Department of Integrative Biology, University of Wisconsin-Madison, 430 Lincoln Drive, Madison, WI, 53706, USA.
Wisconsin Institute for Discovery, University of Wisconsin-Madison, 330 N. Orchard Street, Madison, WI, 53715, USA.
Cogn Res Princ Implic. 2023 Jun 19;8(1):38. doi: 10.1186/s41235-023-00482-1.
In visual communication, people glean insights about patterns of data by observing visual representations of datasets. Colormap data visualizations ("colormaps") show patterns in datasets by mapping variations in color to variations in magnitude. When people interpret colormaps, they have expectations about how colors map to magnitude, and they are better at interpreting visualizations that align with those expectations. For example, they infer that darker colors map to larger quantities (dark-is-more bias) and colors that are higher on vertically oriented legends map to larger quantities (high-is-more bias). In previous studies, the notion of quantity was straightforward because more of the concept represented (conceptual magnitude) corresponded to larger numeric values (numeric magnitude). However, conceptual and numeric magnitude can conflict, such as using rank order to quantify health-smaller numbers correspond to greater health. Under conflicts, are inferred mappings formed based on the numeric level, the conceptual level, or a combination of both? We addressed this question across five experiments, spanning data domains: alien animals, antibiotic discovery, and public health. Across experiments, the high-is-more bias operated at the conceptual level: colormaps were easier to interpret when larger conceptual magnitude was represented higher on the legend, regardless of numeric magnitude. The dark-is-more bias tended to operate at the conceptual level, but numeric magnitude could interfere, or even dominate, if conceptual magnitude was less salient. These results elucidate factors influencing meanings inferred from visual features and emphasize the need to consider data meaning, not just numbers, when designing visualizations aimed to facilitate visual communication.
在视觉传达中，人们通过观察数据集的视觉表示来获取有关数据模式的洞察。颜色映射数据可视化（“颜色映射”）通过将颜色的变化映射到幅度的变化来显示数据集中的模式。当人们解释颜色映射时，他们对颜色到幅度的映射方式有预期，并且更善于解释与这些预期相符的可视化效果。例如，他们推断较暗的颜色表示较大的数量（暗即更多的偏见），并且在垂直方向上的图例中较高的颜色表示较大的数量（高即更多的偏见）。在以前的研究中，数量的概念是直截了当的，因为更多的概念表示（概念幅度）对应于更大的数值（数值幅度）。但是，概念和数值幅度可能会发生冲突，例如使用秩次来量化健康，较小的数字表示更大的健康。在冲突下，是根据数值水平、概念水平还是两者的组合形成推断的映射？我们通过五个实验来解决这个问题，涵盖了数据领域：外星动物、抗生素发现和公共卫生。在所有实验中，高即更多的偏见都在概念层面起作用：当较大的概念幅度在图例中更高时，颜色映射更容易解释，而不管数值幅度如何。暗即更多的偏见倾向于在概念层面起作用，但如果概念幅度不明显，数值幅度可能会干扰甚至占主导地位。这些结果阐明了影响从视觉特征推断出的含义的因素，并强调了当设计旨在促进视觉沟通的可视化效果时，不仅要考虑数字，还要考虑数据含义。
