Abstract
Multinomial processing tree (MPT) models are statistical models that allow for the prediction of categorical frequency data by sets of unobservable (cognitive) states. In MPT models, the probability that an event belongs to a certain category is a sum of products of state probabilities. AppleTree is a computer program for Macintosh for testing user-defined MPT models. It can fit model parameters to empirical frequency data, provide confidence intervals for the parameters, generate tree graphs for the models, and perform identifiability checks. In this article, the algorithms used by AppleTree and the handling of the program are described.
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The work reported here was supported by Grant Bu 945/1-2 from the Deutsche Forschungsgemeinschaft. The author thanks Axel Buchner, Edgar Erdfelder, Xiangen Hu, and Martin Brand for help and feedback in the implementation process of AppleTree. For reviews of the manuscript the author thanks Axel Buchner and Karl F. Wender.
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Rothkegel, R. Appletree: A multinomial processing tree modeling program for macintosh computers. Behavior Research Methods, Instruments, & Computers 31, 696–700 (1999). https://doi.org/10.3758/BF03200748
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DOI: https://doi.org/10.3758/BF03200748