Cognitive modeling commonly relies on asking participants to complete a battery of varied tests in order to estimate attention, working memory, and other latent variables. In many cases, these tests result in highly variable observation models. A near-ubiquitous approach is to repeat many observations for each test, resulting in a distribution over the outcomes from each test given to each subject. In this paper, we explore the usage of latent variable modeling to enable learning across many correlated variables simultaneously. We extend latent variable models (LVMs) to the setting where observed data for each subject are a series of observations from many different distributions, rather than simple vectors to be reconstructed. By embedding test battery results for individuals in a latent space that is trained jointly across a population, we are able to leverage correlations both between tests for a single participant and between multiple participants. We then propose an active learning framework that leverages this model to conduct more efficient cognitive test batteries. We validate our approach by demonstrating with real-time data acquisition that it performs comparably to conventional methods in making item-level predictions with fewer test items.

翻译：认知建模通常依赖于让参与者完成一系列不同类型的测试，以估计注意力、工作记忆等潜变量。在许多情况下，这些测试会产生高度可变的观测模型。一种近乎普遍的方法是针对每项测试重复进行多次观测，从而得到每个受试者每项测试结果的分布。本文探讨了利用潜变量建模同时学习多个相关变量的方法。我们将潜变量模型（LVM）扩展到观测数据为每个受试者来自多个不同分布的一系列观测值（而非待重建的简单向量）的场景。通过将个体测试结果嵌入到跨人群联合训练的潜空间中，我们能够利用单个参与者内部测试之间以及多个参与者之间的相关性。随后，我们提出一种主动学习框架，利用该模型进行更高效的认知测试组合。我们通过实时数据采集验证了该方法，证明其在用更少测试项进行项目级预测时与传统方法性能相当。