RUI: Causes and consequences of early quitting in visual search: Investigating the role of distractors

RUI：视觉搜索中提前退出的原因和后果：调查干扰因素的作用

• 批准号: 2218384
• 负责人: Jeff Moher
• 金额: $ 22.31万
• 依托单位: Connecticut College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2218384&HistoricalAwards=false
• 关键词: RUI; Causes; consequences; early; quitting

Distractions are everywhere in modern life, and they bring consequences ranging from the mundane to the deadly. It is well established that distracting objects can attract attention when a viewer first looks at a scene. For example, a flashing roadside billboard may take a driver’s eyes off the road for a moment. But might distractions change behavior in other, more subtle but equally critical ways? Another key aspect of visual attention arises when a person searches for something that may or may not be present and must decide whether or not they’ve found what they are looking for. An important illustration of this is that when a radiologist searches a medical image, there may or may not be an area of concern present in the scan. In these searches, there is a strategic decision component in which the searcher must decide that they’ve looked thoroughly enough to be confident that indeed no “target” is present. However, there is a gap in our knowledge – there has been little research on how distracting objects might affect this decision component. In the current project, the investigators explore the phenomenon of distractor-induced quitting, in which distracting objects alter this decision process and cause people to terminate search earlier than they otherwise would. This early quitting causes people to entirely miss targets that they would otherwise likely find. Knowledge gained from this project will advance our understanding of how distractions are processed, leading to new insights into human behavior in the fields of attention, distraction, and decision-making. Furthermore, these results have potential real-world implications for tasks that involve high-stakes searches for targets, such as medical image screening or x-ray baggage inspection. In particular, it is worth considering that the use of salient signals (e.g., from artificial intelligence) to convey information to a human observer may inadvertently trigger this exact problematic situation. For example, if a computer system is trained to scan images and highlight potential areas of interest for a radiologist (or security personnel) by using a salient signal, these quitting effects might offset any benefits the computer guidance system might otherwise afford. Finally, undergraduate students participate in the design of these experiments, collection of data, and the presentation of results at conferences including those focused on medical imaging. Some of these students are recruited from the Science Leaders program at Connecticut College, a program dedicated to providing opportunities in the sciences for students from historically excluded identities.Salient signals can alter search strategies when people are looking for targets. More precisely, in recent work, the investigators have discovered that task-irrelevant distractors can cause people to quit searching early. As a result, people more frequently miss targets when these distractors are present. In this project, the investigators use a variety of experimental protocols to explore the impact of salient distractors on visual search in tasks where targets may or may not be present. Participants search for simple targets in visual displays with multiple non-targets and press a key to indicate whether a target is present or not. Eye-tracking is employed to investigate the specific mechanisms that cause participants to quit early as a result of visual distraction – for example, when a distractor is present, does it cause participants to scan the display less exhaustively? Or does it cause participants to look at each item for a shorter time, making them more likely to look at a target but fail to process it correctly? Next, the investigators establish factors that can modulate and potentially eliminate this distractor-induced quitting, such as giving participants control over the appearance and disappearance of a salient cue that may or may not highlight the target. Finally, the investigators examine how the information content of salient signals can impact distractor-induced quitting – in other words, if salient signals sometimes draw attention to the target, do those signals cause as much (or perhaps more) disruption on the occasions where they do not draw attention to the target? Results from these studies may aid our understanding of human visual attention and visual search. Findings are shared with the scientific community at large, but also (more specifically) with colleagues in radiology in order to spark new discussion on how the investigators might apply this research to help improve communications from artificial intelligence systems to human observers in medical settings.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在现代生活中，分心的事情无处不在，它们会带来从平凡到致命的各种后果。众所周知，当观众第一次看到一个场景时，让人分心的物体会吸引注意力。例如，路边闪烁的广告牌可能会让司机的视线暂时离开道路。但分心是否会以其他更微妙但同样关键的方式改变行为呢？视觉注意力的另一个关键方面是，当一个人搜索可能存在或可能不存在的东西时，必须决定他们是否找到了他们正在寻找的东西。这方面的一个重要例证是，当放射科医生搜索医学图像时，扫描中可能存在也可能没有关注的区域。在这些搜索中，有一个战略决策部分，在这个部分中，搜索者必须决定他们已经看得足够彻底，确信确实没有“目标”存在。然而，我们的知识中存在一个缺口--关于分散注意力的物体可能如何影响这一决策部分的研究很少。在目前的项目中，研究人员探索了分心导致的戒烟现象，即分心的物体改变了这一决策过程，并导致人们比其他情况下更早终止搜索。这种提前戒烟会导致人们完全错过他们本来可能找到的目标。从这个项目中获得的知识将促进我们对分心是如何处理的理解，导致对人类在注意力、分心和决策领域的行为的新的见解。此外，这些结果对涉及高风险搜索目标的任务具有潜在的现实意义，例如医学图像筛查或x光行李检查。尤其值得考虑的是，使用显著信号(例如，来自人工智能的信号)向人类观察者传达信息可能会无意中触发这一问题。例如，如果训练计算机系统扫描图像，并通过使用显著信号来突出放射科医生(或安全人员)感兴趣的潜在区域，这些退出效应可能会抵消计算机导航系统本来可能提供的任何好处。最后，本科生参与这些实验的设计，收集数据，并在会议上展示结果，包括那些专注于医学成像的会议。其中一些学生来自康涅狄格大学的科学领袖项目，该项目致力于为历史上被排除在外的学生提供科学方面的机会。当人们寻找目标时，突出的信号可以改变搜索策略。更准确地说，在最近的研究中，研究人员发现，与任务无关的分心因素会导致人们提前停止搜索。因此，当这些干扰因素存在时，人们更容易错过目标。在这个项目中，研究人员使用各种实验方案来探索在目标可能存在或可能不存在的任务中，显著分心因素对视觉搜索的影响。参与者在具有多个非目标的可视显示中搜索简单目标，并按下一个键以指示目标是否存在。眼球跟踪被用来调查导致参与者因视觉分心而提前退场的具体机制--例如，当存在分心物时，它是否会导致参与者不那么详尽地扫描显示器？或者，这会导致参与者看每一项的时间更短，使他们更有可能看着目标，但无法正确处理它？接下来，研究人员建立了一些因素，可以调节并有可能消除这种分心导致的戒烟，比如让参与者控制可能突出目标的显著提示的出现和消失，也可能不突出目标。最后，研究人员研究了显著信号的信息内容如何影响分心者诱导的戒烟--换句话说，如果显著信号有时会将注意力吸引到目标上，那么在不引起目标注意的情况下，这些信号是否会造成同样(或更多)的干扰？这些研究的结果可能有助于我们理解人类的视觉注意和视觉搜索。研究结果不仅与整个科学界分享，而且(更具体地说)与放射学的同事分享，以便引发新的讨论，讨论调查人员如何应用这项研究，以帮助改善医疗环境中人工智能系统与人类观察者的沟通。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。