In mathematical modeling of cognition, it is important to have well-justified criteria for choosing among differing explanations (i.e., models) of observed data. This project investigates those criteria as well as their instantiation in five model selection methods. Two lines of research will be undertaken. In the first, a thorough investigation of model complexity will be conducted. Comprehensive simulations re intended to determine complexity's contribution to model fit and to model selection. An analytical solution will also be sought with the hope of quantifying model complexity. The second line of work examines the utility of each of the five selection methods in choosing among models in three topic areas in cognitive psychology (information integration, categorization, connectionist modeling), the end goal being to identify their merits and shortcomings. Findings should provide a better understanding of model selection than currently available and serve as a useful guide for researchers comparing the suitability of quantitative models of cognition.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH057472-02
Application #
2891003
Study Section
Perception and Cognition Review Committee (PEC)
Program Officer
Kurtzman, Howard S
Project Start
1998-08-10
Project End
2001-07-31
Budget Start
1999-08-01
Budget End
2000-07-31
Support Year
2
Fiscal Year
1999
Total Cost
Indirect Cost
Name
Ohio State University
Department
Psychology
Type
Schools of Arts and Sciences
DUNS #
098987217
City
Columbus
State
OH
Country
United States
Zip Code
43210
Wu, Hao; Myung, Jay I; Batchelder, William H (2010) Minimum description length model selection of multinomial processing tree models. Psychon Bull Rev 17:275-86
Wu, Hao; Myung, Jay I; Batchelder, William H (2010) On the Minimum Description Length Complexity of Multinomial Processing Tree Models. J Math Psychol 54:291-303
Cavagnaro, Daniel R; Myung, Jay I; Pitt, Mark A et al. (2010) Adaptive design optimization: a mutual information-based approach to model discrimination in cognitive science. Neural Comput 22:887-905
Myung, Jay I; Pitt, Mark A (2009) Optimal experimental design for model discrimination. Psychol Rev 116:499-518
Myung, Jay I; Tang, Yun; Pitt, Mark A (2009) Evaluation and comparison of computational models. Methods Enzymol 454:287-304
Myung, Jay I; Montenegro, Maximiliano; Pitt, Mark A (2007) Analytic Expressions for the BCDMEM Model of Recognition Memory. J Math Psychol 51:198-204
Myung, Jay I; Pitt, Mark A; Navarro, Daniel J (2007) Does response scaling cause the generalized context model to mimic a prototype model? Psychon Bull Rev 14:1043-50
Pitt, Mark A; Myung, Jay I; Altieri, Nicholas (2007) Modeling the word recognition data of Vitevitch and Luce (1998): is it ARTful? Psychon Bull Rev 14:442-8
Pitt, Mark A; Kim, Woojae; Navarro, Daniel J et al. (2006) Global model analysis by parameter space partitioning. Psychol Rev 113:57-83
Navarro, Daniel J (2004) A note on the applied use of MDL approximations. Neural Comput 16:1763-8

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