用双维度贝叶斯方法精准评估记忆任务表现，更快发现个体差异。
Multidimensional Bayesian Active Machine Learning of Working Memory Task Performance
- 通过空间与特征绑定双变量控制，用高斯过程模型动态优化实验刺激。
- 仅需约30次采样即可准确拟合完整性能模型，效率显著提升。
- 揭示个体在空间与特征负荷交互中的差异，适合认知神经研究者使用。
尽管自适应实验设计已超越单一维度的阶梯式调节，多数认知实验仍仅控制一个变量，并以标量总结表现。本文验证了一种基于贝叶斯的二维主动分类方法，在沉浸式虚拟环境中进行5×5工作记忆重构任务。同时控制两个变量：空间负荷L（占据格子数）和特征绑定负荷K（不同颜色数）。刺激选取由非参数高斯过程（GP）概率分类器的后验不确定性引导，输出覆盖(L, K)平面的性能表面，而非单一阈值或最大跨度。在年轻成人被试中，将GP驱动的自适应模式（AM）与仅调节L、K=3的传统阶梯模式（CM）对比，两者在该条件下达到一致性，组内相关系数为0.755。此外，AM揭示了个体在空间负荷与特征绑定间的交互差异。相比其他采样策略，AM估计收敛更快，仅需约30个样本即可准确拟合全模型。
原文摘要 · Abstract (English)
While adaptive experimental design has outgrown one-dimensional, staircase-based adaptations, most cognitive experiments still control a single factor and summarize performance with a scalar. We show a validation of a Bayesian, two-axis, active-classification approach, carried out in an immersive virtual testing environment for a 5-by-5 working-memory reconstruction task. Two variables are controlled: spatial load L (number of occupied tiles) and feature-binding load K (number of distinct colors) of items. Stimulus acquisition is guided by posterior uncertainty of a nonparametric Gaussian Process (GP) probabilistic classifier, which outputs a surface over (L, K) rather than a single threshold or max span value. In a young adult population, we compare GP-driven Adaptive Mode (AM) with a traditional adaptive staircase Classic Mode (CM), which varies L only at K = 3. Parity between the methods is achieved for this cohort, with an intraclass coefficient of 0.755 at K = 3. Additionally, AM reveals individual differences in interactions between spatial load and feature binding. AM estimates converge more quickly than other sampling strategies, demonstrating that only about 30 samples are required for accurate fitting of the full model.
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