@article {1522736,
	title = {Visual search errors are persistent in a laboratory analog of the incidental finding problem},
	journal = {Cogn Res Princ Implic},
	volume = {5},
	number = {1},
	year = {2020},
	month = {2020 Jul 29},
	pages = {32},
	abstract = {When radiologists search for a specific target (e.g., lung cancer), they are also asked to report any other clinically significant "incidental findings" (e.g., pneumonia). These incidental findings are missed at an undesirably high rate. In an effort to understand and reduce these errors, Wolfe et al. (Cognitive Research: Principles and Implications 2:35, 2017) developed "mixed hybrid search" as a model system for incidental findings. In this task, non-expert observers memorize six targets: half of these targets are specific images (analogous to the suspected diagnosis in the clinical task). The other half are broader, categorically defined targets, like "animals" or "cars" (analogous to the less well-specified incidental findings). In subsequent search through displays for any instances of any of the targets, observers miss about one third of the categorical targets, mimicking the incidental finding problem. In the present paper, we attempted to reduce the number of errors in the mixed hybrid search task with the goal of finding methods that could be deployed in a clinical setting. In Experiments 1a and 1b, we reminded observers about the categorical targets by inserting non-search trials in which categorical targets were clearly marked. In Experiment 2, observers responded twice on each trial: once to confirm the presence or absence of the specific targets, and once to confirm the presence or absence of the categorical targets. In Experiment 3, observers were required to confirm the presence or absence of every target on every trial using a checklist procedure. Only Experiment 3 produced a marked decline in categorical target errors, but at the cost of a substantial increase in response time.},
	issn = {2365-7464},
	doi = {10.1186/s41235-020-00235-4},
	author = {Nartker, Makaela S and Alaoui-Soce, Abla and Wolfe, Jeremy M}
}
