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

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 ishigher-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 directread-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.