Abstract
We review a current and popular class of cognitive models calledmultinomial processing tree (MPT) models. MPT models are simple, substantively motivated statistical models that can be applied to categorical data. They are useful as data-analysis tools for measuring underlying or latent cognitive capacities and as simple models for representing and testing competing psychological theories. We formally describe the cognitive structure and parametric properties of the class of MPT models and provide an inferential statistical analysis for the entire class. Following this, we provide a comprehensive review of over 80 applications of MPT models to a variety of substantive areas in cognitive psychology, including various types of human memory, visual and auditory perception, and logical reasoning. We then address a number of theoretical issues relevant to the creation and evaluation of MPT models, including model development, model validity, discrete-state assumptions, statistical issues, and the relation between MPT models and other mathematical models. In the conclusion, we consider the current role of MPT models in psychological research and possible future directions.
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This research was supported by NSF Grant SBR-9309667. The authors thank Associate Editor Richard Schweickert and Richard Chechile, Mark Howe, Kevin Murnane, and James Townsend for helpful reviews of an earlier draft of this paper. The authors also thank Ece Batchelder, Edgar Erdfelder, Xiangen Hu, and Christoph Klauer for comments on various aspects of this project.
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Batchelder, W.H., Riefer, D.M. Theoretical and empirical review of multinomial process tree modeling. Psychonomic Bulletin & Review 6, 57–86 (1999). https://doi.org/10.3758/BF03210812
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DOI: https://doi.org/10.3758/BF03210812