Generalized processing tree models: Jointly modeling discrete and continuous variables


Heck, Daniel W. ; Erdfelder, Edgar ; Kieslich, Pascal J.



DOI: https://doi.org/10.1007/s11336-018-9622-0
URL: https://link.springer.com/article/10.1007/s11336-0...
Additional URL: https://www.researchgate.net/publication/325022582...
Document Type: Article
Year of publication: 2018
The title of a journal, publication series: Psychometrika
Volume: 83
Issue number: 4
Page range: 893-918
Place of publication: New York, NY
Publishing house: Springer Science + Business Media
ISSN: 0033-3123 , 1860-0980
Publication language: English
Institution: School of Social Sciences > Kognitive Psychologie u. Differentielle Psychologie (Erdfelder 2002-2019)
Außerfakultäre Einrichtungen > Graduate School of Economic and Social Sciences- CDSS (Social Sciences)
Subject: 150 Psychology
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.




Dieser Eintrag ist Teil der Universitätsbibliographie.




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