Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/23023
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Type: Journal article
Title: Global model analysis by parameter space partitioning
Author: Pitt, M.
Kim, W.
Navarro, D.
Myung, J.
Citation: Psychological Review, 2006; 113(1):57-83
Publisher: Amer Psychological Assoc
Issue Date: 2006
ISSN: 0033-295X
1939-1471
Statement of
Responsibility: 
Mark A. Pitt, Woojae Kim, Daniel J. Navarro, and Jay I. Myung
Abstract: To model behavior, scientists need to know how models behave. This means learning what other behaviors a model can produce besides the one generated by participants in an experiment. This is a difficult problem because of the complexity of psychological models (e.g., their many parameters) and because the behavioral precision of models (e.g., interval-scale performance) often mismatches their testable precision in experiments, where qualitative, ordinal predictions are the norm. Parameter space partitioning is a solution that evaluates model performance at a qualitative level. There exists a partition on the model’s parameter space that divides it into regions that correspond to each data pattern. Three application examples demonstrate its potential and versatility for studying the global behavior of psychological models.
Keywords: Model comparison; model complexity; MCMC; connectionist modeling
Rights: Copyright 2006 American Psychological Association
RMID: 0020060144
DOI: 10.1037/0033-295X.113.1.57
Published version: http://www.apa.org/journals/rev/homepage.html
Appears in Collections:Psychology publications

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