Response time has attracted increased interest in educational and psychological assessment for, e.g., measuring test takers' processing speed, improving the measurement accuracy of ability, and understanding aberrant response behavior. Most models for response time analysis are based on a parametric assumption about the response time distribution. The Cox proportional hazard model has been utilized for response time analysis for the advantages of not requiring a distributional assumption of response time and enabling meaningful interpretations with respect to response processes. In this paper, we present a new version of the proportional hazard model, called a latent space accumulator model, for cognitive assessment data based on accumulators for two competing response outcomes, such as correct vs. incorrect responses. The proposed model extends a previous accumulator model by capturing dependencies between respondents and test items across accumulators in the form of distances in a two-dimensional Euclidean space. A fully Bayesian approach is developed to estimate the proposed model. The utilities of the proposed model are illustrated with two real data examples.

翻译：反应时在教育与心理评估中日益受到关注，例如用于测量受测者的处理速度、提升能力评估精度，以及理解异常作答行为。大多数反应时分析模型基于反应时分布的参数假设。Cox比例风险模型因无需反应时分布假设，并能对反应过程进行有意义的解释，已被应用于反应时分析。本文针对认知评估数据，提出一种基于两类竞争性反应结果（如正确与错误反应）累积器的新型比例风险模型，称为"潜在空间累积模型"。该模型通过二维欧氏空间中的距离形式，捕捉不同累积器中受测者与测验项目之间的依赖关系，从而扩展了先前的累积器模型。我们发展了一种完全贝叶斯方法对模型进行估计，并通过两个真实数据示例展示了模型的应用价值。