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On the Knowledge Organization in Concept Formation: An Exploratory Cognitive Modeling Study

  • Toshihiko Matsuka
  • Hidehito Honda
  • Arieta Chouchourelou
  • Sachiko Kiyokawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5768)

Abstract

Recent cognitive modeling studies suggest the effectiveness of meta-heuristic optimization in describing human cognitive behaviors. Such models are built on the basis of population-based algorithm (e.g., genetic algorithm) and thus hold multiple solutions or notions. There are, however, important yet unaddressed issues in cognitive mechanisms associated with possession of multiple notions. The issues we address in the present research is about how multiple notions are organized in our mind. In particular, we paid close attention to how each notion interact with other notions while learning a new concept. In so doing, we incorporated Particle Swarm Optimization in a cognitive model of concept learning. Three PSO-based concept learning models were developed and compared in the present exploratory cognitive modeling study.

Keywords

Particle Swarm Optimization Concept Formation Swarm Intelligence Concept Learning Knowledge Organization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Anderson, J.R.: The Adaptive Character of Thought. Lawrence Erlbaum, Hillsdale (1990)Google Scholar
  2. 2.
    Anderson, R.C., Pichert, J.W.: Recall of previously unrecallable information following a shift in perspective. Journal of Verbal Learning and Verbal Behavior 17, 1–12 (1978)CrossRefGoogle Scholar
  3. 3.
    Engelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. Wilely, Chichester (2005)Google Scholar
  4. 4.
    Gigerenzer, G., Todd, P.M., The ABC Research Group: Simple heuristics that make us smart. Oxford, New York (1999)Google Scholar
  5. 5.
    Higashi, H., Iba, H.: Particle Swarm Optimization with Gaussian Mutation. In: Proceedings of the IEEE Swarm Intelligence Symposium, pp. 72–79 (2003)Google Scholar
  6. 6.
    Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)Google Scholar
  7. 7.
    Matsuka, T., Corter, J.E.: Observed attention allocation processes in category learning. Quarterly Journal of Experimental Psychology 61, 1067–1097 (2008)CrossRefGoogle Scholar
  8. 8.
    Matsuka, T., Sakamoto, Y., Chouchourelou, A.: Modeling a flexible representation machinery of human concept learning. Neural Networks 21, 289–302 (2008)CrossRefGoogle Scholar
  9. 9.
    Matsuka, T., Sakamoto, Y., Chouchourelou, A., Nickerson, J.V.: Toward a descriptive cognitive model of human learning. Neurocomputing 71, 2446–2455 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Toshihiko Matsuka
    • 1
  • Hidehito Honda
    • 1
  • Arieta Chouchourelou
    • 2
  • Sachiko Kiyokawa
    • 3
  1. 1.Department of Cognitive and Information ScienceChiba UniversityJapan
  2. 2.European University CyprusNicociaCyprus
  3. 3.Department of PsychologyChubu UniversityJapan

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