Prevalence effects in visual search: Theoretical and practical implications

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，如卫星图像解释，机场行李检查和制造业质量监控。在医学中，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，但它们具有重要的特征。它们是要求很高的任务，由要求高精度的专家执行。这些任务需要投入大量资源，错误也会带来大量费用。这些都是生死攸关的问题。然而，敬业的专业人员并没有像人们期望或期望的那样很好地执行这些任务。为什么他们的错误率这么高？搜索任务并不新鲜。动物总是寻找食物和伴侣。这个提议验证了一个假设，即在自然界中，我们作为视觉觅食者的搜索过程可能在我们搜索肿瘤或骨折时不起作用。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。错误可以通过开发和应用对人类搜索引擎的理解来减少。我们的基础研究策略是将临床环境中的问题带入实验室，在那里可以在非专家人群中进行广泛研究，然后使用这些基础研究结果来生成可以在临床环境中进行测试的高度集中的假设。有三个特定的目标：1）许多现代医学成像设备创建3D数据体积（例如CT和MRI）。目标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“觅食”任务，其中观察者在时间和空间上延伸的显示器中搜索多个目标。觅食一直是动物行为文献中的一个感兴趣的主题，但在人类视觉搜索文献中只有最低限度。什么时候该停止觅食继续生活？目的2测试的假设，觅食规则，在自然界中的工作可能是一个来源的错误，在临床上。3)第三个目标是将视觉搜索模型和医学图像感知模型融合成一个更全面的模型。实验室的搜索模型已经处理了持续约一秒的搜索任务。医学图像感知模型涉及持续数分钟或更长时间的任务。目标3测试的假设，基本的搜索模型可以扩展到这个更长的时间框架的变化，将受到这里提出的研究。

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.