[Submitted on 19 May 2026 (v1), last revised 6 Nov 2026 (this version, v2)]
Abstract: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. These results provide evidence for a shared geometry in learned representations of human visual responses: subject-specific fMRI embeddings can be aligned across individuals within a common coordinate system.
Submission history
From: Pablo Marcos-Manchón [view email]
[v1] Tue, 19 May 2026 21:04:15 UTC (948 KB)
[v2] Fri, 6 Nov 2026 14:56:37 UTC (10,085 KB)
