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

TL;DR

This study investigates how reference framing and explicit prompting influence the recognition of missing information in data visualizations. By comparing partial exemplar references with global population baselines and testing spontaneous versus guided prompting across three domains, the authors show that partial framing enhances absence detection when attention is not guided, while guided prompting dramatically boosts detection across both framings. The results reveal a strong prompting effect that can override framing differences, highlighting the importance of attentional guidance in visualization design. The findings have practical implications for designing AI-assisted explanations and dashboards that surface omissions, balance cognitive load, and support more mindful, expectation-aware data reasoning.

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.

Figures (4)

• Figure 1: Overview of the experimental design. The study employed a 2×2 mixed factorial design with Reference Framing (Partial vs. Global) as between-subjects and Prompting Mode (Spontaneous vs. Guided) as within-subjects. Participants (N = 100) were randomly assigned to one of two reference conditions: Partial Reference, where the focal entity (E1) was compared to multiple concrete exemplars, or Global Reference, where E1 was compared to a single aggregate baseline. All participants responded to both prompting conditions across three datasets (political regimes, income brackets, and energy sources), resulting in six open-ended responses per participant.
• Figure 2: Example visualization in the Partial Reference condition using the Regime dataset. The focal entity (E1, highlighted in yellow) is compared with three exemplar regions: Asia, North America, and Europe.
• Figure 3: Example visualization in the Global Reference condition for the same Regime dataset. The focal entity (E1, yellow) is compared with a single World aggregate representing the population distribution across regime types.
• Figure 4: Absence detection across reference framing and prompting conditions. Panel (a) shows per-domain detection rates, highlighting sharp increases from spontaneous to guided prompting across all contexts. Panel (b) summarizes the overall interaction, showing that guided prompting eliminates the small spontaneous advantage of the Global reference. Error bars denote 95% Wilson score confidence intervals.