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）通常用于编码信息。然而，关于线条属性感知的许多问题仍有待解答，例如可以区分的属性变化层级数量，或针对不同任务应优先选择哪些线条属性。我们开展了三项研究以制定使用风格化线条编码标量数据的指导原则。第一项研究中，参与者绘制风格化线条来编码不确定性信息。不确定性通常需要与其他数据同时可视化，因此替代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）对大多数人而言是熟悉的话题。因此我们在第一项研究的可视化场景中选取了该主题。通过分析研究结果，我们确定了绘制不确定性时最常用的四种线条属性：虚线、亮度、波幅和宽度。尽管这些线条属性在绘制不确定性时尤为常见，但它们同样广泛应用于其他领域。在第二和第三项研究中，我们探究了第一项研究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。我们评估了恰好可察觉差异（JND），并推导出关于感知上显著线条层级的建议。结果表明，参与者对宽度属性的辨别层级数量显著多于波幅、虚线或亮度属性。