# The Algonauts Project 2021 Challenge: How the Human Brain Makes Sense of a World in Motion

arXiv: https://arxiv.org/abs/2104.13714 · HTML: https://mlsanity.com/paper/2104.13714/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 28 Apr 2021.

## Builds on (most cited first, 1 of 1)

- [The Algonauts Project: A Platform for Communication between the Sciences of Biological and Artificial Intelligence](https://mlsanity.com/paper/1905.05675/) (2019)

## Built on by (most cited first, 17 of 17)

- [The Algonauts Project 2023 Challenge: How the Human Brain Makes Sense of Natural Scenes](https://mlsanity.com/paper/2301.03198/) (2023)
- [Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)](https://mlsanity.com/paper/2307.10246/) (2023)
- [NeuralBench: A Unifying Framework to Benchmark NeuroAI Models](https://mlsanity.com/paper/2605.08495/) (2026)
- [The Sensorium competition on predicting large-scale mouse primary visual cortex activity](https://mlsanity.com/paper/2206.08666/) (2022)
- [Brain Decodes Deep Nets](https://mlsanity.com/paper/2312.01280/) (2023)
- [The Algonauts Project 2023 Challenge: UARK-UAlbany Team Solution](https://mlsanity.com/paper/2308.00262/) (2023)
- [Animate Your Thoughts: Decoupled Reconstruction of Dynamic Natural Vision from Slow Brain Activity](https://mlsanity.com/paper/2405.03280/) (2024)
- [System identification of neural systems: If we got it right, would we know?](https://mlsanity.com/paper/2302.06677/) (2023)
- [Memory Encoding Model](https://mlsanity.com/paper/2308.01175/) (2023)
- [Relating CNNs with brain: Challenges and findings](https://mlsanity.com/paper/2108.09768/) (2021)
- [Sparse Autoencoders Map Brain-LLM Alignment onto Cortical Semantic Topography](https://mlsanity.com/paper/2605.23035/) (2026)
- [Net2Brain: A Toolbox to compare artificial vision models with human brain responses](https://mlsanity.com/paper/2208.09677/) (2022)
- [Retinotopy Inspired Brain Encoding Model and the All-for-One Training Recipe](https://mlsanity.com/paper/2307.14021/) (2023)
- [The ISLab Solution to the Algonauts Challenge 2025: A Multimodal Deep Learning Approach to Brain Response Prediction](https://mlsanity.com/paper/2508.06499/) (2025)
- [Do Language Models Align with Brains? Prediction Scores Are Not Enough](https://mlsanity.com/paper/2605.14025/) (2026)
- [MIRAGE: Adaptive Multimodal Gating for Whole-Brain fMRI Encoding](https://mlsanity.com/paper/2605.29850/) (2026)
- [Neural Functional Alignment Space: Brain-Referenced Representation of Artificial Neural Networks](https://mlsanity.com/paper/2603.00793/) (2026)

## Abstract (arXiv)

The sciences of natural and artificial intelligence are fundamentally connected. Brain-inspired human-engineered AI are now the standard for predicting human brain responses during vision, and conversely, the brain continues to inspire invention in AI. To promote even deeper connections between these fields, we here release the 2021 edition of the Algonauts Project Challenge: How the Human Brain Makes Sense of a World in Motion (http://algonauts.csail.mit.edu/). We provide whole-brain fMRI responses recorded while 10 human participants viewed a rich set of over 1,000 short video clips depicting everyday events. The goal of the challenge is to accurately predict brain responses to these video clips. The format of our challenge ensures rapid development, makes results directly comparable and transparent, and is open to all. In this way it facilitates interdisciplinary collaboration towards a common goal of understanding visual intelligence. The 2021 Algonauts Project is conducted in collaboration with the Cognitive Computational Neuroscience (CCN) conference.