mlsanityData to 7 Oct 2026

The Algonauts Project 2023 Challenge: How the Human Brain Makes Sense of Natural Scenes

arXiv 2301.03198 ↗First posted 9 Jan 2023cs.CV q-bio.NCcited by 34

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Abstract (arXiv)

The sciences of biological and artificial intelligence are ever more intertwined. Neural computational principles inspire new intelligent machines, which are in turn used to advance theoretical understanding of the brain. To promote further exchange of ideas and collaboration between biological and artificial intelligence researchers, we introduce the 2023 installment of the Algonauts Project challenge: How the Human Brain Makes Sense of Natural Scenes (http://algonauts.csail.mit.edu). This installment prompts the fields of artificial and biological intelligence to come together towards building computational models of the visual brain using the largest and richest dataset of fMRI responses to visual scenes, the Natural Scenes Dataset (NSD). NSD provides high-quality fMRI responses to ~73,000 different naturalistic colored scenes, making it the ideal candidate for data-driven model building approaches promoted by the 2023 challenge. The challenge is open to all and makes results directly comparable and transparent through a public leaderboard automatically updated after each submission, thus allowing for rapid model development. We believe that the 2023 installment will spark symbiotic collaborations between biological and artificial intelligence scientists, leading to a deeper understanding of the brain through cutting-edge computational models and to novel ways of engineering artificial intelligent agents through inductive biases from biological systems.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Builds on 11 of its 39 references that are in the map. First posted on arXiv 9 Jan 2023.

Builds on

11 of 39 references.

References · 11

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  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
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  4. 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,430
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  6. 2019Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)cited by 128
  7. 2019Learning From Brains How to Regularize Machinescited by 37
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  9. 2021The Algonauts Project 2021 Challenge: How the Human Brain Makes Sense of a World in Motioncited by 17
  10. 2022The Sensorium competition on predicting large-scale mouse primary visual cortex activitycited by 13
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 10; 2024: 11; 2025: 7; 2026: 6.

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  1. 2023fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decodingcited by 16
  2. 2024The Wisdom of a Crowd of Brains: A Universal Brain Encodercited by 15
  3. 2025MindSimulator: Exploring Brain Concept Localization via Synthetic FMRIcited by 15
  4. 2023Brain Decodes Deep Netscited by 14
  5. 2026NeuralBench: A Unifying Framework to Benchmark NeuroAI Modelscited by 13
  6. 2023The Algonauts Project 2023 Challenge: UARK-UAlbany Team Solutioncited by 11
  7. 2025Transformer brain encoders explain human high-level visual responsescited by 10
  8. 2024Towards Neural Foundation Models for Vision: Aligning EEG, MEG, and fMRI Representations for Decoding, Encoding, and Modality Conversioncited by 9
  9. 2025SynBrain: Enhancing Visual-to-fMRI Synthesis via Probabilistic Representation Learningcited by 9
  10. 2023Joint fMRI Decoding and Encoding with Latent Embedding Alignmentcited by 8
  11. 2023Memory Encoding Modelcited by 7
  12. 2024BRACTIVE: A Brain Activation Approach to Human Visual Brain Learningcited by 6
22 more
  1. 2024AlignedCut: Visual Concepts Discovery on Brain-Guided Universal Feature Spacecited by 5
  2. 2025BrainExplore: Large-Scale Discovery of Interpretable Visual Representations in the Human Braincited by 5
  3. 2023Applicability of scaling laws to vision encoding modelscited by 4
  4. 2025Brain-Language Model Alignment: Insights into the Platonic Hypothesis and Intermediate-Layer Advantagecited by 4
  5. 2025Brain-IT: Image Reconstruction from fMRI via Brain-Interaction Transformercited by 4
  6. 2023Brainformer: Mimic Human Visual Brain Functions to Machine Vision Models via fMRIcited by 3
  7. 2023A Parameter-efficient Multi-subject Model for Predicting fMRI Activitycited by 2
  8. 2023Unidirectional brain-computer interface: Artificial neural network encoding natural images to fMRI response in the visual cortexcited by 2
  9. 2024Aligned with LLM: a new multi-modal training paradigm for encoding fMRI activity in visual cortexcited by 2
  10. 2024NeuralOOD: Improving Out-of-Distribution Generalization Performance with Brain-machine Fusion Learning Frameworkcited by 2
  11. 2023Exploration and Comparison of Deep Learning Architectures to Predict Brain Response to Realistic Picturescited by 1
  12. 2024Bayesian Comparisons Between Representationscited by 1
  13. 2024Brain-like emergent properties in deep networks: impact of network architecture, datasets and trainingcited by 1
  14. 2025The ISLab Solution to the Algonauts Challenge 2025: A Multimodal Deep Learning Approach to Brain Response Predictioncited by 1
  15. 2026MIRAGE: Adaptive Multimodal Gating for Whole-Brain fMRI Encodingcited by 1
  16. 2024Saliency Suppressed, Semantics Surfaced: Visual Transformations in Neural Networks and the Braincited by 0
  17. 2024Generalizability analysis of deep learning predictions of human brain responses to augmented and semantically novel visual stimulicited by 0
  18. 2024Contrastive Learning to Fine-Tune Feature Extraction Models for the Visual Cortexcited by 0
  19. 2026Gaslight, Gatekeep, V1-V3: Early Visual Cortex Alignment Shields Vision-Language Models from Sycophantic Manipulationcited by 0
  20. 2026ViBE: Visual-to-M/EEG Brain Encoding via Spatio-Temporal VAE and Distribution-Aligned Projectioncited by 0
  21. 2026Brain-IT-VQA: From Brain Signals to Answerscited by 0
  22. 2026Task-guided cross-subject latent alignment: a multi-encoder-decoder VAEcited by 0