# How Much is Brain Data Worth for Machine Learning?

arXiv: https://arxiv.org/abs/2605.09243 · HTML: https://mlsanity.com/paper/2605.09243/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 10 May 2026.

## Builds on (most cited first, 22 of 22)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Exact solutions to the nonlinear dynamics of learning in deep linear neural networks](https://mlsanity.com/paper/1312.6120/) (2013)
- [Scaling Laws for Generative Mixed-Modal Language Models](https://mlsanity.com/paper/2301.03728/) (2023)
- [Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)](https://mlsanity.com/paper/1905.11833/) (2019)
- [On the Optimal Weighted ℓ2 Regularization in Overparameterized Linear Regression](https://mlsanity.com/paper/2006.05800/) (2020)
- [Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs](https://mlsanity.com/paper/1909.06161/) (2019)
- [Scaling laws for language encoding models in fMRI](https://mlsanity.com/paper/2305.11863/) (2023)
- [A Unified, Scalable Framework for Neural Population Decoding](https://mlsanity.com/paper/2310.16046/) (2023)
- [Learning From Brains How to Regularize Machines](https://mlsanity.com/paper/1911.05072/) (2019)
- [Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle](https://mlsanity.com/paper/2303.14151/) (2023)
- [Understanding Forgetting in Continual Learning with Linear Regression](https://mlsanity.com/paper/2405.17583/) (2024)
- [Scaling laws for decoding images from brain activity](https://mlsanity.com/paper/2501.15322/) (2025)
- [Improving Semantic Understanding in Speech Language Models via Brain-tuning](https://mlsanity.com/paper/2410.09230/) (2024)
- [Using Human Brain Activity to Guide Machine Learning](https://mlsanity.com/paper/1703.05463/) (2017)
- [NeuroAI for AI Safety](https://mlsanity.com/paper/2411.18526/) (2024)
- [BrainWavLM: Fine-tuning Speech Representations with Brain Responses to Language](https://mlsanity.com/paper/2502.08866/) (2025)
- [A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision](https://mlsanity.com/paper/2503.06286/) (2025)
- [Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation](https://mlsanity.com/paper/2403.19720/) (2024)
- [Cognitive Dark Matter: Measuring What AI Misses](https://mlsanity.com/paper/2603.03414/) (2026)
- [Divergences between Language Models and Human Brains](https://mlsanity.com/paper/2311.09308/) (2023)
- [Biological connectomes as a representation for the architecture of artificial neural networks](https://mlsanity.com/paper/2209.14406/) (2022)

## Abstract (arXiv)

If a person can solve a task, can measuring their brain make it easier to train a model to solve that task too? Recent NeuroAI work suggests that supplementing task training with neural recordings can modestly improve model performance and robustness. However, it is unclear when there should be a benefit from using neural data and how much benefit to expect. We formulate this question mathematically, and begin to address it theoretically using a simple, analytically tractable linear gaussian model of task targets and neural recordings. For a multimodal estimator trained on both brain data and task labels, we derive scaling laws for how performance scales with the numbers of brain and task samples. From these laws we derive relative value and exchange rates between brain samples and task samples, quantifying how much extra task samples neural data is worth as a function of task-brain alignment, neural and task noise, latent dimension, and brain data sample size. We also analyze test distribution shift, to identify conditions where brain-regularized learning can produce substantial robustness gains through learned invariances. Finally, under a fixed collection budget, we characterize the regimes in which brain data is worth collecting. Our results provide a foundation for understanding how valuable brain data could be for improving machine learning.