Generalized Processing Tree Models: Jointly Modeling Discrete and Continuous Variables

Daniel W. Heck (), Edgar Erdfelder and Pascal J. Kieslich
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Daniel W. Heck: University of Mannheim
Edgar Erdfelder: University of Mannheim
Pascal J. Kieslich: University of Mannheim

Psychometrika, 2018, vol. 83, issue 4, No 7, 893-918

Abstract: Abstract 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.

Keywords: multinomial processing tree model; discrete states; mixture model; cognitive modeling; response times; mouse-tracking (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11336-018-9622-0

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