Educational Implications of the ‘Self-Made Worldview’ Concept

  • Alexandra Maland
  • Liane GaboraEmail author
Part of the Creativity Theory and Action in Education book series (CTAE, volume 4)


Immersion in a creative task can be an intimate experience. It can feel like a mystery: intangible, inexplicable, and beyond the reach of science. However, science is making exciting headway into understanding creativity. While the mind of a highly uncreative individual consists of a collection of items accumulated through direct experience and enculturation, the mind of a creative individual is self-organizing and self-mending; thus, experiences and items of cultural knowledge are thought through from different perspectives such that they cohere together into a loosely integrated whole. The reweaving of items in memory is elicited by perturbations: experiences that increase psychological entropy because they are inconsistent with one’s web of understandings. The process of responding to one perturbation often leads to other perturbations, i.e., other inconsistencies in one’s web of understandings. Creative thinking often requires the capacity to shift between divergent and convergent modes of thought in response to the ever-changing demands of the creative task. Since uncreative individuals can reap the benefits of creativity by imitating creators, using their inventions, or purchasing their artworks, it is not necessary that everyone be creative. Agent based computer models of cultural evolution suggest that society functions best with a mixture of creative and uncreative individuals. The ideal ratio of creativity to imitation increases in times of change, such as we are experiencing now. Therefore it is important to educate the next generation in ways that foster creativity. The chapter concludes with suggestions for how educational systems can cultivate creativity.



This work was supported by a grant (62R06523) from the Natural Sciences and Engineering Research Council of Canada.


  1. Andreasen, N. C. (1987). Creativity and mental illness: Prevalence rates in writers and their first-degree relatives. American Journal of Psychiatry, 144, 1288–1292.CrossRefGoogle Scholar
  2. Bak, P., Tang, C., & Weisenfeld, K. (1988). Self-organized criticality. Physical Review A, 38, 364.CrossRefGoogle Scholar
  3. Beghetto, R. A. (2007). Ideational code-switching: Walking the talk about supporting student creativity in the classroom. Roeper Review, 29, 265–270.CrossRefGoogle Scholar
  4. Beghetto, R. A. (2013). Killing ideas softly?: The promise and perils of creativity in the classroom. Charlotte: Information Age Publishing.Google Scholar
  5. Chrusch, C., & Gabora, L. (2014). A tentative role for FOXP2 in the evolution of dual processing modes and generative abilities. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of 36th annual meeting of cognitive science society (pp. 499–504). Austin: Cognitive Science Society.Google Scholar
  6. Donald, M. (1991). Origins of the modern mind: Three stages in the evolution of culture and cognition. Cambridge, MA: Harvard University Press.Google Scholar
  7. Edelman, G. (1987). Neural Darwinism: The theory of neuronal group selection. New York: Basic Books.Google Scholar
  8. Eldridge, N., & Gould, S. J. (1972). Punctuated equilibria: An alternative to phyletic gradualism. In T. Schopf (Ed.), Models in paleobiology (pp. 82–115). New York: Freeman, Cooper &.Google Scholar
  9. Ellamil, M., Dobson, C., Beeman, M., & Christoff, K. (2012). Evaluative and generative modes of thought during the creative process. NeuroImage, 59, 1783–1794.CrossRefGoogle Scholar
  10. Feinstein, J. S. (2006). The nature of creative development. Stanford: Stanford University Press.Google Scholar
  11. Feist, G. J. (1998). A meta-analysis of personality in scientific and artistic creativity. Personality and Social Psychology Review, 2(4), 290–309.CrossRefGoogle Scholar
  12. Gabora, L. (1995). Meme and Variations: A computer model of cultural evolution. In L. Nadel & D. L. Stein (Eds.), 1993 lectures in complex systems (pp. 471–486). Boston: Addison Wesley.Google Scholar
  13. Gabora, L. (1997). The origin and evolution of culture and creativity. Journal of Memetics: Evolutionary Models of Information Transmission, 1(1).
  14. Gabora, L. (1998). Weaving, bending, patching, mending the fabric of reality: A cognitive science perspective on worldview inconsistency. Foundations of Science, 3(2), 395–428.CrossRefGoogle Scholar
  15. Gabora, L. (2003). Contextual focus: A cognitive explanation for the cultural transition of the middle/upper paleolithic. In R. Alterman & D. Hirsch (Eds.), Proceedings of 25th annual meeting of cognitive science society (pp. 432–437). Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  16. Gabora, L. (2004). Ideas are not replicators but minds are. Biology and Philosophy, 19(1), 127–143.CrossRefGoogle Scholar
  17. Gabora, L. (2006). Self-other organization: Why early life did not evolve through natural selection. Journal of Theoretical Biology, 241, 443–450.CrossRefGoogle Scholar
  18. Gabora, L. (2010). Revenge of the ‘neurds’: Characterizing creative thought in terms of the structure and dynamics of human memory. Creativity Research Journal, 22, 1–13.CrossRefGoogle Scholar
  19. Gabora, L. (2011). Five clarifications about cultural evolution. Journal of Cognition and Culture, 11, 61–83.CrossRefGoogle Scholar
  20. Gabora, L. (2013). An evolutionary framework for culture: Selectionism versus communal exchange. Physics of Life Reviews, 10(2), 117–145.CrossRefGoogle Scholar
  21. Gabora, L. (2017a). Honing theory: A complex systems framework for creativity. Nonlinear Dynamics, Psychology, and Life Sciences, 21(1), 35–88.Google Scholar
  22. Gabora, L. (2017b, August 30). What creativity really is – and why schools need it. The Conversation. Retrieved from
  23. Gabora, L. (2018). The neural basis and evolution of divergent and convergent thought. In O. Vartanian & R. Jung (Eds.), The Cambridge handbook of the neuroscience of creativity. Cambridge, MA: Cambridge University Press.Google Scholar
  24. Gabora, L., & Merrifield, M. (2012). Dynamical disequilibrium, transformation, and the evolution and development of sustainable worldviews. In F. Orsucci & N. Sala (Eds.), Complexity science, living systems, and reflexing interfaces (pp. 69–77). Hershey: IGI Global.Google Scholar
  25. Gabora, L., & Ranjan, A. (2013). How insight emerges in distributed, content-addressable memory. In A. Bristol, O. Vartanian, & J. Kaufman (Eds.), The neuroscience of creativity (pp. 19–43). Cambridge, MA: MIT Press.CrossRefGoogle Scholar
  26. Gabora, L., & Smith, C. (in press). Two cognitive transitions underlying the capacity for cultural evolution. Journal of Anthropological Sciences.Google Scholar
  27. Gabora, L., & Tseng, S. (2017). The social benefits of balancing creativity and imitation: Evidence from an agent-based model. Psychology of Aesthetics, Creativity, and the Arts, 11(4), 457–473.CrossRefGoogle Scholar
  28. Gabora, L., Chia, W. W., & Firouzi, H. (2013). A computational model of two cognitive transitions underlying cultural evolution. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of 35th annual meeting of cognitive science society (pp. 2344–2349). Austin: Cognitive Science Society.Google Scholar
  29. Gick, M. L., & Lockhart, R. S. (1995). Creative insight and preventive forms. In R. J. Sternberg & J. E. Davidson (Eds.), The nature of insight. Cambridge: MIT Press.Google Scholar
  30. Greenwald, A., Banaji, M., Rudman, L. A., Farnham, S., Nosek, B., & Mellott, D. (2002). A unified theory of implicit attitudes, stereotypes, self-esteem, and self-concept. Psychological Review, 109, 3–25.CrossRefGoogle Scholar
  31. Gregerson, M., Kaufman, J., & Snyder, H. (Eds.). (2013). Teaching creatively and teaching creativity. New York: Springer.Google Scholar
  32. Guastello, S. J. (2002). Managing emergent phenomena: Nonlinear dynamics in work organizations. Mahwah: Lawrence Erlbaum Associates.Google Scholar
  33. Hebb, D. (1949). The organization of behavior. New York: Wiley.Google Scholar
  34. Hirsh, J., Mar, R., & Peterson, J. (2012). Psychological entropy: A framework for understanding uncertainty-related anxiety. Psychological Review, 119, 304–320.CrossRefGoogle Scholar
  35. Hordijk, W., Hein, J., & Steel, M. (2010). Autocatalytic sets and the origin of life. Entropy, 12, 1733–1742.CrossRefGoogle Scholar
  36. Jacobsen, J. J., & Guastello, S. J. (2011). Diffusion models for innovation: S-curves, networks, power laws, catastrophes, and entropy. Nonlinear Dynamics, Psychology, and Life Sciences, 15, 307–333.Google Scholar
  37. Josselyn, S., et al. (2015). Finding the engram. Nature Reviews Neuroscience, 16(9), 521–534.CrossRefGoogle Scholar
  38. Kanerva, P. (1988). Sparse distributed memory. Cambridge: MIT Press.Google Scholar
  39. Kauffman, S. (1993). Origins of order. New York: Oxford University Press.Google Scholar
  40. Kitzbichler, M. G., Smith, M. L., Christensen, S. R., & Bullmore, E. (2009). Broadband criticality of human brain network synchronization. PLoS Computational Biology, 5, e1000314.CrossRefGoogle Scholar
  41. Kounios, J., & Beeman, M. (2009). The “Aha!” moment: The cognitive neuroscience of insight. Current Directions in Psychological Science, 18, 210–216.CrossRefGoogle Scholar
  42. Kounios, J., & Beeman, M. (2014). The cognitive neuroscience of insight. Annual Review of Psychology, 65, 71–93.CrossRefGoogle Scholar
  43. Mayer, R. E. (1995). The search for insight: Grappling with gestalt psychology’s unanswered questions. In R. J. Sternberg & J. E. Davidson (Eds.), The nature of insight. Cambridge, MA: MIT Press.Google Scholar
  44. McClelland, J. L. (2011). Memory as a constructive process: The parallel distributed processing approach. In S. Nalbantian, P. M. Matthews, J. L. McClelland, S. Nalbantian, P. M. Matthews, & J. L. McClelland (Eds.), The memory process: Neuroscientific and humanistic perspectives (pp. 129–155). Cambridge, MA: MIT Press.Google Scholar
  45. McClelland, J. L., Rumelhart, D. E., & Hinton, G. E. (2003). Parallel distributed processing: Explorations in the microstructure of cognition. In M. P. Munger (Ed.), The history of psychology: Fundamental questions (pp. 478–492). New York: Oxford University Press.Google Scholar
  46. Mithen, S. (1998). Creativity in human evolution and prehistory. London: Routledge.Google Scholar
  47. Nelson, D. L., McEvoy, C. L., & Schreiber, T. A. (2004). The University of South Florida word association, rhyme and word fragment norms. Behavior Research Methods, Instruments, & Computers, 36, 408–420.CrossRefGoogle Scholar
  48. Orsucci, F. (2008). Reflexing interfaces: The complex coevolution of information technology ecosystems. Hershey: Idea Books.CrossRefGoogle Scholar
  49. Osgood, C. E., & Tannenbaum, P. H. (1955). The principle of congruity in the prediction of attitude change. Psychological Review, 62, 42–55.CrossRefGoogle Scholar
  50. Paterson, H. M., Kemp, R. I., & Forgas, J. P. (2009). Co-witnesses, confederates, and conformity: The effects of discussion and delay on eyewitness memory. Psychiatry, Psychology and Law, 16(1), S112–S124.CrossRefGoogle Scholar
  51. Pelaprat, E., & Cole, M. (2011). “Minding the gap”: Imagination, creativity and human cognition. Integrative Psychological and Behavioral Science, 45(4), 397–418. Scholar
  52. Piaget, J. (2013). The construction of reality in the child (Vol. 82). London: Routledge.Google Scholar
  53. Ranjan, A., & Gabora, L. (2012). Creative ideas for actualizing student potential. In H. Snyder, M. Gregerson, & J. Kaufman (Eds.), Teaching creatively (pp. 119–132). Berlin: Springer.Google Scholar
  54. Salvi, C., Bricolo, E., Kounios, J., Bowden, E., & Beeman, M. (2016). Insight solutions are correct more often than analytic solutions. Thinking & Reasoning, 22, 443–460. Scholar
  55. Schacter, D. L. (2001). The seven sins of memory: How the mind forgets and remembers. Reading: Houghton Mifflin.Google Scholar
  56. Stephen, D. G., Boncoddo, R. A., Magnuson, J. S., & Dixon, J. (2009). The dynamics of insight: Mathematical discovery as a phase transition. Memory & Cognition, 37, 1132–1149.CrossRefGoogle Scholar
  57. Steyvers, M., & Tenenbaum, J. B. (2005). The large-scale structure of semantic networks: Statistical analyses and a model of semantic growth. Cognitive Science, 29, 41–78.CrossRefGoogle Scholar
  58. Sutton, R. S. (1996). Generalization in reinforcement learning: Successful examples using sparse coarse coding. In Advances in neural information processing systems (pp. 1038–1044). Cambridge: MIT Press.Google Scholar
  59. Topolinski, S., & Reber, R. (2010). Gaining insight into the “Aha” experience. Current Directions in Psychological Science, 19, 402–402.CrossRefGoogle Scholar
  60. Vetsigian, K., Woese, C., & Goldenfeld, N. (2006). Collective evolution and the genetic code. Proceedings of the National Academy of Science, 103, 10696–10701.CrossRefGoogle Scholar
  61. Ward, T., Smith, S., & Vaid, J. (1997). Conceptual structures and processes in creative thought. In T. Ward, S. Smith, & J. Vaid (Eds.), Creative thought: An investigation of conceptual structures and processes (pp. 1–27). Washington, DC: American Psychological Association.CrossRefGoogle Scholar
  62. Wilkenfeld, M. J., & Ward, T. B. (2001). Similarity and emergence in conceptual combination. Journal of Memory and Cognition, 45, 21–38.Google Scholar
  63. Yoruk, S., & Runco, M. A. (2014). The neuroscience of divergent thinking. Activitas Nervosa Superior, 56, 1–16.CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of PsychologyFipke Centre for Innovative ResearchKelownaCanada

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