Using Ensembles of Cognitive Models to Answer Substantive Questions

Henrik Singmann, David Kellen, Eda Mızrak, Ilke Öztekin

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

Abstract

Cognitive measurement models decompose observed behavior into latent cognitive processes. For situations with more than one condition, such models allow to test hypotheses on the level of the latent processes. We propose a fully Bayesian ensemble model approach to test hypotheses on the level of the latent processes in situations in which multiple measurement models or model classes exist. In the first step, one needs to perform a Bayesian model selection step comparing the hypotheses within each model class. Aggregating the results of the first step yields ensemble posterior model probabilities. We provide an example for a working memory data set using an ensemble of a resource model and a slots model.

Original languageEnglish (US)
Title of host publicationProceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018
PublisherThe Cognitive Science Society
Pages1068-1073
Number of pages6
ISBN (Electronic)9780991196784
StatePublished - 2018
Event40th Annual Meeting of the Cognitive Science Society: Changing Minds, CogSci 2018 - Madison, United States
Duration: Jul 25 2018Jul 28 2018

Publication series

NameProceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018

Conference

Conference40th Annual Meeting of the Cognitive Science Society: Changing Minds, CogSci 2018
Country/TerritoryUnited States
CityMadison
Period7/25/187/28/18

Keywords

  • Bayesian inference
  • ensemble models
  • model selection

ASJC Scopus subject areas

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

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