The Erlangen Program in lateral occipital cortex: Hierarchical encoding of emergent features
Year: 2026
Type: article
Abstract: • LOC as Hierarchical Encoder : The lateral occipital cortex (LOC) hierarchically encodes emergent features according to the geometric transformations defined by the Erlangen Program. • Quantifying Feature Superiority : Neural representational in LOC directly reflect the relative superiority of geometric properties predicted by the Erlangen hierarchy, providing a neural metric for Gestalt feature primacy. • Preserved Mathematical Structure : The Erlangen Program's geometric hierarchy is conserved across levels—from stimulus variations, through LOC's neural representations, to behavioral reaction times—revealing a unified principle for perceptual invariance. Emergent features are fundamental concepts in Gestalt psychology, yet the neural encoding of these features, particularly a quantitative understanding of their relative superiority, remains elusive. This study bridges this gap by conceptualizing emergent features through geometric transformations within the Erlangen Program, which provides a principled framework to quantify their hierarchical relationships. We propose that the lateral occipital cortex (LOC) encodes these emergent features in accordance with the geometric hierarchies defined by this program. Using fMRI and multivariate pattern analysis, we demonstrate that LOC reliably discriminates between distinct geometric transformations (Euclidean, affine, projective, and topology). Critically, representational similarity analysis reveals that neural dissimilarities in LOC align with the relative stability of geometries predicted by the Erlangen Program. However, the LOC exhibits similar representational structures for lower-order transformations like Euclidean and affine geometries, suggesting a potential collapse of these distinctions in the region’s global geometric hierarchy. Furthermore, transfer learning confirms hierarchical nesting relationships among the geometries: classifiers trained on specific geometric distinctions generalize to others in a manner consistent with the Erlangen hierarchy. These findings establish LOC as the neural substrate where emergent features are organized hierarchically by geometric stability, revealing how the visual system prioritizes invariant global structures to optimize perceptual efficiency.
Source: NeuroImage
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,540
Award: G2004902191
OpenAlex ID: W7130958410