Between Material and Ideas: A Process-Based Spatial Model of Artistic Creativity

  • Palle Dahlstedt


In this chapter, I propose a model of an artistic creative process, based on study of my own creative processes over twenty years of activities as composer and improviser. The model describes the creative process as a structured exploration of the space of the possible, emphasising the interplay between a dynamic concept and the changing material form of the work. Combining ideas, tools, material and memory, creativity is described as a coherent, dynamic, and iterative process that navigates the space of the chosen medium, guided by the tools at hand, and by the continuously revised ideas, significantly extending previous spatial models of creativity. This involves repeated misinterpretation and coincidences, which are crucial in human creative processes, adding meaning and depth to the artwork. A few examples from real life are given as illustrations of the model, together with a discussion of phenomena such as appreciation, skill and collaborative creativity. Finally, I discuss how the proposed model could form a foundation for computer implementations of artistic creative process, to increase our understanding of human creativity, and to possibly enable believable artistic behaviour in machines.


Creative Process Material Form Conceptual Representation Conceptual Space Temporary Result 
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.



A major part of the research behind this chapter was funded by a research grant from the Swedish Research Council, for the project “Potential Music”.


  1. Barron, F. (1972). Artists in the making. New York: Seminar Press. Google Scholar
  2. Barron, F. (Ed.) (1997). Creators on creating. Tarcher. Google Scholar
  3. Boden, M. (2004). The creative mind: myths and mechanisms (2nd ed.). London: Routledge. Google Scholar
  4. Buchanan, B. G. (2001). Creativity at the meta-level: AAAI 2000 presidential address. AI Magazine Volume, 22(3), 13–28. MathSciNetGoogle Scholar
  5. Cope, D. (2005). Computer models of musical creativity. Cambridge: MIT Press. Google Scholar
  6. Dahlstedt, P. (2001). A MutaSynth in parameter space: interactive composition through evolution. Organised Sound, 6(2), 121–124. CrossRefGoogle Scholar
  7. Dahlstedt, P. (2004). Sounds unheard of: evolutionary algorithms as creative tools for the contemporary composer. PhD thesis, Chalmers University of Technology. Google Scholar
  8. Dahlstedt, P. (2005). Defining spaces of potential art: the significance of representation in computer-aided creativity. Paper presented at the description & creativity conference, King’s College, Cambridge, UK, 3–5 July 2005 Google Scholar
  9. Dahlstedt, P. (2007) Evolution in creative sound design. In E. R. Miranda & J. A. Biles (Eds.), Evolutionary computer music (pp. 79–99). London: Springer. CrossRefGoogle Scholar
  10. Dahlstedt, P. (2009a). Ideas and tools in material space—an extended spatial model of creativity. In M. Boden, M. d’Inverno & J. McCormack (Eds.), Dagstuhl seminar proceedings: Vol. 09291. Computational creativity: an interdisciplinary approach. Dagstuhl: Schloss Dagstuhl—Leibniz-Zentrum fuer Informatik. Google Scholar
  11. Dahlstedt, P. (2009b). Thoughts on creative evolution: a meta-generative approach to composition. Contemporary Music Review, 28(1), 43–55. CrossRefGoogle Scholar
  12. Dahlstedt, P. (2012). Ossia II: autonomous evolution of complete piano pieces and performances. In A-Life for music:music and computer models of living systems. Middleton: A-R Editions. Google Scholar
  13. Denton, R. (2004). The atheism tapes: Jonathan Miller in conversation (TV program), episode 6, interview with Daniel Dennet. London: BBC, TV program. Google Scholar
  14. Ebcioglu, K. (1988). An expert system for harmonising four-part chorales. Computer Music Journal, 12(3), 43–51. CrossRefGoogle Scholar
  15. Gabora, L. (2005). Creative thought as a non-Darwinian evolutionary process. Journal of Creative Behavior, 39(4), 65–87. CrossRefGoogle Scholar
  16. Gibson, J. (1977). The theory of affordances. In R. Shaw & J. Bransford (Eds.), Perceiving, acting and knowing, Hillsdale: Erlbaum. Google Scholar
  17. Gregory, R. L. (1981). Mind in science. London: Weidenfeld and Nicolson. Google Scholar
  18. Harrison, A. (1978). Making and thinking. Harvester Press. Google Scholar
  19. Hofstadter, D. (1985). Variations on a theme as the crux of creativity. In Metamagical themas. New York: Basic Books. Google Scholar
  20. Hofstadter, D., & The Fluid Analogies Research Group (1995). Fluid concepts and creative analogies: computer models of the fundamental mechanisms of thought. New York: Basic Books. Google Scholar
  21. Jacob, B. (1996). Algorithmic composition as a model of creativity. Organised Sound, 1(3), 157–165. CrossRefGoogle Scholar
  22. Karmiloff-Smith, A. (1994). Beyond modularity: a developmental perspective on cognitive science. Behavioral and Brain Sciences, 17(4), 693–745. CrossRefGoogle Scholar
  23. Klein, G., & Ödman, M. (Eds.) (2003). Om kreativitet och flow. Bromberg. Google Scholar
  24. Konecni, V. J. (1991). Portraiture: an experimental study of the creative process. Leonardo, 24(3). Google Scholar
  25. Lenat, D. (1983). Eurisko: a program that learns new heuristics and domain concepts. Artificial Intelligence, 21, 61–98. CrossRefGoogle Scholar
  26. Lindsay, R. K., Buchanan, B. G., Feigenbaum, E. A., & Lederberg, J. (1980). Applications of artificial intelligence for organic chemistry: the DENDRAL project. New York: McGraw-Hill. Google Scholar
  27. McCorduck, P. (1990). AARON’S CODE: meta-art, artificial intelligence, and the work of Harold Cohen. New York: Freeman. Google Scholar
  28. McCormack, J. (2005). Open problems in evolutionary music and art. In F. Rothlauf et al. (Eds.), LNCS: Vol. 3449. EvoWorkshops 2005 (pp. 428–436). Berlin: Springer. Google Scholar
  29. Mednick, S. A. (1962). The associative basis of the creative process. Psychological Review, 69(3), 220–232. CrossRefGoogle Scholar
  30. Nachmanovitch, S. (1990). Free play: improvisation in life and art. New York: Jeremy P. Tarcher/Penguin-Putnam Publishing. Google Scholar
  31. Norman, D. A. (1988). The psychology of everyday things. New York: Basic Books. Google Scholar
  32. Papadopoulos, G., & Wiggins, G. (1999). AI methods for algorithmic composition: a survey, a critical view and future prospects. In A. Patrizio (Ed.), Proceedings of the AISB’99 symposium on musical creativity, Edinburgh, UK. Google Scholar
  33. Pearce, M., & Wiggins, G. A. (2002). Aspects of a cognitive theory of creativity in musical composition. In Proceedings of 2nd international workshop on creative systems, European conference on artificial intelligence, Lyon, France. Google Scholar
  34. Sims, K. (1991). Artificial evolution for computer graphics. In ACM SIGGRAPH ’91 conference proceedings, Las Vegas, Nevada, July 1991 (pp. 319–328). Google Scholar
  35. Todd, P. M., & Werner, G. M. (1999). Frankensteinian methods for evolutionary music composition. In N. Griffith & P. M. Todd (Eds.), Musical networks: parallel distributed perception and performance, Cambridge: MIT Press—Bradford Books. Google Scholar
  36. Valkare, G. (1997). Det audiografiska fältet: om musikens förhållande till skriften och den unge Bo Nilssons strategier. PhD thesis, University of Gothenburg. Google Scholar
  37. Wiggins, G. A. (2006). A preliminary framework for description, analysis and comparison of creative systems. Knowledge-Based Systems, 19, 449–458. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Dept. of Applied Information TechnologyUniversity of GothenburgGöteborgSweden

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