Generalized Processing Tree Models: Jointly Modeling Discrete and Continuous Variables
- 24 May 2018
- journal article
- research article
- Published by Springer Science and Business Media LLC in Psychometrika
- Vol. 83 (4), 893-918
- https://doi.org/10.1007/s11336-018-9622-0
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
Funding Information
- Deutsche Forschungsgemeinschaft (GRK 2277)
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