发表机构
University of San Francisco; Worcester Polytechnic Institute（旧金山大学; 伍斯特理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过受控两选一强制选择实验评估分层透明度可视化中的最小可觉差异，发现配色方案间差异小，个体差异大，中间范围表现稳定，极端值易出错，并提供研究材料与数据。
AI 中文摘要
透明度是数据可视化中广泛使用的通道，但与颜色、长度、大小等通道相比，它仍然不太被理解。Meng等人最近的研究调查了透明度在不同竞争性配色方案中的影响，发现某些配色方案与更高的参与者准确性相关。我们在一个受控的两选一强制选择设置中进一步检验这些效应，以确定透明度差异在所有可能的透明度比较范围内是否真正相等。在一项包含96次试验的受试者内研究中，包括两种竞争性配色方案（来自Meng等人的最佳和最差方案）和48对透明度对，我们发现配色方案之间的差异很小，但个体间的准确性差异较大。此外，结果显示在中间透明度范围内表现稳定，而在比较极端值时出现更多错误。我们讨论了设计指南和进一步研究的潜在影响，并在以下网址提供了我们的研究材料、分析脚本和数据：此https URL。
英文摘要
Opacity is a widely used channel in data visualization, but it remains less well understood compared to channels such as color, length, size, etc. Recent work from Meng et al. investigated the impact of opacity across competing color schemes, finding that certain color schemes were associated with better participant accuracy. We examine these effects further in a controlled two-alternative forced-choice setup to determine whether opacity differences are truly equal across possible opacity comparison ranges. In a within-subjects study with 96 trials, including two competing color schemes (best and worst from Meng et al.) and 48 opacity pairs, we find little differences between color schemes but larger individual differences in accuracy. Further, results show stable performance in middle opacity ranges, with more errors occurring when comparing extreme values. We discuss potential implications for design guidelines and further study and make our study materials, analysis scripts, and data available at https://osf.io/zv9dx/overview?view_only=38d03cea1b3d42788593e3e6b1016cfd.
