Due process in dual process: A model-recovery analysis of Smith et al. (2014)

Charlotte E.R. Edmunds, Fraser Milton, Andy J. Wills

Research output: Chapter in Book/Report/Conference proceedingConference proceedings published in a bookpeer-review

Abstract

Considerable behavioral evidence has been cited in support of the COVIS dual-system model of category learning (Ashby & Valentin, 2016). The validity of the inferences drawn from these data critically depend on the accurate identification of participants' categorization strategies. In the COVIS literature, participants' strategies are identified using a model-based analysis inspired by General Recognition Theory (Maddox, 1999). Here, we examine the accuracy of this analysis in a model-recovery simulation. We find that participants can appear to be using implicit, procedural strategies when their responses were actually generated by explicit rule-based strategies. The implications of this for the COVIS literature are discussed.

Original languageEnglish
Title of host publicationCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society
Subtitle of host publicationComputational Foundations of Cognition
PublisherThe Cognitive Science Society
Pages1979-1984
Number of pages6
ISBN (Electronic)9780991196760
Publication statusPublished - 2017
Event39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017 - London, United Kingdom
Duration: 26 Jul 201729 Jul 2017

Publication series

NameCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition

Conference

Conference39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017
Country/TerritoryUnited Kingdom
CityLondon
Period26/07/1729/07/17

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

Keywords

  • categorization
  • COVIS
  • dual-systems accounts
  • GRT
  • model-recovery

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