Guiding Attention in Real-World Scenes

项目摘要 现实世界的场景包含的信息远远超过我们在任何给定时刻所能感知的。场景感知 因此需要注意选择相关场景区域以进行优先处理。那些东西怎么样？ 世界上哪些方面应该优先选择？尽管过去的许多研究都集中在如何 注意力是由场景的视觉特性引导的，这是从意义地图中开发的新证据 以前的资助期，确立了意义在一个场景中的分布起到了中心作用，而且经常 在引导注意力方面起主导作用。这一令人惊讶的发现提出了许多关于 情景意义的本质及其在注意引导中的具体作用。这个项目的首要目标是 详细了解场景对象和功能空间的语义特征如何影响 在复杂的真实世界场景中引导视觉注意。具体目标是：(1)确定 情景注意引导中的客体语义学；(2)确定功能空间在注意中的作用 场景中的引导；(3)确定观看任务与场景语义在引导注意中的相互作用。 本课题的创新之处在于拓展了传统的注意力研究，明确考虑了注意的作用 意思是。为此，将使用利用意义地图概念的新的语义地图来捕获本地 在场景上连续的区域含义，允许直接调查不同的关系 注意的意义类型。本课题的创新之处在于：(1)拓展了传统的视觉研究 注意明确考虑语义的作用；(2)注重场景内容的语义 (3)语境中意义在注意引导中的作用 观察任务；(4)综合运用服务中的各种认知科学方法 理解在真实世界场景中意义对视觉注意的影响，包括眼神跟踪， 大规模众包、计算图像处理、计算语义建模和深度 卷积神经网络。该项目对挑战当前的视觉注意模型具有重要意义 说明了场景的作用意义。由于拟议的研究测试了相互竞争的模型，结果将 导致了综合理论框架的发展，无论结果如何，都能推动该领域的发展。 虽然这些研究的重点是基础科学，但通过提供一种 更完整地描述与视觉注意相关的过程。拟议的研究可能 最终导致制定视觉注意的康复策略，因为它在真实中运行 世界，更好地利用观众的知识来抵消那些有注意力的人中断的功能 以及视力缺陷。

Project Summary Real-world scenes contain far more information than we can perceive at any given moment. Scene perception therefore requires attentional selection of relevant scene regions for prioritized processing. How are those aspects of the world that should receive priority selected? Although much past research has focused on how attention is guided by the visual properties of a scene, new evidence from meaning maps, developed in the previous funding period, established that the distribution of meaning across a scene plays a central and often dominant role in guiding attention. This surprising finding raises many important new questions about the nature of scene meaning and its specific role in attentional guidance. The overarching goal of this project is to understand in detail how the semantic features of a scene’s objects and functional spaces influence the guidance of visual attention in complex real-world scenes. The specific aims are: (1) To determine the role of object semantics in attentional guidance in scenes; (2) To determine the role of functional spaces in attentional guidance in scenes; (3) To determine how viewing task interacts with scene semantics in guiding attention. The project is innovative in expanding the traditional study of attention to explicitly consider the role of meaning. To this end, new semantic maps capitalizing on the meaning map concept will be used capture local region meaning continuously over a scene, allowing for direct investigation of the relationships of different types of meaning with attention. The project is innovative in (1) expanding the traditional study of visual attention to explicitly consider the role of semantics; (2) focusing on the semantics of both scene content (objects) and scene structure (space); (3) considering the role of meaning in attentional guidance in the context of viewing task; (4) integrating the use of a wide variety of cognitive science methods marshalled in the service of understanding the influence of meaning on visual attention in real-world scenes, including eyetracking, large-scale crowd-sourcing, computational image processing, computational semantic modeling, and deep convolutional neural networks. The project is significant in challenging current models of visual attention to account for the role of scene meaning. Because the proposed studies test competing models, the results will lead to the development of integrative theoretical frameworks that advance the field regardless of the outcome. While focused on basic science, the studies have potentially important translational implications by providing a more complete characterization of the processes associated with visual attention. The proposed studies may ultimately lead to the development of rehabilitation strategies for visual attention as it operates in the real world, better capitalizing on the use of a viewer’s knowledge to offset disrupted functions in those with attention and vision deficits.