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Minimum description length model selection of multinomial processing tree models
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DOI：10.3758/PBR.17.3.275.png)
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摘要
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Multinomial processing tree (MPT) modeling has been widely and successfully applied as a statistical methodology for measuring hypothesized latent cognitive processes in selected experimental paradigms. In this article, we address the problem of selecting the best MPT model from a set of scientifically plausible MPT models, given observed data. We introduce a minimum description length (MDL) based model-selection approach that overcomes the limitations of existing methods such as the G(2)-based likelihood ratio test, the Akaike information criterion, and the Bayesian information criterion. To help ease the computational burden of implementing MDL, we provide a computer program in MATLAB that performs MDL-based model selection for any MPT model, with or without inequality constraints. Finally, we discuss applications of the MDL approach to well-studied MPT models with real data sets collected in two different experimental paradigms: source monitoring and pair clustering. The aforementioned MATLAB program may be downloaded from http://pbr.psychonomic-journals.org/content/supplemental.
Keyword:
BAYESIAN INFORMATION CRITERION
STATISTICAL-ANALYSIS
SPECIAL-ISSUE
COMPLEXITY
STORAGE
CRITIQUE
MEMORY
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