Excellence in Research: Predicting computations that lead to a stable perception of object lightness under spectral variabilities of the visual scene

卓越的研究：预测计算，在视觉场景的光谱变化下实现物体亮度的稳定感知

• 批准号: 2054900
• 负责人: Vijay Singh
• 金额: $ 37.36万
• 依托单位: North Carolina Agricultural & Technical State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2054900&HistoricalAwards=false
• 关键词: Excellence; Research; Predicting; computations; lead

The human visual system allows people to see objects in a scene as unchanging, despite variation in the light reaching the eyes. For example, consider the task of estimating the lightness of an object (i.e., the fraction of light reflected by the object’s surface). Properties of the object (e.g., its color), will change the fraction of light it reflects and hence the amount of light available to the eyes. However, the light available to the eyes also depends on several other aspects of the scene, such as the intensity of the light source and how background objects also reflect the light. Despite all this variation, the human visual system allows people to stably perceive an object’s lightness. This project aims to identify the mental computations that lead to stable perception of object lightness. Lightness perception is just one example of a large variety of scene invariant perception problems that the human brain deals with and thus, an understanding of these mental computations could open the door to a better grasp of other brain functions as well. This interdisciplinary project will establish a color perception lab at an Historically Black College and University (HBCU), which will train students from under-represented communities in valuable job-oriented analytical, computational, and experimental skills that are in high demand in academia and industry.To identify these computations, the investigator proposes to combine computational methods – mathematical models that mimic the behavior of complex systems – with experiments involving human participants to study how changes in scene properties affect people’s perception of lightness. Human participants will view computer-generated pictures of natural looking scenes using specifically calibrated equipment and will be asked to discriminate between two objects based on their lightness. For instance, a participant may see back-to-back images of the same scene and be asked to indicate in which of the two scenes a target object (e.g., a spherical object) is lighter. The images will be generated using the investigator’s custom-built software pipeline; each image will be a two-dimensional rendering of a three-dimensional scene. The software employed here can be used to generate large databases of images with precise control over the properties of the objects in the scene, such as the position and color of the objects, and the intensity and color of the light sources. During the experiment, the investigator will vary the intensity of light sources and the color of the background objects and measure human participants’ ability to distinguish the lightness of the target objects. A computational model will then be created to compare with the performance of human observers. The computational learning methods will be designed to perform the same task as the human participants. If the model performs in a similar manner to the humans, it will suggest that the underlying mathematical computations of the model are similar to the mental computations of the human observers. Taken together, the experiments and model will provide a rich description of how human beings perceive the lightness of objects under varying conditions and will provide insights into the possible biological mechanisms involved in stable lightness perception.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.

人类的视觉系统允许人们将场景中的对象视为不变的，尽管到达眼睛的光线会发生变化。例如，考虑估计对象的亮度的任务（即，被物体表面反射的光的分数）。对象的属性（例如，它的颜色），将改变它反射的光的比例，从而改变眼睛可用的光的量。 然而，眼睛可用的光还取决于场景的其他几个方面，例如光源的强度以及背景物体如何反射光。尽管有这些变化，人类的视觉系统允许人们稳定地感知物体的亮度。 该项目旨在确定导致物体亮度稳定感知的心理计算。亮度感知只是人类大脑处理的各种场景不变感知问题中的一个例子，因此，对这些心理计算的理解也可以为更好地掌握其他大脑功能打开大门。这个跨学科的项目将在历史上的黑人学院和大学（HBCU）建立一个颜色感知实验室，该实验室将培养来自代表性不足社区的学生，使他们掌握学术界和工业界高度需求的有价值的面向工作的分析，计算和实验技能。研究者建议将联合收割机计算方法--模拟复杂系统行为的数学模型--与实验涉及人类参与者研究如何在场景属性的变化影响人们的感知亮度。人类参与者将使用专门校准的设备观看计算机生成的自然场景图片，并将被要求根据亮度区分两个物体。例如，参与者可能会看到同一场景的连续图像，并被要求指示目标对象位于两个场景中的哪一个场景中（例如，球形物体）更轻。 这些图像将使用调查人员定制的软件管道生成;每张图像都是三维场景的二维渲染。 这里使用的软件可用于生成大型图像数据库，并精确控制场景中对象的属性，例如对象的位置和颜色以及光源的强度和颜色。 在实验过程中，研究人员将改变光源的强度和背景物体的颜色，并测量人类参与者区分目标物体亮度的能力。 然后将创建一个计算模型，与人类观察员的表现进行比较。 计算学习方法将被设计为执行与人类参与者相同的任务。 如果模型以与人类相似的方式执行，则表明模型的底层数学计算与人类观察者的心理计算相似。 这些实验和模型将为人类在不同条件下如何感知物体的亮度提供丰富的描述，并将为稳定的亮度感知所涉及的可能生物机制提供深入的见解。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

Equivalent noise characterization of human lightness constancy.

• DOI: 10.1167/jov.22.5.2
• 发表时间: 2022-04-06
• 期刊: Journal of vision
• 影响因子: 1.8
• 作者: Singh V;Burge J;Brainard DH
• 通讯作者: Brainard DH