Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry
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| Format: | Preprint |
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2026
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| author | Marcos-Manchón, Pablo Jha, Rishi Fuentemilla, Lluís |
| author_facet | Marcos-Manchón, Pablo Jha, Rishi Fuentemilla, Lluís |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_20496 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry Marcos-Manchón, Pablo Jha, Rishi Fuentemilla, Lluís Neurons and Cognition Computer Vision and Pattern Recognition 68T07 I.2.6; I.2.10; J.3 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. |
| title | Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry |
| topic | Neurons and Cognition Computer Vision and Pattern Recognition 68T07 I.2.6; I.2.10; J.3 |
| url | https://arxiv.org/abs/2605.20496 |