Bayesian Distributional Models of Executive Functioning
This work provides a principled basis for more efficient cognitive assessments, addressing a domain-specific problem in psychology and neuroscience.
The study tackled the problem of estimating executive functioning parameters under sparse or incomplete data by comparing Distributional Latent Variable Models (DLVM) and Bayesian Distributional Active Learning (DALE) against conventional methods, finding that DLVM consistently outperformed Independent Maximum Likelihood Estimation (IMLE) with smaller data and converged faster, while DALE outperformed random and fixed sampling within the first 80 trials.
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. DLVM consistently outperformed IMLE, especially under with 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.