# BrainCognizer: Brain Decoding with Human Visual Cognition Simulation for fMRI-to-Image Reconstruction

arXiv: https://arxiv.org/abs/2510.20855 · HTML: https://mlsanity.com/paper/2510.20855/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2025.

## Built on by (most cited first, 3 of 3)

- [Plug-and-Play Fidelity Optimization for Diffusion Transformer Acceleration via Cumulative Error Minimization](https://mlsanity.com/paper/2512.23258/) (2025)
- [SODA: Sensitivity-Oriented Dynamic Acceleration for Diffusion Transformer](https://mlsanity.com/paper/2603.07057/) (2026)
- [HyNeuralMap: Hyperbolic Mapping of Visual Semantics to Neural Hierarchies](https://mlsanity.com/paper/2605.09392/) (2026)

## Abstract (arXiv)

Brain decoding is a key neuroscience field that reconstructs the visual stimuli from brain activity with fMRI, which helps illuminate how the brain represents the world. fMRI-to-image reconstruction has achieved impressive progress by leveraging diffusion models. However, brain signals infused with prior knowledge and associations exhibit a significant information asymmetry when compared to raw visual features, still posing challenges for decoding fMRI representations under the supervision of images. Consequently, the reconstructed images often lack fine-grained visual fidelity, such as missing attributes and distorted spatial relationships. To tackle this challenge, we propose BrainCognizer, a novel brain decoding model inspired by human visual cognition, which explores multi-level semantics and correlations without fine-tuning of generative models. Specifically, BrainCognizer introduces two modules: the Cognitive Integration Module which incorporates prior human knowledge to extract hierarchical region semantics; and the Cognitive Correlation Module which captures contextual semantic relationships across regions. Incorporating these two modules enhances intra-region semantic consistency and maintains inter-region contextual associations, thereby facilitating fine-grained brain decoding. Moreover, we quantitatively interpret our components from a neuroscience perspective and analyze the associations between different visual patterns and brain functions. Extensive quantitative and qualitative experiments demonstrate that BrainCognizer outperforms state-of-the-art approaches on multiple evaluation metrics.