Correlation Between Human Aesthetic Judgement and Spatial Complexity Measure

  • Mohammad Ali Javaheri Javid
  • Tim Blackwell
  • Robert Zimmer
  • Mohammad  Majid al-Rifaie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9596)

Abstract

The quantitative evaluation of order and complexity conforming with human intuitive perception has been at the core of computational notions of aesthetics. Informational theories of aesthetics have taken advantage of entropy in measuring order and complexity of stimuli in relation to their aesthetic value. However entropy fails to discriminate structurally different patterns in a 2D plane. This paper investigates a computational measure of complexity, which is then compared to a results from a previous experimental study on human aesthetic perception in the visual domain. The model is based on the information gain from specifying the spacial distribution of pixels and their uniformity and non-uniformity in an image. The results of the experiments demonstrate the presence of correlations between a spatial complexity measure and the way in which humans are believed to aesthetically appreciate asymmetry. However the experiments failed to provide a significant correlation between the measure and aesthetic judgements of symmetrical images.

Keywords

Human aesthetic judgements Spatial complexity Information theory Symmetry Complexity 

Notes

Acknowledgements

We are grateful to Thomas Jacobsen of Helmut Schmidt University for granting permission to use his experimental stimuli.

References

  1. 1.
    Andrienko, Y.A., Brilliantov, N.V., Kurths, J.: Complexity of two-dimensional patterns. Eur. Phys. J. B 15(3), 539–546 (2000)CrossRefGoogle Scholar
  2. 2.
    Arnheim, R.: Art and Visual Perception: A Psychology of the Creative Eye. Univ of California Press, Berkeley (1954)Google Scholar
  3. 3.
    Arnheim, R.: Towards a Psychology of Art/entropy and Art an Essay on Disorder and Order. The Regents of the University of California (1966)Google Scholar
  4. 4.
    Arnheim, R.: Visual Thinking. Univ of California Press, Berkeley (1969)Google Scholar
  5. 5.
    Bates, J.E., Shepard, H.K.: Measuring complexity using information fluctuation. Phys. Lett. A 172(6), 416–425 (1993)CrossRefGoogle Scholar
  6. 6.
    Bense, M., Nee, G.: Computer grafik. In: Bense, M., Walther, E. (eds.) Edition Rot, vol. 19. Walther, Stuttgart (1965)Google Scholar
  7. 7.
    Bense, M.: Aestetica: Programmierung des Schönen, allgemeine Texttheorie und Textästhetik [Aesthetica : Programming of beauty, general text theory and aesthetics]. Agis-Verlag (1960)Google Scholar
  8. 8.
    Bense, M.: Kleine abstrakte ästhetik [small abstract aesthetics]. In: Walther, E. (ed.) Edition Rot, vol. 38 (1969)Google Scholar
  9. 9.
    Birkhoff, G.: Aesthetic Measure. Harvard University Press, Cambridge (1933)CrossRefMATHGoogle Scholar
  10. 10.
    Ciesielski, V., Barile, P., Trist, K.: Finding image features associated with high aesthetic value by machine learning. In: Machado, P., McDermott, J., Carballal, A. (eds.) EvoMUSART 2013. LNCS, vol. 7834, pp. 47–58. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley Series in Telecommunications and Signal Processing. Wiley-Interscience, New York (2006)MATHGoogle Scholar
  12. 12.
    Dawkins, R.: The Blind Watchmaker. W. W. Norton, New York (1986)Google Scholar
  13. 13.
    den Heijer, E., Eiben, A.E.: Comparing aesthetic measures for evolutionary art. In: Chio, C., Brabazon, A., Caro, G.A., Ebner, M., Farooq, M., Fink, A., Grahl, J., Greenfield, G., Machado, P., O’Neill, M., Tarantino, E., Urquhart, N. (eds.) EvoApplications 2010, Part II. LNCS, vol. 6025, pp. 311–320. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Eysenck, H.J.: An experimental study of aesthetic preference for polygonal figures. J. Gen. Psychol. 79(1), 3–17 (1968)CrossRefGoogle Scholar
  15. 15.
    Eysenck, H.J.: The empirical determination of an aesthetic formula. Psychol. Rev. 48(1), 83 (1941)CrossRefGoogle Scholar
  16. 16.
    Eysenck, H.J.: The experimental study of the ‘good gestalt’ –a new approach. Psychol. Rev. 49(4), 344 (1942)CrossRefGoogle Scholar
  17. 17.
    Franke, H.W.: A cybernetic approach to aesthetics. Leonardo 10(3), 203–206 (1977)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Galanter, P.: Computational aesthetic evaluation: past and future. In: McCormack, J., d’IInverno, M. (eds.) Computer and Creativity, pp. 255–293. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  19. 19.
    den Heijer, E.: Autonomous Evolutionary Art, Ph.D. thesis. Vrije Universiteit, Amsterdam (2013)Google Scholar
  20. 20.
    Jacobsen, T.: Beauty and the brain: culture, history and individual differences in aesthetic appreciation. J. Anat. 216(2), 184–191 (2010)CrossRefGoogle Scholar
  21. 21.
    Jacobsen, T., Hofel, L.: Aesthetic judgments of novel graphic patterns: analyses of individual judgments. Percept. Mot. Skills 95(3), 755–766 (2002)CrossRefGoogle Scholar
  22. 22.
    Javaheri Javid, M.A., Blackwell, T., Zimmer, R., Al-Rifaie, M.M.: Spatial complexity measure for characterising cellular automata generated 2D patterns. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds.) EPIA 2015. LNCS, vol. 9273, pp. 201–212. Springer, Heidelberg (2015)Google Scholar
  23. 23.
    Javid, M.A.J., al-Rifaie, M.M., Zimmer, R.: An informational model for cellular automata aesthetic measure. In: AISB Symposium on Computational Creativity. University of Kent, Canterbury, UK (2015)Google Scholar
  24. 24.
    Latham, W.H., Todd, S.: Computer sculpture. IBM Syst. J. 28(4), 682–688 (1989)CrossRefGoogle Scholar
  25. 25.
    Li, M.: An introduction to Kolmogorov complexity and its applications. Springer, New York (1997)CrossRefMATHGoogle Scholar
  26. 26.
    Machado, P., Cardoso, A.: Computing aesthetics. In: de Oliveira, F.M. (ed.) SBIA 1998. LNCS (LNAI), vol. 1515, pp. 219–228. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  27. 27.
    Machado, P., Romero, J., Manaris, B.: Experiments in computational aesthetics: an iterative approach to stylistic change in evolutionary art. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music, pp. 381–415. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  28. 28.
    McCormack, J.: Open problems in evolutionary music and art. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 428–436. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  29. 29.
    McCormack, J.: Facing the future: evolutionary possibilities for human-machine creativity. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution, pp. 417–451. Springer, Heidleberg (2008)CrossRefGoogle Scholar
  30. 30.
    Moles, A.: Information Theory and Esthetic Perception. Illinois Press, Urbana (1968). Trans. JE Cohen. UGoogle Scholar
  31. 31.
    Nake, F.: Information aesthetics: an heroic experiment. J. Math. Arts 6(2–3), 65–75 (2012)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Noll, A.M.: The digital computer as a creative medium. IEEE Spectr. 4(10), 89–95 (1967)CrossRefGoogle Scholar
  33. 33.
    Rigau, J., Feixas, M., Sbert, M.: Informational aesthetics measures. IEEE Comput. Graph. Appl. 28(2), 24–34 (2008)CrossRefGoogle Scholar
  34. 34.
    Rigau, J., Feixas, M., Sbert, M.: Conceptualizing Birkhoff’s aesthetic measureusing shannon entropy and kolmogorov complexity. In: Cunningham, D.W., Meyer, G., Neumann, L. (eds.) Workshop on Computational Aesthetics, pp. 105–112. Eurographics Association, Banff, Alberta, Canada (2007)Google Scholar
  35. 35.
    Shannon, C.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423, 623–656 (1948)Google Scholar
  36. 36.
    Sims, K.: Artificial evolution for computer graphics. Technical Report, TR-185, Thinking Machines Corporation (1991)Google Scholar
  37. 37.
    Staudek, T.: Exact aesthetics, object and scene to message. Ph.D. thesis, Faculty of Informatics, Masaryk University of Brno (2002)Google Scholar
  38. 38.
    Wackerbauer, R., Witt, A., Atmanspacher, H., Kurths, J., Scheingraber, H.: A comparative classification of complexity measures. Chaos, Solitons & Fractals 4(1), 133–173 (1994)MathSciNetCrossRefMATHGoogle Scholar
  39. 39.
    Wilson, D.J.: An experimental investigation of Birkhoff’s aesthetic measure. J. Abnorm. Soc. Psychol. 34(3), 390 (1939)CrossRefGoogle Scholar
  40. 40.
    Zurek, W.H.: Algorithmic randomness and physical entropy. Phys. Rev. A 40(8), 4731 (1989)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mohammad Ali Javaheri Javid
    • 1
  • Tim Blackwell
    • 1
  • Robert Zimmer
    • 1
  • Mohammad  Majid al-Rifaie
    • 1
  1. 1.Department of ComputingGoldsmiths, University of LondonLondonUK

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