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.

人类对抽象结构的识别和操作能力十分出色，尤其在几何领域中显得更为明显。最近的认知科学研究表明，神经网络并没有共享这种能力，得出结论认为人类的几何能力来自人类心智表示中的离散符号结构。然而，人工智能领域的进展表明，经过标准架构的扩展模型规模和训练数据量后，神经网络开始表现出更类似人类推理的能力。在本研究中，我们重新审视认知科学对几何视觉处理的实证结果，并确定了几何视觉处理中的三个关键偏见：对复杂性、规律性和部分与关系的感知敏感性。我们测试了从文献中探索这些偏见的任务，并发现在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。

大规模预训练神经网络中的人类化几何抽象