# Studying the neural representations of uncertainty

arXiv: https://arxiv.org/abs/2202.04324 · HTML: https://mlsanity.com/paper/2202.04324/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2022.

## Built on by (most cited first, 3 of 3)

- [Characterizing higher-order representations through generative diffusion models explains human decoded neurofeedback performance](https://mlsanity.com/paper/2503.14333/) (2025)
- [Active Sensing Subserves Task-Level Control](https://mlsanity.com/paper/2605.22988/) (2026)
- [An entropic explanation of insistence on sameness in autism](https://mlsanity.com/paper/2608.04616/) (2026)

## Abstract (arXiv)

The study of the brain's representations of uncertainty is a central topic in neuroscience. Unlike most quantities of which the neural representation is studied, uncertainty is a property of an observer's beliefs about the world, which poses specific methodological challenges. We analyze how the literature on the neural representations of uncertainty addresses those challenges and distinguish between "code-driven" and "correlational" approaches. Code-driven approaches make assumptions about the neural code for representing world states and the associated uncertainty. By contrast, correlational approaches search for relationships between uncertainty and neural activity without constraints on the neural representation of the world state that this uncertainty accompanies. To compare these two approaches, we apply several criteria for neural representations: sensitivity, specificity, invariance, functionality. Our analysis reveals that the two approaches lead to different, but complementary findings, shaping new research questions and guiding future experiments.