Learning to Pay Attention: Unsupervised Modeling of Attentive and Inattentive Respondents in Survey Data

• URL: http://arxiv.org/abs/2603.02427v1
• Date: Mon, 02 Mar 2026 22:11:51 GMT
• Title: Learning to Pay Attention: Unsupervised Modeling of Attentive and Inattentive Respondents in Survey Data
• Authors: Ilias Triantafyllopoulos, Panos Ipeirotis,
• Abstract summary: Traditional safeguards, such as attention checks, are often costly, reactive, and inconsistent.<n>We propose a unified, label-free framework for inattentiveness detection using complementary unsupervised views.
• Score: 0.14323566945483493
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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