Advertisement

Metric Properties of Visual Perception of Mirror Symmetry

  • T. RakcheevaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1126)

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.

Keywords

Psychophysics Symmetry Visual perception Metric properties Mirror symmetry Experimental research Engineering psychology 

References

  1. 1.
    Weil, H.: Symmetry, p. 192c. Nauka, Moscow (1968)Google Scholar
  2. 2.
    Shubnikov, A.V., Koptsik, V.A.: Symmetry in Science and Art, 560p. Computer Research Institute, Moscow-Ijevsk (2004)Google Scholar
  3. 3.
    Gregory, R.L.: A reasonable eye, 232 p. Editorial URSS (2003)Google Scholar
  4. 4.
    Fress, P., Piaget, J.: Experimental Psychology, Issue 4, Progress, Moscow, 344 p. (1978)Google Scholar
  5. 5.
    Hamada, J., Amano, K., Fukuda, S.T., Uchiumi, C., Fukushi, K., van der Helm, P.A.: A group theoretical model of symmetry cognition. Acta Psychol. 171, 128–137 (2016).  https://doi.org/10.1016/j.actpsy.2016.10.002CrossRefGoogle Scholar
  6. 6.
    Treder, M.S.: Behind the looking-glass: a review on human symmetry perception. Symmetry 2, 1510–1543 (2010).  https://doi.org/10.3390/sym2031510CrossRefGoogle Scholar
  7. 7.
    Van der Helm, P.A.: Symmetry perception. In: Wagemans, J. (ed.) Oxford Handbook of Perceptual Organization, pp. 108–128. Oxford University Press, Oxford (2015).  https://doi.org/10.1093/oxfordhb/9780199686858.013.056
  8. 8.
    Poirier, F.J.A.M., Wilson, H.R.: A biologically plausible model of human shape symmetry perception. J. Vis. 10, 1–16 (2010)CrossRefGoogle Scholar
  9. 9.
    Artemenkov, S.L., Shukova, G.V., Mironova, K.V.: Visual perception of symmetry as a factor of aesthetic experience. Exp. Psychol. 11(1), 166–177 (2018).  https://doi.org/10.17759/exppsy.2018110110CrossRefGoogle Scholar
  10. 10.
    Turoman, N., Spence, Ch.: Cross-modal correspondence between visual symmetry and taste. Perception 45(2), 329 (2016). Suppl, 39th European Conference on Visual Perception (ECVP) 2016 BarcelonaGoogle Scholar
  11. 11.
    Pecchinenda, A., Bertamini, M., Makin, A.D.J., Ruta, N.: The pleasantness of visual symmetry: always, never or sometimes. PLoS ONE 9(3), e92685 (2014).  https://doi.org/10.1371/journal.pone.0092685CrossRefGoogle Scholar
  12. 12.
    Van Tonder, G.J., Lyons, M.J.: Visual perception in Japanese rock garden design. Axiomathes 15, 353–371 (2005).  https://doi.org/10.1007/s10516-004-5448-8CrossRefGoogle Scholar
  13. 13.
    Van der Helm, P.A.: Weber-Fechner behavior in symmetry perception? Attention Percept. Psychophysics 72, 1854–1864 (2010)Google Scholar
  14. 14.
    Zhukova, K.V., Reier, I.A.: Basic skeleton connectivity and a parametric shape descriptor. Mach. Learn. Data Anal. 1(10), 1354–1368 (2014)Google Scholar
  15. 15.
    Rakcheeva, T.A.: The factor of symmetrization in the identification of forms of curves. In: Seventh Kurdyumov readings. Synergetics in the Natural Sciences: Materials intern. interdisciplinary Scientific Conf. Tver, pp. 156–158 (2011)Google Scholar
  16. 16.
    Rakcheeva, T.A.: The influence of form on the perception of distance. In: Eighths Anniversary Kurdyumov Readings: Synergetics in the Natural Sciences, Proceedings of the International Interdisciplinary Conference, pp. 117–119 (2012)Google Scholar
  17. 17.
    Rakcheeva, T.A., Nikolaeva, E.C., et al.: Orthogonal illusion of visual perception. Modern Med. Theory Practice 1, 42–51 (2004)Google Scholar
  18. 18.
    Rakcheeva, T.A.: Metric invariants of the illusion of intersection. In: Proceedings of the International Conference “Machines, Technologies and Materials for Modern Engineering, dedicated to the 80th Anniversary of IMASH RAS, p. 160 (2018)Google Scholar
  19. 19.
    Al Walid, M.H., Anisuzzaman, D.M., Saif, A.S.: Data analysis and visualization of continental cancer situation by Twitter scraping. Int. J. Modern Educ. Comput. Sci. (IJMECS) 11(7), 23–31 (2019).  https://doi.org/10.5815/ijmecs.2019.07.03CrossRefGoogle Scholar
  20. 20.
    Hamd, M.H., Ahmed, S.K.: Biometric system design for iris recognition using intelligent algorithms. Int. J. Modern Educ. Comput. Sci. (IJMECS) 10(3), 9–16 (2018).  https://doi.org/10.5815/ijmecs.2018.03.02CrossRefGoogle Scholar
  21. 21.
    Marouf, A.A., Ashrafi, A.F., Ahmed, T., Emon, T.: A machine learning based approach for mapping personality traits and perceived stress scale of undergraduate students. Int. J. Modern Educ. Comput. Sci. (IJMECS) 11(8), 42–47 (2019).  https://doi.org/10.5815/ijmecs.2019.08.05CrossRefGoogle Scholar
  22. 22.
    Isong, B.: A methodology for teaching computer programming: first year students perspective. Int. J. Modern Educ. Comput. Sci. (IJMECS) 6(9), 15–21 (2014)CrossRefGoogle Scholar
  23. 23.
    Dominic, M., Francis, S.: An adaptable e-learning architecture based on learners’ profiling. Int. J. Modern Educ. Comput. Sci. (IJMECS) 7(3), 26–31 (2015)CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Mechanical Engineering Research Institute of the Russian Academy of SciencesMoscowRussia

Personalised recommendations