Toward a descriptive cognitive model of human learning

Toshihiko Matsuka, Yasuaki Sakamoto, Arieta Chouchourelou, Jeffrey V. Nickerson

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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 important observed psychological phenomena.

Original languageEnglish
Pages (from-to)2446-2455
Number of pages10
JournalNeurocomputing
Volume71
Issue number13-15
DOIs
StatePublished - Aug 2008

Keywords

  • Cognitive modeling
  • Evolutionary computation
  • Human learning

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