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cs.LG updates on arXiv.org

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Bayesian Distributional Models of Executive Functioning
[Submitted on 1 Oct 2025 (v1), last revised 13 Aug 2026 (this ve · 2026-05-26 · via cs.LG updates on arXiv.org

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Abstract:This study uses controlled simulations with known ground-truth parameters to evaluate how Distributional Latent Variable Models (DLVM) and Bayesian Distributional Active LEarning (DALE) perform in comparison to conventional Independent Maximum Likelihood Estimation (IMLE). DLVM integrates observations across multiple executive function tasks and individuals, allowing parameter estimation even under sparse or incomplete data conditions. To establish known-ground truth, we uniformly sample individual sessions from a neural network learned latent space and map them to distributional cognitive performance across different tasks. The individual test-items are then sampled from these distributions using either DALE, random procedure or a standard fixed battery approach. When given the same set of observations, DLVM consistently outperformed IMLE, especially under smaller amounts of data, and converges faster to highly accurate estimates of the true distributions. In a second set of analyses, DALE adaptively guided sampling to maximize information gain, outperforming random sampling and fixed test batteries, particularly within the first 80 trials. These findings establish the advantages of combining DLVM's cross-task inference with DALE's optimal adaptive sampling, providing a principled basis for more efficient cognitive assessments.

Submission history

From: Dennis Barbour [view email]
[v1] Wed, 1 Oct 2025 00:59:31 UTC (7,283 KB)
[v2] Tue, 7 Oct 2025 20:12:14 UTC (7,283 KB)
[v3] Fri, 22 May 2026 22:17:21 UTC (7,304 KB)
[v4] Thu, 13 Aug 2026 15:06:02 UTC (17,060 KB)