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Perceptual Similarity and Analogy in Creativity and Cognitive Development

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 548)

Abstract

We argue for the position that analogy represents the core mechanism in human cognitive development rather than being a special cognitive skill among many. We review some developmental psychology results that support this claim. Analogy and metaphor, on the other hand, are seen as central for the creative process. Whereas mainstream research in artificial creativity and computational models of reasoning by analogy stresses the importance of matching the structure between the source and the target domains, we suggest that perceptual similarities play a much more important role. We provide some empirical data to support these claims and discuss their consequences.

Keywords

Creativity  Analogy Perceptual Similarity Cognitive Development 

References

  1. 1.
    Indurkhya, B.: Metaphor and Cognition. Kluwer Academic Publishers, Dordrecht (1992)CrossRefGoogle Scholar
  2. 2.
    Polya, G.: How to Solve It, 2nd edn. Princeton University Press, Princeton (1957)Google Scholar
  3. 3.
    Richards, I.A.: Philos. Rhetoric. Oxford University Press, Oxford (1936)Google Scholar
  4. 4.
    Mark T. Keane.: Analogical Problem Solving. Wiley, New York (1988)Google Scholar
  5. 5.
    Nersessian, N.J., Chandrasekharan, S.: Hybrid Analogies in Conceptual Innovation in Science. Cogn. Syst. Res. 10(3), 178–188 (2009)CrossRefGoogle Scholar
  6. 6.
    Keith, J.: Holyoak and Paul Thagard. Mental Leaps: Analogy in Creative Thought. The MIT Press, Cambridge (1995)Google Scholar
  7. 7.
    Ashok K. Goel.: Design, Analogy, and Creativity. IEEE Expert: Intell. Syst. Appl. 12(3), 62–70 (1997)Google Scholar
  8. 8.
    Gordon, W.J.J.: Synectics: The Development of Creative Capacity. Harper & Row, New York (1961)Google Scholar
  9. 9.
    Schön, D.A.: Displacement of Concepts. Humanities Press, New York (1963)Google Scholar
  10. 10.
    Stojanov, G.: Computational Models of Creativity: Taking Early Cognitive Development Seriously, to appear. In: Indyrkhya, B., Manjalay, J. (eds.) Cognition, Experience and Creativity. Orient Blackswan, New Delhi (2012)Google Scholar
  11. 11.
    Kokinov, B., Petrov, A.: Integration of memory and reasoning in analogy-making: The AMBR model. In: Gentner, D., Holyoak, K., Kokinov, B. (eds.) The Analogical Mind: Perspectives from Cognitive Science. MIT Press, Cambridge (2001)Google Scholar
  12. 12.
    Davies, J., Goel, A.: Visual re-representation in creative analogies. Open AI J. 2, 11–20 (2008)Google Scholar
  13. 13.
    Hofstadter, D.: Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought. Basic Books, New York (1995)Google Scholar
  14. 14.
    Hofstadter, D.: Analogy as the core of cognition. In: Gentner, D., Holyoak, K., Kokinov, B. (eds.) The Analogical Mind: Perspectives from Cognitive Science, pp. 499–538. The MIT Press/Bradford Book, Cambridge (2001)Google Scholar
  15. 15.
    Indurkhya, B.: On modeling creativity in legal reasoning. In: Proceedings of the Sixth International Conference on AI and Law, pp. 180–189. ACM, New York (1997)Google Scholar
  16. 16.
    Ogawa, S., Indurkhya, B., Byrski, A.: A meme-based architecture for modeling creativity. In: Proceedings of the International Conference on Computational Creativity, Dublin, Ireland (2012)Google Scholar
  17. 17.
    Bonnardel, N.: Towards understanding and supporting creativity in design: analogies in a constrained cognitive environment. Knowl.-Based Syst. 13, 505–513 (2000)CrossRefGoogle Scholar
  18. 18.
    Koestler, A.: The Act of Creation. Hutchinsons, London (1964)Google Scholar
  19. 19.
    Okada, T., Yokochi, S., Ishibashi, K., Ueda, K.: Analogical modification in the creation of contemporary art. Cogn. Syst. Res. 10(3), 189–203 (2009)CrossRefGoogle Scholar
  20. 20.
    Bhatta, S., Goel, A.: An analogical theory of creativity in design. In: Proceedings 2nd International Conference on Case-Based Reasoning, Lecture Notes in Computer Science, vol: 1266, pp. 565–574. (1997)Google Scholar
  21. 21.
    Swaroop S. Vattam, Michael E. Helms, Ashok K. Goel. A content account of creative analogies in biologically inspired design. Artif. Intell. Eng. Des. Anal. Manuf. 24(4), 467–481 (2010)Google Scholar
  22. 22.
    Indurkhya, B.: On the role of metaphor in creative cognition. In: Proceedings of the International Conference on Computational Creativity: ICCC-X, Lisbon, Portugal (2010)Google Scholar
  23. 23.
    Indurkhya, B.: Rationality and reasoning with metaphors. New Ideas in Psychology 25(1), 16–36 (2007)Google Scholar
  24. 24.
    Blank, D.: Implicit Analogy-Making: A Connectionist Exploration. MAICS, Bloomington (1996)Google Scholar
  25. 25.
    Falkenhainer, B., Forbus, K.D., Gentner, D.: The structure-mapping engine: algorithm and examples. Artif. Intell. 41, 1–63 (1989)CrossRefMATHGoogle Scholar
  26. 26.
    Gentner, D.: Structure-mapping: a theoretical framework for analogy. Cogn. Sci. 7, 155–170 (1983)CrossRefGoogle Scholar
  27. 27.
    Holyoak, K., Thagard, P.: Analogical mapping by constraint evaluation. Cogn. Sci. 13, 295–355 (1989)CrossRefGoogle Scholar
  28. 28.
    Hummel, J.E., Holyoak, K.J.: Distributed representations of structure: a theory of analogical access and mapping. Psychol. Rev. 104, 427–466 (1997)Google Scholar
  29. 29.
    Hummel, J.E., Holyoak, K.J.: A symbolic-connectionist theory of relational inference and generalization. Psychol. Rev. 110, 220–264 (2003)CrossRefGoogle Scholar
  30. 30.
    Chalmers, D.J., French, R.M., Hofstadter, D.R.: High-level perception, representation, and analogy: a critique of artificial intelligence methodology. J. Exp. Theor. Artif. Intell. 4(3), 185–211 (1992)CrossRefGoogle Scholar
  31. 31.
    Sun, R.: A microfeature based approach towards metaphor interpretation, In: Proceedings of the 14th IJCAI, pp. 424–429. Morgan Kaufman, San Francisco (1995)Google Scholar
  32. 32.
    Ward, T.B.: Analogies. In: Runco, M.A., Pritzker, S.R. (eds.) Encyclopedia of Creativity, 2nd edn. Academic Press, New York (2011)Google Scholar
  33. 33.
    Mitchell, M.: Analogy-Making as Perception. ISBN 0-262-13289-3 (1993)Google Scholar
  34. 34.
    O’Hara, S., Indurkhya, B.: Incorporating (Re)-Interpretation in case-based reasoning. In: Wess, S., Althoff, K.-D., Richter, M.M. (eds.) Topics in Case-Based Reasoning: LNCS, pp. 246–260. Springer, Berlin (1994)CrossRefGoogle Scholar
  35. 35.
    O’Hara, S., Indurkhya, B.: Adaptation and re-description in the context of geometric proportional analogies. In: AAAI-95 Fall Symposium Series: Adaptation of Knowledge for Reuse, pp. 80–86 (1995)Google Scholar
  36. 36.
    Dastani, M., Indurkhya, B.: Modeling context effect in perceptual domains. In: Modeling and Using Context: Proceedings of the Third International Conference on Modeling and Using Context: CONTEXT 2001, pp. 129–142. University of Dundee, Dundee (2001)Google Scholar
  37. 37.
    Dastani, M., Indurkhya, B., Scha, R.: An algebraic approach to modeling analogical projection in pattern perception. J. Exp. Theor. Artif. Intell. 15(4), 489–511 (2003)CrossRefMATHGoogle Scholar
  38. 38.
    Hofstadter, D., Sander., E.: Surfaces and Essences: Analogy as the Fuel and Fire of Thinking, Basic Books, New York (2013)Google Scholar
  39. 39.
    Schwering, A., Krumnack, K., K uhnberger, K.-U., Gust, H.: Using gestalt principles to compute analogies of geometric figures. In: 19th Meeting of the Cognitive Science Society (2007)Google Scholar
  40. 40.
    Hofstadter, D.R.: THE COPYCAT PROJECT: An Experiment in Nondeterminism and Creative Analogies, AI Memo 755. MIT, The Artificial Intelligence Laboratory, Cambridge (1984)Google Scholar
  41. 41.
    Hofstadter, D.R., Mitchell, M.: An Overview of the Copycat Project, Technical Report CRCC. Indiana University, Center for Research on Concepts and Cognition, Bloomington (1991)Google Scholar
  42. 42.
    Indurkhya, B., Kattalay, K., Ojha, A., Tandon, P.: Experiments with a creativity-support system based on perceptual similarity. In: Fujita, H., Zualkernan, I. (eds.) New Trends in Software Methodologies. IOS Press, Tools and Techniques, Amsterdam (2008)Google Scholar
  43. 43.
    Keane, M.T.: Analogical Problem Solving. (Cognitive Science Series). Ellis Horwood, Chichester (1988)Google Scholar
  44. 44.
    Klein, S., Aeschliman, J.F., Balsiger, D., Converse, S.L., Court, C., Foster, M. et al.: Automatic Novel Writing: A Status Report, Technical Report 186. The University of Wisconsin, Madison, Wisconsin, Computer Science Department (1973)Google Scholar
  45. 45.
    Dehn, N.: Story generation after tale-spin. In: Proceedings of the Seventh International Joint Conference on Artificial Intelligence, pp. 16–18. William Kaufmann Inc, Los Altos (1981)Google Scholar
  46. 46.
    Lebowitz, M.: Story-telling as planning and learning, In: Proceedings of the Tenth International Joint Conference on Artificial Intelligence, vol. 1, William Kaufmann Inc, Los Altos (1983)Google Scholar
  47. 47.
    Turner, S. R., Minstrel: a computer model of creativity and storytelling. Ph.D. dissertation, University of California at Los Angeles, Los Angeles (1993)Google Scholar
  48. 48.
    Pérez y Pérez, R.: MEXICA: a computer model of creativity in writing, Ph.D. dissertation, The University of Sussex (1999)Google Scholar
  49. 49.
    Theune, M., Faas, E., Nijholt, A., Heylen, D.: The virtual storyteller: Story creation by intelligent agents. In: Proceedings of the Technologies for Interactive Digital Storytelling and Entertainment (TIDSE) Conference, pp. 204–215. Springer, Berlin (2003)Google Scholar
  50. 50.
    Stock, O., Strapparava, C.: The act of creating humorous acronyms. Appl. Artif. Intell. 19(2), 137–151 (2005)CrossRefGoogle Scholar
  51. 51.
    Manurung, R., Ritchie, G., Pain, H., Waller, A., O’Mara, D., Black, R.: The construction of a pun generator for language skills development. Appl. Artif. Intell. 22(9), 841–869 (2008)CrossRefGoogle Scholar
  52. 52.
    Tinholt, H. W., Nijholt, A.: Computational humour: utilizing cross-reference ambiguity for conversational fokes, In: Masulli, F., Mitra, S., Pasi, G. (eds.) 7th International Workshop on Fuzzy Logic and Applications (WILF 2007), Camogli (Genova), Italy, Vol. 4578 of Lecture Notes in Artificial Intelligence, pp. 477–483. Springer Verlag, Berlin (2007)Google Scholar
  53. 53.
    Binsted, K., Ritchie, G.: An implemented model of punning riddles. In: Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94). AAAI Press, Menlo Park (1994)Google Scholar
  54. 54.
    Cope, D.: Experiments in musical intelligence (EMI): non-linear linguistic-based composition. Interface 18, 117–139 (1989)CrossRefGoogle Scholar
  55. 55.
    Grindlay, G., Helmbold, D.: Modeling, analyzing, and synthesizing expressive performance with graphical models. Mach. Learn. 65(2–3), (2006)Google Scholar
  56. 56.
    Widmer, G., Flossmann, S., Grachten, M., YQX Plays Chopin, AI Mag. 30(3), 35–48 (2009)Google Scholar
  57. 57.
    Colton, S.: Creativity versus the perception of creativity in computational systems. In: Proceedings of the AAAI Spring Symposium on Creative Systems (2008)Google Scholar
  58. 58.
    Cohen, H.: On Modeling of Creative Behavior, Rand Corporation Report Series, P-6681, (1981)Google Scholar
  59. 59.
    Indurkhya, B.: Whence is Creativity? In: Proceedings of the International Conference on Computational Creativity: ICCC-2012, pp. 62–66. Dublin, Ireland (2012)Google Scholar
  60. 60.
    Boden, M.: The creative mind. Weidenfeld and Nicholson, London (1990)Google Scholar
  61. 61.
    Wiggins, G.A.: A preliminary framework for description, analysis and comparison of creative systems, Knowl. Based Syst. 19(2006), 449–458, Elsevier (2006)Google Scholar
  62. 62.
    Turner, S.A.: The Creative Process: A Computer Model of Storytelling and Creativity, Lawrence Erlbaum Associates, Hillsdale (1994)Google Scholar
  63. 63.
    Hugo Liu, and Push Singh.: Makebelieve: Using Commonsense Knowledge to Generate Stories. In: Proceedings of the Eighteenth National Conference on Artificial Intelligence, pp. 957–958, AAAI Press, Edmonton (2002)Google Scholar
  64. 64.
    McCorduck, P.: Aaron’s code: Meta-art, artificial intelligence, and the work of Harold Cohen. Freeman, New York (1991)Google Scholar
  65. 65.
    Zhu, J., Ontanon, S.: Towards analogy-based story generation. In: the Proceedings of ICCC-X, Lisbon (2010)Google Scholar
  66. 66.
    Bhatta, S., Goel, A.: A functional theory of design patterns. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 294–300. Nagoya, Japan (1997)Google Scholar
  67. 67.
    Davies, J., Goel, A.K., Nersessian, N.J.: A computational model of visual analogies in design. Cogn. Syst. Res. 10(3), 204–215 (2009)CrossRefGoogle Scholar
  68. 68.
    Goswami, U.: Analogical reasoning in children. In: Campione, J., Metz, K., Sullivan Palincsar, A. (eds.) Children’s Learning in Laboratory and Classroom Contexts Essays in Honor of Ann Brown. Routledge, London (2007)Google Scholar
  69. 69.
    Piaget, J.: Play, Dreams and Imitation in Childhood (Trans. Gattegno. C., Hodgson, F.M) W.W. Norton, New York. (Original work published 1945) (1962)Google Scholar
  70. 70.
    Stojanov, G.: Embodiment as metaphor: metaphorizing-in the environment. In: C. Nehaniv (ed.) LNAI, vol. 1562, pp. 88–101, Springer (1999)Google Scholar
  71. 71.
    Poprcova, V., Stojanov, G., Kulakov, A.: Inductive Logic Programming (ILP) and reasoning by analogy in context of embodied robot learning. In: Proceedings of IJATS, pp. 64–73 (2010)Google Scholar
  72. 72.
    Levitt, S.D., Dubner, S.J.: Superfreakonomics. William Morrow, New York (2009)Google Scholar
  73. 73.
    Karmiloff-Smith, A.: From metaprocess to conscious access: Evidence from children’s metalinguistic and repair data. Cognition 23, 95–147 (1986)Google Scholar
  74. 74.
    Karmiloff-Smith, A.: Beyond Modularity: A Developmental Perspective on Cognitive Science. MIT Press, Cambridge (1992)Google Scholar
  75. 75.
    Barsalou, L.W., Prinz, J.J.: Mundane creativity in perceptual symbol systems. In: Ward, T.B., Smith, S.M., Vaid, J. (eds.) Creative Thought: An Investigation of Conceptual Structures and Processes, pp. 267–307. American Psychological Association, Washington (1997)CrossRefGoogle Scholar
  76. 76.
    Barsalou, L.W.: Grounding symbolic operations in the brain’s modal systems. In: Semin, G.R., Smith, E.R. (eds.) Embodied Grounding: Social, Cognitive, Affective, and Neuroscientific Approaches, pp. 9–42. Cambridge University Press, New York (2008)CrossRefGoogle Scholar
  77. 77.
    Keane, M.T.: Incremental analogising: Theory and model. In: Gilhooly, K.J., Keane, M.T., Logie, R., Erdos, G. (eds.) Lines of Thinking: Reflections on the Psychology of Thought. Wiley, Chichester (1990)Google Scholar
  78. 78.
    Shapira, O., Liberman, N.: An Easy Way to Increase Creativity, Scientific American (Mind Matters), July 21 (2009)Google Scholar
  79. 79.
    Barnden, J.A., Holyoak, K.J. (eds.).: Analogy, metaphor, and reminding. Intellect Books (1994)Google Scholar
  80. 80.
    Faries, J.M., Schlossberg, K.R., The effect of similarity on memory for prior problems. In: Proceedings of the 16\(^{\rm {th}}\) Annual Conference of the Cognitive Science Society, pp. 278–282. (1994)Google Scholar
  81. 81.
    de Bono, E.: New Think: The Use of Lateral Thinking in the Generation of New Ideas. Basic Books, New York (1975)Google Scholar
  82. 82.
    Ojha, A., Indurkhya, B.: Perceptual versus conceptual similarities and creation of new features in visual metaphor. In: Kokinov, B., Holyoak, K., Gentner, D. (eds.) New Frontiers in Analogy Research. New Bulgarian University Press, Sofia (2009)Google Scholar
  83. 83.
    Ojha, A., Indurkhya, B.: On the role of perceptual features in metaphor, In: Gola, E., Ervas, F. (eds.) Metaphor and Communication. To appear.Google Scholar
  84. 84.
    Rodari, G.: The Grammar of Fantasy (Trans. Zipes, J.) Teachers & Writers Collaborative, New York (1996)Google Scholar
  85. 85.
    Riesbeck, C.K., Schank, R.C.: Inside Case-Based Reasoning. Lawrence Erlbaum & Associates, Hillsdale (1989)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.The American University of ParisParisFrance
  2. 2.Computer Science departmentAGH University of Science and TechnologyCracowPoland

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