CHS: Small: Using Virtual Reality for the Dynamic, Real-Time Optimization of Human Visual Perception

CHS：小型：利用虚拟现实动态、实时优化人类视觉感知

• 批准号: 1524888
• 负责人: Patrick Shafto
• 金额: $ 49.94万
• 依托单位: Rutgers University Newark
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-12-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1524888&HistoricalAwards=false
• 关键词: CHS; Small; Using; Virtual; Reality

Computational vision and vision science have traditionally looked to the statistics of the natural world and each other for insights into visual processing. Until recently, these approaches have been primarily static and correlational: the natural world has been treated as a collection of images for which processing should be optimized, and the averaged regularities in natural scenes have been shown to be correlated with perceptual biases. Any dynamic adjustment to recent experience influencing perception has often been minimized, in large part because there have not been ways to disrupt the environment and test the effects. But recent advances in computing and virtual reality hardware have made possible the manipulation of visual input in near-real time.This research combines mobile computing technology with immersive augmented reality to explore how visual perception dynamically adapts to encountered regularities in the environment. The PI will investigate perception of orientation, a feature of the first cortical layer of human visual processing, and thus a logical starting point. If stimuli are encoded under a framework that uses recent environmental statistics to dynamically optimize perception, then altering the typical environmental regularities should have predictable effects on human visual performance. The PI argues that existing computational models of human perception can be extended to predict which changes in the input will improve (or inhibit) human perceptual performance. This, in turn, will open up the possibility of training human perception to optimize performance on real world tasks that previously required extensive specialized training or costly, custom-built software. With the goal of creating a more precise model of the flexibility of the human visual system by quantifying the extent to which encoding biases can be altered or obliterated, this project will include three interrelated thrusts. First, the PI will develop a suite of software tools to process the visual environment in near real-time, and will use these tools to systematically investigate changes in human perception in response to experience with environments whose statistical content is atypical. He will measure changes in human perceptual performance on a variety of real-world tasks (e.g., object detection), in response to immersive experience with atypical environmental input. And he will develop and test a computational model of this human perceptual learning. Preliminary research suggests that the combination of computer image-filtering and virtual reality hardware can be used to change subsequent visual processing in ways that are predictable based on the filtered input.

传统上，计算视觉和视觉科学一直关注自然世界的统计数据以及彼此之间的统计数据，以获取对视觉处理的见解。 直到最近，这些方法主要是静态的和相关的：自然世界被视为图像的集合，处理应该被优化，自然场景中的平均亮度已被证明与感知偏差相关。 对影响感知的最近经验的任何动态调整往往被最小化，这在很大程度上是因为没有办法破坏环境并测试效果。 本研究将移动的计算技术与沉浸式增强现实技术相结合，探讨视觉感知如何动态地适应环境中遇到的障碍物。 PI将研究方向感知，这是人类视觉处理的第一皮层的特征，因此是逻辑起点。 如果刺激是在一个使用最近的环境统计数据来动态优化感知的框架下编码的，那么改变典型的环境统计数据应该会对人类的视觉表现产生可预测的影响。 PI认为，可以扩展现有的人类感知计算模型，以预测输入中的哪些变化将改善（或抑制）人类感知性能。 反过来，这将打开训练人类感知的可能性，以优化真实的世界任务的性能，而这些任务以前需要广泛的专业培训或昂贵的定制软件。为了通过量化编码偏差可以改变或消除的程度来创建人类视觉系统灵活性的更精确模型，该项目将包括三个相互关联的目标。 首先，PI将开发一套软件工具来近实时地处理视觉环境，并将使用这些工具系统地调查人类感知的变化，以响应统计内容不典型的环境的经验。 他将测量人类在各种现实世界任务中感知性能的变化（例如，对象检测），以响应具有非典型环境输入的沉浸式体验。 他将开发并测试一个人类感知学习的计算模型。 初步研究表明，计算机图像过滤和虚拟现实硬件的结合可以用来改变随后的视觉处理的方式是可预测的基础上过滤输入。