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

人类的视觉系统允许人们看到场景中的物体是不变的，尽管到达眼睛的光线不同。例如，考虑估计对象的亮度(即对象表面反射的光的比例)的任务。物体的属性(例如，其颜色)将改变其反射的光的部分，从而改变眼睛可用的光量。但是，可供眼睛使用的灯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对象如何也反射灯光。尽管有这些差异，人类的视觉系统仍能让人稳定地感知物体的亮度。这个项目的目的是找出能够稳定感知物体亮度的心理计算。亮度感知只是人脑处理的大量场景不变感知问题中的一个例子，因此，理解这些心理计算可能也会为更好地掌握大脑的其他功能打开大门。这个跨学科的项目将在一个历史悠久的黑人学院和大学(HBCU)建立一个颜色感知实验室，该实验室将培训来自代表性不足社区的学生，学习面向有价值的工作的分析、计算和实验技能，这些技能在学术界和工业界都很受欢迎。为了确定这些计算，研究人员提议将计算方法--模拟复杂系统行为的数学模型--与涉及人类参与者的实验相结合，研究场景属性的变化如何影响人们对亮度的感知。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，并将被要求根据两个物体的亮度区分它们。例如，参与者可以观看同一场景的背靠背图像，并且被要求指示在两个场景中的哪个场景中目标对象(例如，球形对象)较轻。这些图像将使用调查员定制的软件管道生成；每一张图像都将是三维场景的二维渲染。这里使用的软件可以用来生成大型图像数据库，并精确控制场景中对象的属性，如对象的位置和颜色，以及光源的强度和颜色。在实验中，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，以与人类观察者的表现进行比较。计算学习方法将被设计为执行与人类参与者相同的任务。如果该模型的执行方式与人类相似，则表明该模型的基本数学计算类似于人类观察者的心理计算。综上所述，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，并将为稳定亮度感知所涉及的可能生物机制提供洞察。这一奖项反映了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。

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.