通过参考样本和提示，提升用户发现数据缺失的能力
Making Absence Visible: The Roles of Reference and Prompting in Recognizing Missing Information
- 用具体样例替代全局基准，帮助用户形成预期
- 提示用户寻找缺失信息后，识别率显著提升
- 适合设计需要用户发现异常或遗漏的交互界面
交互系统在解释数据或支持决策时，往往强调已存在的内容，而忽略本应存在却缺失的信息。这种‘存在偏见’限制了用户对数据集或情境形成完整认知。识别缺失信息依赖于对‘应有内容’的预期，但现有界面很少帮助用户建立此类预期。我们通过实验研究了参考框架与提示对用户识别数据中预期但缺失类别能力的影响。参与者在能源、财富、政体三个领域进行比较，参考条件分为‘全局’（提供统一人口基准）和‘局部’（展示多个具体实例）。结果表明，采用局部参考时，用户对缺失信息的识别率更高，说明基于样例的局部框架有助于预期形成与缺失检测。当用户被提示关注缺失项时，识别率显著上升。研究讨论了对交互界面与基于预期的可视化设计的启示，同时考虑了不同参考结构与引导注意力带来的认知权衡。
原文摘要 · Abstract (English)
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.
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