Locatability and Locatability Robustness of Visual Variables in Single Target Localization

TL;DR

This work tackles the lack of empirical grounding for locating a target among many visual items by evaluating seven visual variables across large set sizes and two layout types. Using two crowdsourced experiments and Bayesian analysis, it shows that no single variable is immune to increases in set size, and it reveals systematic differences in locatability and robustness tied to target location and grid versus non-grid layouts. The authors introduce locatability and locatability robustness as task-specific metrics, provide an empirical variable ranking, and offer design implications and a web tool to apply the findings. Overall, the study bridges visual search theory and practical visualization design, highlighting layout choices and color-based variables as key levers for efficient localization in dense displays.

Abstract

Finding a particular object in a display is important for viewers in many visualizations, for example, when reacting to brushing or to a highlighted object. This can be enabled by making the target object different in one of the visual variables that determine the object's appearance; for example, by changing its color or size. Certain interpretations of the visual search literature have promoted the view that using visual variables such as hue-often labeled as preattentive-would make the target object automatically "popout," implying that an object can be located almost instantly, regardless of the number of objects in the display. In this paper we present a study that serves as a bridge between the extensive visual search literature and visualization, establishing empirical base measurements for the localization task. By testing displays with up to hundreds of objects, we are able to show that none of the common visual variables is immune to the increase in the number of objects. We also provide the first empirically informed comparisons between visual variables for this task in the context of visualization, and show how different visual variables have varying robustness with respect to two additional dimensions: the location of the target and the overall visual arrangement (layout). A free copy of this paper and all supplemental materials are available on our online repository: https://osf.io/z68ak/overview.

Figures (14)

• Figure 1: The target and distractor for each visual variable.
• Figure 2: Left: illustration of parafovea and fovea (orange), redrawn from wikimedia_Macula. Right: area division for target location (orange: Center, white: Periphery).
• Figure 3: Flowchart of an example trial with Luminance in our study.
• Figure 4: Experiment 1: Estimated Completion Time (black dot and floating numbers, in milliseconds) and 95% High-Density Interval (HDI, whiskers) for each visual variable. Colored violins show data distribution (not estimated mean distribution). A rounded rectangle outline enclosing several conditions indicates that they are not statistically distinguishable. All other pairwise comparisons are distinguishable with probability $>95\%$.
• Figure 5: Experiment 1: Task CT for different set sizes (left), target locations (middle), and layouts (right) in experiment one. All pairwise comparisons $>95\%$ probability. Other notation as in Figure \ref{['fig:H4']}