Bivariate Separable-Dimension Glyphs can Improve Visual Analysis of Holistic Features

TL;DR

This study evaluates five bivariate glyph designs to improve visual analysis of holistic features, revealing that certain combinations like length-texture enhance perception, while traditional integral pairs perform poorly unless combined with color.

Contribution

The paper provides an empirical evaluation of bivariate glyphs based on psychophysics principles, identifying effective design pairs for visual analysis tasks.

Findings

length-texture glyphs facilitate global pattern detection

length-color glyphs yield highest accuracy and speed

integral dimensions perform poorly unless combined with color

Abstract

We introduce the cause of the inefficiency of bivariate glyphs by defining the corresponding error. To recommend efficient and perceptually accurate bivariate-glyph design, we present an empirical study of five bivariate glyphs based on three psychophysics principles: integral-separable dimensions, visual hierarchy, and pre-attentive pop out, to choose one integral pair ($length_y-length_x$), three separable pairs ($length-color$, $length-texture$, $length_y-length_y$), and one redundant pair ($length_y-color/length_x$). Twenty participants performed four tasks requiring: reading numerical values, estimating ratio, comparing two points, and looking for extreme values among a subset of points belonging to the same sub-group. The most surprising result was that $length-texture$ was among the most effective methods, suggesting that local spatial frequency features can lead to global pattern detection that facilitate visual search in complex 3D structure. Our results also reveal the following: $length-color$ bivariate glyphs led to the most accurate answers and the least task execution time, while $length_y-length_x$ (integral) dimensions were among the worst and is not recommended; it achieved high performance only when pop-up color was added.