Revealing higher-order neural representations of uncertainty with the Noise Estimation through Reinforcement-based Diffusion (NERD) model

• URL: http://arxiv.org/abs/2503.14333v3
• Date: Thu, 04 Sep 2025 21:35:59 GMT
• Title: Revealing higher-order neural representations of uncertainty with the Noise Estimation through Reinforcement-based Diffusion (NERD) model
• Abstract summary: We study noise expectation" HORs using neural data from a task which may require the brain to learn about its own noise.<n>We develop and apply a Noise Estimation through Reinforcement-based Diffusion model to characterize how brains may undertake this process.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Studies often aim to reveal ``first-order" representations (FORs), which encode aspects of an observer's environment, such as contents or structure. A less-common target is ``higher-order" representations (HORs), which are ``about" FORs -- e.g., their strength or uncertainty -- and which may contribute to learning. HORs about uncertainty are unlikely to be direct ``read-outs" of FOR characteristics, instead reflecting noisy estimation processes incorporating prior expectations about uncertainty, but how the brain represents such expected uncertainty distributions remains largely unexplored. Here, we study ``noise expectation" HORs using neural data from a task which may require the brain to learn about its own noise: decoded neurofeedback, wherein human subjects learn to volitionally produce target neural patterns. We develop and apply a Noise Estimation through Reinforcement-based Diffusion (NERD) model to characterize how brains may undertake this process, and show that NERD offers high explanatory power for human behavior.

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