Do neural network models of vision learn brain-aligned representations because they share architectural constraints and task objectives with biological vision or because they learn universal features of natural image processing? We characterized the universality of hundreds of thousands of representational dimensions from visual neural networks with varied construction. We found that networks with varied architectures and task objectives learn to represent natural images using a shared set of latent dimensions, despite appearing highly distinct at a surface level. Next, by comparing these networks with human brain representations measured with fMRI, we found that the most brain-aligned representations in neural networks are those that are universal and independent of a network's specific characteristics. Remarkably, each network can be reduced to fewer than ten of its most universal dimensions with little impact on its representational similarity to the human brain. These results suggest that the underlying similarities between artificial and biological vision are primarily governed by a core set of universal image representations that are convergently learned by diverse systems.

翻译：视觉神经网络模型之所以学习到与大脑对齐的表征，是因为它们与生物视觉共享架构约束和任务目标，还是因为它们学习了自然图像处理的普适性特征？本研究系统刻画了来自不同构建方式的视觉神经网络中数十万个表征维度的普适性。研究发现，尽管在表层表现上差异显著，但具有不同架构和任务目标的网络都倾向于使用一组共享的潜在维度来表征自然图像。进一步通过将这些网络与fMRI测量的人类大脑表征进行比较，我们发现神经网络中与大脑最对齐的表征正是那些具有普适性且独立于网络具体特性的维度。值得注意的是，每个网络可被缩减至其最具普适性的十个维度以内，而其与人类大脑表征相似性的影响微乎其微。这些结果表明，人工视觉与生物视觉之间的本质相似性主要由一组核心的普适性图像表征所主导，这些表征能被多样化的系统通过趋同学习获得。