How Ordered Is It? On the Perceptual Orderability of Visual Channels

Abstract

The design of effective glyphs for visualisation involves a number of different visual encodings. Since spatial position is usually already specified in advance, we must rely on other visual channels to convey additional relationships for multivariate analysis. One such relationship is the apparent order present in the data. This paper presents two crowdsourcing empirical studies that focus on the perceptual evaluation of orderability for visual channels, namely Bertin's retinal variables. The first study investigates the perception of order in a sequence of elements encoded with different visual channels. We found evidence that certain visual channels are perceived as more ordered (for example, value) while others are perceived as less ordered (for example, hue) than the measured order present in the data. As a result, certain visual channels are more/less sensitive to disorder. The second study evaluates how visual orderability affects min and max judgements of elements in the sequence. We found that visual channels that tend to be perceived as ordered, improve the accuracy of identifying these values.

Crowdsourcing Versus the Laboratory: Towards Human-Centered Experiments Using the Crowd

“…As a result, experiments often employ a number of techniques. Consistency checks are conducted as a post process on the experimental results to ensure reasonably consistent answers for the same question or a set of sufficiently diverse answers [4,5,38]. Given drastically different answers for the exact same question (or the exact same answer for all questions even though they differ substantially), one could assume that the participants were not invested in the experimental tasks.…”

“…https://www.mturk.com/ last accessed 14 Jun 2017 4. http://www.crowdflower.com/ last accessed 14 Jun 2017.…”

Emoji and Chernoff - A Fine Balancing Act or are we Biased?

“…In our designs the main visual channels to be considered are size, shape, orientation and curvature. For size, shape and orientation we follow the same approach to compute JND thresholds as used by Chung et al [13]. Size is therefore mapped to circle radius, following results which demonstrate how perception of size (e.g., area) is logarithmic and can be modelled using Weber-Fechners Law [3].…”

“…Shape differences are measured using image moment statistics [27]. Orientation follows the more conservative measurement proposed by Chung et al [13], with changes between consecutive elements of at least 11.3°. We similarly avoid ambiguous orientations.…”

Exploring the Potential of Physical Visualizations

“…Based on this work a great amount of studies has been conducted to extend and validate the variables (e.g., Cleveland and McGill [1984, 1985, 1987; Mackinlay [1986]) and is still an important topic in current research (e.g., Chung et al [2015Chung et al [ , 2016; Skau and Kosara [2016]). Among other things those studies led to a ranking of perceptual tasks which is useful for designers of visualizations by supporting the data mapping process.…”