Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map

Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical under constraints. Symmetry-based feature extraction representation neural networks may unravel the most informative contents large image databases. Despite significant achievements artificial intelligence recognition classification regular patterns, problem uncertainty remains major challenge ambiguous data. In this study, we present an network that detects symmetry states human observers. To end, exploit metric output biologically inspired Self-Organizing Map Quantization Error (SOM-QE). Shape pairs with perfect geometry mirror but non-homogenous appearance, caused local variations hue, saturation, lightness within and/or across shapes given pair produce, as shown here, longer choice response time (RT) for “yes” responses relative to symmetry. These data are consistently mirrored SOM-QE from unsupervised analysis same stimulus images. The thus capable detecting scaling patterns. Such capacity tightly linked metric’s proven selectivity contrast color highly complex