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arXiv AI By Serwan Jassim, Victoria Bosch, Daniel Anthes, Elia Bruni, Tim Kietzmann

Latent Prediction Yields Cross-Subject Alignable Representations in Sensory-Evoked fMRI

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The study investigates whether self‑supervised learning can produce useful representations from fMRI data alone. Using a joint embedding predictive architecture, participant‑specific encoders are trained without access to stimulus images or derived representations. The resulting embeddings perform competitively on brain captioning, image reconstruction, and cross‑participant generalization, and can be aligned unsupervised across participants, enabling a decoding head trained on one subject to work on another with minimal loss of performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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