A foundation model of vision, audition, and language for in-silico neuroscience

Stéphane d'Ascoli; Jérémy Rapin; Yohann Benchetrit; Teon Brooks; Katelyn Begany; Joséphine Raugel; Hubert Banville; Jean-Rémi King

arXiv · 2605.04326

A foundation model of vision, audition, and language for in-silico neuroscience

Stéphane d'Ascoli·Jérémy Rapin·Yohann Benchetrit·Teon Brooks·Katelyn Begany·Joséphine Raugel·Hubert Banville·Jean-Rémi King

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Abstract

Cognitive neuroscience is fragmented into specialized models, each tailored to specific experimental paradigms, hence preventing a unified model of cognition in the human brain. Here, we introduce TRIBE v2, a tri-modal (video, audio and language) foundation model capable of predicting human brain activity in a variety of naturalistic and experimental conditions. Leveraging a unified dataset of over 1,000 hours of fMRI across 720 subjects, we demonstrate that our model accurately predicts high-resolution brain responses for novel stimuli, tasks and subjects, superseding traditional linear encoding models, delivering several-fold improvements in accuracy. Critically, TRIBE v2 enables in silico experimentation: tested on seminal visual and neuro-linguistic paradigms, it recovers a variety of results established by decades of empirical research. Finally, by extracting interpretable latent features, TRIBE v2 reveals the fine-grained topography of multisensory integration. These results establish artificial intelligence as a unifying framework for exploring the functional organization of the human brain.

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Stéphane d'Ascoli, Jérémy Rapin, Yohann Benchetrit, Teon Brooks, Katelyn Begany, Joséphine Raugel, Hubert Banville, Jean-Rémi King. 2026-05-05. A foundation model of vision, audition, and language for in-silico neuroscience. https://arxiv.org/abs/2605.04326

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