Detection of Bilateral Symmetry in Complex Biological Images

Author:

Evans Christopher S1,Wenderoth Peter1,Cheng Ken1

Affiliation:

1. Department of Psychology, Macquarie University, Sydney, NSW 2109, Australia

Abstract

The recognition of bilateral symmetry in simple dot patterns is reliably influenced by orientation. Performance is best when the axis of symmetry is vertical. We conducted two experiments to determine whether stimulus orientation also affects detection of the low levels of naturally occurring asymmetry in complex biological images. University students judged whether colour images displayed on a computer monitor possessed perfect bilateral symmetry. Stimuli were generated from high-resolution plan-view images of crabs and insects. In experiment 1, the asymmetric stimuli were the original animals, displayed on a standard black background. Symmetrical versions of each natural image were generated by sectioning the shape at the midline, copying and reflecting one side, and then fusing the two halves together. To facilitate comparison of results with those obtained in earlier studies, we also presented dot patterns based upon both the slightly asymmetric and perfectly symmetrical natural images. Experiment 2 was designed to assess whether symmetry detection was dependent upon the markings and patterns on the body and appendages of the animals. The natural images were converted to silhouettes and tested against matched dot patterns. In both studies, images were presented in a random sequence with the axis of symmetry vertical, horizontal, oblique left, and oblique right. Performance with the biological images was consistently better than with the dot patterns. Abolishing fine detail did not appreciably reduce this effect. A pronounced vertical advantage was apparent with all stimuli, demonstrating that this phenomenon is robust despite considerable variation in image complexity. The implications of orientation effects for perception of natural structures are discussed.

Publisher

SAGE Publications

Subject

Artificial Intelligence,Sensory Systems,Experimental and Cognitive Psychology,Ophthalmology

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