Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 17 January 2021 / Approved: 18 January 2021 / Online: 18 January 2021 (10:18:03 CET)

A peer-reviewed article of this Preprint also exists.

Dresp-Langley, B.; Wandeto, J.M. Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map. Symmetry 2021, 13, 299. Dresp-Langley, B.; Wandeto, J.M. Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map. Symmetry 2021, 13, 299.

Journal reference: Symmetry 2021, 13, 299
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, 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 and/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 metric’s proven selectivity to local contrast and color variations in large and highly complex image data.

Subject Areas

symmetry; shape; local color; self-organized visual map; quantization error; SOM-QE; choice response time; human decision; uncertainty

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.