Prevalence effects in visual research: Theoretical and practical implications

低流行率搜索形成了一类重要的和有问题的视觉搜索任务。这些是 搜索目标罕见的任务。许多社会重要的任务，如机场安全或癌症 筛查是低流行率的任务。以前的工作，大部分来自我们的实验室，已经表明， 流行可能具有不期望的效果。最值得注意的是，漏失（假阴性）错误是显著的。 在低发病率下升高。如果搜索的目的是为了检测某些东西， 像癌症或恐怖威胁一样罕见但重要。我们之前的工作已经记录了这种模式， 包括细胞学（宫颈癌筛查）在内的一些专家领域的失误增加， 机场行李检查和乳腺癌检查。假警报（误报）错误率通常 在低流行率时下降，与未命中错误方向相反。这表明， 观察者的判断标准。在低流行率下，观察者变得更不愿意称之为 目标我们和其他人的一些研究表明，这种“保守”的标准转变并不是 足以解释整个流行效应。Wolfe和VanWert（2010）开发了一种“双重- 阈值”模型，该模型通过提出以下建议， 低流行率的两个影响：（1）决定是否参加项目的标准的保守转变 是一个目标，（2）降低“戒烟门槛”。退出阈值决定何时 观察员结束搜索。过早退出也会增加观察者错过目标的机会。 流行效应的研究是在与其他方面的搜索实验隔离的情况下进行的。然而，在这方面， 在乳腺癌筛查等任务中，其他因素与患病率相互作用。四个项目在 目前的建议，每个调查这些相互作用之一。项目1研究了 在任务中随着时间的流逝而出现的“警惕性下降”。在搜索中，观察员 必须对搜索目标（或多个目标）保持一种内在的心理表征。项目2关注 流行程度对这些“目标模板”的影响。人工智能的进步（特别是 深度学习）正在开发工具来帮助专家搜索。然而，一旦部署，这些人工智能工具 比理论预测的效果要差项目3测试的假设，部分问题是 低流行率的另一个副作用，该项目测试了一种潜在的干预措施。最后，临床医生， 搜索一种类型的目标（例如肺炎）应该报告其他可能的迹象 问题（如肺癌）。项目4探讨了流行率在未报告此类疾病中的作用， “偶然发现”我们再次测试了几种干预措施。这是一项“基于使用的基础研究”， 研究结果将为专家执行具有社会重要性的低流行率任务提供指导。

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.