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Sub-Symbols and Icons

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Abstract

There is a relationship between perception through biological senses and simple problem-solving. According to the production system theory, we can define a geometrically based problem-solving model as a production system operating on vectors of fixed dimensions (Icons). Our goal is to form a sequence of associations, which lead to a desired state represented by a vector, from an initial state represented by a vector. We define a simple and universal heuristics function, which takes into account the relationship between the vector and the corresponding similarity of the represented object or state in the real world.

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References

  1. Aleksander I, Dunmall B, Del Frate V. Neurocomputational models of visualisation: a preliminary report. In: Proc. IWANN, Alicante, Spain; 1999.

  2. Aleksander I, Morton H. Neurons and symbols, the stuff that mind is made of. London: Chapman and Hall; 1993.

    Google Scholar 

  3. Aleksander I, Morton H, Dunmall B. Seeing is believing. In: Proc. IWANN, Granada; 2001.

  4. Allport A. Visual attention. In: Posner M, editor. Foundations of cognitive science. Cambridge, MA: The MIT Press; 1989. p. 631–82.

    Google Scholar 

  5. Anderson J. The architecture of cognition. Cambridge: Harvard University Press; 1983.

    Google Scholar 

  6. Anderson JR. Cognitive psychology and its implications. 4th ed. New York: W. H. Freeman and Company; 1995.

    Google Scholar 

  7. Arp R. Scenario visualization: an evolutionary account of creative problem solving. Cambridge: MIT Press; 2008.

    Google Scholar 

  8. Biederman I, Ju G. Surface vs. edge-based determinants of visual recognition. Cogn Psychol. 1988;20:38–64.

    Article  CAS  PubMed  Google Scholar 

  9. Browne A, Sun R. Connectionistic variable binding. Expert Systems. 1999;16(3):189–206.

    Article  Google Scholar 

  10. Browne A, Sun R. Connectionistic inference models. Neural Networks. 2001;14:1331–55.

    Article  CAS  PubMed  Google Scholar 

  11. Downing C, Oinker S. The spatial structure of visual attention. In: Posner M, Marin O, editors. Attention and performance XI. Hillsdale, NJ: Erlbaum; 1985. p. 171–87.

    Google Scholar 

  12. Feldman J. Four frames suffice: a provisional model of vision and space. Behavioral and Brain Sciences. 1985;8:265–89.

    Article  Google Scholar 

  13. Gilovich T. Tversky. In: Wilson RA, Keil FC, editors. The MIT encyclopedia of the cognitive sciences. New York: The MIT Press; 1999. p. 849–850.

  14. Goldstone R. Similarity. In: Wilson RA, Keil FC, editors. The MIT encyclopedia of the cognitive sciences. New York: The MIT Press; 1999. p. 763–765.

  15. Hofstadter D. Fluid concepts and creative analogies: computer models of the fundamental mechanisms of thought. New York: Basic Books; 1995.

    Google Scholar 

  16. Johnson-Laird P. Mental models: toward a cognitive science of language, inference and consciousness. Cambridge: Harvard University Press; 1983.

    Google Scholar 

  17. Klahr P, Waterman D. Expert systems: techniques, tools and applications. Longman: Addison-Wesley; 1986.

    Google Scholar 

  18. Kosslyn SM. Image and brain, the resolution of the imagery debate. Cambridge: The MIT Press; 1994.

    Google Scholar 

  19. Kurbat M, Smith E, Medin D. Categorization, typicality, and shape similarity. In: Proceedings of the cognitive science meeting. Atlanta, GA; 1994.

  20. Lancy D. Cross-cultural studies in cognition and mathematics. New York: Academic Press; 1983.

    Google Scholar 

  21. Luger GF, Stubblefield WA. Artificial intelligence, structures and strategies for complex problem solving. 3rd ed. Longman: Addison-Wesley; 1998.

    Google Scholar 

  22. McClelland J, Rumelhart D. Distributed memory and the representation of general and specific memory. J Exp Psychol Gen. 1985;114:159–88.

    Article  CAS  PubMed  Google Scholar 

  23. Newell A. Physical symbol systems. San Francisco: Norman; 1981.

    Google Scholar 

  24. Newell A. Unified theories of cognition. Cambridge: Harvard University Press; 1990.

    Google Scholar 

  25. Newell A, Simon H. Human problem solving. Englewood Cliffs: Prentice-Hall; 1972.

    Google Scholar 

  26. Newell A, Simon H. Computer science as empirical inquiry: symbols and search. Communication of the ACM. 1976;19(3):113–26.

    Article  Google Scholar 

  27. Opwis K, Ploetzner R. Kognitive psychologie mit dem computer. Heidelberg: Spektrum Akademischer Verlag; 1996.

    Google Scholar 

  28. Osherson DN. New axioms for the contrast model of similarity. J Math Psychol. 1987;31:93–103.

    Article  Google Scholar 

  29. Osherson DN. Probability judgment. In: Smith EE, Osherson DN, editors. Thinking, 2nd edn, vol. 3. Chap. 2, New York: MIT Press; 1995. p. 35–75.

  30. Plate T. Holographic reduced representations. IEEE Transactions on Neural Networks. 1995;6(3):623–41.

    Article  CAS  PubMed  Google Scholar 

  31. Plate T. Distributed representations. In: Nadel L, editor. Macmillan encyclopedia of cognitive science. London: Macmillan; 2002.

    Google Scholar 

  32. Posner MI, Raichle EM. Images of mind. New York: Scientific American Library; 1994.

    Google Scholar 

  33. Russell SJ, Norvig P. Artificial intelligence: a modern approach. Englewood Cliffs: Prentice-Hall; 1995.

    Google Scholar 

  34. Simon HA. The science of the artificial. New York: The MIT Press; 1969.

    Google Scholar 

  35. Simon HA. Models of my life. New York: Basic Books; 1991.

    Google Scholar 

  36. Smith E, Sloman S. Similarity vs. rulebased categorization. Memory Cognition. 1994;22:377–86.

    CAS  PubMed  Google Scholar 

  37. Smith EE. Concepts and categorization. In: Smith EE, Osherson DN, editors. Thinking, 2nd edn, vol. 3. Chap. 1, New York: MIT Press; 1995, p. 3–33.

  38. Stiny G. Shape: talking about seeing and doing. New York: MIT Press; 2006.

    Google Scholar 

  39. Sun R. A two-level hybrid architecture for structuring knowledge for commonsense reasoning. In: Sun R, Bookman LA, editors. Computational architectures integrating neural and symbolic processing. Chap. 8, Boston: Kluwer Academic Publishers; 1995, p. 247–182.

  40. Sussman G. A computer model of skill acquisition. Cambridge: MIT; 1975.

    Google Scholar 

  41. Tarski A. The semantic conception of truth and foundations of semantics. Philos. and Phenom. Res. 1944;4:241–376.

    Google Scholar 

  42. Tarski A. Logic, semantics, metamathematics. London: Oxford University Press; 1956.

    Google Scholar 

  43. Tarski A. Pisma logiczno-filozoficzne. Prawda, vol. 1. Warszawa: Wydawnictwo Naukowe PWN; 1995.

    Google Scholar 

  44. Tversky A. Feature of similarity. Psychol Rev. 1977;84:327–52.

    Article  Google Scholar 

  45. Wichert A. Pictorial reasoning with cell assemblies. Connection Science. 2001;13(1):1–42.

    Article  Google Scholar 

  46. Wichert A. Learning of associative prediction by experience. Neurocomputing. 2002;48(1–4):741–2.

    Article  Google Scholar 

  47. Wichert A. Associative computer: a hybrid connectionistic production system. Cognitive Systems Research. 2005;6(2):111–44.

    Article  Google Scholar 

  48. Wichert A, Pereira JD, Carreira P. Visual search light model for mental problem solving. Neurocomputing. 2008;71(13–15):2806–22.

    Article  Google Scholar 

  49. Winston PH. Artificial intelligence. 3rd ed. Longman: Addison-Wesley; 1992.

    Google Scholar 

  50. Wermter S, Sun R. Hybrid neural systems. Berlin: Springer–Verlag; 2000.

    Google Scholar 

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  1. Department of Informatics, INESC-ID/IST—Technical University of Lisboa, Lisbon, Portugal

    Andreas Wichert

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  1. Andreas Wichert
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Correspondence to Andreas Wichert.

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Wichert, A. Sub-Symbols and Icons. Cogn Comput 1, 342–347 (2009). https://doi.org/10.1007/s12559-009-9027-6

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