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.

翻译：尽管自适应实验设计已超越一维阶梯式适应方法，但大多数认知实验仍仅控制单一变量并以标量总结表现。本研究展示了一种贝叶斯双轴主动分类方法在沉浸式虚拟测试环境中的验证，该环境用于执行5×5工作记忆重建任务。实验控制两个变量：项目的空间负荷L（占用网格数）和特征绑定负荷K（不同颜色数量）。刺激采集由非参数高斯过程（GP）概率分类器的后验不确定性指导，该分类器输出(L, K)平面上的曲面而非单一阈值或最大跨度值。在青年成年群体中，我们将GP驱动的自适应模式（AM）与仅当K=3时调整L的传统自适应阶梯经典模式（CM）进行比较。两种方法在该群体中达到等效性，在K=3时组内相关系数为0.755。此外，AM揭示了空间负荷与特征绑定之间相互作用的个体差异。AM估计比其他采样策略收敛更快，证明仅需约30个样本即可精确拟合完整模型。