arXiv:2503.14333v3 Announce Type: replace-cross 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.
Original: https://arxiv.org/abs/2503.14333