Radiologists miss as many as 30% of cancers in their examinations (Berlin, 1994; Bird, Wallace, & Yankaskas, 1992). Eye-tracking studies indicate that more than one-third of these misses are the result of an incomplete search in which the radiologist fails to fixate the area around the cancer (Bird et al., 1992; Krupinski, 1995). One possible reason why misses are so high is because cancers are quite rare in scans.
Research suggests that miss rates are far higher for low- than for high-prevalence search targets (Ishibashi, Kita, & Wolfe, 2012; Mitroff & Biggs, 2014; Rich et al., 2008; Schwark, Sandry, Macdonald, & Dolgov, 2012; Wolfe et al., 2007; Wolfe, Horowitz, & Kenner, 2005). Similarly to what occurs in radiology, many of these misses are selection errors (Peltier & Becker, 2016a) whereby the observer fails to inspect the target before responding target absent. To explain this pattern, researchers have posited that rare targets result in low quitting thresholds (Wolfe et al., 2005; Wolfe & Van Wert, 2010). With low quitting thresholds, observers inspect less of the display before responding that the target is absent, resulting in faster reaction times but many misses (Gur et al., 2003; Rich et al., 2008; Schwark et al., 2012; Schwark, Macdonald, Sandry, & Dolgov, 2013; Schwark, Sandry, & Dolgov, 2013; Van Wert, Horowitz, & Wolfe, 2009; Wolfe et al., 2005; Wolfe et al., 2007).
Because most errors are caused by an incomplete search (Peltier & Becker, 2016a), a manipulation that delays search termination might entice observers to perform a more thorough search, thereby increasing the hit rate. On the basis of this logic, Wolfe et al. (2007) attempted to alleviate the low prevalence effect (LPE) by giving “speeding tickets” when responses were made too quickly. This manipulation successfully increased latency to absent responses. Unexpectedly, there was no change in accuracy, criterion, or sensitivity, despite the increased response time. One possible explanation for this pattern of results is that observers made an internal target-absent decision and ceased active search but delayed making a target-absent response to avoid a speeding ticket. These results suggest that simple delay manipulations are particularly ineffective; searches take longer but are no more accurate.
Wolfe et al. (2007) conducted seven experiments and found only one technique that was successful in increasing hit rates in low-prevalence searches. This technique involved interspersing low-prevalence search blocks without feedback with blocks of high-prevalence trials with trial-by-trial performance-based feedback (information about correct or incorrect responses). Although this approach increased target detections in the low-prevalence search blocks, sensitivity did not increase. Instead, the increased hit rate was accompanied by increased false alarm rates, a signature of a shift toward a more liberal decision criterion or an informed increase in target-present guessing as prevalence increases (Peltier & Becker, 2017a, 2017b). This type of criterion shift may not be desirable, particularly because there are many opportunities for false alarms in a low-prevalence search. If this method were applied to a real-world situation, observers’ workloads would increase to allow for “dummy blocks” of high-prevalence searches, and the manipulation would result in a higher proportion of false alarms. Ideally, a manipulation that reduces the LPE would increase hits without an increase in false alarms.
Although Wolfe et al.’s (2007) interspersed high-prevalence block experiment did not achieve this ideal, it did demonstrate the important role that feedback can play in setting quitting thresholds. Indeed, according to an influential model of quitting thresholds, feedback about misses is critical to adaptably setting quitting thresholds (Chun & Wolfe, 1996; Danielmeier & Ullsperger, 2011). Under this theory, feedback about a missed target provides information that the previous search was not adequate, thereby increasing quitting thresholds. In low-prevalence and real-world search scenarios, there may be insufficient feedback about misses to set optimal quitting thresholds. In low-prevalence search tasks, the sparse targets result in few opportunities for feedback about misses. In real-world search tasks, the ability to give accurate feedback about one’s performance is often unavailable because the ground truth is unknown.
Given the important role that feedback can play in setting quitting thresholds, as well as the problems associated with providing performance-based feedback (feedback that a target was missed) in low-prevalence and real-world search scenarios, here we attempt to improve search by providing a different form of feedback, namely “eye movement feedback (EMF),” that provides real-time feedback about an observer’s scanning pattern, allowing an observer to see what aspects of the scene they have and have not searched. The ability to provide this type of EMF requires no foreknowledge of a target’s presence or absence and is not impacted by target prevalence rates. Even so, it may act as a substitute for performance-based feedback and may influence quitting thresholds to make searches more complete.
To investigate this possibility, across four experiments, we provided observers with EMF while they searched for rare targets. Three of these experiments used an eye tracker to provide real-time feedback about the portions of the scene that had been inspected during the trial. A fourth automatically revealed sections of the display one at a time, thereby providing observers information about the amount of the scene that had not yet been searched. Although our initial experiment showed promise for the EMF method, across the experiments and manipulations, it became clear that the EMF approach is not a panacea that mitigates the high miss rates that occur when search targets are rare.