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The Erlangen Program in lateral occipital cortex: Hierarchical encoding of emergent features
Junjun Zhang1, Shi Zeng1, Baochen Wang1
1MOE Key Lab for Neuroinformation, Brain-Computer Interface & Brain-Inspired Intelligence Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Neuroimage
|February 24, 2026
Summary
This study reveals how the brain encodes emergent features using geometric principles. The lateral occipital cortex (LOC) organizes visual information hierarchically based on geometric stability, optimizing perception.
Area of Science:
- Neuroscience
- Cognitive Science
- Computer Vision
Background:
- Emergent features are key in Gestalt psychology, but their neural basis is unclear.
- Quantifying the hierarchy of emergent features requires a principled framework.
Purpose of the Study:
- To investigate the neural encoding of emergent features using geometric transformations.
- To determine if the lateral occipital cortex (LOC) represents these features hierarchically.
- To link geometric stability to neural representations in the LOC.
Main Methods:
- Functional magnetic resonance imaging (fMRI) and multivariate pattern analysis.
- Representational similarity analysis (RSA).
- Transfer learning with machine classifiers.
Main Results:
- LOC discriminates between Euclidean, affine, projective, and topological transformations.
- Neural dissimilarities in LOC correlate with geometric stability from the Erlangen Program.
- LOC shows similar representations for Euclidean and affine geometries, indicating potential hierarchical collapse.
- Transfer learning confirms hierarchical nesting of geometries in LOC, aligning with the Erlangen hierarchy.
Conclusions:
- The LOC is the neural substrate for hierarchically organizing emergent features by geometric stability.
- The visual system prioritizes invariant global structures for perceptual efficiency.
- Geometric principles from the Erlangen Program provide a framework for understanding visual feature encoding.
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