Prevalence effects in visual research: Theoretical and practical implications

低流行率搜索形成了一类重要且有问题的视觉搜索任务。这些是 搜索目标很少的任务。许多重要的社会任务，如机场安检或癌症 筛查是一项低患病率的任务。之前的工作，其中大部分来自我们的实验室，已经表明低 流行可能会产生令人不快的影响。最值得注意的是，遗漏(假阴性)错误非常明显 在低流行率时升高。如果搜索的目的是检测某些东西，这是一个明显的问题 罕见但重要的疾病，比如癌症或恐怖威胁。我们以前的工作记录了这种模式 包括细胞学(宫颈癌筛查)在内的许多专家领域的漏诊错误增加， 机场行李检查和乳腺癌检查。错误警报(误报)错误率通常 在低流行率的情况下下降，与错过错误的方向相反。这表明 观察者的决策标准。在低流行率的情况下，观察者变得更不愿意将某事称为 目标。几项研究--我们和其他人--已经表明，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)发展了一种“双重-- 更好地捕捉患病率影响数据的重要方面的“阈值”模型 低患病率的两个影响：(1)决定是否参加的项目的标准的保守转变 是一个目标，以及(2)降低“退出门槛”。退出阈值决定何时退出 观察员结束了一次搜索。过早退出也会增加观察者错过目标的机会。 流行率的影响已经在实验中与搜索的其他方面隔离进行了研究。然而， 在像乳腺癌筛查这样的任务中，其他因素与患病率相互作用。中的四个项目 目前的提案每个都研究这些交互作用中的一种。项目1考察了 “警觉性减退”被认为是一项任务中的时间流逝。在搜索中，观察者 必须保持搜索目标(或多个目标)的内部心理表征。项目2涉及到 以及流行率对这些“目标模板”的影响。人工智能的进展(特别是 深度学习)正在制作协助专家搜索者的工具。然而，一旦部署，这些人工智能工具 并不像理论预测的那样有效。项目3测试了这样的假设，即部分问题是 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(例如肺癌)。项目4探讨了流行率在未报告此类事件中的作用 “偶然发现”。我们再次测试了几种干预措施。这是一项“受使用启发的基础研究” 结果将为执行具有重要社会意义的低流行率任务的专家提供指导。

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.