Literature DB >> 24297778

Prevalence effects in newly trained airport checkpoint screeners: trained observers miss rare targets, too.

Jeremy M Wolfe1, David N Brunelli, Joshua Rubinstein, Todd S Horowitz.   

Abstract

Many socially important search tasks are characterized by low target prevalence, meaning that targets are rarely encountered. For example, transportation security officers (TSOs) at airport checkpoints encounter very few actual threats in carry-on bags. In laboratory-based visual search experiments, low prevalence reduces the probability of detecting targets (Wolfe, Horowitz, & Kenner, 2005). In the lab, this "prevalence effect" is caused by changes in decision and response criteria (Wolfe & Van Wert, 2010) and can be mitigated by presenting a burst of high-prevalence search with feedback (Wolfe et al., 2007). The goal of this study was to see if these effects could be replicated in the field with TSOs. A total of 125 newly trained TSOs participated in one of two experiments as part of their final evaluation following training. They searched for threats in simulated bags across five blocks. The first three blocks were low prevalence (target prevalence ≤ .05) with no feedback; the fourth block was high prevalence (.50) with full feedback; and the final block was, again, low prevalence. We found that newly trained TSOs were better at detecting targets at high compared to low prevalence, replicating the prevalence effect. Furthermore, performance was better (and response criterion was more "liberal") in the low-prevalence block that took place after the high-prevalence block than in the initial three low-prevalence blocks, suggesting that a burst of high-prevalence trials may help alleviate the prevalence effect in the field.

Keywords:  airport security; criterion shift; error rates; prevalence effects; visual attention; visual search

Mesh:

Year:  2013        PMID: 24297778      PMCID: PMC3848386          DOI: 10.1167/13.3.33

Source DB:  PubMed          Journal:  J Vis        ISSN: 1534-7362            Impact factor:   2.240


  32 in total

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  31 in total

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