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Exploring the Diversity of Evolved Cognitive Models with Cluster Analysis

  • Peter Lane
  • , Noman Javed
  • , L. K. Bartlett
  • , D. Bennett
  • , Fernand Gobet

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

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Abstract

Cognitive scientists often represent theories of cognitive behavior in the form of computer programs which simulate and model the performance of humans in experimental settings. Earlier work has demonstrated that evolutionary techniques, specifically genetic programming (GP), can be used to generate a pool of candidate models in the form of executable computer programs. However, previous work has not considered the impact of changes to hyper-parameter values, such as those controlling the behavior and timing of operators or those controlling the operation of the GP process. In this paper, we develop and use a cluster analysis technique based around the Silhouette index to investigate the impact of hyper-parameter changes on the composition of evolved populations of programs. Our metrics support visualizations and enable a user to assess both qualitatively and quantitatively the diversity of candidates from different populations. In this way, a cognitive scientist can analyze the output of the evolutionary system in order to uncover or inspire potentially novel theories of human behavior.
Original languageEnglish
Title of host publicationEvolution in Computational Intelligence - Proceedings of the 12th International Conference on Frontiers in Intelligent Computing
Subtitle of host publicationTheory and Applications, FICTA 2024
EditorsVikrant Bhateja, Preeti Patel, Jinshan Tang
PublisherSpringer Singapore
Pages51-63
Number of pages13
ISBN (Electronic)978-981-96-2124-8
ISBN (Print)9789819621231
DOIs
Publication statusPublished - 20 Jun 2025

Publication series

NameSmart Innovation, Systems and Technologies
PublisherSpringer Singapore
Volume436
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Keywords

  • Cluster analysis
  • Cognitive science
  • Evolutionary computing
  • Genetic programming
  • Silhouette index
  • Visualization

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