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 independently, resulting in a distribution over the outcomes from each test given to each subject. Latent variable models (LVMs), if employed, are only added after data collection. In this paper, we explore the usage of LVMs to enable learning across many correlated variables simultaneously. We extend 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 can leverage correlations both between disparate test data 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多样化的测试，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。一种近乎普遍的做法是独立地对每个测试进行多次重复观测，从而得到每个受试者在每个测试结果上的分布。若使用潜变量模型（LVMs），通常仅在数据收集完成后才引入。本文探讨了利用LVMs同时学习多个相关变量的方法。我们将LVMs扩展到以下场景：每个受试者的观测数据是来自多种不同分布的一系列观测值，而非待重构的简单向量。通过将个体的测试组结果嵌入到在群体上联合训练的潜空间中，我们可以同时利用单个参与者不同测试数据之间以及多个参与者之间的相关性。随后，我们提出了一种主动学习框架，该框架利用此模型以进行更高效的认知测试组。我们通过实时数据采集验证了所提方法，结果表明其在实现项目级预测时，使用更少的测试项目即可达到与传统方法相当的性能。