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arXiv · 2610.04192

Evaluating Just Noticeable Differences in Layered Opacity Visualizations

Caterina Ponti·Shano Liang·Lane Harrison·Alark Joshi
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Abstract

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.

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Caterina Ponti, Shano Liang, Lane Harrison, Alark Joshi. 2026-10-03. Evaluating Just Noticeable Differences in Layered Opacity Visualizations. https://arxiv.org/abs/2610.04192

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