Advertisement

A Cognitive Model of Multi-objective Multi-concept Formation

  • Toshihiko Matsuka
  • Yasuaki Sakamoto
  • Jeffrey V. Nickerson
  • Arieta Chouchourelou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)

Abstract

The majority of previous computational models of high-order human cognition incorporate gradient descent algorithms for their learning mechanisms and strict error minimization as the sole objective of learning. Recently, however, the validity of gradient descent as a descriptive model of real human cognitive processes has been criticized. In the present paper, we introduce a new framework for descriptive models of human learning that offers qualitatively plausible interpretations of cognitive behaviors. Specifically, we apply a simple multi-objective evolutionary algorithm as a learning method for modeling human category learning, where the definition of the learning objective is not based solely on the accuracy of knowledge, but also on the subjectively and contextually determined utility of knowledge being acquired. In addition, unlike gradient descent, our model assumes that humans entertain multiple hypotheses and learn not only by modifying a single existing hypothesis but also by combining a set of hypotheses. This learning-by-combination has been empirically supported, but largely overlooked in computational modeling research. Simulation studies show that our new modeling framework successfully replicated observed phenomena.

Keywords

Gradient Descent Learning Objective Human Learning Category Learning Attention Allocation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Solomon, K.O., Medin, D.L., Lynch, E.: Concepts Do More Than Categorize. Trends in Cognitive Sciences 3, 99–105 (1999)CrossRefGoogle Scholar
  2. 2.
    Kruschke, J.K.: ALCOVE: An Exemplar-Based Connectionist Model of Category Learning. Psychological Review 99, 22–44 (1992)CrossRefGoogle Scholar
  3. 3.
    Love, B.C., Medin, D.L., Gureckis, T.M.: SUSTAIN: A Network Model of Human Category Learning. Psychological Review 111, 309–332 (2004)CrossRefGoogle Scholar
  4. 4.
    Matsuka, T.: Simple, Individually Unique, and Context-Dependent Learning Method for Models of Human Category Learning. Behavior Research Methods 37, 240–255 (2005)CrossRefGoogle Scholar
  5. 5.
    Matsuka, T., Corter, J.E.: Process Tracing of Attention Allocation in Category Learning (2006) (Under review)Google Scholar
  6. 6.
    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
  7. 7.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Berlin (2003)MATHGoogle Scholar
  8. 8.
    Markman, A.B., Baldwin, G.C., Maddox, W.T.: The Interaction of Payoff Structure and Regulatory Focus in Classification. Psychological Science 16, 852–855 (2005)CrossRefGoogle Scholar
  9. 9.
    Matsuka, T., Chouchourelou, A.: A Model of Human Category Learning with Dynamic Multi-Objective Hypotheses Testing with Retrospective Verifications. In: Proc. IJCNN (2006) (Forthcoming)Google Scholar
  10. 10.
    Barsalou, L.W.: Ad Hoc Categories. Memory and Cognition 11, 211–227 (1983)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Toshihiko Matsuka
    • 1
  • Yasuaki Sakamoto
    • 1
  • Jeffrey V. Nickerson
    • 1
  • Arieta Chouchourelou
    • 2
  1. 1.Stevens Institute of TechnologyHobokenUSA
  2. 2.Rutgers UniversityNewarkUSA

Personalised recommendations