| Title: | Distributional latent variable models with an application in active cognitive testing |
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| Authors: | ID Kasumba, Robert (Author)
ID Marticorena, Dom C. P. (Author)
ID Pahor, Anja (Author)
ID Ramani, Geetha B. (Author)
ID Goffney, Imani (Author)
ID Jaeggi, Susanne M. (Author)
ID Seitz, Aaron R. (Author)
ID Gardner, Jacob R. (Author)
ID Barbour, Dennis L. (Author) |
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| Files: |  RAZ_Kasumba_Robert_2025.pdf (1,52 MB)
MD5: 8B4FA4499BA3F05CE46B5CDF1F37B3E8
 https://ieeexplore.ieee.org/document/10916781
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FF - Faculty of Arts
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| Abstract: | Cognitive modeling commonly relies on asking par ticipants 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. |
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| Keywords: | active machine learning, executive function, latent variable modeling, cognition |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Article acceptance date: | 05.02.2025 |
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| Publication date: | 15.10.2025 |
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| Publisher: | Institute of Electrical and Electronics Engineers |
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| Year of publishing: | 2025 |
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| Number of pages: | str. 1212-1222 |
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| Numbering: | Letn. 17, št. 5 |
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| PID: | 20.500.12556/DKUM-94775  |
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| UDC: | 159.95:159.98 |
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| ISSN on article: | 2379-8939 |
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| COBISS.SI-ID: | 231658755 |
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| DOI: | 10.1109/TCDS.2025.3548962 |
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| Publication date in DKUM: | 27.01.2026 |
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| Views: | 139 |
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| Downloads: | 1 |
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| Metadata: |    |
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| Categories: | Misc.
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