CAREER: Efficient coding of visual,structural, and semantic scene information

职业：视觉、结构和语义场景信息的高效编码

• 批准号: 2240815
• 负责人: Michelle Greene
• 金额: $ 65.4万
• 依托单位: Barnard College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240815&HistoricalAwards=false
• 关键词: CAREER; Efficient; coding; visual; structural

It is commonly said that “a picture is worth a thousand words”, a phrase that evokes the rich amount of information we can gain from the scenes that make up our visual world. But do all scenes contain the same amount of information? Intuitively, the answer seems to be ‘no’ -- we often encounter situations where we are overwhelmed with visual information, such as in a crowded concert venue or a cluttered desk. Further, when overwhelmed with visual information, we may make consequential mistakes, such as failing to find a tumor on a medical scan or crashing one’s car. This CAREER award aims to understand what types of scene information create overload and the time course of neural processing when overcoming information overload. Using both behavioral and electroencephalography (EEG) measures, we assess four levels of information, ranging from purely visual to semantic. These experiments provide insights into the mechanisms of visual perception and may enable designers to create spaces that minimally tax our cognitive resources. This award also takes meaningful steps toward democratizing training in basic computing. The PI and students work to create an open educational multi-media textbook that trains students in scientific computing skills.This CAREER award aims to gain insights into the mechanisms of scene perception by assessing the system under information overload. We gain insights into cognitive and neural mechanisms when we push systems to their limits. Rapid visual perception has intrigued researchers because the speed of perception places bounds on the types of neural mechanisms that can achieve recognition. However, most work centers around the successes of rapid scene understanding than its failures. This work assesses how four levels of increasing informational complexity (visual, object-based, semantic, and experiential) contribute to early scene processing. Specifically, the research tests how each information level affects performance in rapid scene detection and classification tasks and how each alters the time course of information processing using EEG. The results of these experiments reveal what types of information affect visual processing and at what time scales, providing critical insights into the mechanisms of rapid visual perception. The PI collaborates with students to create an open multimedia textbook on scientific computing skills that are often missing from early computer science classes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人们常说，一张图片胜过千言万语，这句话唤起了我们从构成我们视觉世界的场景中获得的丰富信息。但是，所有场景都包含相同数量的信息量吗？直觉上，答案似乎是否定的--我们经常遇到视觉信息淹没的情况，比如在拥挤的演唱会场地或凌乱的办公桌上。此外，当我们被视觉信息淹没时，我们可能会犯相应的错误，比如在医学扫描中找不到肿瘤或撞坏了自己的汽车。这项职业奖旨在了解哪些类型的场景信息会造成过载，以及在克服信息过载时神经加工的时间进程。使用行为和脑电(EEG)测量，我们评估了从纯视觉到语义的四个级别的信息。这些实验提供了对视觉感知机制的洞察，并可能使设计师能够创造出最大限度地减少我们认知资源负担的空间。该奖项还向基础计算培训的民主化迈出了有意义的一步。PI和学生们致力于创建一种开放的教育多媒体教科书，培训学生的科学计算技能。这个职业奖旨在通过评估信息过载下的系统来深入了解场景感知的机制。当我们将系统推向极限时，我们就会获得对认知和神经机制的洞察。快速视觉感知引起了研究人员的兴趣，因为感知的速度取决于能够实现识别的神经机制的类型。然而，大多数工作都集中在快速场景理解的成功上，而不是失败上。这项工作评估了四个级别的不断增加的信息复杂性(视觉、基于对象、语义和经验)是如何促进早期场景处理的。具体地说，这项研究测试了每个信息水平如何影响快速场景检测和分类任务的性能，以及每个信息水平如何改变使用脑电进行信息处理的时间进程。这些实验的结果揭示了什么类型的信息影响视觉处理，以及在什么时间尺度上，为快速视觉感知的机制提供了关键的见解。PI与学生合作创建了一本关于科学计算技能的开放式多媒体教科书，这些技能经常在早期的计算机科学课程中缺失。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。