Selecting appropriate visual encodings is critical to designing effective visualization recommendation systems, yet few findings from graphical perception are typically applied within these systems. We observe two significant limitations in translating graphical perception knowledge into actionable visualization recommendation rules/constraints: inconsistent reporting of findings and a lack of shared data across studies. How can we translate the graphical perception literature into a knowledge base for visualization recommendation? We present a review of 59 papers that study user perception and performance across ten visual analysis tasks. Through this study, we contribute a JSON dataset that collates existing theoretical and experimental knowledge and summarizes key study outcomes in graphical perception. We illustrate how this dataset can inform automated encoding decisions with three representative visualization recommendation systems. Based on our findings, we highlight open challenges and opportunities for the community in collating graphical perception knowledge for a range of visualization recommendation scenarios.

翻译：选择合适的视觉编码对于设计有效的可视化推荐系统至关重要，但现有系统中通常只应用了少量图形感知研究结果。我们观察到，将图形感知知识转化为可操作的可视化推荐规则/约束存在两个显著局限：研究结果报告不一致以及跨研究缺乏共享数据。如何将图形感知文献转化为可视化推荐的知识库？我们回顾了59篇研究十类视觉分析任务中用户感知与表现的相关论文。通过此项研究，我们贡献了一个JSON数据集，该数据集整理了现有的理论与实验知识，并总结了图形感知领域的核心研究结论。我们通过三个具有代表性的可视化推荐系统，展示了该数据集如何为自动化编码决策提供依据。基于研究结果，我们指出了在面向各类可视化推荐场景整理图形感知知识时，学术界面临的开放挑战与机遇。