Todorović Dejan, Zdravković Sunčica
Department of Psychology, Faculty of Philosophy, University of Belgrade, Cika Ljubina 18-20, 11000 Belgrade, Serbia; Laboratory for Experimental Psychology, University of Belgrade, Cika Ljubina 18-20, 11000 Belgrade, Serbia.
Laboratory for Experimental Psychology, University of Belgrade, Cika Ljubina 18-20, 11000 Belgrade, Serbia; Department of Psychology, Faculty of Philosophy, University of Novi Sad, Dr Zorana Djindjica 2, 21000 Novi Sad, Serbia.
Vision Res. 2014 Apr;97:1-15. doi: 10.1016/j.visres.2014.01.015. Epub 2014 Feb 7.
The snake illusion is an effect in which the lightness of target patches is strongly affected by the luminance of remote patches. One explanation is that such images are decomposed into a pattern of illumination and a pattern of reflectance, involving a classification of luminance edges into illumination and reflectance edges. Based on this decomposition, perceived reflectance is determined by discounting the illumination. A problem for this account is that image decomposition is not unique, and that different decompositions may lead to different lightness predictions. One way to rule out alternative decompositions and ensure correct predictions is to postulate that the visual system tends to classify curved luminance edges as reflectance edges rather than illumination edges. We have constructed several variations of the basic snake display in order to test the proposed curvature constraint and the more general image decomposition hypothesis. Although the results from some displays have confirmed previous findings of the effect of curvature, the general pattern of data questions the relevance of the shape of luminance edges for the determination of lightness in this class of displays. The data also argue against an image decomposition mechanism as an explanation of this effect. As an alternative, a tentative neurally based account is sketched.
蛇形错觉是一种现象，即目标斑块的亮度会受到远处斑块亮度的强烈影响。一种解释是，此类图像会被分解为光照模式和反射模式，这涉及将亮度边缘分类为光照边缘和反射边缘。基于这种分解，通过去除光照来确定感知到的反射率。该解释存在的一个问题是，图像分解并非唯一的，不同的分解可能会导致不同的亮度预测。排除其他分解方式并确保做出正确预测的一种方法是假定视觉系统倾向于将弯曲的亮度边缘分类为反射边缘而非光照边缘。我们构建了几种基本蛇形显示的变体，以测试所提出的曲率约束以及更普遍的图像分解假设。尽管一些显示的结果证实了先前关于曲率效应的发现，但总体数据模式对亮度边缘形状与此类显示中亮度确定的相关性提出了质疑。这些数据也反对将图像分解机制作为对此效应的一种解释。作为替代方案，勾勒出了一种基于神经学的初步解释。
