视觉传达中直接联想和关系联想的统一效应
Unifying Effects of Direct and Relational Associations for Visual Communication
摘要：
人们对颜色如何映射到可视化中的概念有期望，并且他们更善于解释符合他们期望的可视化。传统上，对这些期望（推断映射）的研究区分了与分类与连续信息可视化相关的不同因素。对分类信息的研究侧重于直接关联（例如，芒果与黄色相关）而对连续信息的研究集中于关系关联（例如，较暗的颜色映射到较大的量;较暗的偏置）。我们将这两个领域结合在一个单一的分配推理框架内。分配推理是人们推断感知特征与编码系统中表示的概念之间的映射的过程。观察者通过最大化每个可能分配的“优点”或“优点”来推断全局最优分配。以前的工作分配推理集中在可视化的分类信息。我们将这种方法扩展到连续数据的可视化，方法是：（a）将优点的概念扩大到包括关系关联;（b）开发一种方法，用于结合多个（有时是冲突的）优点来源来预测人们的推断映射。我们在实验数据上开发和测试了我们的模型，在实验中，参与者解释了色彩图数据可视化，代表了关于环境概念（阳光，阴影，野火，海水，冰川冰）的虚构数据。我们发现，直接和关系的关联独立的推断映射。这些结果可用于优化可视化设计，以促进视觉传达。
英文摘要：
People have expectations about how colors map to concepts in visualizations, and they are better at interpreting visualizations that match their expectations. Traditionally, studies on these expectations (inferred mappings) distinguished distinct factors relevant for visualizations of categorical vs. continuous information. Studies on categorical information focused on direct associations (e.g., mangos are associated with yellows) whereas studies on continuous information focused on relational associations (e.g., darker colors map to larger quantities; dark-is-more bias). We unite these two areas within a single framework of assignment inference. Assignment inference is the process by which people infer mappings between perceptual features and concepts represented in encoding systems. Observers infer globally optimal assignments by maximizing the "merit," or "goodness," of each possible assignment. Previous work on assignment inference focused on visualizations of categorical information. We extend this approach to visualizations of continuous data by (a) broadening the notion of merit to include relational associations and (b) developing a method for combining multiple (sometimes conflicting) sources of merit to predict people's inferred mappings. We developed and tested our model on data from experiments in which participants interpreted colormap data visualizations, representing fictitious data about environmental concepts (sunshine, shade, wild fire, ocean water, glacial ice). We found both direct and relational associations contribute independently to inferred mappings. These results can be used to optimize visualization design to facilitate visual communication.
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