ND COBRE: ATTENTION AND THE REPRESENTATION OF VISUAL ENVIRONMENTS

想象一下，在一条繁忙的街道上开车，街上挤满了行人、其他车辆、交通标志和信号，以及潜在的道路危险。为了安全地行驶在这条街上，你必须持续监测所有这些刺激，准备好对任何需要立即做出反应的变化做出反应。如果大脑同时代表环境中的所有信息，处理这种复杂性将是直截了当的。然而，最近的证据表明，一个人对视觉环境的表示是相当稀疏的；在任何给定的时间，只有一小部分对象在环境中被表示。要了解人类如何与复杂的视觉环境交互，关键是要确定环境的哪些方面包含在其表示中。这个项目的目标是调查指导视觉信息获取的因素，以及由此产生的表征的性质。该项目的第一个具体目标是 描述与场景表征发展相关的神经活动。为了做到这一点，我们提出了几个实验，让受试者在记录电生理数据的同时观看场景；这些数据将被用来得出关于语义加工、注意力和场景表征之间的关系的结论。第二个目标是使用电生理数据结合眼动记录来研究语义对注意力和眼动控制的影响。这些效应以前没有使用允许直接测量潜在过程的工具来研究；电生理学方法提供了这样的工具。该项目的第三个具体目标 是研究动态、交互环境的表示。在一系列实验中，考察了模拟驾驶环境中的场景表示。与更传统的静态场景使用相比，这种方法在理论上具有几个优势。第四个目标是确定分心任务的性质如何影响外周信息的获取和表征。除了对我们理解场景表征的贡献外，这些实验还可能对现实世界的问题有重要的影响，例如驾驶分心或界面设计。

Imagine driving down a busy street full of pedestrians, other cars, traffic signs and signals, and potential road hazards. In order to drive safely down this street, you must continuously monitor all of these stimuli, ready to react to any changes that require immediate response. Dealing with this complexity would be straightforward if the brain simultaneously represented all information in the environment. Recent evidence suggests, however, that one's representation of the visual environment is quite sparse; at any given time, only a small number of objects in the environment are represented. To understand how humans interact with a complex visual environment, it is critical to determine which aspects of the environment are included in its representation. The objective of this project is to investigate factors that guide the acquisition of visual information, and the nature of the resulting representation. The first specific aim of the project is to characterize the neural activity associated with the development of a scene representation. To do this, several experiments are proposed in which subjects view scenes while electrophysiological data are recorded; these data will be used to draw conclusions concerning the relationships among semantic processing, attention, and scene representation. A second aim is to use electrophysiological data in conjunction with eye movement recording to investigate semantic effects on attention and eye movement control. These effects have not previously been studied using tools that permit direct measurement of underlying processes; electrophysiological methods provide such tools. The third specific aim of the project is to examine the representation of dynamic, interactive environments. In a series of experiments, scene representation is examined within a simulated driving environment. This approach provides several theoretical advantages over the more traditional use of static scenes. The fourth aim is to determine how the acquisition and representation of peripheral information is affected by the properties of a distracting task. In addition to its contributions to our understanding of scene representation, these experiments may also have important implications for real-world problems, such as driving distraction or interface design.