mlsanityData to 7 Oct 2026

The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies

arXiv 2501.00504 ↗First posted 31 Dec 2024q-bio.NCcited by 19

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Abstract (arXiv)

There is growing symbiosis between artificial and biological intelligence sciences: neural principles inspire new intelligent machines, which are in turn used to advance our theoretical understanding of the brain. To promote further collaboration between biological and artificial intelligence researchers, we introduce the 2025 edition of the Algonauts Project challenge: How the Human Brain Makes Sense of Multimodal Movies (https://algonautsproject.com/). In collaboration with the Courtois Project on Neuronal Modelling (CNeuroMod), this edition aims to bring forth a new generation of brain encoding models that are multimodal and that generalize well beyond their training distribution, by training them on the largest dataset of fMRI responses to movie watching available to date. Open to all, the 2025 challenge provides transparent, directly comparable results through a public leaderboard that is updated automatically after each submission to facilitate rapid model assessment and guide development. The challenge will end with a session at the 2025 Cognitive Computational Neuroscience (CCN) conference that will feature winning models. We welcome researchers interested in collaborating with the Algonauts Project by contributing ideas and datasets for future challenges.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Dec 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 9; 2026: 10.

19 papers.

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  1. 2025TRIBE: TRImodal Brain Encoder for whole-brain fMRI response predictioncited by 17
  2. 2026NeuralBench: A Unifying Framework to Benchmark NeuroAI Modelscited by 13
  3. 2026A foundation model of vision, audition, and language for in-silico neurosciencecited by 12
  4. 2025VIBE: Video-Input Brain Encoder for fMRI Response Modelingcited by 7
  5. 2025Multimodal Recurrent Ensembles for Predicting Brain Responses to Naturalistic Movies (Algonauts 2025)cited by 5
  6. 2025CNeuroMod-THINGS, a densely-sampled fMRI dataset for visual neurosciencecited by 2
  7. 2025A Multimodal Sequence-to-Sequence Model for Cross-Subject Prediction of Brain Responses to Naturalistic Stimulicited by 2
  8. 2025Improving Multimodal Brain Encoding Model with Dynamic Subject-awareness Routingcited by 2
  9. 2025Probing Multimodal Fusion in the Brain: The Dominance of Audiovisual Streams in Naturalistic Encodingcited by 1
  10. 2025The ISLab Solution to the Algonauts Challenge 2025: A Multimodal Deep Learning Approach to Brain Response Predictioncited by 1
  11. 2025Stacked Regression using Off-the-shelf, Stimulus-tuned and Fine-tuned Neural Networks for Predicting fMRI Brain Responses to Movies (Algonauts 2025 Report)cited by 1
  12. 2026MIRAGE: Adaptive Multimodal Gating for Whole-Brain fMRI Encodingcited by 1
7 more
  1. 2026BrainVista: Modeling Naturalistic Brain Dynamics as Multimodal Next-Token Predictioncited by 0
  2. 2026Neuro-Symbolic Decoding of Neural Activitycited by 0
  3. 2026Probabilistic Prediction of Neural Dynamics via Autoregressive Flow Matchingcited by 0
  4. 2026A global predicted-fMRI drive signal from TRIBE does not predict YouTube replay heatmapscited by 0
  5. 2026Platonic Projection Structures: Operator-Induced Observability in Representation Learningcited by 0
  6. 2026The Platonic brain bridge hypothesis: human brain networks as an architectural prior for multimodal large language modelscited by 0
  7. 2026Cross-attention encoding models reveal dynamic spatiotemporal routing across human higher visual cortexcited by 0