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