We study attention in mobile Augmented Reality (AR) using object recall as a proxy outcome. We observe that the ability to recall an object (physical or virtual) that was encountered in a mobile AR experience depends on many possible impact factors and attributes, with some objects being readily recalled while others are not, and some people recalling objects overall much better or worse than others. This opens up a potential cognitive attack in which adversaries might create conditions that make an AR user not recall certain potentially mission-critical objects. We explore whether a calibrated predictor of object recall can help shield against such cognitive attacks. We pool data from four mobile AR studies (with a total of 1,152 object recall probes) and fit a Partial Least Squares Structural Equation Model (PLS-SEM) with formative Object, Scene, and User State composites predicting recall, also benchmarking against Random Forest and multilayer perceptron classifiers. PLS-SEM attains the best F1 score in three of four studies. Additionally, path estimates identify lighting, augmentation density, AR registration stability, cognitive load, and AR familiarity as primary drivers. The model outputs per-object recall probabilities that can drive interface adjustments when predicted recall falls. Overall, PLS-SEM provides competitive accuracy with interpretable levers for design and evaluation in mobile AR.

翻译：本研究以对象回忆作为代理指标，探究移动增强现实（AR）中的注意力机制。我们观察到，用户在移动AR体验中遇到的物体（物理或虚拟）能否被回忆取决于多种潜在影响因素与属性：部分物体容易被回忆，而其他则不然；同时，某些用户的整体回忆能力显著优于或弱于他人。这一现象揭示了一种潜在的认知攻击风险——攻击者可能通过特定条件使AR用户无法回忆起某些关键任务相关的物体。我们探讨了经过校准的对象回忆预测器是否有助于防御此类认知攻击。通过整合四项移动AR研究的数据（共包含1,152次对象回忆测试），我们构建了基于偏最小二乘结构方程模型（PLS-SEM）的预测框架，该模型以形成性的对象、场景及用户状态复合变量作为回忆预测因子，并与随机森林及多层感知机分类器进行性能对比。结果显示，PLS-SEM在四项研究中的三项取得了最优的F1分数。此外，路径估计表明照明条件、增强内容密度、AR配准稳定性、认知负荷及AR熟悉度是主要驱动因素。该模型可输出每个对象的回忆概率，当预测回忆率较低时可驱动界面调整。总体而言，PLS-SEM在移动AR领域提供了具有竞争力的预测精度，并为设计与评估提供了可解释的调控维度。