Please use this identifier to cite or link to this item:
https://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 |
DOI: | 10.1037/0033-295X.113.1.57 |
Published version: | http://www.apa.org/journals/rev/homepage.html |
Appears in Collections: | Aurora harvest 7 Psychology publications |
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hdl_23023.pdf | Accepted version | 541.43 kB | Adobe PDF | View/Open |
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