Eye movements during real-world visual search: A behavioral & computational study

现实世界视觉搜索期间的眼球运动：行为

• 批准号: 7789584
• 负责人: GREGORY J ZELINSKY
• 金额: $ 22.72万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-05 至 2014-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7789584
• 关键词: Accounting; Address; Affect; Appearance; Appetitive Behavior; Attention; Behavior; Behavioral; Behavioral Model; Behavioral Research; Categories; Code; Cognitive; Color; Communities; Complex; Computer Simulation; Data; Diagnosis; Disease; Emergency Situation; Emotional disorder; Event; Eye Movements; Goals; Human; Image; Joints; Knowledge; Label; Laboratories; Laboratory Study; Lead; Learning; Left; Literature; Location; Manuals; Methods; Modeling; Molds; Motor; Movement; Neurons; Pattern; Population; Process; Public Health; Research; Research Personnel; Retina; Shapes; Simulate; Skiing; Sorting - Cell Movement; Staging; Stimulus; System; Techniques; Telephone; Text; Time; Uncertainty; Ursidae Family; Visual; Visual attention; Visual impairment; Work; aged; base; behavior prediction; computer studies; effective therapy; field study; frontier; gaze; improved; instrument; interest; neuropsychological; oculomotor behavior; payment; public health relevance; response; sample fixation; theories; tool; visual search

DESCRIPTION (provided by applicant): A joint behavioral/modeling approach is used to better understand the top-down constraints that guide overt visual attention in realistic contexts. In previous work we developed a biologically-plausible model of eye movements during search that used oriented and color-selective linear filters, population averaging over time, and an artificial retina to represent stimuli of arbitrary complexity. The simulated fixation-by-fixation behavior of this model compared well to human behavior, using stimuli ranging from Os and Qs to fully realistic scenes. However, this model was limited in that it had to be shown the target's exact appearance, and it could not exploit scene context to constrain attention to likely target locations. Consequently, it is largely unknown how people shift their attention as they look for scene constrained targets or targets that are defined categorically. These limitations are addressed in six studies. Studies 1-2 explore how people use scene context to narrow their search for a specific target in realistic scenes. A text precue provides information about the target's location in relation to a region of the scene ("in the field"; Study 1) or a scene landmark ("next to the blue building"; Study 2). Behavioral work quantifies the effects of these informational manipulations on search guidance; computational work implements the behavioral scene constraints and integrates them into the existing search model. Studies 3-6 address the relationship between search guidance and the level of detail in a target's description. Study 3 builds on previous work by designating targets either categorically (e.g., "find the teddy bear") or through use of a preview (e.g., a picture of a specific teddy bear), but increases the number of target categories to determine the boundary conditions on categorical search. Study 4 asks whether categorical targets are coded at the basic or subordinate levels, and Study 5 analyzes the distractors fixated during search to determine the features used to code these categorical targets. In Study 6 we use text labels to vary the degree of information in a target precue (e.g., a work boot target might be described as "footwear", a "boot", or a "tan work boot with red laces"). Study 7 describes the sorts of questions that can be asked once scene constraints and categorical target descriptions are integrated under a single theoretical framework, and Study 8 points to an entirely new research direction made possible by the modeling techniques that will be developed for this project. All of these studies are synergistic in that model predictions are used to guide behavioral studies, which in turn produce the data needed to refine the model and to make even more specific behavioral predictions. The project's long term objective is to obtain an understanding of how people allocate their overt visual attention in realistic contexts, specifically in terms of how partial information about an object's location in a scene or its appearance can be used to acquire targets in a search task. This understanding is expressed in the form of a computational model, one that can now use simple spatial relations and the visual features of learned target classes to acquire semantically-defined targets. PUBLIC HEALTH RELEVANCE: The attention system has been implicated in a host of neuropsychological disorders, and a visual search task is a key component in diagnoses of attention deficits. By increasing our understanding of the neuronal computations underlying overt search behavior, the proposed work is relevant to the public health in its potential to improve the validity of existing instruments for diagnosing attention disorders, and ultimately in better understanding these disorders so as to provide more effective treatments.

描述（由申请人提供）：使用联合行为/建模方法来更好地了解自上而下的约束，从而在现实的环境中指导公开的视觉关注。在先前的工作中，我们在搜索过程中开发了一种具有生物学性能的眼动模型，该模型使用了定向和颜色选择性的线性过滤器，平均群体随着时间的推移和人工视网膜来表示代表任意复杂性的刺激。使用从OS和QS到完全逼真的场景的刺激，该模型的模拟固定行为与人类行为进行了很好的比较。但是，该模型受到限制，因为它必须显示目标的确切外观，并且无法利用场景上下文以限制对目标位置的关注。因此，当人们寻找明确定义的场景限制目标或目标时，人们如何转移注意力。这些局限性在六项研究中得到了解决。研究1-2探讨人们如何使用场景上下文来缩小对现实场景中特定目标的搜索。文本预处理提供了有关目标与场景区域（“在现场”；研究1）或场景地标（“蓝色建筑物旁边”；研究2）的信息。行为工作量化了这些信息操纵对搜索指导的影响；计算工作实现了行为场景的约束，并将其集成到现有的搜索模型中。研究3-6解决了目标描述中搜索指导与细节水平之间的关系。研究3通过明确指定目标（例如，“找到泰迪熊”）或使用预览（例如，特定的泰迪熊的图片）来基于先前的工作，但增加了目标类别的数量，以确定分类搜索的边界条件。研究4询问分类目标是在基本还是从属级别上编码，并研究5分析搜索过程中固定的干扰因素，以确定用于编码这些分类目标的特征。在研究6中，我们使用文本标签来改变目标准则中的信息程度（例如，可以将工作引导目标描述为“鞋类”，“引导”或“带有红色鞋带的棕褐色工作靴”）。研究7描述了一旦场景约束和分类目标描述都集成在一个理论框架下，并研究了8点，并研究了将为该项目开发的建模技术提供了一个全新的研究方向，就可以提出各种问题。所有这些研究都是协同作用的，因为该模型预测用于指导行为研究，这又产生了完善模型并做出更具体的行为预测所需的数据。该项目的长期目标是了解人们如何在现实的上下文中分配公开的视觉关注，特别是关于如何在场景中的对象位置的部分信息或其外观来获取搜索任务中的目标。这种理解是以计算模型的形式表达的，现在可以使用简单的空间关系和学习目标类的视觉特征来获取语义定义的目标。公共卫生相关性：注意力系统与许多神经心理障碍有关，视觉搜索任务是诊断注意力缺陷的关键组成部分。通过提高我们对公开搜索行为基础的神经元计算的理解，拟议的工作与公共卫生有关，其潜力是提高现有工具诊断注意力障碍的有效性，并最终更好地了解这些疾病，以提供更有效的治疗方法。