塑造它：一种设计形状调色板的实证基础方法
Shape It Up: An Empirically Grounded Approach for Designing Shape Palettes
AI总结：
本文通过四项实验评估39种形状在三类任务中的感知效率，构建基于形状成对关系的模型与设计工具，为多类散点图提供优化感知效率的形状调色板推荐。
摘要：
形状通常用于在多类散点图中区分类别。然而，现有的选择有效形状调色板的指南在很大程度上依赖于直觉，并且没有考虑随着类别数量的增加，这些需求可能会如何变化。尽管与颜色相比，形状的数量是有限的，但它们无法用数值空间来表示，这使得很难为形状选择提出一般性指南，也难以阐明设计师制作的形状调色板的设计启发式方法。本文提出了一系列四个实验，评估了39种形状在三个任务中的效率——相对均值判断任务、专家选择和数据相关性估计。鉴于结果的复杂性和纠缠性，我们不再依赖传统特征进行建模，而是构建了一个模型，并引入了相应的设计工具，为形状编码提供推荐。形状的感知有效性在特定对之间显著变化，某些形状可能会提高感知效率和准确性。然而，性能的变化并不能很好地映射到形状的经典特征，如角度、填充或凸包。我们基于在实验中测量的形状之间的成对关系以及所需的形状数量开发了一个模型，以智能地为给定设计推荐形状调色板。该工具为设计师提供了对形状选择的控制权，同时纳入了我们研究中捕获的感知性能的实证元素。我们的模型推进了对可视化环境中形状感知的理解，并为可视化设计中的高级形状使用提供了实用的设计指南，以优化感知效率。
英文摘要：
Shape is commonly used to distinguish between categories in multi-class scatterplots. However, existing guidelines for choosing effective shape palettes rely largely on intuition and do not consider how these needs may change as the number of categories increases. Although shapes can be a finite number compared to colors, they can not be represented by a numerical space, making it difficult to propose a general guideline for shape choices or shed light on the design heuristics of designer-crafted shape palettes. This paper presents a series of four experiments evaluating the efficiency of 39 shapes across three tasks -- relative mean judgment tasks, expert choices, and data correlation estimation. Given how complex and tangled results are, rather than relying on conventional features for modeling, we built a model and introduced a corresponding design tool that offers recommendations for shape encodings. The perceptual effectiveness of shapes significantly varies across specific pairs, and certain shapes may enhance perceptual efficiency and accuracy. However, how performance varies does not map well to classical features of shape such as angles, fill, or convex hull. We developed a model based on pairwise relations between shapes measured in our experiments and the number of shapes required to intelligently recommend shape palettes for a given design. This tool provides designers with agency over shape selection while incorporating empirical elements of perceptual performance captured in our study. Our model advances the understanding of shape perception in visualization contexts and provides practical design guidelines for advanced shape usage in visualization design that optimize perceptual efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
