JACIII Vol.17 No.4 pp. 663-669
doi: 10.20965/jaciii.2013.p0663


Aligning Mental Representations

Fumiko Kano Glückstad

Department of International Business Communication, Copenhagen Business School, Dalgas Have 15, DK-2000 Frederiksberg, Denmark

December 29, 2012
June 10, 2013
July 20, 2013
similarity, generalization, semantic representation, communication theory, knowledge alignment
This work introduces a framework that implements asymmetric communication theory proposed by Sperber and Wilson [1]. The framework applies a generalization model known as the Bayesian model of generalization (BMG) [2] for aligning knowledge possessed by two communicating parties. The work focuses on the application of the BMG to publicly available datasets, the Leuven natural concept database [3] representing semantic structures of domain knowledge possessed by individual subjects [3]. Results indicate that the BMG is potentially a model applicable to simulating the alignment of domain knowledge from the information receiver’s viewpoint.
Cite this article as:
F. Glückstad, “Aligning Mental Representations,” J. Adv. Comput. Intell. Intell. Inform., Vol.17 No.4, pp. 663-669, 2013.
Data files:
  1. [1] D. Sperber and D. Wilson, “Relevance: Communication and Cognition,” Oxford: Blackwell, 1986.
  2. [2] J. B. Tenenbaum and T. L. Griffiths, “Generalization, similarity, and Bayesian inference,” in Behavioral and Brain Sciences, Vol.24, No.4, pp. 629-640, 2001.
  3. [3] S. De Deyne, S. Verheyen, E. Ameel, W. Vanpaemel, M. J. Dry, W. Voorspoels, and G. Storms, “Exemplar by feature applicability matrices and other Dutch normative data for semantic concepts,” in Behavior Research Methods, Vol.40, No.4, pp. 1030-1048, 2008.
  4. [4] T. Gruber, “Toward principles for the design of ontologies used for knowledge sharing,” in Int. J. Human-Computer Studies, Vol.43, pp. 907-928, 1992.
  5. [5] B. N. Madsen, H. E. Thomsen, and C. Vikner, “Principles of a system for terminological concept modelling,” in Proc. of the 4th Int. Conf. on Language Resources and Evaluation, ELRA, pp. 15-19, 2004.
  6. [6] C. S. Peirce, “Collected papers of Charles Sanders Peirce, I-VIII,” Cambridge: Cambridge University Press, pp. 1932-1958.
  7. [7] E. Rosch and C. B. Mervis, “Family resemblances: studies in the internal structure of categories,” in Cognitive Psychology, Vol.7, pp. 573-605, 1975.
  8. [8] G. Storms and P. De Boeck, “Formal models for intracategorical structure that can be used for data-analysis,” in K. Lamberts and D. Shanks (Eds.), Knowledge, concepts, and categories, London: UCL Press, pp. 439-459, 1997.
  9. [9] T. Declerck, H. U. Krieger, S. M. Thomas, P. Buitelaar, S. O’Riain, T. Wunner, G. Maguet, J. McCrae, D. Spohr, and E. Montiel-Ponsoda, “Ontology-based multilingual access to financial reports for sharing business knowledge across Europe,” in J. Rooz and J. Ivanyos (Eds.), Internal Financial Control Assessment Applying Multilingual Ontology Framework, Budapest: HVG Press, 2010.
  10. [10] P. Jaccard, “Distribution de la flore alpine dans le bassin des dranses et dans quelques regions voisines,” in Bulletin de la societe vaudoise des sciences naturelles, Vol.37, pp. 241-272, 1901.
  11. [11] G. L. Murphy, “The Big Book of Concepts,” Cambridge, Massachusetts: The MIT Press, 2004.
  12. [12] D. L. Medin and M. M. Schaffer, “Context Theory of Classification Learning,” in Psychological Review, Vol.85, No.3, pp. 207-238, May 1978.
  13. [13] A. Tversky, “Features of similarity,” in Psychological Review, Vol.87, No.4, pp. 327-352, 1977.
  14. [14] G. L. Murphy and P. D. Allopenna, “The Locus of Knowledge Effects in Concept Learning,” in J. of Experimental Psychology: Learning, Memory, and Cognition, Vol.20, No.4, pp. 904-919, 1994.
  15. [15] A. B. Markman and E. J. Wisniewski, “Similar and Different,” in J. of Experimental Psychology: Learning, Memory, and Cognition, Vol.23, No.1, pp. 54-70, 1997.
  16. [16] E. J. Wisniewski and D. L. Medin, “On the Interaction of Theory and Data in Concept Learning,” in Cognitive Science, Vol.18, pp. 221-281, 1994.
  17. [17] T. L. Spalding and G. L. Murphy, “Effects of Background Knowledge on Category Construction,” in J. of Experimental Psychology: Learning, Memory, and Cognition, Vol.22, No.2, pp. 525-538, 1996.
  18. [18] M. E. Lassaline and G. L. Murphy, “Induction and Category Coherence,” in Psychonomic Bulletin and Review, Vol.3, pp. 95-99, 1996.
  19. [19] R. N. Shepard, “Towards a universal law of generalization for psychological science,” in Science, Vol.237, pp. 1317-1323, 1987.
  20. [20] N. Chomsky, “Language and Problems of Knowledge: The Managua lectures,” Cambridge, MA: MIT Press, 1986.
  21. [21] D. N. Osherson, E. E. Smith, O. Wilkie, A. Lopez, and E. Shafir, “Category-based induction,” in Psychological Review, Vol.97, No.2, pp. 185-200, 1990.
  22. [22] S. A. Sloman, “Feature-based induction,” in Cognitive Psychology, Vol.25, pp. 231-280, 1993.
  23. [23] C. Kemp, P. Shafto, and J. B. Tenenbaum, “An integrated account of generalization across objects and features,” in Cognitive Psychology, Vol.64, pp. 35-73, 2012.

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