Modern feedforward convolutional neural networks (CNNs) can now solve some computer vision tasks at super-human levels. However, these networks only roughly mimic human visual perception. One difference from human vision is that they do not appear to perceive illusory contours (e.g. Kanizsa squares) in the same way humans do. Physiological evidence from visual cortex suggests that the perception of illusory contours could involve feedback connections. Would recurrent feedback neural networks perceive illusory contours like humans? In this work we equip a deep feedforward convolutional network with brain-inspired recurrent dynamics. The network was first pretrained with an unsupervised reconstruction objective on a natural image dataset, to expose it to natural object contour statistics. Then, a classification decision head was added and the model was finetuned on a form discrimination task: squares vs. randomly oriented inducer shapes (no illusory contour). Finally, the model was tested with the unfamiliar “illusory contour” configuration: inducer shapes oriented to form an illusory square. Compared with feedforward baselines, the iterative “predictive coding” feedback resulted in more illusory contours being classified as physical squares. The perception of the illusory contour was measurable in the luminance profile of the image reconstructions produced by the model, demonstrating that the model really “sees” the illusion. Ablation studies revealed that natural image pretraining and feedback error correction are both critical to the perception of the illusion. Finally we validated our conclusions in a deeper network (VGG): adding the same predictive coding feedback dynamics again leads to the perception of illusory contours.

现代前馈卷积神经网络 (CNN) 现在可以解决一些超人类水平的计算机视觉任务。然而，这些网络只是粗略地模仿了人类的视觉感知。与人类视觉的一个不同之处在于，它们似乎不像人类那样感知虚幻的轮廓（例如 Kanizsa 方块）。来自视觉皮层的生理证据表明，对虚幻轮廓的感知可能涉及反馈连接。循环反馈神经网络会像人类一样感知虚幻的轮廓吗？在这项工作中，我们为深度前馈卷积网络配备了受大脑启发的循环动力学。该网络首先在自然图像数据集上使用无监督重建目标进行预训练，以将其暴露于自然对象轮廓统计数据。然后，添加了一个分类决策头，并对模型进行了形式识别任务的微调：正方形与随机定向的诱导器形状（没有虚幻的轮廓）。最后，该模型用陌生的“虚幻轮廓”配置进行了测试：诱导器形状定向形成一个虚幻的正方形。与前馈基线相比，迭代的“预测编码”反馈导致更多虚幻的轮廓被归类为物理方块。幻觉轮廓的感知可以在模型生成的图像重建的亮度分布中进行测量，这表明模型确实“看到”了幻觉。消融研究表明，自然图像预训练和反馈纠错对于错觉的感知都至关重要。最后，我们在更深层次的网络 (VGG) 中验证了我们的结论：