| title | Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry |
|---|---|
| authors | Pablo Marcos-Manchón, Rishi Jha, Lluís Fuentemilla |
| date | 2026-05-19 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.20496 |
| affiliation | Universitat de Barcelona |
| upvotes | 2 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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, 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. |
cite
@misc{marcosmanchn2026platonic,
title={Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry},
author={Pablo Marcos-Manchón and Rishi Jha and Lluís Fuentemilla},
year={2026},
eprint={2605.20496},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.20496}
}