Prevalence effects in visual search: Theoretical and practical implications

描述（由申请人提供）：考虑查看骨扫描或CT，以确定患者是否对癌症治疗有反应。或者，作为一个不那么生死攸关的案例，考虑在一个孩子的玩具盒里寻找所有可以用来建造今天项目屋顶的乐高积木。这些是复杂的，扩展的视觉搜索，用于多种目标类型的多个实例，无论是癌症的转移还是特定形状的构建块。人类如何执行这些任务？我们如何帮助专家更有效地执行我们的文明在诊所、机场和其他地方创造的关键扩展搜索任务？在过去的25年里，我们在理解视觉选择性注意的基本过程方面取得了巨大的进展。也就是说，现有的大部分工作都涉及对单一目标类别的单个实例进行短暂的搜索（秒）（例如找到红色垂直线或该特定对象）。通常，该目标将出现在50%或更多的试验中。为了理解和改进放射学和其他地方的社会重要的搜索任务，我们必须研究扩展的视觉搜索任务。拟议的工作利用三个扩展的搜索范式，主要是新的人类搜索的研究。觅食任务在实验室中类似于在田野中采摘浆果或在胸部CT中采摘肺结节。观察者试图在固定的时间段内选择尽可能多的目标项。为了模拟这种行为，引导搜索（GS）模型增加了动物觅食文献以及决策理论和神经经济学的思想。混合搜索任务涉及在视觉显示器上搜索几种可能的目标类型中的任何一种（想想购物清单或疾病迹象清单）。这些是视觉搜索和记忆搜索的组合。搜索存储在内存中的数百个不同对象是可能的。为了模拟这种行为，工作记忆和长期记忆（LTM）研究的想法被纳入GS框架。最后，混合觅食任务联合收割机结合了觅食和混合任务的需求。在这里，观察者试图收集尽可能多的多类型目标的例子。我们使用这些方法中的每一个在实验室研究与非专家观察员和实验与专家放射科医生。我们试图描述和模拟扩展搜索的基本过程，并提高放射科医生和其他专家执行此类搜索的能力。特别令人感兴趣的是对罕见、低患病率目标的搜索（例如，乳腺癌筛查人群中的癌症出现在约0.3%的检查中）。在临床环境中，假阴性、漏诊错误的发生率较低。拟议中的工作提出了改善这种效果的技术，并探讨了在扩展搜索任务的流行的影响。总之，这项工作开发了GS的跨学科扩展，应用于扩展搜索任务，最终目标是提高在放射学和其他社会重要环境中发现的关键搜索任务的性能。

DESCRIPTION (provided by applicant): Consider looking at a bone scan or a CT to determine if a patient is responding to cancer treatment. Or, as a less life-and-death case, consider searching through a child's toy box for all the Lego pieces that can be used to build a roof on today's project. These are complex, extended visual searches for multiple instances of multiple target types, be they metastases of cancer or building blocks of a particular shape. How do humans perform such tasks and how can we help experts to perform more effectively the critical extended search tasks that our civilization has created in the clinic, the airport, and elsewhere? The past 25+ years have seen great progress in understanding the basic process of visual selective attention. That said, the bulk of the existing work involves brief episodes of search (seconds) for a single instance of a single class of target (e.g. find a red vertical line or this specific object). Typically, that target will be present on 50% or more of trials. In order to understand and improve upon socially important search tasks in radiology and elsewhere, we must investigate extended visual search tasks. The proposed work exploits three extended search paradigms, largely new to the study of human search. Foraging tasks are laboratory analogs of picking berries in a field or lung nodules in a chest CT. Observers try to select as many target items as possible in a fixed period of time. To model this behavior, the Guided Search (GS) model is augmented with ideas from the literature on animal foraging as well as decision theory and neuroeconomics. Hybrid Search tasks involve searching visual displays for any of several possible types of target (think of a shopping list or a checklist of signs of disease). These are combinations of visual search and memory search. It is possible to search for literally hundreds of distinct objects, held in memory. To model this behavior, ideas from the study of working memory and long-term memory (LTM) are brought into the GS framework. Finally, Hybrid Foraging tasks combine the demands of the foraging and hybrid tasks. Here, observers attempt to collect as many examples as possible of multiple types of targets. We use each of these methods in laboratory studies with non-expert observers and in experiments with expert radiologists. We seek to describe and model the fundamental processes of extended search and to improve the ability of radiologists and other experts to perform such searches. Of particular interest are searches for rare, low prevalence targets (e.g. cancer in a breast cancer screening population appears in about 0.3% of exams). False negative, miss errors are elevated at low prevalence in clinical settings. The proposed work suggests techniques for ameliorating this effect and examines the impact of prevalence in extended search tasks. In summary, this work develops an interdisciplinary extension of GS, applied to extended search tasks, with the ultimate goal of improving performance on the critical search tasks found in radiology and other socially important settings.