On the learning algorithms of descriptive models of high-order human cognition

  • Authors:

  • Toshihiko Matsuka;Arieta Chouchourelou

  • Affiliations:

  • Center for Decision Technologies, Stevens Institute of Technology, Hoboken, NJ;Rutgers University, Newark, NJ

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
  • Year:
  • 2006

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Abstract

The primary focus in computational modeling research in high order human cognition is to compare how realistically embedded algorithm describes human cognitive processes. However, several current models incorporated learning algorithms that apparently have questionable descriptive validity or qualitative plausibleness. The present research attempts to bridge this gap by identifying five critical issues overlooked by previous modeling research and then introducing a modeling framework that addresses the issues and offers better qualitative plausibleness. A simulation study utilizing the present framework with two distinctive implementation approaches shows their descriptive validity.