Paper Review: TRImodal Brain Encoder for whole-brain fMRI response prediction (TRIBE)
Or the snappier[1] title from my email inbox: "Meta's mind-reading movie AI" Paper: TRIBE: TRImodal Brain Encoder for whole-brain fMRI response prediction (arXiv:2507.22229) Summary * Meta's Brain & AI Research team[2] won first place at Algonauts 2025 with TRIBE, a deep neural network trained to predict brain responses to stimuli across multiple modalities (text, audio, video), cortical areas (superior temporal lobe, ventral, and dorsal visual cortices), and individuals (four people). * The model is the first brain encoding pipeline which is simultaneously non-linear, multi-subject, and multi-modal. * The team show that these features improve model performance (exemplified by their win!) and provide extra insights, including improved neuroanatomical understandings. * Specifically, the model predicts blood-oxygen-level-dependent (BOLD) signals (a proxy for neural activity) in the brains of human participants exposed to video content: Friends, The Bourne Supremacy, Hidden Figures, The Wolf of Wall Street and Life (a BBC Nature documentary). Epistemic Status I provide a summary with key points from the paper alongside my takeaways, keeping things light and engaging! I'm a data scientist with a deep interest in AI alignment and neuroscience but not a neuroscientist. I have moderate confidence in my statements but errors are possible. I spent 2-3 hours deeply reading the paper, performing supplementary research, and drafting notes. I spent 4 hours writing up notes. Review (1) Review preamble A few days ago I published a [long] piece on why we might model superintelligence as third-order cognition — and the implications this would have for AI alignment. Part of my post talks about appreciating Meta as a key-player in this world model, particularly due to their focus on highly individualised superintelligent AI. I state that we can model "humans with superintelligence" as third-order cognition beings, one crucial property of which being lower order irreco
I think this framing also aligns with the discussion of whether we should focus on making AI corrigible or virtuous.