Abstract
Researchers working across a variety of domains investigate the effects of visual clutter on performance have developed a handful of measures for characterizing this clutter. Although these measures can provide coarse predictions of search performance, they do not account for the properties of search targets and how these interact with clutter. In particular, they do not consider target-background similarity in quantifying the effect of clutter on search performance. However, when search targets and backgrounds share similar features, performance declines (Semizer & Michel, 2017). Here, we propose two new clutter metrics based on different measures of target-background similarity (i.e., exemplar level and category level) to predict the effect of clutter on search performance. Our metrics compute the similarity between target and background features (i.e., orientation subbands) in images while also accounting for size of a search target. The exemplar-level metric quantified the overlap between features of a specific search target (present in the search image) and features of a search background, while the categorical-level metric quantified the overlap between features of a search target category and features of a search background, where the latter can be used to predict search performance when the target is absent. We tested the predictive power of these metrics, along with that of an existing target-agnostic clutter metric, using a set of search data where the task was to detect and locate categorical targets in a set of natural images. Our results demonstrate that both clutter metrics successfully contributed to explaining search performance as a function of the search target. More importantly, these metrics can predict such differences even for scenes in which the target is absent, suggesting that the categorical representation of the target guides the search.