, Volume 83, Issue 4, pp 893–918 | Cite as

Generalized Processing Tree Models: Jointly Modeling Discrete and Continuous Variables

  • Daniel W. HeckEmail author
  • Edgar Erdfelder
  • Pascal J. Kieslich


Multinomial processing tree models assume that discrete cognitive states determine observed response frequencies. Generalized processing tree (GPT) models extend this conceptual framework to continuous variables such as response times, process-tracing measures, or neurophysiological variables. GPT models assume finite-mixture distributions, with weights determined by a processing tree structure, and continuous components modeled by parameterized distributions such as Gaussians with separate or shared parameters across states. We discuss identifiability, parameter estimation, model testing, a modeling syntax, and the improved precision of GPT estimates. Finally, a GPT version of the feature comparison model of semantic categorization is applied to computer-mouse trajectories.


multinomial processing tree model discrete states mixture model cognitive modeling response times mouse-tracking 

Supplementary material

11336_2018_9622_MOESM1_ESM.pdf (184 kb)
Supplementary material 1 (pdf 184 KB)


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Copyright information

© The Psychometric Society 2018

Authors and Affiliations

  • Daniel W. Heck
    • 1
    Email author
  • Edgar Erdfelder
    • 1
  • Pascal J. Kieslich
    • 1
  1. 1.Department of PsychologyUniversity of MannheimMannheimGermany

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