Abstract
The work is devoted to an experimental investigation of visual perception of symmetry. Symmetry participates in the mechanism of the evolutionary adaptability of a living organism, providing the form that is optimal for existence. Not only all living beings, but also objects created by their creativity, one way or another, correspond to the laws of symmetry. Functional expediency was transformed into such aesthetic categories as structural proportionality, harmony. The main developments of psychophysical research are carried out in areas in which qualitative characteristics of the perception of symmetry are used, such as emotional reaction, aesthetic experience, imaginative association, and even taste sensations. The goal of this work is an experimental study of the metric properties of perception and reconstruction of elementary symmetries as a manifestation of symmetry models of human intelligence. The experimental research was implemented on a computer in the format of the reconstruction of a partially given symmetry composition. The subject had to finish the composition in accordance with the transformation of the given symmetry. Independent or regulated factors are symmetry elements, symmetry objects and symmetry transformations. The dependent or resulting factors are functions of the regulated factors and reflect the accuracy of the reconstruction of a given symmetry composition. The visual perception of mirror symmetry in a vertical configuration for a “point” object is characterized by statistically significant stable correctness of the average value on the population and the horizontal-vertical anisotropy of errors for individuals; axis symmetry direction is a matter of principle. This work represents the first and necessary stage of research related to the perception of arbitrary forms.
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Rakcheeva, T. (2020). Metric Properties of Visual Perception of Mirror Symmetry. In: Hu, Z., Petoukhov, S., He, M. (eds) Advances in Artificial Systems for Medicine and Education III. AIMEE 2019. Advances in Intelligent Systems and Computing, vol 1126. Springer, Cham. https://doi.org/10.1007/978-3-030-39162-1_19
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