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

TL;DR

This paper introduces a biologically inspired neural network that detects symmetry uncertainty in human perception by analyzing local variations in images, linking neural metrics to human response times.

Contribution

It demonstrates that a Self Organizing Map's quantization error can reliably reflect human symmetry uncertainty in complex visual patterns.

Findings

Neural network outputs correlate with human response times.

SOM QE detects local contrast and color variations affecting symmetry perception.

The approach links neural metrics to perceptual uncertainty in ambiguous images.

Abstract

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.