An individual differences approach to improving low target prevalence visual search performance

"Critical real-world visual search tasks such as radiology and baggage screening rely on the detection of rare targets that may only be present on as few as .3% of searches (Gur et al., 2004). When targets are rare, observers search for a shorter amount of time and miss targets more often than when targets are common, a phenomenon known as the low prevalence effect (LPE). Given the real-world importance of the detection of low prevalence targets, researchers have attempted to improve search performance. There have been several experimental attempts to reduce the LPE, but none have been wholly successful, as even the best methods have increased hits at the cost of more false alarms. As an alternative to improving visual search performance through experimental manipulations, researchers have recently started using an individual differences approach to predict those who would be best at rare target detection. The individual differences approach has shown that it is possible to predict low prevalence target detection using working memory capacity (WMC) (Peltier & Becker, 2016b; Schwark et al., 2012) and moderate prevalence target detection using a personality assessment (Biggs, Clark, & Mitroff, 2017) and vigilance (Adamo, Cain, & Mitroff, 2016). Experiment 1 expands on the previous research by predicting low prevalence visual search performance using measures of WMC, near transfer high prevalence visual search accuracy, vigilance, attentional control, and introversion. The regression using these predictors accounts for 52% of the variance in accuracy. Experiment 2 addresses practical and theoretical limitations of Experiment 1 by replicating the original finding, including new potential predictors of low prevalence search performance (fluid intelligence, task unrelated thought frequency, and far transfer search accuracy), using more realistic search stimuli to increase external validity, and using eye tracking to investigate how individual differences relate to specific components of performance. The results show that near transfer search, far transfer search, WMC, introversion, and fluid intelligence account for 53% of the variance in accuracy in a more realistic low prevalence search. Using the beta weights from Experiment 1's significant predictors and each observer's score on the corresponding measures in Experiment 2, I find that the old predictors account for 42% of the variance in a novel search task's accuracy. Finally, the eye-tracking results show that we can significantly predict quitting thresholds (the number of items inspected before terminating search), selection error rates (misses caused by never inspecting the target), identification error rates (misses caused by misidentifying an inspected target), item re-inspection rates, target decision times, and distractor decision times. I conclude that the individual differences approach has the potential to be a highly effective tool in selecting those who are most likely to perform at a high level in real-world searches."--Pages ii-iii.