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

TL;DR

This study investigates how reference framing and prompting affect users' ability to recognize missing information in datasets, highlighting that partial references and prompts significantly improve absence detection.

Contribution

The paper introduces an experimental analysis of how different reference structures and prompts influence the recognition of missing data in interactive systems.

Findings

Partial reference framing enhances absence detection.

Prompting significantly increases recognition of missing categories.

Partial references support better expectation formation.

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.