Simulating feature- and relation-based categorisation with a symbolic-connectionist model

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Participants in Goldwater et al. (2018) reported using either feature- or relation-based strategy during a series of category learning tasks. A computational modeling study was conducted to investigate whether performance on Experiments 1 and 2 of Goldwater et al. (2018) might be explained by the assumption that participants used either feature- or relation-based representational encoding during learning. Human participants' and model performance are compared and implications are discussed.
Original languageEnglish
Title of host publicationProceedings of the 42nd Annual Meeting of the Cognitive Science Society
Place of PublicationAustin, TX
PublisherThe Cognitive Science Society
Pages3412-3418
Number of pages7
Volume42
ISBN (Print)9781713818977
Publication statusPublished - 1 Aug 2020
Event42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020 - Virtual, Online
Duration: 29 Jul 20201 Aug 2020

Publication series

NameProceedings of the Annual Meeting of the Cognitive Science Society
PublisherThe Cognitive Science Society

Conference

Conference42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020
CityVirtual, Online
Period29/07/201/08/20

Keywords / Materials (for Non-textual outputs)

  • categorisation
  • computational modeling
  • featural categories
  • relational categories
  • symbolic-connectionist model

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