Center for Perceptual Systems and Department of Psychology, University of Texas at Austin, Austin, TX 78712.
Center for Perceptual Systems and Department of Psychology, University of Texas at Austin, Austin, TX 78712
Proc Natl Acad Sci U S A. 2020 Nov 24;117(47):29363-29370. doi: 10.1073/pnas.1912331117.
A fundamental natural visual task is the identification of specific target objects in the environments that surround us. It has long been known that some properties of the background have strong effects on target visibility. The most well-known properties are the luminance, contrast, and similarity of the background to the target. In previous studies, we found that these properties have highly lawful effects on detection in natural backgrounds. However, there is another important factor affecting detection in natural backgrounds that has received little or no attention in the masking literature, which has been concerned with detection in simpler backgrounds. Namely, in natural backgrounds the properties of the background often vary under the target, and hence some parts of the target are masked more than others. We began studying this factor, which we call the "partial masking factor," by measuring detection thresholds in backgrounds of contrast-modulated white noise that was constructed so that the standard template-matching (TM) observer performs equally well whether or not the noise contrast modulates in the target region. If noise contrast is uniform in the target region, then this TM observer is the Bayesian optimal observer. However, when the noise contrast modulates then the Bayesian optimal observer weights the template at each pixel location by the estimated reliability at that location. We find that human performance for modulated noise backgrounds is predicted by this reliability-weighted TM (RTM) observer. More surprisingly, we find that human performance for natural backgrounds is also predicted by the RTM observer.
一个基本的自然视觉任务是识别我们周围环境中特定的目标对象。长期以来，人们一直知道背景的某些属性对目标可见性有很强的影响。最著名的属性是背景的亮度、对比度和与目标的相似性。在之前的研究中，我们发现这些属性对自然背景下的检测有高度的规律影响。然而，还有另一个重要因素影响自然背景下的检测，在掩蔽文献中很少或没有关注到，这些文献主要关注更简单背景下的检测。也就是说，在自然背景下，背景的属性通常在目标下发生变化，因此目标的某些部分比其他部分更容易被掩蔽。我们通过测量对比度调制的白噪声背景下的检测阈值来开始研究这个我们称之为“部分掩蔽因子”的因素，这种噪声是这样构建的，即标准模板匹配（TM）观察者无论噪声对比度是否在目标区域调制，表现都一样好。如果噪声在目标区域均匀调制，那么这个 TM 观察者就是贝叶斯最优观察者。然而，当噪声对比度调制时，贝叶斯最优观察者会根据该位置的估计可靠性来加权每个像素位置的模板。我们发现，调制噪声背景下的人类表现可以通过这种可靠性加权的 TM（RTM）观察者来预测。更令人惊讶的是，我们发现自然背景下的人类表现也可以通过 RTM 观察者来预测。
