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A Tutorial

  • James J. Palestro
  • Per B. Sederberg
  • Adam F. Osth
  • Trisha Van Zandt
  • Brandon M. Turner
Chapter
Part of the Computational Approaches to Cognition and Perception book series (CACP)

Abstract

In this chapter, we apply some of the likelihood-free algorithms discussed in previous chapters in a tutorial fashion with an application to the Minerva 2 model, a global matching model of recognition memory. First, we will briefly describe the model. Then, we will fit the Minerva 2 model to simulated data using three different likelihood-free algorithms and compare how well each technique provides fits to the data. Finally, we will fit a hierarchical version of the Minerva 2 model to real-world data using a combination of the probability density approximation method and the Gibbs ABC algorithm.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • James J. Palestro
    • 1
  • Per B. Sederberg
    • 1
  • Adam F. Osth
    • 2
  • Trisha Van Zandt
    • 1
  • Brandon M. Turner
    • 1
  1. 1.Department of PsychologyThe Ohio State UniversityColumbusUSA
  2. 2.University of MelbourneParkvilleAustralia

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