Prevalence effects in visual search: Theoretical and practical implications

视觉搜索中的流行效应：理论和实践意义

• 批准号: 7777292
• 负责人: Jeremy M Wolfe
• 金额: $ 40.71万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-04-01 至 2011-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7777292
• 关键词: Accounting; Adverse effects; Banana; Basic Science; Breast; Breast Cancer Detection; Cervical Cancer Screening; Characteristics; Clinical; Costs and Benefits; Data; Detection; Error Sources; Frequencies; Fruit; Goals; Gur; High Prevalence; Human; Image; Imaging Techniques; Intervention; Laboratories; Literature; Low Prevalence; Malignant neoplasm of cervix uteri; Mammography; Measures; Medical; Modeling; Modification; Monitor; Noise; Pap smear; Pathology; Performance; Positioning Attribute; Prevalence; Prevalence Study; Public Health; Radiology Specialty; Research; Rest; Screening procedure; Series; Signal Transduction; Societies; Solutions; Source; Stimulus; Structure; Techniques; Testing; Time; Training; Translating; Visual; Work; Workload; base; falls; improved; malignant breast neoplasm; programs; research study; response; simulation; successful intervention; theories; tumor; vigilance; visual performance; visual search

DESCRIPTION (provided by applicant): PROJECT SUMMARY: In visual search tasks, observers (Os) look for a target in a visual scene containing distracting stimuli. Some medical screening tasks (e.g. breast cancer & cervical cancer) are difficult visual search tasks. A characteristic of these tasks is low target prevalence. That is, unlike many other real world search tasks (e.g. finding bananas in the fruit aisle) and unlike typical laboratory search tasks, these are searches for targets that appear only rarely. In routine mammography, for example, pathology is present on less than 1% of breast images. We have found that this low target prevalence, by itself, can be a potent source of miss errors in visual search (Wolfe, et al, 2005). In exps. with a range of different search stimuli, we have found that Os miss 0.30 to 0.40 of targets when those targets are present on only 1-2% of search trials. Os miss only 0.05 to 0.10 of the same targets when those targets are present on 50% of search trials. Visual search tasks can be studied as signal detection problems. Os are trying to distinguish displays containing a target signal from those containing only noise. Miss errors can arise from a lack of sensitivity to the target or from setting a decision criterion to a position that causes detectable targets to be classified as non-targets. In other contexts (e.g. vigilance & categorization literatures), the response to a change in target frequency is understood as a shift in criterion, not in sensitivity. We have shown that this is also true in visual search. Low prevalence produces a large shift in criterion that is surprisingly hard to counteract (e.g. by manipulations of costs and benefits for different types of response). Relevance: Medical screening tasks (such as mammograms and pap smears) are examples of searches for rare targets. Our basic research suggests that this rarity, by itself, makes targets more difficult to find. Because of differences between laboratory search tasks and clinical screening tasks and because clinical tasks are performed by highly trained professionals, it would be unwise to generalize from the existing data to the conclusions about errors in medical screening. Therefore, we propose to determine whether this is really true for medical screening tasks, and, if so, test theory-based solutions. The proposed research has three specific aims: 1) to test the hypothesis that prevalence effects are a potential source of errors in breast cancer and cervical cancer screening, 2) to develop and test a model of the effects of prevalence in visual search and 3) to test theoretically and clinically motivated strategies to reduce miss errors.

描述（由申请人提供）： 项目摘要：在视觉搜索任务中，观察者 (O) 在包含干扰刺激的视觉场景中寻找目标。一些医学筛查任务（例如乳腺癌和宫颈癌）是困难的视觉搜索任务。这些任务的一个特点是目标发生率低。也就是说，与许多其他现实世界的搜索任务（例如在水果过道中寻找香蕉）和典型的实验室搜索任务不同，这些是对很少出现的目标的搜索。例如，在常规乳房 X 光检查中，只有不到 1% 的乳房图像出现病理情况。我们发现，这种低目标发生率本身可能是视觉搜索中遗漏错误的一个重要来源（Wolfe 等，2005）。在经验中。通过一系列不同的搜索刺激，我们发现，当这些目标仅出现在 1-2% 的搜索试验中时，Os 会错过 0.30 至 0.40 个目标。当这些目标出现在 50% 的搜索试验中时，Os 仅错过 0.05 至 0.10 个相同目标。视觉搜索任务可以作为信号检测问题来研究。 Os 试图区分包含目标信号的显示器和仅包含噪声的显示器。未命中错误可能是由于对目标缺乏敏感性或由于将决策标准设置到导致可检测目标被分类为非目标的位置而引起的。在其他情况下（例如警戒和分类文献），对目标频率变化的响应被理解为标准的转变，而不是敏感性的转变。我们已经证明，这在视觉搜索中也是如此。低流行率会导致标准发生巨大变化，而这种变化很难抵消（例如通过操纵不同类型应对措施的成本和收益）。相关性：医学筛查任务（例如乳房 X 光检查和子宫颈抹片检查）是搜索稀有目标的示例。我们的基础研究表明，这种稀有性本身就使得目标更难找到。由于实验室检索任务和临床筛查任务之间存在差异，而且临床任务是由训练有素的专业人员执行的，因此从现有数据归纳出有关医学筛查错误的结论是不明智的。因此，我们建议确定这对于医学筛查任务是否确实如此，如果是，则测试基于理论的解决方案。拟议的研究有三个具体目标：1）检验患病率影响是乳腺癌和宫颈癌筛查中潜在错误来源的假设，2）开发和测试视觉搜索中患病率影响的模型，3）测试减少漏检错误的理论和临床策略。