Feed it video, get 70k brain predictions: Meta's TRIBE v2

You know that feeling when you need to know whether content actually engages people — not clicks, not dwell time, but genuine cognitive attention? fMRI studies cost thousands per session, take hours, and give you data from maybe a dozen people. Neuroscience has been stuck studying brain responses one modality, one brain region, one small experiment at a time. TRIBE v2 gives you a computational shortcut: predict the brain response instead of measuring it.

Think of it like a weather forecast for your brain. Instead of predicting rain, it predicts neural activity. You feed in a video (or audio, or text). Three pretrained AI models — one for language (LLaMA 3.2-3B), one for video (V-JEPA2), one for audio (Wav2Vec-BERT 2.0) — each extract features from the content. A Transformer called FmriEncoder then maps those features onto a standard brain surface (fsaverage5 mesh, ~20,000 vertices, ~70,000 voxels). The predictions shift 5 seconds backward to account for the hemodynamic lag — the delay between neural firing and the blood-oxygen signal fMRI actually measures.

• Zero-shot brain prediction — feed any content, get predictions for people the model has never seen, in languages it wasn't explicitly trained on, without retraining
• 70,000 voxel resolution — 70x jump from TRIBE v1's 1,000 voxels, covering the full cortical surface on the standard fsaverage5 mesh
• Trimodal input — handles video, audio, and text simultaneously, or any single modality; text gets auto-converted to speech with word-level timing
• Open weights on HuggingFace — 90,599 downloads in the first month, with a Colab notebook that gets you from zero to predictions in minutes
• Tribev2 mini variant — a smaller model added March 30 for faster inference when you don't need full resolution
• Standard neuroimaging output — predictions land on the fsaverage5 mesh and work directly with Nilearn and PyVista for visualization
• 2-3x improvement over prior methods — Meta's claim for both movie and audiobook brain response prediction benchmarks

Who It Is For

If you work in computational neuroscience, neuroAI, or multimodal ML research and want a pretrained brain encoding model instead of training your own from scratch. Also relevant if you build content analysis tools and want a 'neural engagement' signal — but the CC-BY-NC-4.0 license means you'll need a separate license for anything commercial. Not useful if you need subject-specific predictions (t...

Worth Exploring?

Absolutely worth exploring if you're in research or non-commercial tooling. The Colab notebook gets you to a working demo in under 10 minutes with zero local setup. But treat it as experimental — 5 commits, no releases, paper not on arxiv, and BrainVista (Feb 2026) already beats it on some long-horizon benchmarks. The ecosystem is moving fast: 14 open PRs suggest active community patches, but the non-commercial license gates the most interesting applications.