Bayesian Distributional Models of Executive Functioning
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914593084801024 |
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| author | Kasumba, Robert Lu, Zeyu Marticorena, Dom CP Zhong, Mingyang Beggs, Paul Pahor, Anja Ramani, Geetha Goffney, Imani Jaeggi, Susanne M Seitz, Aaron R Gardner, Jacob R Barbour, Dennis L |
| author_facet | Kasumba, Robert Lu, Zeyu Marticorena, Dom CP Zhong, Mingyang Beggs, Paul Pahor, Anja Ramani, Geetha Goffney, Imani Jaeggi, Susanne M Seitz, Aaron R Gardner, Jacob R Barbour, Dennis L |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00387 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Distributional Models of Executive Functioning Kasumba, Robert Lu, Zeyu Marticorena, Dom CP Zhong, Mingyang Beggs, Paul Pahor, Anja Ramani, Geetha Goffney, Imani Jaeggi, Susanne M Seitz, Aaron R Gardner, Jacob R Barbour, Dennis L Machine Learning Human-Computer Interaction 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. |
| title | Bayesian Distributional Models of Executive Functioning |
| topic | Machine Learning Human-Computer Interaction |
| url | https://arxiv.org/abs/2510.00387 |