学习如何关注：在调查数据中无监督建模注意力集中与不集中受访者
Learning to Pay Attention: Unsupervised Modeling of Attentive and Inattentive Respondents in Survey Data
AI总结：
本文提出了一种无监督框架，通过几何重建和概率依赖建模检测调查数据中的不集中受访者，揭示心理测量与机器学习的对齐关系。
摘要：
行为和社会科学调查的完整性依赖于检测不集中受访者，这些受访者提供随机或低努力度的回答。传统保障措施，如注意力检查，往往成本高、反应性强且不一致。我们提出了一种统一的、无标签框架，用于检测不集中性，该框架通过互补的无监督视角对响应一致性进行评分：几何重建（自编码器）和概率依赖建模（Chow-Liu树）。虽然我们引入了“百分位损失”目标来提高自编码器对异常值的鲁棒性，但我们的主要贡献是识别使无监督质量控制可行的结构条件。在九个异质现实世界数据集中，我们发现检测效果主要由调查结构而非模型复杂性驱动：具有连贯、重叠项目电池的仪器表现出强烈的协方差模式，使即使线性模型也能可靠地区分注意力集中与不集中受访者。这揭示了关键的“心理测量-机器学习对齐”：最大化测量可靠性（例如内部一致性）的设计原则也最大化算法检测性。该框架为调查平台提供了一种可扩展、领域无关的诊断工具，将数据质量直接与仪器设计联系起来，使审计无需额外的受访者负担。
英文摘要：
The integrity of behavioral and social-science surveys depends on detecting inattentive respondents who provide random or low-effort answers. Traditional safeguards, such as attention checks, are often costly, reactive, and inconsistent. We propose a unified, label-free framework for inattentiveness detection that scores response coherence using complementary unsupervised views: geometric reconstruction (Autoencoders) and probabilistic dependency modeling (Chow-Liu trees). While we introduce a "Percentile Loss" objective to improve Autoencoder robustness against anomalies, our primary contribution is identifying the structural conditions that enable unsupervised quality control. Across nine heterogeneous real-world datasets, we find that detection effectiveness is driven less by model complexity than by survey structure: instruments with coherent, overlapping item batteries exhibit strong covariance patterns that allow even linear models to reliably separate attentive from inattentive respondents. This reveals a critical ``Psychometric-ML Alignment'': the same design principles that maximize measurement reliability (e.g., internal consistency) also maximize algorithmic detectability. The framework provides survey platforms with a scalable, domain-agnostic diagnostic tool that links data quality directly to instrument design, enabling auditing without additional respondent burden.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
