Mechanisms of spatial localization in human vision

当你要穿过一条忙碌的街道时，你如何确定一辆车在哪里？内向，这似乎是毫不费力和不言自明的，但在世界上定位一个对象远非简单。例如，确定汽车的位置需要考虑眼睛位置、头部位置、汽车如何移动以及它相对于场景的其他部分（如人行横道）的位置。所有这些都必须准确地完成，因为即使是在确定汽车位置时的一个小错误也可能导致毁灭性的撞车事故。我们与世界互动的能力取决于我们如何确定物体的位置，但我们缺乏对我们如何做到这一点的完整描述。本提案中的项目旨在开发一个可测试的模型，该模型基于两个组成部分，解释了这些类型的复杂环境中物体的感知位置。第一个是视网膜图像内容（我们所看到的）-这包括考虑与物体运动的相互作用，其他物体的位置，以及我们现在正在看的地方和最近一直在看的地方。另一个组成部分是感知位置的个体差异。即使在最简单的情况下，我们定位和与世界上的物体互动的能力在个体之间以及视野中的不同位置之间也有很大差异。当对位置做出困难的感知判断时，参与者一致地和特异地错误判断物体的位置，在不同的位置表现出错误的“签名”或指纹。为了开发这个模型，首先必须准确地理解这种个体差异如何影响感知位置。因此，为了实现这一长期目标，该提案提出了以下问题：（1）我们个人的空间表征是否固定不变，或者它们是否会随着时间的推移而改变;（2）它们在视觉处理的哪个阶段影响定位;以及（3）我们如何将个人地图与来自世界的信息联合收割机结合起来。除了告知基本的视觉过程之外，视觉定位的综合模型对于开发支持复杂环境中的感知决策的技术至关重要，并对人类感知世界中的物体的位置做出假设，包括自动驾驶系统，放射学中的计算机辅助诊断和基于AI的导航系统。世界上有很多技术可以帮助我们，所有这些技术都需要了解计算机和用户之间的差距。拟议的工作还将为学生提供丰富的培训机会，在实验设计，分析和沟通技巧。这将使学生为一系列职业做好准备（例如，这将对加拿大未来劳动力的发展产生积极影响。

How do you determine where a car is when you're about to cross a busy street? Introspectively, this seems effortless and self-evident, but localizing an object in the world is far from simple. Determining the car's location, for example, requires taking into account eye position, head position, how the car is moving and where it is relative to other parts of the scene (such as the crosswalk). All this must be done accurately, as even a small error in determining the location of a car could lead to a devastating crash. Our ability to interact with the world depends on how we determine an object's location, yet we lack a complete description of how we do this. The projects in this proposal are aimed at developing a testable model that accounts for the perceived locations of objects in these types of complex environments, based on two components. The first is retinal image content (what we see) - this includes taking into account interactions with the motion of the object, the positions of other objects now, as well as where we're looking now and have been looking recently. The other component is individual variability in perceived position. Even in the simplest of situations, our ability to localize and interact with objects in the world varies considerably between individuals and across different locations in the visual field. When making difficult perceptual judgments of location, participants consistently and idiosyncratically misjudge the locations of objects, exhibiting `signatures' or fingerprints of error at different locations. In order to develop this model, it is essential to first understand exactly how this individual variability contributes to perceived location. Therefore, to move towards this long-term goal, this proposal asks: (1) whether our individual spatial representations fixed, or do they change over time, (2) at what stage of visual processing do they impact localization, and (3) how we combine our individual maps with information from the world. In addition to informing fundamental visual processes, a comprehensive model of visual localization is essential to develop technologies that support perceptual decisions in complex environments and make assumptions about where humans perceive objects in the world, including autonomous driving systems, computer-aided diagnosis in radiology, and AI-based guidance systems. There are a host of technologies that exist to help us in the world, and all of them need to have some understanding of the gap between where a computer and the user might localize an object. The proposed work will also provide a rich source of training opportunities for students, in experiment design, analysis and communication skills. This will prepare students for a range of careers (e.g., natural sciences, health sciences, engineering, the private sector or in government), and will have a positive impact on the development of Canada's future work force.