The Statistical Saliency Model Can Choose Colors for Items on Maps

Abstract

We show how a model of visual salience that was originally developed to explain human visual search performance can suggest display design choices that reduce search time for items. The statistical saliency model proposes that the time to find an item on a visual display depends on the similarity between a target item's features and the statistical distribution of display features. In the present study, observers rated the amount of display clutter on a set of MapQuest maps containing colored pushpins. We identified a group of “high-clutter” maps and a group of “low-clutter” maps. Next, we used the statistical saliency model to choose colors for new pushpins placed on those maps. We show that the model's color assignments depend on the colors the display contains. Map designs produced using this method were tested in a visual search experiment. Search time decreased as a pushpin's predicted salience increased. In addition, choosing low salience colors led to slower search times for items on high-clutter displays than for items on low-clutter displays. The method we describe works with real images and does not require any parameter fitting. This study provides evidence that computational models of visual perception have potential as display design tools.