Deep neural network (DNN) models for computer vision are capable of human-level object recognition. Consequently, similarities between DNN and human vision are of interest. Here, we characterize DNN representations of Scintillating grid visual illusion images in which white disks are perceived to be partially black. Specifically, we use VGG-19 and ResNet-101 DNN models that were trained for image classification and consider the representational dissimilarity (\(L^1\) distance in the penultimate layer) between pairs of images: one with white Scintillating grid disks and the other with disks of decreasing luminance levels. Results showed a nonmonotonic relation, such that decreasing disk luminance led to an increase and subsequently a decrease in representational dissimilarity. That is, the Scintillating grid image with white disks was closer, in terms of the representation, to images with black disks than images with gray disks. In control nonillusion images, such nonmonotonicity was rare. These results suggest that nonmonotonicity in a deep computational representation is a potential test for illusion-like response geometry in DNN models.

中文翻译：

---

ImageNet 训练的深度神经网络对闪烁网格表现出类似幻觉的响应。

用于计算机视觉的深度神经网络 (DNN) 模型能够进行人类级别的对象识别。因此，DNN 和人类视觉之间的相似性是令人感兴趣的。在这里，我们描述了闪烁网格视觉错觉图像的 DNN 表示，其中白色磁盘被认为是部分黑色的。具体来说，我们使用经过训练用于图像分类的 VGG-19 和 ResNet-101 DNN 模型，并考虑图像对之间的表示差异（倒数第二层中的 \(L^1\) 距离）：一个具有白色闪烁网格磁盘和另一个带有亮度级别降低的磁盘。结果显示出非单调关系，因此降低磁盘亮度会导致表示差异增加并随后减少。也就是说，带有白色圆盘的闪烁网格图像更接近，在表示方面，黑色磁盘的图像比灰色磁盘的图像。在对照非幻觉图像中，这种非单调性很少见。这些结果表明，深度计算表示中的非单调性是 DNN 模型中类似错觉的响应几何的潜在测试。