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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. We then sample individual test items from these distributions using DALE, a random procedure, or a standard fixed-battery approach. Given the same observations, DLVM consistently outperformed IMLE, especially with smaller amounts of data, and converged 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\u2019s cross-task inference with DALE\u2019s optimal adaptive sampling, providing a principled basis for more efficient cognitive assessments.<\/jats:p>","DOI":"10.3758\/s13428-026-03191-x","type":"journal-article","created":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T21:59:32Z","timestamp":1791237572000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bayesian distributional models of executive functioning"],"prefix":"10.3758","volume":"58","author":[{"given":"Robert","family":"Kasumba","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyu","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dom C. 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