽样模型中的公共参数定义聚类，并通过不同分布识别受试者组别。我们通过一项试点研究的鼠标追踪数据集和模拟研究展示了HSP模型的独特特性。结果表明，所提出的探索性框架在聚类及揭示不同受试者组间潜在行为模式差异方面具有显著能力与有效性。