Studies of Brightness, Brightness Contrast and Lightness: Methods and Modeling

亮度是视觉的基本品质。亮度感知中的一个核心问题是理解视觉系统如何以及何时能够将表面的物理不变反射率与其潜在变化的照明分开。落在感光器阵列上的亮度分布以及由此产生的神经活动是环境中物体的反射率与其照度的代数乘积。因此，从原始亮度信号恢复表面反射率和照度信息是不适定的问题，因为存在可能潜在地产生给定强度分布的照度和反射率的无数组合，并且在没有附加信息的情况下，没有算法解决方案。然而，我们从日常经验中知道，视觉系统在大多数情况下都能以令人满意的方式解决逆问题。因此，视觉系统所使用的启发法必须涉及使用额外的信息来源和/或假设。事实上，目前研究人员在亮度和亮度感知方面的大部分争论，以及本研究项目的重点，都集中在发现视觉系统用来解决这个问题的机制上。 对Arend和Spehar的工作[（1993）Perception Psychophysics，54，446-468]的仔细检查清楚地表明，仅定义为感知反射率的亮度是不够详细的，因为它指的是三种非常不同类型的不可比较的非彩色判断。这是一个直接的结果的事实，在其启发式解决方案的逆问题，视觉系统使用不同的策略来估计表面反射率在不同的情况下。事实上，亮度判断是基于不同条件下的不同信息，并且术语亮度的各种定义指的是这些不同类型的亮度判断的全部或仅一个子集，这在文献中造成了混乱。因此，在一项研究中产生的“亮度”数据可能与其他研究人员使用相同名称进行的研究具有可比性或相关性。 所提出的实验旨在解决在文献中的感知照明和感知深度对亮度感知的影响的差异。据推测，这些差异是不同的研究挖掘不同类型的亮度判断的结果。为了最终解决这个问题，我们将获得一组关键刺激的亮度，亮度对比度和亮度判断。明确区分推断亮度判断（目标亮度的认知判断）和基于亮度或亮度对比度的亮度判断（表面颜色或对比度的感觉水平判断）是批判性评估亮度和亮度感知模型的重要步骤。这些数据将与亮度感知的多尺度过滤模型[Blakeslee McCourt（1999）Vision Research，39，4361-4377]以及亮度感知的锚定模型[Gilchrist et al.（1999）Psychological Review，106，795-834]的预测进行比较。该项目将通过让本科生尽可能多地参与研究过程的每个阶段来整合研究和教育。将努力让来自代表性不足群体的学生参与，如女性和美洲土著人。除了通过扩大基本感觉处理的知识而给社会带来的好处外，本研究还将广泛适用于受视力正常的人类和各种机器之间界面影响的任何领域。

Brightness is a fundamental quality of vision. A central problem in brightness perception is understanding how and when the visual system is able to separate the physically invariant reflectances of surfaces from their potentially changing illumination. The luminance distribution falling on the photoreceptor array, and the neural activity this engenders, is the algebraic product of the reflectance of objects in the environment and their illuminance. The recovery of surface reflectance and illuminance information from the raw luminance signal is thus an ill-posed problem, in that there are myriad combinations of illumination and reflectance that can potentially give rise to a given intensity distribution, and in the absence of additional information there is no algorithmic solution. We know from everyday experience, however, that the visual system solves the inverse problem in a satisfactory manner much of the time. The heuristic used by the visual system must, therefore, involve the use of additional sources of information and/or assumptions. Indeed, much of the current debate among researchers in brightness and lightness perception, and the focus of this research project, centers on discovering the mechanisms the visual system uses to solve this problem. A careful examination of the work of Arend and Spehar [(1993) Perception & Psychophysics, 54, 446-468] makes it clear that lightness defined only as perceived reflectance is under-specified, since it refers to three very different types of achromatic judgments that are not comparable. This is a direct result of the fact that, in its heuristic solution to the inverse problem, the visual system uses different strategies to estimate surface reflectance in different situations. The fact that lightness judgments are based on different information under different conditions, and that the various definitions of the term lightness refer to either all or only a subset of these different types of lightness judgments, has created confusion in the literature. Thus, "lightness" data generated in one study may or may not be comparable or relevant to what other investigators study using the same name. The proposed experiments are designed to resolve large discrepancies in the literature over the effects of perceived illumination and the effects of perceived depth on lightness perception. It is hypothesized that these discrepancies are the result of different studies tapping different types of lightness judgments. To definitively resolve this issue we will obtain brightness, brightness contrast and lightness judgments for a set of critical stimuli. A clear distinction between inferred-lightness judgments (cognitive judgments of target lightness) and lightness judgments based on brightness or brightness contrast (sensory-level judgments of surface color or contrast) is an essential step in critically evaluating models of brightness and lightness perception. These data will be compared to predictions of both a multiscale filtering model of brightness perception [Blakeslee & McCourt (1999) Vision Research, 39, 4361-4377] as well as to an anchoring model of lightness perception [Gilchrist et al. (1999) Psychological Review, 106, 795-834].The project will integrate research and education by involving undergraduate students as much as possible in every phase of the research process. Effort will be made to involve students from underrepresented groups, such as females and Native Americans. In addition to the benefits conferred on society by expanding knowledge of basic sensory processing, the present research will be of broad applicability to any area impacted by the interface between sighted humans and machines of all descriptions.