From Simple Machines to Eureka in Four Not-So-Easy Steps: Towards Creative Visuospatial Intelligence

Chapter
Part of the Synthese Library book series (SYLI, volume 376)

Abstract

This chapter builds an account of the cognitive abilities and mechanisms required to produce creative problem-solving and insight. Such mechanisms are identified in an essentialized set of human abilities: making visuospatial inferences, creatively solving problems involving object affordances, using experience with previously solved problems to find solutions for new problems, generating new concepts out of old ones. Each such cognitive ability is selected to suggests a principle necessary for the harder feat of engineering insight. The features such abilities presuppose in a cognitive system are addressed. A core set of mechanisms able to support such features is proposed. A unified system framework in line with cognitive research is suggested, in which the knowledge-encoding supports the variety of such processes efficiently.

Keywords

Creativity Problem-solving Insight Visuospatial intelligence 

References

  1. Aerts, D., & Gabora, L. (2005). A theory of concepts and their combinations II: A hilbert space representation. Kybernetes, 34(1/2), 192–221.CrossRefGoogle Scholar
  2. Barsalou, L. (2003). Abstraction in perceptual symbol systems. Philosophical Transactions of the Royal Society of London, B, Biological Sciences, 358, 1177–1187.CrossRefGoogle Scholar
  3. Barsalou, L., & Wiemer-Hastings, K. (2005). Situating abstract concepts. In D. Pecher & R. A. Zwaan (Eds.), Grounding cognition: The role of perception and action in memory, language, and thought (pp. 129–163). New York: Cambridge University Press.CrossRefGoogle Scholar
  4. Batchelder, W. H., & Alexander, G. E. (2012). Insight problem solving: A critical examination of the possibility of formal theory. The Journal of Problem Solving, 5(1), 56–100.CrossRefGoogle Scholar
  5. Boden, M. (2003). The creative mind: Myths and mechanisms. London/New York: Routledge.Google Scholar
  6. Booker, C. (2004). The seven basic plots: Why we tell stories. London/New York: Continuum.Google Scholar
  7. Chambers, N., & Jurafsky, D. (2010). A database of narrative schemas. In Proceedings of the LREC, Valletta.Google Scholar
  8. Dunbar, K. (1993). Concept discovery in a scientific domain. Cognitive Science, 17(3), 397–434.CrossRefGoogle Scholar
  9. Duncker, K. (1945). On problem solving (Psychological monographs, Vol. 58(5, Whole No.270)). Washington, DC: American Psychological Association.Google Scholar
  10. Falomir, Z., Gonzalez-Abril, L., Museros, L., & Ortega, J. (2013). Measures of similarity between objects from a qualitative shape description. Spatial Cognition and Computation, 13, 181–218.CrossRefGoogle Scholar
  11. Fauconnier, G., & Turner, M. (1998). Conceptual integration networks. Cognitive Science, 22(2), 133–187.CrossRefGoogle Scholar
  12. Forsyth, D., & Ponce, J. (2003). Computer vision: A modern approach. Upper Saddle River: Prentice Hall.Google Scholar
  13. Freksa, C. (1991). Qualitative spatial reasoning. In D. Mark & A. Frank (Eds.), Cognitive and linguistic aspects of geographic space (pp. 361–372). Dordrecht/Holland: Kluwer.CrossRefGoogle Scholar
  14. Freksa, C. (2013). Spatial computing – how spatial structures replace computational effort. In M. Raubal, D. Mark, & A. Frank (Eds.), Cognitive and linguistic aspects of geographic space (Lecture notes in geoinformation and cartography, pp. 23–42). Berlin/Heidelberg/New York: Springer.CrossRefGoogle Scholar
  15. Gärdenfors, P. (2004). Conceptual spaces: The geometry of thought. Cambridge: Bradford Books.Google Scholar
  16. Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7(2), 155–170.CrossRefGoogle Scholar
  17. Gentner, D. (2010). Where hypotheses come from: Learning new relations by structural alignment. Journal of Cognition and Development, 11(3), 356–373.CrossRefGoogle Scholar
  18. Gibson, J. J. (1977). The theory of affordance. In R. Shaw & J. Bransford (Eds.), Perceiving, acting, and knowing. Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  19. Gillan, D. J., Premack, D., & Woodruff, G. (1981). Reasoning in the chimpanzee: I. analogical reasoning. Journal of Experimental Psychology: Animal Behavior Processes, 7(1), 1.Google Scholar
  20. Guilford, J. (1967). The nature of human intelligence. New York: McGraw-Hill.Google Scholar
  21. Haven, K. (2006). 100 greatest science inventions of all time. Westport: Libraries Unlimited.Google Scholar
  22. Hebb, D. (1949). The organization of behavior. New York: Wiley.Google Scholar
  23. Hélie, S., & Sun, R. (2010). Incubation, insight, and creative problem solving: A unified theory and a connectionist model. Psychological Review, 117(3), 994.CrossRefGoogle Scholar
  24. Holyoak, K., & Thagard, P. (1996). Mental leaps: Analogy in creative thought. Cambridge: MIT.Google Scholar
  25. Imai, M., Gentner, D., & Uchida, N. (1994). Children’s theories of word meaning: The role of shape similarity in early acquisition. Cognitive Development, 9(1), 45–75.CrossRefGoogle Scholar
  26. insight. (2014). Encyclopaedia britannica online academic edition. http://www.britannica.com/EBchecked/topic/289152/insight.
  27. Kim, K. H. (2006). Can we trust creativity tests? A review of the torrance tests of creative thinking (ttct). Creativity Research Journal, 18(1), 3–14.CrossRefGoogle Scholar
  28. Klahr, D., & Dunbar, K. (1988). Dual space search during scientific reasoning. Cognitive Science, 12(1), 1–48.CrossRefGoogle Scholar
  29. Koestler, A. (1964). The act of creation. New York: Macmillan.Google Scholar
  30. Köhler, W. (1976). The mentality of apes. New York: Liveright. (Originally published in 1925).Google Scholar
  31. Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 59–69.CrossRefGoogle Scholar
  32. Lakoff, G., & Johnson, M. (1980). Metaphors we live by. Chicago: University of Chicago Press.Google Scholar
  33. Lakoff, G., & Johnson, M. (1999). Philosophy in the flesh: The embodied mind and its challenge to Western thought. New York: Basic Books.Google Scholar
  34. Landau, B., Smith, L. B., & Jones, S. S. (1988). The importance of shape in early lexical learning. Cognitive Development, 3(3), 299–321.CrossRefGoogle Scholar
  35. Langley, P. (2000). The computational support of scientific discovery. International Journal of Human-Computer Studies, 53(3), 393–410.CrossRefGoogle Scholar
  36. Maier, N. R. (1931). Reasoning in humans. II. The solution of a problem and its appearance in consciousness. Journal of Comparative Psychology, 12(2), 181.Google Scholar
  37. Mandler, J. M. (2010). The spatial foundations of the conceptual system. Language and Cognition, 2(1), 21–44.CrossRefGoogle Scholar
  38. Medin, D. L., & Shoben, E. J. (1988). Context and structure in conceptual combination. Cognitive Psychology, 20(2), 158–190.CrossRefGoogle Scholar
  39. Metcalfe, J., & Wiebe, D. (1987). Intuition in insight and noninsight problem solving. Memory & Cognition, 15(3), 238–246.CrossRefGoogle Scholar
  40. Murphy, G. L., & Medin, D. L. (1985). The role of theories in conceptual coherence. Psychological Review, 92(3), 289.CrossRefGoogle Scholar
  41. Nersessian, N. (2008). Creating scientific concepts. Cambridge: MIT.Google Scholar
  42. Newell, A. (1969). Heuristic programming: Ill-structured problems. In J. Aronofsky (Ed.), Progress in operations research, III. New York: Wiley.Google Scholar
  43. Newell, A. (1994). Unified theories of cognition. Cambridge: Harvard University Press.Google Scholar
  44. Newell, A., & Simon, A. (1972). Human problem solving. Englewood Cliffs: Prentice Hall.Google Scholar
  45. Philbin, T. (2005). The 100 greatest inventions of all time: A ranking past and present. New York: Citadel Press.Google Scholar
  46. Ritchie, G. (2001). Assessing creativity. In Proceedings of AISB’01 Symposium, Citeseer.Google Scholar
  47. Rosch, E. (1975). Cognitive representations of semantic categories. Journal of experimental psychology: General, 104(3), 192.CrossRefGoogle Scholar
  48. Samuelson, L. K., & Smith, L. B. (1999). Early noun vocabularies: Do ontology, category structure and syntax correspond? Cognition, 73(1), 1–33.CrossRefGoogle Scholar
  49. Schultheis, H., & Barkowsky, T. (2011). Casimir: An architecture for mental spatial knowledge processing. topiCS – Topics in Cognitive Science, 3, 778–795.CrossRefGoogle Scholar
  50. Simon, H. A. (1974). The structure of ill structured problems. Artificial Intelligence, 4(3), 181–201.Google Scholar
  51. Sloman, A. (1971). Interactions between philosophy and artificial intelligence: The role of intuition and non-logical reasoning in intelligence. Artificial Intelligence, 2(3), 209–225.CrossRefGoogle Scholar
  52. Sowa, J. (1992). Semantic networks. In S. Shapiro (Ed.), Encyclopedia of artificial intelligence (pp. 1493–1511). New York: Wiley.Google Scholar
  53. Sternberg, R., & Davidson, J. (1996). The nature of insight. Cambridge: MIT.Google Scholar
  54. Thagard, P. (2012). Creative combination of representations: Scientific discovery and technological invention. In R. W. Proctor & E. J. Capaldi (Eds.), Psychology of science: Implicit and explicit processes (pp 389–405). Oxford/New York: Oxford University Press.CrossRefGoogle Scholar
  55. Tower-Richardi, S. M., Brunye, T. T., Gagnon, S. A., Mahoney, C. R., & Taylor, H. A. (2012). Abstract spatial concept priming dynamically influences real-world action. Front Psychology, 3, 361.CrossRefGoogle Scholar
  56. Vitruvius Pollio, M. (1914). The ten books on architecture (pp. 253–254, M. Hicky Morgan, Trans.). Cambridge: Harvard University Press.Google Scholar
  57. Wallas, G. (1926). The art of thought. London: Cape.Google Scholar
  58. Watson, P. (2005). Ideas: A history of thought and invention, from fire to Freud. New York: HarperCollins.Google Scholar
  59. Watson, P. (2011). The modern mind: An intellectual history of the 20th century. London: HarperCollins.Google Scholar
  60. Wertheimer, M. (1945). Productive thinking. New York: Harper and Row.Google Scholar
  61. Wiggins, G. A. (2001). Towards a more precise characterisation of creativity in AI. In Case-Based Reasoning: Papers from the Workshop Programme at ICCBR (Vol. 1, pp. 113–120).Google Scholar
  62. Zhang, J. (1997). The nature of external representations in problem solving. Cognitive Science, 21(2), 179–217.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Universität BremenBremenGermany

Personalised recommendations