# Distributional Latent Variable Models with an Application in Active Cognitive Testing

arXiv: https://arxiv.org/abs/2312.09316 · HTML: https://mlsanity.com/paper/2312.09316/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 33 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Builds on (most cited first, 1 of 1)

- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)

## Built on by (most cited first, 2 of 2)

- [Goal Recognition Design for General Behavioral Agents using Machine Learning](https://mlsanity.com/paper/2404.03054/) (2024)
- [Early Prediction of Future Behavioral Strategy from Process Traces](https://mlsanity.com/paper/2605.30550/) (2026)

## Abstract (arXiv)

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.