An Automated Approach Using Sparse Dictionary Learning to Discover Visual Representations in the Human Visual Cortex
Year: 2026
Type: dissertation
Abstract: Visual features are encoded by the population activity of neurons across the human visual hierarchy, from primary visual features in early areas to semantic visual features in the higher visual cortex. Sparse dictionary learning and sparse autoencoders have recently emerged as powerful automated and unsupervised methods for discovering monosemantic representations of features in the massive data of artificial and biological neural networks. The present study set out to discover, validate, and interpret the visual features encoded by fMRI activity across the visual hierarchy by analyzing a publicly released dataset of 7T fMRI responses collected from eight participants viewing over 70,000 natural images, with additional data augmentation. The study focused particularly on expanding the scope of visual feature discovery, separating feature recovery from its interpretation, and reducing agentic bias in discovering and interpreting visual representations. Participant-wise fMRI responses were decomposed by a sparse autoencoder into localised fMRI activity patterns. Each pattern was independently evaluated under two dedicated streams. In total, 35,181 and 6,603 fMRI patterns were discovered, validated, and interpreted, representing 113,714 semantic and 6,870 primary visual features, respectively. Two limitations identified in past literature were also tested quantitatively. The findings confirmed the dissociation between the selectivity and the interpretability of primary visual features, and that interpreting primary visual features as semantic risks representational misattribution. The present work contributes a novel method for the automated discovery of visual representations in human fMRI data, emphasising the authenticity of representation discovery and interpretation.
Author: Qi, Qinglin
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7213485174