Characterizing higher-order representations through generative diffusion models explains human decoded neurofeedback performance
Brains construct not only "first-order" representations of the environment but also "higher-order" representations about those representations -- including higher-order uncertainty estimates that guide learning and adaptive behavior. Higher-order expectations about representational uncertainty -- i.e., learned through experience -- may play a key role in guiding behavior and learning, but their characterization remains empirically and theoretically challenging. Here, we introduce the Noise Estimation through Reinforcement-based Diffusion (NERD) model, a novel computational framework that trains denoising diffusion models via reinforcement learning to infer distributions of noise in functional MRI data from a decoded neurofeedback task, where healthy human participants learn to achieve target neural states. We hypothesize that participants accomplish this task by learning about and then minimizing their own representational uncertainty. We test this hypothesis with NERD, which mirrors brain-like unsupervised learning. Our results show that NERD outperforms backpropagation-trained control models in capturing human performance with explanatory power enhanced by clustering learned noise distributions. Importantly, our results also reveal individual differences in expected-uncertainty representations that predict task success, demonstrating NERD's utility as a powerful tool for probing higher-order neural representations.