用分布潜变量模型和自适应采样，提升认知评估的效率与准确性。
Bayesian Distributional Models of Executive Functioning
- 融合多任务数据的分布潜变量模型，支持稀疏数据下的参数估计。
- 在少于80次测试时，自适应采样比随机或固定测试更高效，准确率更高。
- 适合需要精准、低耗认知评估的研究者和临床应用。
本研究通过已知真实参数的受控模拟，评估分布潜变量模型（DLVM）与贝叶斯分布主动学习（DALE）相较于传统独立最大似然估计（IMLE）的表现。DLVM整合多个执行功能任务及个体数据，在数据稀疏或不完整时仍可进行参数估计。为建立真实基准，我们从神经网络学习的潜在空间中均匀采样个体会话，并映射到不同任务上的分布认知表现；随后使用DALE、随机方法或标准固定题库方式采样测试项。在相同观测条件下，DLVM始终优于IMLE，尤其在数据量较小时收敛更快，且更接近真实分布。第二组分析显示，DALE通过自适应采样最大化信息增益，在前80次测试内显著优于随机采样和固定题库。结果表明，结合DLVM的跨任务推断与DALE的最优自适应采样，为更高效的认知评估提供了理论基础。
原文摘要 · Abstract (English)
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.
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