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: 8258718
• 负责人: Jeremy M Wolfe
• 金额: $ 43.87万
• 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 2014-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8258718
• 关键词: Accounting; Animal Behavior; Animals; Appetitive Behavior; Basic Science; Berlin; Breast Cancer Detection; Cessation of life; Characteristics; Chest; Civilization; Clinic; Clinical; Clinical Research; Commit; Complex; Cytology; Data; Data Set; Detection; Disease; Error Sources; Evaluation; Food; Fracture; Future; Goals; Grant; Human; Image; Imaging Device; Injury; Intervention; Learning; Left; Life; Literature; Low Prevalence; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of lung; Mammography; Measures; Medical; Medical Imaging; Medicine; Modeling; Modification; Monitor; Partner in relationship; Perception; Performance; Population; Prevalence; Process; Raspberries; Redness; Reporting; Research; Resources; Science; Screening procedure; Semantics; Signal Transduction; Staging; Stretching; Stroke; Structure; Techniques; Testing; Time; Tissues; Training; Trauma; Visual; Work; analog; base; cost; disease diagnosis; impression; improved; insight; interest; meetings; prevent; public health relevance; radiologist; selective attention; theories; tool; tumor; visual search

DESCRIPTION (provided by applicant): Modern civilization asks trained experts to perform a range of critical visual search tasks such as satellite image interpretation, airport baggage screening, and quality monitoring in manufacturing. In medicine, visual search through complex medical images is critical to detection and diagnosis of disease (e.g. screening for breast or lung cancer, evaluation of stroke, detection of internal injuries after trauma). The important search tasks of modern civilization are quite varied but they share significant characteristics. They are demanding tasks, carried out by experts who are asked to perform with high accuracy. Very significant resources are devoted to these tasks and very substantial costs accompany errors. These are quite literally matters of life and death. Nevertheless, committed professionals do not perform those tasks as well as would be desired or expected. Why are their error rates as high as they are? Search tasks are not new. Animals have always searched for food and mates. This proposal tests the hypothesis that the processes of search that served us well as visual foragers in the natural world may not serve as well when we search for tumors or fractures. Errors can arise when natural mechanisms of search meet the artificial demands of medical image perception. Errors can be reduced by developing and applying an understanding of the human search engine. Our basic research strategy is to bring problems from the clinical setting into the lab where they can be extensively studied in non-expert populations and then to use those basic research results to generate highly focused hypotheses that can be tested in the clinical setting. There are three specific aims: 1) Many modern medical imaging devices create 3D volumes of data (e.g. CT and MRI). Aim 1 tests the hypothesis that the presentation of 3D data changes the sources of errors in medical image perception (e.g. by increasing the chance that a specific region of a 3D dataset will not be attended). 2) Many medical image perception tasks are "foraging" tasks where observers searching for multiple targets in displays extended in time and space. Foraging has been a topic of interest in the animal behavior literature but only minimally in the human visual search literature. When is it time to stop foraging and move on? Aim 2 tests the hypothesis that the foraging rules that work in the natural world may be a source of errors in the clinic. 3) The third aim is to fuse models of visual search and models of medical image perception into a more comprehensive model. Search models from the lab have dealt with search tasks that last for about a second. Medical image perception models are concerned with tasks that last for minutes or more. Aim 3 tests the hypothesis that basic search models can be extended to this longer time frame with changes that will be constrained by the research proposed here. PUBLIC HEALTH RELEVANCE: Errors in medical image perception tasks like screening for breast cancer are distressingly high. In a substantial proportion of cases, the sign of disease is visible but, nevertheless, missed by a well-intentioned, well-trained expert. Since humans will be performing these tasks for the foreseeable future, the goal of this research is to understand the perceptual and attentional factors that produce these errors so that counter-measures can be devised to prevent them.

描述(申请人提供)：现代文明要求训练有素的专家执行一系列关键的视觉搜索任务，如卫星图像解译、机场行李检查和制造过程中的质量监控。在医学上，通过复杂的医学图像进行视觉搜索对于疾病的检测和诊断至关重要(例如，乳腺癌或肺癌的筛查，中风的评估，创伤后内伤的检测)。现代文明的重要搜索任务是多种多样的，但它们有显著的特点。他们是高要求的任务，由专家执行，他们被要求以高精度执行。非常大量的资源被投入到这些任务上，并且伴随着错误而来的是非常巨大的成本。这些简直就是生死攸关的问题。然而，尽职尽责的专业人员并没有像人们希望或期望的那样出色地完成这些任务。为什么他们的错误率会这么高？搜索任务并不新鲜。动物总是寻找食物和配偶。这一建议验证了一种假设，即当我们搜索肿瘤或骨折时，帮助我们在自然界进行视觉搜寻的搜索过程可能不会起到同样的作用。当自然的搜索机制满足医学图像感知的人工需求时，可能会出现错误。通过开发和应用对人工搜索引擎的理解，可以减少错误。我们的基本研究策略是将临床环境中的问题带到实验室，在那里可以在非专家群体中广泛研究这些问题，然后使用这些基础研究结果来生成可以在临床环境中测试的高度集中的假设。它有三个具体的目标：1)许多现代医学成像设备创建3D数据量(如CT和MRI)。AIM 1测试了3D数据的呈现改变了医学图像感知中的错误来源的假设(例如，通过增加3D数据集的特定区域将不被关注的机会)。2)许多医学图像感知任务是观察者在时间和空间上扩展的显示器上搜索多个目标的“觅食”任务。在动物行为文献中，觅食一直是人们感兴趣的话题，但在人类视觉搜索文献中，觅食的兴趣很小。什么时候该停止觅食，继续前进？Aim 2测试了一种假设，即在自然界中起作用的觅食规则可能是临床错误的一个来源。第三个目标是将视觉搜索模型和医学图像感知模型融合为一个更全面的模型。该实验室的搜索模型处理的搜索任务持续了大约一秒钟。医学图像感知模型关注持续几分钟或更长时间的任务。Aim 3测试了这样一种假设，即基本搜索模式可以扩展到这个更长的时间框架，但更改将受到这里提出的研究的限制。 与公共健康相关：医学图像感知任务中的错误，如乳腺癌筛查，令人担忧地高得令人担忧。在相当大比例的病例中，疾病的迹象是可见的，但受过良好训练的善意专家却忽视了这一点。由于人类将在可预见的未来执行这些任务，因此本研究的目标是了解产生这些错误的感知和注意因素，以便能够设计出防止它们的对策。