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

人们常说“一张图片胜过千言万语”，这句话唤起了我们可以从组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吗？直觉上，答案似乎是“不”--我们经常遇到被视觉信息淹没的情况，比如在拥挤的音乐会场地或杂乱的办公桌上。此外，当我们被视觉信息淹没时，我们可能会犯一些严重的错误，比如在医学扫描中找不到肿瘤，或者撞车。这个CAREER奖项旨在了解什么类型的场景信息会产生过载，以及克服信息过载时神经处理的时间过程。使用行为和脑电图（EEG）的措施，我们评估四个层次的信息，从纯粹的视觉到语义。这些实验提供了对视觉感知机制的深入了解，并可能使设计师能够创造出最低限度地消耗我们认知资源的空间。该奖项也为基础计算培训的民主化迈出了有意义的一步。PI和学生们共同创作了一本开放式的多媒体教材，培养学生的科学计算能力。该CAREER奖项旨在通过评估信息过载下的系统，深入了解场景感知机制。当我们将系统推向极限时，我们可以深入了解认知和神经机制。快速视觉感知引起了研究人员的兴趣，因为感知的速度限制了可以实现识别的神经机制的类型。然而，大多数工作都围绕着快速场景理解的成功而不是失败。这项工作评估了四个层次的信息复杂性（视觉，基于对象的，语义和经验）的增加如何有助于早期场景处理。具体来说，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EEG的信息处理的时间过程。这些实验的结果揭示了什么类型的信息会影响视觉处理，以及在什么时间尺度上，为快速视觉感知的机制提供了重要的见解。PI与学生合作，创建了一个开放的多媒体教科书的科学计算技能，往往是从早期的计算机科学课程失踪。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

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.