Bayesian distributional models of executive functioning.

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 compared with 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. 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's cross-task inference with DALE's optimal adaptive sampling, providing a principled basis for more efficient cognitive assessments.