# Prevalence effects in visual research: Theoretical and practical implications

> **NIH NIH R01** · BRIGHAM AND WOMEN'S HOSPITAL · 2020 · $16,110

## Abstract

In medical exams, “incidental findings” are findings that should be noted but that are incidental to the
purpose of the exam (e.g. looking for pneumonia and unexpectedly finding cancer). Missed incidental
findings can have significant medical and legal consequences. These errors can be considered as
“satisfaction of search” errors and are closely related to “inattentional blindness” in basic vision
research. Such errors occur regularly in daily life, as when, after a search of the supermarket, you
arrive home with the tomatoes, but not the basil. The goal of this proposal is to understand the
conditions that produce incidental finding errors in socially-important search tasks and to use that
understanding to generate interventions that can improve the efficiency of those searches and reduce
failure rates. We will develop a laboratory model of incidental findings built on aspects of search
behavior studied extensively in our lab in the prior grant period: First, we have studied “hybrid search”
tasks in which observers search for multiple types of target at the same time, combining visual search
and memory search. Second, we pioneered the study of human “foraging” search - search for an
unknown numbers of instances of one target type (e.g. picking berries). Combining those two
paradigms, we have studied “hybrid foraging” in which observers search for an unknown number of
instances of multiple targets (analogous to some medical situations). Finally, we have extensively
studied `prevalence effects'; the finding that rare items are found less successfully in search, simply
as a function of their low prevalence. We have documented these prevalence effects in the lab, in
baggage screening and in cancer screening. Building on this prior work, there are three specific aims:
1) We will combine hybrid search, foraging, and low prevalence methods into a model system to test
 the hypothesis that the structure of some clinical search tasks makes it more likely that incidental
 findings will be missed. We will compare our model system to radiologists' behavior and we will
 assess interventions to reduce these false negative errors.
2) We will use a second model system to study the impact of vigilance/time-on-task and interruption.
 Here we use signal detection methods to distinguish changes in detectability from criterion shifts.
3) Our Guided Search (GS) model has been a leading account of search performance in classic
 search tasks. The third aim is to have a GS model that can predict conditions that will produce
 high rates of incidental finding errors. More importantly, the model will be a tool that can be used
 to identify testable conditions that might minimize those errors.
Our experiments will provide insights into fundamental processes of visual search and can lead to
interventions that can be used to reduce a dangerous type of medical error.

## Key facts

- **NIH application ID:** 10181436
- **Project number:** 3R01EY017001-12S1
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Jeremy M Wolfe
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $16,110
- **Award type:** 3
- **Project period:** 2020-03-01 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10181436

## Citation

> US National Institutes of Health, RePORTER application 10181436, Prevalence effects in visual research: Theoretical and practical implications (3R01EY017001-12S1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10181436. Licensed CC0.

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