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Applications

  • 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 highlight a few real-world applications where truly simulation-based models (with intractable likelihood functions) are fit using likelihood-free techniques. First, we use likelihood-free techniques to fit a hierarchical version of the Retrieving Effectively from Memory model to data from a recognition memory task. Next, we fit a dynamic extension of the signal detection theory model and compare the fit generated using likelihood-free methods to hand-held fits previously attained using the approximate least squares method. Finally, we fit two complex, stochastic accumulator models of decision making to data from a perceptual decision-making task.

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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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