Making Absence Visible: The Roles of Reference and Prompting in Recognizing Missing Information

2026-01-12

AI Summary

Interactive visualization systems often suffer from presence bias, overlooking information absent from the display that users nonetheless expect to see, thereby hindering the formation of a complete mental model of the data. This study investigates how reference frames—global baselines versus concrete exemplars—and prompting strategies influence users’ ability to identify missing categories across three domains: energy, wealth, and political regimes. Through controlled experiments integrating methods from cognitive psychology and interactive visualization techniques, the research demonstrates that reference frames grounded in concrete exemplars are significantly more effective than global baselines in facilitating the detection of missing information. Furthermore, active prompting mechanisms markedly enhance users’ awareness of such omissions. These findings offer empirical support and practical guidance for designing expectation-driven visualizations that better align with users’ cognitive processes.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Preferences
• Data Mining & Knowledge Management: Data Visualization & Summarization

Application Category

• Web Mining and Content Analysis: Web data visualization
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Interactive systems that explain data, or support decision making often emphasize what is present while overlooking what is expected but missing. This presence bias limits users'ability to form complete mental models of a dataset or situation. Detecting absence depends on expectations about what should be there, yet interfaces rarely help users form such expectations. We present an experimental study examining how reference framing and prompting influence people's ability to recognize expected but missing categories in datasets. Participants compared distributions across three domains (energy, wealth, and regime) under two reference conditions: Global, presenting a unified population baseline, and Partial, showing several concrete exemplars. Results indicate that absence detection was higher with Partial reference than with Global reference, suggesting that partial, samples-based framing can support expectation formation and absence detection. When participants were prompted to look for what was missing, absence detection rose sharply. We discuss implications for interactive user interfaces and expectation-based visualization design, while considering cognitive trade-offs of reference structures and guided attention.

Problem

• absence detection
• presence bias
• expectation formation
• missing information
• reference framing

Innovation

• absence detection
• reference framing
• prompting
• expectation formation
• interactive visualization