The Brain and Creativity

Abstract

Modern abstract art is considered complex and the extraction of meaning from some works of art is largely controversial. However, some artists have explicitly tried to produce paintings in accordance with specific goals. This means that behind their artwork there is a project realized through creativity. Their paintings clearly reflect those efforts and are able to show the emergence of complex ideas reproducing a non-linear and uncertain world. This chapter investigates the link between brain-states of a subjectʼs perception of art with the complexity of the art. More than 25 paintings of famous artists of modern art are studied and evaluated. The concept of artistic complexity, CA has been introduced as a metric for assessing the complexity of paintings of different artists. The results achieved have been compared to the saliency maps earlier introduced in computer vision as computational models of bottom-up VA. The measure proposed is based on an interplay between top-down and bottom-up approaches, manifesting the difficulty of the human brain in extracting invariants from some abstract representations. The intriguing relationships shown may offer a paradigm for testing novel computational models on brain-like machines. The methodologies described are likely to be of interest for multimedia quality assessment as metrics able to emulate the integral mechanisms of human visual systems as well as to correlate well with visual perception of quality.

Keywords

Visual Attention Shannon Entropy Kolmogorov Complexity Tsallis Entropy Salient Location 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

2-D

two-dimensional

AM

aesthetic measure

MI

mutual information

QoS

quality of service

TE

Tsallis entropy

VA

visual attention

fMRI

functional magnetic resonance imaging

References

  1. 61.1.
    M. Livingstone: Vision and Art: The Biology of Seeing (Harry N. Abrams, New York 2002)Google Scholar
  2. 61.2.
    S. Zeki: Artistic creativity and the brain, Science 293, 51–52 (2001)CrossRefGoogle Scholar
  3. 61.3.
    R. Rao, D. Ballard: Predicting coding in visual cortex: A functional interpretation of some extra-classical receptive-field effects, Nat. Neurosci. 2(1), 79–87 (1999)CrossRefGoogle Scholar
  4. 61.4.
    S. David, W. Vinje, J. Gallant: Natural stimulus statistics alter the receptive field structure of V1 neurons, J. Neurosci. 24(31), 6991–7006 (2004)CrossRefGoogle Scholar
  5. 61.5.
    W. Shultz, A. Dickinson: Neuronal coding of prediction errors, Annu. Rev. Neurosci. 23, 473–500 (2000)CrossRefGoogle Scholar
  6. 61.6.
    A. Treisman, G. Gelade: A feature-integration theory of attention, Cogn. Psychol. 12(1), 97–136 (1980)CrossRefGoogle Scholar
  7. 61.7.
    E. Korner, G. Matsumoto: Cortical architecture and self-referential control for brain-like computation, IEEE Eng. Med. Biol. Mag. 21(5), 121–133 (2002)CrossRefGoogle Scholar
  8. 61.8.
    A. Forsythe, G. Mulhern, M. Sawey: Confounds in pictorial sets: The role of complexity and familiarity in basic-level picture processing, Behav. Res. Methods 40(1), 116–129 (2008)CrossRefGoogle Scholar
  9. 61.9.
    L. Itti, P. Baldi: Bayesian surprise attracts human attention, Vis. Res. 49, 1295–1306 (2009)CrossRefGoogle Scholar
  10. 61.10.
    F. Porikli: Multimedia Quality Assessment, IEEE Signal Process. Mag. 28(6), 164–177 (2011)CrossRefGoogle Scholar
  11. 61.11.
    G. Tononi, O. Sporns, G. Edelman: A measure for brain complexity: Relating functional segregation and integration in the nervous system, Proc. Natl. Acad. Sci. USA 91, 5033–5037 (1994)CrossRefGoogle Scholar
  12. 61.12.
    C. Tsallis: Possible generalization of Boltzmann–Gibbs statistics, J. Stat. Phys. 52, 479–487 (1988)MathSciNetCrossRefMATHGoogle Scholar
  13. 61.13.
    F.C. Morabito: Artistic Complexity and brain: Quantitative measurement of creativity, Tri-Soc. Newsl. 8(2), 10–11 (2010), INNS/ENNS/JNNSGoogle Scholar
  14. 61.14.
    F.C. Morabito, M. Cacciola, G. Occhiuto: Creative brain and abstract art: A quantitative study on Kandinskij paintings, Proc. IJCNN (2011) pp. 2387–2394Google Scholar
  15. 61.15.
    G. Birkhoff: Aesthetic Measure (Harvard Univ., Cambridge, USA 1933)CrossRefMATHGoogle Scholar
  16. 61.16.
    M. Bense: Einführung in die Informationstheoretische Ästhetik (Rowohlt Taschenbuch, Hamburg 1969)Google Scholar
  17. 61.17.
    L. Itti, C. Koch, E. Niebur: A model of saliency based visual attention for rapid scene analysis, IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  18. 61.18.
    L. Zhang, M.H. Tong, T.K. Marks, H. Shan, G.W. Cottrell: SUN: A Bayesian framework for saliency using natural statistics, J. Vis. 8(7), 1–20 (2008)Google Scholar
  19. 61.19.
    K. Jones-Smith, H. Mathur: Fractal analysis: Revisiting Pollockʼs drip paintings, Nature 444, E9–E10 (2006)CrossRefGoogle Scholar
  20. 61.20.
    G. Tononi, O. Sporns, G.M. Edelman: Complexity and coherency: Integrating information in the brain, Trends Cogn. Sci. 2(12), 474–484 (1998)CrossRefGoogle Scholar
  21. 61.21.
    International Telecommunication Union: Parameter values for the HDTV standards for production and international programme exchange (2002), available online at www.itu.int/dms_pubrec/itu-r/rec/bt/R-REC-BT.709-5-200204-I!!PDF-E.pdf
  22. 61.22.
    O.A. Rosso, M.T. Martin, A. Plastino: Brain electrical activity analysis using wavelet-based informational tools (II): Tsallis non-extensivity and complexity measures, Physica A 320, 497–511 (2003)CrossRefMATHGoogle Scholar
  23. 61.23.
    M. Gell-Mann, C. Tsallis (Eds.): Nonextensive Entropy-Interdisciplinary Applications (Oxford Univ. Press, New York 2004)MATHGoogle Scholar
  24. 61.24.
    T. Kadir, M. Brady: Saliency, Scale and Image Description (University of Oxford, UK 2000)MATHGoogle Scholar
  25. 61.25.
    L. Itti, C. Koch: Computational modelling of visual attention, Nature 2, 194–203 (2001)Google Scholar
  26. 61.26.
    B. Julesz: Dialogues on Perception (MIT Press, Cambridge 1995)Google Scholar

Copyright information

© Springer-Verlag 2014

Authors and Affiliations

  1. 1.DICEAMUniversity MediterraneaReggio CalabriaItaly
  2. 2.University of PaviaPaviaItaly
  3. 3.DICEAMUuniversity Mediterranea of Reggio CalabriaReggio CalabriaItaly
  4. 4.DICEAMUniversity MediterraneaReggio CalabriaItaly

Personalised recommendations