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

Birgitta Dresp-Langley,John M Wandeto

doi:10.3390/sym13020299

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 extraction 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 Quantization Error (SOM-QE). Shape pairs with perfect geometry mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, a longer choice response time (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 metric’s proven selectivity to local contrast and color variations in large and highly complex image data.

Highlights

Symmetry in biological and physical systems is a product of self-organization [1] driven by evolutionary processes and/or mechanical systems under constraints
Visual system uncertainty associated with the symmetry of shape pairs was varied experimentally in a series of two-dimensional images showing shape pairs with perfect geometrical symmetry but varying amounts of local color information
To quantify human uncertainty in response to the variable amounts of local color information, images were presented in random order a computer screen to observers tion, images were presented in random order on aon computer screen to observers whowho had had to decide as quickly as possible whether two shapes in a given images were symmetto decide as quickly as possible whether two shapes in a given images were symmetrical rical or(yes/no not

Summary

Introduction

Symmetry in biological and physical systems is a product of self-organization [1] driven by evolutionary processes and/or mechanical systems under constraints. Human symmetry detection [14,15] in patterns or shapes involves visual and cognitive processes from lower to higher levels of functional organization [16,17,18,19,20,21,22,23,24]. Vertical mirror symmetry is a salient form of visual symmetry [23,24,25], processed at early stages in human vision and producing greater or lesser detection reliability [23]

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Conclusion