Graphical perception studies typically measure visualization encoding effectiveness using the error of an "average observer", leading to canonical rankings of encodings for numerical attributes: e.g., position > area > angle > volume. Yet different people may vary in their ability to read different visualization types, leading to variance in this ranking across individuals not captured by population-level metrics using "average observer" models. One way we can bridge this gap is by recasting classic visual perception tasks as tools for assessing individual performance, in addition to overall visualization performance. In this paper we replicate and extend Cleveland and McGill's graphical comparison experiment using Bayesian multilevel regression, using these models to explore individual differences in visualization skill from multiple perspectives. The results from experiments and modeling indicate that some people show patterns of accuracy that credibly deviate from the canonical rankings of visualization effectiveness. We discuss implications of these findings, such as a need for new ways to communicate visualization effectiveness to designers, how patterns in individuals' responses may show systematic biases and strategies in visualization judgment, and how recasting classic visual perception tasks as tools for assessing individual performance may offer new ways to quantify aspects of visualization literacy. Experiment data, source code, and analysis scripts are available at the following repository: https://osf.io/8ub7t/?view\_only=9be4798797404a4397be3c6fc2a68cc0.
翻译:图形化感知研究通常使用“ 平均观察者” 错误来衡量视觉化编码的有效性, 从而得出数字属性编码的卡通排序: 例如, 位置 > 面积 > 角度 > 数量。 然而, 不同的人读取不同视觉化类型的能力可能不同, 导致人口级指标没有通过“ 平均观察者” 模型衡量的个人排名的差异。 我们缩小这一差距的方法之一是, 除总体可视化绩效外, 将经典视觉化任务作为评估个人性能的工具。 在本文中, 我们复制和扩展克利夫兰和麦吉尔的图形比较实验, 使用巴耶西亚多级回归, 使用这些模型从多个角度探索可视化技能中的个体差异。 实验和建模的结果表明, 一些人显示的准确性模式与视觉化效果的卡通性排序有明显的差异。 我们讨论这些发现的影响, 例如需要以新的方式向设计者传达可视化效果, 个人反应的格局如何显示视觉化判断中的系统偏见和战略, 以及如何重新描绘可视化的视觉认知性认知性任务: 用于评估个人业绩的视觉分析源码 。