"It's Not a Tumor": A Framework for Capitalizing on Individual Diversity to Boost Target Detection

Even experts routinely miss infrequent targets, such as weapons in baggage scans or tumors in mammograms, because the visual system is not equipped to notice the unusual. To date, limited progress has been made toward improving human factors that mediate such critical diagnostic tasks. Here, we present a novel framework for pairing individuals' estimates to increase target detection. Using a wisdom-of-crowds approach that capitalizes on the visual system's ability to efficiently combine information, we demonstrated how averaging two noninteracting individuals' continuous estimates of whether a briefly presented image contained a prespecified target can significantly boost detection across a range of tasks. Furthermore, we showed how pairing individuals' estimates to maximize decorrelated patterns of performance in one task can optimize performance on a separate task. These results make significant advances toward combating severe deficits in target detection using straightforward applications for maximizing performance within limited pools of observers.