Minimum description length model selection of multinomial processing tree models

Psychonomic Bulletin and Review

Volumn 17, Issue 3, 2010, Pages 275-286

  Wu, Hao ^a   Myung, Jay I ^a   Batchelder, William H ^b  

^a OHIO STATE UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ATTENTION; BAYES THEOREM; COGNITION; COMPUTER PROGRAM; HUMAN; INFORMATION PROCESSING; LEARNING; MATHEMATICAL COMPUTING; RECOGNITION; STATISTICAL MODEL; STATISTICS;

ATTENTION; BAYES THEOREM; COGNITION; DATA COLLECTION; HUMANS; LIKELIHOOD FUNCTIONS; MATHEMATICAL COMPUTING; MODELS, STATISTICAL; RECOGNITION (PSYCHOLOGY); SOFTWARE; VERBAL LEARNING;

EID: 77957275564     PISSN: 10699384     EISSN: 15315320     Source Type: Journal    
DOI: 10.3758/PBR.17.3.275     Document Type: Article

References (50)

1. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Csaki (Eds.), Proceedings from the Second Symposium on Information Theory (pp. 267-281). Budapest: Akadémiai Kiadó.
2. Batchelder, W. H. (2009). Cognitive psychometrics: Using multinomial processing tree models as measurement tools. In S. E. Embretson (Ed.), Measuring psychological constructs: Advances in model-based approaches (pp. 71-93). Washington, DC: American Psychological Association.
3. Batchelder, W. H., & Riefer, D. M. (1980). Separation of storage and retrieval factors in free recall of clusterable pairs. Psychological Review, 87, 375-397.
4. Batchelder, W. H., & Riefer, D. M. (1986). The statistical analysis of a model for storage and retrieval processes in human memory. British Journal of Mathematical & Statistical Psychology, 39, 129-149.
5. Batchelder, W. H., & Riefer, D. M. (1990). Multinomial processing models of source monitoring. Psychological Review, 97, 548-564.
6. Batchelder, W. H., & Riefer, D. M. (1999). Theoretical and empirical review of multinomial process tree modeling. Psychonomic Bulletin & Review, 6, 57-86.
7. Batchelder, W. H., & Riefer, D. M. (2007). Using multinomial processing tree models to measure cognitive deficits in clinical populations. In R. W. J. Neufeld (Ed.), Advances in clinical cognitive science: Formal modeling of processes and symptoms (pp. 19-50). Washington, DC: American Psychological Association.
8. Bishara, A. J., & Payne, B. K. (2008). Multinomial process tree models of control and automaticity in weapon misidentification. Journal of Experimental Social Psychology, 45, 524-534.
9. nd ed.). Belmont, CA: Duxbury Press.
10. Chechile, R. A. (1998). A new method for estimating model parameters for multinomial data. Journal of Mathematical Psychology, 42, 432-471.
11. Chechile, R. A. (2004). New multinomial models for the Chechile-Meyer task. Journal of Mathematical Psychology, 48, 364-384.
12. Chechile, R. A. (2007). A model-based storage-retrieval analysis of developmental dyslexia. In R. W. J. Neufeld (Ed.), Advances in clinical cognitive sciences: Formal modeling of processes and symptoms (pp. 51-80). Washington, DC: American Psychological Association.
13. Erdfelder, E., Auer, T.-S., Hilbig, B. E., Aßfalg, A., Moshagen, M., & Nadarevic, L. (2009). Multinomial processing tree models: A review of the literature. Zeitschrift für Psychologie, 217, 108-124.
14. Grünwald, P. [D.] (2000). Model selection based on minimum description length. Journal of Mathematical Psychology, 44, 133-152.
15. Grünwald, P. D. (2007). The minimum description length principle. Cambridge, MA: MIT Press.
16. Grünwald, P. D., Myung, I. J., & Pitt, M. A. (Eds.) (2005). Advances in minimum description length: Theory and applications. Cambridge, MA: MIT Press.
17. Hu, X., & Batchelder, W. H. (1994). The statistical analysis of general processing tree models with the EM algorithm. Psychometrika, 59, 21-47.
18. Hu, X., & Phillips, G. A. (1999). GPT.EXE: A powerful tool for the visualization and analysis of general processing tree models. Behavior Research Methods, Instruments, & Computers, 31, 220-234.
19. Klauer, K. C. (2010). Hierarchical multinomial processing tree models: A latent-trait approach. Psychometrika, 75, 70-98. doi:10.1007/s11336-009-9141-0
20. Knapp, B. R., & Batchelder, W. H. (2004). Representing parametric order constraints in multitrial applications of multinomial processing tree models. Journal of Mathematical Psychology, 48, 215-229.
21. Lee, M. D. (2001). On the complexity of additive clustering models. Journal of Mathematical Psychology, 45, 131-148.
22. Lee, M. D., & Pope, K. J. (2006). Model selection for the rate problem: A comparison of significance testing, Bayesian, and minimum description length statistical inference. Journal of Mathematical Psychology, 50, 193-202.
23. Myung, I. J. (2000). The importance of complexity in model selection. Journal of Mathematical Psychology, 44, 190-204.
24. Myung, I. J., Forster, M. R., & Browne, M. W. (2000). Guest editors' introduction: Special issue on model selection. Journal of Mathematical Psychology, 44, 1-2.
25. Myung, J. I., Navarro, D. J., & Pitt, M. A. (2006). Model selection by normalized maximum likelihood. Journal of Mathematical Psychology, 50, 167-179.
26. Myung, I. J., & Pitt, M. A. (1997). Applying Occam's razor in modeling cognition: A Bayesian approach. Psychonomic Bulletin & Review, 4, 79-95.
27. Myung, J. I., Pitt, M. A., & Navarro, D. J. (2007). Does response scaling cause the generalized context model to mimic a prototype model? Psychonomic Bulletin & Review, 14, 1043-1050.
28. Navarro, D. J., & Lee, M. D. (2004). Common and distinctive features in stimulus similarity: A modified version of the contrast model. Psychonomic Bulletin & Review, 11, 961-974.
29. Pitt, M. A., Myung, I. J., & Zhang, S. (2002). Toward a method of selecting among computational models of cognition. Psychological Review, 109, 472-491.
30. Purdy, B. P., & Batchelder, W. H. (2009). A context-free language for binary multinomial processing tree models. Journal of Mathematical Psychology, 53, 547-561.
31. Raftery, A. E. (1999). Bayes factors and BIC: Comment on "A critique of the Bayesian information criterion for model selection." Sociological Methods & Research, 27, 411-427.
32. Read, T. R. C., & Cressie, N. A. C. (1988). Goodness-of-fit statistics for discrete multivariate data. New York: Springer.
33. Riefer, D. M., & Batchelder, W. H. (1988). Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339.
34. Riefer, D. M., & Batchelder, W. H. (1991). Statistical inference for multinomial processing tree models. In J.-P. Doignon & J.-C. Falmagne (Eds.), Mathematical psychology: Current developments (pp. 313-335). New York: Springer.
35. Riefer, D. M., & Batchelder, W. H. (1995). A multinomial modeling analysis of the recognition-failure paradigm. Memory & Cognition, 23, 611-630.
36. Riefer, D. M., Hu, X., & Batchelder, W. H. (1994). Response strategies in source monitoring. Journal of Experimental Psychology: Learning, Memory, & Cognition, 20, 680-693.
37. Riefer, D. M., Knapp, B. R., Batchelder, W. H., Bamber, D., & Manifold, V. (2002). Cognitive psychometrics: Assessing storage and retrieval deficits in special populations with multinomial processing tree models. Psychological Assessment, 14, 184-201.
38. Rissanen, J. J. (1996). Fisher information and stochastic complexity. IEEE Transactions on Information Theory, 42, 40-47.
39. Rissanen, J. J. (2001). Strong optimality of the normalized ML models as universal codes and information in data. IEEE Transactions on Information Theory, 47, 1712-1717.
40. Rose, R. G., Rose, P. R., King, N., & Perez, A. (1975). Bilingual memory for related and unrelated sentences. Journal of Experimental Psychology: Human Learning & Memory, 1, 599-606.
41. Saegert, J., Hamayan, E., & Ahmar, H. (1975). Memory for language of input in polyglots. Journal of Experimental Psychology: Human Learning & Memory, 1, 607-613.
42. Schwartz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
43. Schweickert, R. (1993). A multinomial processing tree model for degradation and redintegration in immediate recall. Memory & Cognition, 21, 168-175.
44. Schweickert, R., & Chen, S. (2008). Tree inference with factors selectively influencing processes in a processing tree. Journal of Mathematical Psychology, 52, 158-183.
45. Shapiro, A. (1988). Towards a unified theory of inequality constrained testing in multivariate analysis. International Statistical Review, 56, 49-62.
46. Smith, J. B., & Batchelder, W. H. (2010). Beta-MPT: Multinomial processing tree models for addressing individual differences. Journal of Mathematical Psychology, 54, 167-183.
47. Wagenmakers, E.-J. (2007). A practical solution to the pervasive problems of p values. Psychonomic Bulletin & Review, 14, 779-804.
48. Wagenmakers, E.-J., & Waldorp, L. (2006). Editors' introduction. Journal of Mathematical Psychology, 50, 99-100.
49. Weakliem, D. L. (1999). A critique of the Bayesian information criterion for model selection. Sociological Methods & Research, 27, 359-397.
50. Wu, H., Myung, J. I., & Batchelder, W. H. (2010). On the minimum description length complexity of multinomial processing tree models. Journal of Mathematical Psychology, 54, 291-303.