d’Ascoli, S., Rapin, J. et al. (2026)
Meta
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.
Here are some thoughts:
This paper matters to psychologists because it introduces a single AI model capable of predicting brain responses across vision, language, and auditory processing simultaneously. Rather than relying on separate, task-specific models, TRIBE v2 offers a unified framework for understanding how the brain integrates multisensory information. It can replicate classic experimental findings without running new studies, potentially reducing the cost and time of psychological research. Its ability to generalize across hundreds of subjects also opens new possibilities for studying individual differences in cognitive and neural functioning.
