Characterizing higher-order representations through generative diffusion models explains human decoded neurofeedback performance

[2503.14333] Characterizing higher-order representations through generative diffusion models explains human decoded neurofeedback performance Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2503.14333 (cs) [Submitted on 18 Mar 2025 ( v1 ), last revised 14 Apr 2026 (this version, v4)] Title: Characterizing higher-order representations through generative diffusion models explains human decoded neurofeedback performance Authors: Hojjat Azimi Asrari , Megan A. K. Peters View a PDF of the paper titled Characterizing higher-order representations through generative diffusion models explains human decoded neurofeedback performance, by Hojjat Azimi Asrari and 1 other authors View PDF HTML (experimental) Abstract: 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. Comments: 25 pages, 7 figures Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Neurons and Cognition (q-bio.NC) Cite as: arXiv:2503.14333 [cs.LG] (or arXiv:2503.14333v4 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2503.14333 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Hojjat Azimi Asrari [ view email ] [v1] Tue, 18 Mar 2025 15:08:19 UTC (4,821 KB) [v2] Wed, 25 Jun 2025 19:04:21 UTC (2,847 KB) [v3] Thu, 4 Sep 2025 21:35:59 UTC (2,864 KB) [v4] Tue, 14 Apr 2026 05:13:11 UTC (13,342 KB) Full-text links: Access Paper: View a PDF of the paper titled Characterizing higher-order representations through generative diffusion models explains human decoded neurofeedback performance, by Hojjat Azimi Asrari and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2025-03 Change to browse by: cs cs.AI q-bio q-bio.NC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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