Academic journal article Attention, Perception and Psychophysics

Perceptual Failures in the Selection and Identification of Low-Prevalence Targets in Relative Prevalence Visual Search

Academic journal article Attention, Perception and Psychophysics

Perceptual Failures in the Selection and Identification of Low-Prevalence Targets in Relative Prevalence Visual Search

Article excerpt

Published online: 12 September 2014

© The Psychonomic Society, Inc. 2014

Abstract Previous research has shown that during visual search tasks target prevalence (the proportion of trials in which a target appears) influences both the probability that a target will be detected, and the speed at which participants will quit searching and provide an 'absent' response. When prevalence is low (e.g., target presented on 2 % of trials), participants are less likely to detect the target than when prevalence is higher (e.g., 50 % of trials). In the present set of experiments, we examined perceptual failures to detect low prevalence targets in visual search. We used a relative prevalence search task in order to be able to present an overall 50%target prevalence and thereby prevent the results being accounted for by early quitting behavior. Participants searched for two targets, one of which appeared on 45 % of trials and another that appeared on 5 % of trials, leaving overall target prevalence at 50 %. In the first experiment, participants searched for two dissimilar targets; in the second experiment, participants searched for two similar targets. Overall, the results supported the notion that a reduction in prevalence primarily influenced perceptual failures of identification, rather than of selection. Together, these experiments add to a growing body of research exploring how and why observers fail to detect low prevalence targets, especially in real-world tasks in which some targets are more likely to appear than others.

Keywords Visual search . Relative prevalence . Eyemovements

Introduction

A vital component of visual search is the perceptual selection and identification of objects that could be the target (Schwark, MacDonald, Sandry, & Dolgov, 2013; Wolfe, Cave, & Franzel, 1989; Wolfe, 2007). Previous research examining the eye movement behavior of radiographers searching Xrays for tumors has explored how this perceptual selection and identification process can result in the failure to detect targets (Nodine & Kundel, 1987). Observers can fail to select (i.e., directly fixate) a target, thereby leading them to fail to detect that target. In addition, merely fixating a target does not guarantee that it will be detected: observers can also fail to identify a fixated target, and incorrectly decide that the object is not a target (see also Cain, Adamo, & Mitroff, 2013).1

In the present set of experiments, we explored how failures to perceptually select and identify objects in visual search are modulated by the prevalence of a search target. Target prevalence refers to the proportion of trials in which that target is presented, and previous work (Wolfe, Horowitz, & Kenner, 2005) has demonstrated that, when prevalence is low (e.g., a target is presented on 2 % of trials), participants are less likely to detect a target than when prevalence is higher (50 % of trials). Early accounts of the prevalence effect suggested that it was the result of motor priming of target-absent responses (Fleck & Mitroff, 2007), though this was discounted as a complete explanation after it was demonstrated that the prevalence effect was still present when motor priming was controlled for (Godwin, Menneer, Cave, Helman, et al., 2010; Wolfe et al., 2007).

More recently, the prevalence effect has been explained in terms of a multiple-decision model (Wolfe & VanWert, 2010; Wolfe et al., 2007). This model explains prevalence effects in terms of two discrete decisions. The first of these occurs at a perceptual level (Schwark et al., 2013), and relates to the object currently under examination, addressing whether that object is a target (or not). When prevalence is low, the search system becomes biased towards responding 'no' to this question. The second of these decisions occurs at the level of the search array as a whole, and has been modeled as the accumulation of evidence towards a quitting threshold. …

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