Zhao Henan, Bryant Garnett W, Griffin Wesley, Terrill Judith E, Chen Jian
IEEE Trans Vis Comput Graph. 2024 Apr;30(4):1868-1884. doi: 10.1109/TVCG.2022.3232591. Epub 2024 Feb 28.
We present experimental results to explore a form of bivariate glyphs for representing large-magnitude-range vectors. The glyphs meet two conditions: (1) two visual dimensions are separable; and (2) one of the two visual dimensions uses a categorical representation (e.g., a categorical colormap). We evaluate how much these two conditions determine the bivariate glyphs' effectiveness. The first experiment asks participants to perform three local tasks requiring reading no more than two glyphs. The second experiment scales up the search space in global tasks when participants must look at the entire scene of hundreds of vector glyphs to get an answer. Our results support that the first condition is necessary for local tasks when a few items are compared. But it is not enough for understanding a large amount of data. The second condition is necessary for perceiving global structures of examining very complex datasets. Participants' comments reveal that the categorical features in the bivariate glyphs trigger emergent optimal viewers' behaviors. This work contributes to perceptually accurate glyph representations for revealing patterns from large scientific results. We release source code, quantum physics data, training documents, participants' answers, and statistical analyses for reproducible science at https://osf.io/4xcf5/?view_only=94123139df9c4ac984a1e0df811cd580.
我们展示了实验结果，以探索一种用于表示大数值范围向量的二元图形形式。这些图形满足两个条件：（1）两个视觉维度是可分离的；（2）两个视觉维度中的一个使用分类表示（例如，分类颜色映射）。我们评估这两个条件在多大程度上决定了二元图形的有效性。第一个实验要求参与者执行三项局部任务，每项任务只需读取不超过两个图形。第二个实验在全局任务中扩大了搜索空间，此时参与者必须查看数百个向量图形的整个场景才能得到答案。我们的结果支持，在比较少数项目时，第一个条件对于局部任务是必要的。但对于理解大量数据来说还不够。第二个条件对于感知非常复杂数据集的全局结构是必要的。参与者的评论表明，二元图形中的分类特征触发了最优的查看者行为。这项工作有助于实现感知准确的图形表示，以便从大型科学结果中揭示模式。我们在https://osf.io/4xcf5/?view_only=94123139df9c4ac984a1e0df811cd580上发布了源代码、量子物理数据、训练文档、参与者答案和统计分析，以实现可重复科学。
