Low prevalence searches form an important and problematic class of visual search tasks. These are tasks where the search target is rare. Many socially important tasks like airport security or cancer screening are low prevalence tasks. Previous work, much of it from our lab, has shown that low prevalence can have undesirable effects. Most notably, miss (false negative) errors are markedly elevated at low prevalence. This is a clear problem if the purpose of the search is to detect something rare but important like cancer or a terrorist threat. Our previous work has documented this pattern of increased miss errors in a number of expert domains including cytology (cervical cancer screening), airport baggage screening, and breast cancer screening. False alarm (false positive) error rates typically decline at low prevalence, moving in the opposite direction from miss errors. This indicates a shift in the observer?s decision criterion. At low prevalence, observers become more reluctant to call something a target. Several studies ? ours and others - have shown that this ?conservative? criterion shift is not adequate to explain the entire prevalence effect. Wolfe and VanWert (2010) developed a ?Dual- Threshold? model that better captures the important aspects of the prevalence effect data by proposing two effects of low prevalence: (1) the conservative shift in the criterion for deciding if an attended item is a target, and (2) a lowering of the ?quitting threshold.? The quitting threshold determines when observers end a search. Quitting too soon also increases the chance that the observer will miss a target. Prevalence effects have been studied in experimental isolation from other aspects of search. However, in tasks like breast cancer screening, other factors interact with prevalence. The four projects in the present proposal each investigate one of these interactions. Project 1 examines the relationship of prevalence to the ?vigilance decrements? that are seen as time elapses in a task. In search, observers must maintain an internal, mental representation of the search target (or targets). Project 2 is concerned with the impact of prevalence on these ?target templates?. Advances in artificial intelligence (notably deep learning) are producing tools to assist expert searchers. However, once deployed, these AI tools have been less effective than theory predicts. Project 3 tests the hypothesis that part of the problem is another side-effect of low prevalence and the project tests a potential intervention. Finally, clinicians, searching for one type of target (e.g. pneumonia) are supposed to report signs of other possible problems (e.g. lung cancer). Project 4 probes the role of prevalence in the failure to report such ?incidental findings?. Again, we test several interventions. This is ?use-inspired, basic research? whose results will provide guidance for experts performing socially important low prevalence tasks.

Public Health Relevance

Important tasks like breast cancer screening involve visual search for rare (?low prevalence?) targets but, unfortunately, low prevalence is known to increase the percentage of targets that are missed even by well-trained experts. In a task like breast cancer screening, prevalence interacts with other factors like observer vigilance or the effectiveness of an artificial intelligence tool. This proposal studies four of these interactions with the goal of counteracting the malign effects of prevalence; thus making it possible for experts to perform their critical search tasks more effectively.

National Institute of Health (NIH)
National Eye Institute (NEI)
Research Project (R01)
Project #
Application #
Study Section
Cognition and Perception Study Section (CP)
Program Officer
Wiggs, Cheri
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Brigham and Women's Hospital
United States
Zip Code
Chin, Michael D; Evans, Karla K; Wolfe, Jeremy M et al. (2018) Inversion effects in the expert classification of mammograms and faces. Cogn Res Princ Implic 3:31
Wolfe, Jeremy M; Utochkin, Igor S (2018) What is a preattentive feature? Curr Opin Psychol 29:19-26
Boettcher, Sage E P; Drew, Trafton; Wolfe, Jeremy M (2018) Lost in the supermarket: Quantifying the cost of partitioning memory sets in hybrid search. Mem Cognit 46:43-57
Kok, Ellen M; Aizenman, Avi M; Võ, Melissa L-H et al. (2017) Even if I showed you where you looked, remembering where you just looked is hard. J Vis 17:2
Wolfe, Jeremy M; Alaoui Soce, Abla; Schill, Hayden M (2017) How did I miss that? Developing mixed hybrid visual search as a 'model system' for incidental finding errors in radiology. Cogn Res Princ Implic 2:35
Cunningham, Corbin A; Drew, Trafton; Wolfe, Jeremy M (2017) Analog Computer-Aided Detection (CAD) information can be more effective than binary marks. Atten Percept Psychophys 79:679-690
Drew, Trafton; Boettcher, Sage E P; Wolfe, Jeremy M (2017) One visual search, many memory searches: An eye-tracking investigation of hybrid search. J Vis 17:5
Aizenman, Avi; Drew, Trafton; Ehinger, Krista A et al. (2017) Comparing search patterns in digital breast tomosynthesis and full-field digital mammography: an eye tracking study. J Med Imaging (Bellingham) 4:045501
Sareen, Preeti; Ehinger, Krista A; Wolfe, Jeremy M (2016) CB Database: A change blindness database for objects in natural indoor scenes. Behav Res Methods 48:1343-1348
Josephs, Emilie L; Draschkow, Dejan; Wolfe, Jeremy M et al. (2016) Gist in time: Scene semantics and structure enhance recall of searched objects. Acta Psychol (Amst) 169:100-108

Showing the most recent 10 out of 69 publications