The gist of the space: A space centered approach to visual scene perception

项目摘要 视觉是我们与世界互动的核心。除了能认出面孔， 与人沟通，我们的日常活动也围绕着两个基本任务组织： 认识我们的环境并在其中导航。奥德·奥利瓦博士的研究计划 构成了行为，计算和认知神经科学研究的新整合， 场景感知越来越多的证据来自行为、成像和计算 研究表明，对复杂的现实世界场景的感知涉及不同的 认知和神经机制从那些从事物体识别。然而，迄今为止， 证据并没有导致一个全面的框架，了解现场处理。 在这里，PI提出测试新的假设，即现实世界的场景分析是在 一个由不同的大脑区域组成的网络，每个区域专门代表不同的水平， 场景信息。由于场景本质上是三维空间，她将表明， 大脑利用的是从场景所包围的空间中获得的唯一信息， 而不是完全基于对象的描述。换句话说，在知道“一个场景的要点”之前， 我们分析了“空间的主旨”。" 理解大脑对视觉场景的表征的本质是一项事业， 将推动发展快速和可靠的康复策略，为个人的视觉和 空间障碍，并推动发展基于援助的系统， 理解视觉空间。现实世界的场景识别是一个未解之谜， 对神经科学、计算视觉、人工智能、机器人和 心理学

Project Summary Vision is central to our interactions with the world. Aside from recognizing faces and communicating with people, our daily activities are also organized around two fundamental tasks: recognizing our environment and navigating through it. The research program of Dr. Aude Oliva constitutes a new integration of behavioral, computational and cognitive neuroscience research on scene perception. A growing body of evidence from behavioral, imaging and computational investigations has shown that the perception of complex real-world scenes engages distinct cognitive and neural mechanisms from those engaged in object recognition. To date, however, this evidence has not resulted in a comprehensive framework for understanding scene processing. Here, the PI proposes to test the novel hypothesis that real-world scene analysis is performed in a network of distinctive brain regions, with each region specialized in representing a different level of scene information. Since scenes are inherently three-dimensional spaces, she will show that the brain capitalizes on information uniquely derived from the space encompassed by a scene, rather than an exclusively object-based description. In other words, before knowing the "gist of a scene," we analyze the "gist of the space." Understanding the nature of the brain's representations of visual scenes is an enterprise that will push the development of fast and reliable rehabilitation strategies for individuals with visual and spatial impairments, and push forward the development of aid-based systems that rely on an understanding of the visual space. Real-world scene recognition is an unsolved mystery that will have implications for neuroscience, computational vision, artificial intelligence, robotics and psychology.