Line attributes such as width and dashing are commonly used to encode information. However, many questions on the perception of line attributes remain, such as how many levels of attribute variation can be distinguished or which line attributes are the preferred choices for which tasks. We conducted three studies to develop guidelines for using stylized lines to encode scalar data. In our first study, participants drew stylized lines to encode uncertainty information. Uncertainty is usually visualized alongside other data. Therefore, alternative visual channels are important for the visualization of uncertainty. Additionally, uncertainty -- e.g., in weather forecasts -- is a familiar topic to most people. Thus, we picked it for our visualization scenarios in study 1. We used the results of our study to determine the most common line attributes for drawing uncertainty: Dashing, luminance, wave amplitude, and width. While those line attributes were especially common for drawing uncertainty, they are also commonly used in other areas. In studies 2 and 3, we investigated the discriminability of the line attributes determined in study 1. Studies 2 and 3 did not require specific application areas; thus, their results apply to visualizing any scalar data in line attributes. We evaluated the just-noticeable differences (JND) and derived recommendations for perceptually distinct line levels. We found that participants could discriminate considerably more levels for the line attribute width than for wave amplitude, dashing, or luminance.

翻译：线条属性（如宽度和虚线）常被用于编码信息。然而，关于线条属性感知的诸多问题仍未解决，例如：可区分的属性变化层级有多少？不同任务中哪种线条属性是更优选择？我们开展了三项研究，旨在制定使用风格化线条编码标量数据的指南。第一项研究中，受试者绘制风格化线条以编码不确定性信息。不确定性通常需与其他数据共同可视化，因此替代性视觉通道对不确定性可视化至关重要。此外，不确定性（如天气预报中的不确定性）是大众熟悉的议题，故研究1将其作为可视化场景。我们根据实验结果确定了绘制不确定性最常用的线条属性：虚线、亮度、振幅和宽度。这些属性虽常用于表示不确定性，在其他领域同样普遍。研究2和3探讨了研究1所确定线条属性的可辨别性。这两项研究不限定具体应用领域，因此其结果适用于任何通过线条属性可视化标量数据的场景。我们评估了恰可察觉差异（JND），并推导出感知上可区分线条层级的建议。结果表明，受试者在宽度属性上可区分的层级数量显著多于振幅、虚线或亮度。