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.

翻译：解释数据或支持决策的交互式系统通常强调现有信息，而忽略了预期存在却实际缺失的内容。这种存在性偏差限制了用户对数据集或情境形成完整心智模型的能力。缺失检测依赖于对"应存在内容"的预期，然而交互界面很少帮助用户建立此类预期。我们通过实验研究探讨了参考框架与提示如何影响人们识别数据集中预期存在却实际缺失类别的能力。参与者在两种参考条件下比较了三个领域（能源、财富与政体）的数据分布：全局参考呈现统一总体基线，局部参考展示若干具体示例。结果表明，局部参考条件下的缺失检测率高于全局参考条件，这表明基于部分样本的参考框架有助于预期形成与缺失检测。当参与者被提示寻找缺失内容时，缺失检测率显著上升。我们讨论了该发现对交互式用户界面与基于预期的可视化设计的启示，同时考虑了参考结构与引导注意力的认知权衡。