Humans possess a remarkable capacity to recognize and manipulate abstract structure, which is especially apparent in the domain of geometry. Recent research in cognitive science suggests neural networks do not share this capacity, concluding that human geometric abilities come from discrete symbolic structure in human mental representations. However, progress in artificial intelligence (AI) suggests that neural networks begin to demonstrate more human-like reasoning after scaling up standard architectures in both model size and amount of training data. In this study, we revisit empirical results in cognitive science on geometric visual processing and identify three key biases in geometric visual processing: a sensitivity towards complexity, regularity, and the perception of parts and relations. We test tasks from the literature that probe these biases in humans and find that large pre-trained neural network models used in AI demonstrate more human-like abstract geometric processing.

翻译：人类具有识别和操作抽象结构的卓越能力，这在几何领域尤为明显。最近认知科学的研究表明，神经网络并不具备这种能力，并认为人类的几何能力源于心智表征中的离散符号结构。然而，人工智能领域的进展表明，当标准架构在模型规模和训练数据量上扩展后，神经网络开始表现出更类似人类的推理能力。在本研究中，我们重新审视了认知科学中关于几何视觉处理的实证结果，并识别出几何视觉处理中的三个关键偏好：对复杂性、规律性以及部分与关系感知的敏感性。我们测试了文献中用于探究人类这些偏好的任务，发现人工智能中使用的大型预训练神经网络模型表现出更类似人类的抽象几何处理能力。