Examining perceptual and conceptual set biases in multiple-target visual search

Abstract Visual search is a common practice conducted countless times every day, and one important aspect of visual search is that multiple targets can appear in a single search array. For example, an X-ray image of airport luggage could contain both a water bottle and a gun. Searchers are more likely to miss additional targets after locating a first target in multiple-target searches, which presents a potential problem: If airport security officers were to find a water bottle, would they then be more likely to miss a gun? One hypothetical cause of multiple-target search errors is that searchers become biased to detect additional targets that are similar to a found target, and therefore become less likely to find additional tar-gets that are dissimilar to the first target. This particular hy-pothesis has received theoretical, but little empirical, support. In the present study, we tested the bounds of this idea by utilizing “big data ” obtained from the mobile application Airport Scanner. Multiple-target search errors were substan-tially reduced when the two targets were identical, suggesting that the first-found target did indeed create biases during sub-sequent search. Further analyses delineated the nature of the biases, revealing both a perceptual set bias (i.e., a bias to find additional targets with features similar to those of the first-found target) and a conceptual set bias (i.e., a bias to find additional targets with a conceptual relationship to the first-found target). These biases are discussed in terms of the im-plications for visual-search theories and applications for pro-fessional visual searchers