Likelihood Systems Can Improve Hit Rates in Low-Prevalence Visual Search Over Binary Systems.

Abstract

Objective: To study the performance consequences of binary versus likelihood decision support systems in low-prevalence visual search.

Background: Hit rates in visual search are often low if target prevalence is low, an issue that is relevant for numerous real-world visual search tasks (e.g., luggage screening and medical imaging). Given that binary decision support systems produce many false alarms at low prevalence, they have often been discounted as a solution to this low-prevalence problem. By offering additional information about the certainty of target-present indications through splitting these into warnings and alarms, likelihood-based systems could potentially boost hit rates without raising the number of false alarms.

Method: We used a simulated medical search task with low target prevalence in a paradigm where participants sequentially uncovered parts of the stimulus with their mouse. In two sessions, participants completed the task either while being supported by a binary or a likelihood system.

Results: Hit rates were higher when interacting with the likelihood systems than with the binary system, at no cost of higher false alarms.

Conclusion: Likelihood systems are a promising way to tackle the low-prevalence problem, and might further be an effective means to make systems more transparent.

Application: Simple-to-process information about system certainty for each case might be a solution to low hit rates in domains with low target prevalence, such as radiology.