The Erlangen Program in lateral occipital cortex: Hierarchical encoding of emergent features

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.