Gestalt psychologists have identified a range of conditions in which humans
organize elements of a scene into a group or whole, and perceptual grouping
principles play an essential role in scene perception and object
identification. Recently, Deep Neural Networks (DNNs) trained on natural images
(ImageNet) have been proposed as compelling models of human vision based on
reports that they perform well on various brain and behavioral benchmarks. Here
we test a total of 16 networks covering a variety of architectures and learning
paradigms (convolutional, attention-based, supervised and self-supervised,
feed-forward and recurrent) on dots (Experiment 1) and more complex shapes
(Experiment 2) stimuli that produce strong Gestalts effects in humans. In
Experiment 1 we found that convolutional networks were indeed sensitive in a
human-like fashion to the principles of proximity, linearity, and orientation,
but only at the output layer. In Experiment 2, we found that most networks
exhibited Gestalt effects only for a few sets, and again only at the latest
stage of processing. Overall, self-supervised and Vision-Transformer appeared
to perform worse than convolutional networks in terms of human similarity.
Remarkably, no model presented a grouping effect at the early or intermediate
stages of processing. This is at odds with the widespread assumption that
Gestalts occur prior to object recognition, and indeed, serve to organize the
visual scene for the sake of object recognition. Our overall conclusion is
that, albeit noteworthy that networks trained on simple 2D images support a
form of Gestalt grouping for some stimuli at the output layer, this ability
does not seem to transfer to more complex features. Additionally, the fact that
this grouping only occurs at the last layer suggests that networks learn
fundamentally different perceptual properties than humans.

本文研究了 DNN 模型在场景感知和对象识别中是否存在类似人类感知的 Gestalt 现象，并发现不同类型的 DNN 模型在处理不同的刺激时，只在最后一个处理层表现出一定的 Gestalt 效应，但这种效应与人类感知的 Gestalt 效应不同，从而表明 DNN 学习到的感知属性与人类有根本的差异。