Contribution of higher-order structure to perception of mirror symmetry: Role of shapes and corners

Abstract

Visual mirror symmetry is a global feature that is dependent on specific low-level relationships between component elements. Initially conceptualized as virtual lines between paired elements, it has been suggested that higher-order structure between pairs of symmetric elements forming virtual four cornered shapes may also be important for strengthening the percept of mirror symmetry. We utilize corner elements, formed by joining two Gabor elements along a central midline creating vertices with variable internal angles, in a temporal integration paradigm. This allows us to specifically manipulate the presence and type of higher-order versus lower-order structure in patterns with symmetrically placed elements. We show a significant contribution of higher-order structure to the salience of visual symmetries compared with patterns with only lower-order structures. We also find that although we are more sensitive to patterns with higher-order structure, there is no difference in the temporal processing of higher-order versus lower-order patterns. These findings have important implications for existing spatial filter models of symmetry perception that rely on lower-order structures alone and reinforces the need for elaborated models that can more readily capture the complexities of real-world symmetries.

Symmetry Perception and Psychedelic Experience

“…Skewing destroys quadrangle alignment in single reflections and impairs symmetry detection. Bellagarda et al [96] provided further support for the bootstrapping model using Gabor-corners as elements rather than dots. When Gabor-corners lay on top of the corner of virtual quadrangles, symmetry detection was enhanced.…”

“…When Gabor-corners lay on top of the corner of virtual quadrangles, symmetry detection was enhanced. Bellagarda et al [96] noted that blob-alignment type filter models do not account for the facilitatory role of second-order structure in symmetry detection. Dry [97] provided a mathematically sophisticated extension of the bootstrapping model, called the Voronoi tessellation model.…”