人類大腦中的柏拉圖式表徵：無監督恢復普遍幾何

摘要

强柏拉图表征假说指出，人工神经网络中的表征收敛可以被建设性地利用：无需配对数据，嵌入即可通过通用潜在空间在不同模型间进行翻译。我们探究人脑中是否存在类似的几何结构。利用自然场景数据集的功能性磁共振成像数据，我们提出一种自监督编码器，该编码器通过重复刺激呈现，仅从脑数据中学习受试者特定的嵌入。我们证明，这些独立学习的空间可以通过无监督正交旋转在不同受试者之间进行翻译，无需跨受试者配对样本或中间模型表征。将成对旋转同步至单一共享潜在空间，能进一步提升跨受试者检索效果，表明受试者特定空间与公共坐标系统相互兼容。这些结果为人类视觉皮层中存在共享神经几何结构提供了证据：受试者特定的fMRI表征在不同个体之间近似等距，并可通过纯几何变换进行翻译。

English

The Strong Platonic Representation Hypothesis suggests that representational convergence in artificial neural networks can be harnessed constructively: embeddings can be translated across models through a universal latent space without paired data. We ask whether an analogous geometry can be recovered across human brains. Using fMRI data from the Natural Scenes Dataset, we propose a self-supervised encoder that learns subject-specific embeddings from brain data alone by exploiting repeated stimulus presentations. We show that these independently learned spaces can be translated across subjects using unsupervised orthogonal rotations, without paired cross-subject samples or intermediate model representations. Synchronizing pairwise rotations into a single shared latent space further improves cross-subject retrieval, indicating that subject-specific spaces are mutually compatible with a common coordinate system. These results provide evidence for a shared neural geometry in the human visual cortex: subject-specific fMRI representations are approximately isometric across individuals and can be translated through purely geometric transformations.