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.

翻译：本研究采用已知真实参数的受控模拟方法，系统评估了分布潜变量模型（DLVM）与贝叶斯分布主动学习（DALE）相较于传统独立极大似然估计（IMLE）的性能表现。DLVM通过整合多个执行功能任务及个体观测数据，即使在数据稀疏或不完整条件下仍能实现参数估计。为建立已知真实基准，我们从神经网络学习的潜空间中均匀采样个体会话，并将其映射至不同任务的分布化认知表现。随后采用DALE、随机采样或标准固定测试组合方式，从这些分布中抽取个体测试项目。在相同观测集条件下，DLVM始终优于IMLE，尤其在数据量较小时表现更为显著，且能更快收敛至高度精确的真实分布估计。在第二轮分析中，DALE通过自适应引导采样最大化信息增益，其性能在最初80个试验中即显著超越随机采样与固定测试组合。这些发现确立了DLVM跨任务推理与DALE最优自适应采样相结合的显著优势，为构建更高效的认知评估体系提供了理论依据。