Model evaluation, testing and selection

Handbook of Cognition

Volumn , Issue , 2005, Pages 422-436

  Myung, Jae I ^a   Pitt, Mark A ^a   Kim, Woojae ^a  

^a OHIO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33750393339     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4135/9781848608177.n19     Document Type: Chapter

References (53)

1. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrox & F. Caski (Eds.), Second International Symposium on Information Theory (pp. 267-281). Budapest: Akademiai Kiado.
2. Ashby F. G., Maddox W. T. Relations between prototype, exemplar and decision bound models of categorization Journal of Mathematical Psychology 37 (1993) 372-400 doi:10.1006/jmps.1993.1023 http://dx.doi.org/10.1006/jmps.1993.1023
3. Bamber D., van Santen J. P. H. How many parameters can a model have and still be testable? Journal of Mathematical Psychology 29 (1985) 443-473 doi:10.1016/0022-2496(85)90005-7 http://dx.doi.org/10.1016/0022-2496%2885%2990005-7
4. Bellman, R. (1961). Adaptive control processes: a guided tour. Princeton, NJ: Princeton University Press.
5. Brent M. Speech segmentation and word discovery: a computational perspective Trends in Cognitive Science 3 (1999) 294-301 doi:10.1016/S1364-6613(99)01350-9 http://dx.doi.org/10.1016/S1364-6613%2899%2901350-9
6. Browne M. W. Cross-validation methods Journal of Mathematical Psychology 44 (2000) 108-132 doi:10.1006/jmps.1999.1279 http://dx.doi.org/10.1006/jmps.1999.1279
7. Burnham, K. P., & Anderson, D. R. (1998). Model selection and inference: a practical information-theoretic approach. New York: Springer.
8. Busemeyer J. R., Wang Y.-M. Model comparisons and model selections based on generalization criterion methodology Journal of Mathematical Psychology 44 (2000) 171-189 doi:10.1006/jmps.1999.1282 http://dx.doi.org/10.1006/jmps.1999.1282
9. Casella, G., & Berger, R. (2002). Statistical inference (2nd ed., Chap. 7). Pacific Grove, CA: Duxberry.
10. Elman, J. L., Bates, E. A., Johnson, M. H., KarmiloffSmith, A., Parisi, D., & Plunkett, K. (1996). Rethinking innateness: a connectionist perspective on development. Cambridge, MA: MIT Press.
11. Fishburn, P. C. (1982). The foundations of expected utility. Dordrecht: Reidel.
12. Gescheider, G. A. (1985). Psychophysics: method, theory, and application. Mahwah, NJ: Erlbaum.
13. Gilks, W. R., Richardson, S., & Spiegelhalter, D. J. (1996). Markov chain Monte Carlo in Practice. London: Chapman & Hall.
14. Gluck M. A. Stimulus generalization and representation in adaptive network models of category learning Psychological Science 2 (1991) 50-55 doi:10.1111/j.1467-9280.1991.tb00096.x http://dx.doi.org/10.1111/j.1467-9280.1991.tb00096.x
15. Grainger, J., & Jacobs, A. M. (1998). Localist connectionist approaches to human cognition. Mahwah, NJ: Erlbaum.
16. Grunwald, P. (1999). Viewing all models as ‘probabilistic’. Proceedings of the Twelfth Annual Conference on Computational Learning Theory (COLT’ 99), Santa Cruz, CA.
17. Grunwald P. The minimum description length principle Journal of Mathematical Psychology 44 (2000) 133-152 doi:10.1006/jmps.1999.1280 http://dx.doi.org/10.1006/jmps.1999.1280
18. Grunwald, P. (2002). Minimum description length and maximum probability. Boston, MA: Kluwer.
19. Hansen M. H., Yu B. Model selection and the principle of minimum description length Journal of the American Statistical Association 96 (2001) 746-774 doi:10.1198/016214501753168398 http://dx.doi.org/10.1198/016214501753168398
20. Jacobs A. M., Grainger J. Models of visual word recognition - sampling the state of the art Journal of Experimental Psychology: Human Perception and Performance 29 (1994) 1311-1334 doi:10.1037/0096-1523.20.6.1311 http://dx.doi.org/10.1037/0096-1523.20.6.1311
21. Joanisse M. F., Seidenberg M. S. Impairments in verb morphology after brain injury: a connectionist model Proceedings of the National Academy of Sciences of the USA 96 (1999) 7592-7597 doi:10.1073/pnas.96.13.7592 http://dx.doi.org/10.1073/pnas.96.13.7592
22. Johnson, R. A., & Wichern, D. W. (1998). Applied multivariate statistical analysis (pp. 234-235). Upper Saddle River, NJ: Prentice-Hall.
23. Karabatsos G. The Rasch model, additive conjoint measurement and new models of probabilistic measurement theory Journal of Applied Measurement 2 (2001) 389-423
24. Knowlton B. J. What can neuropsychology tell us about category learning? Trends in Cognitive Science 3 (1999) 123-124 doi:10.1016/S1364-6613(99)01292-9 http://dx.doi.org/10.1016/S1364-6613%2899%2901292-9
25. Knowlton B. J., Squire L. R. The learning of categories: parallel brain systems for item memory and category knowledge Science 262 (1993) 1747-1749 doi:10.1126/science.8259522 http://dx.doi.org/10.1126/science.8259522
26. Kruschke J. K. ALCOVE: an exemplar-based connectionist model of category learning Psychological Review 99 (1992) 22-44 doi:10.1037/0033-295X.99.1.22 http://dx.doi.org/10.1037/0033-295X.99.1.22
27. Li, M., & Vitanyi, P. (1997). An introduction to Kolmogorov complexity and its applications. New York: Springer.
28. Linhart, H., & Zucchini, W. (1986). Model selection. New York: Wiley.
29. Luce, R. D. (1959). Individual choice behavior. New York: Wiley.
30. McClelland J. L., Elman J. L. The TRACE model of speech perception Cognitive Psychology 18 (1986) 1-86 doi:10.1016/0010-0285(86)90015-0 http://dx.doi.org/10.1016/0010-0285%2886%2990015-0
31. McKinley S. C., Nosofsky R. M. Investigations of exemplar and decision bound models in large, ill-defined category structures Journal of Experimental Psychology: Human Perception and Performance 21 (1995) 128-148 doi:10.1037/0096-1523.21.1.128 http://dx.doi.org/10.1037/0096-1523.21.1.128
32. Moody, J. E. (1992). The effective number of parameters: an analysis of generalization and regularization in nonlinear modeling. In J.E. Moody, S.J. Hanson, & R. P. Lippmann (Eds.), Advances in neural information processing systems (Vol. 4, pp. 847-854). Cambridge, MA: MIT Press.
33. Murata N., Yoshizawa S., Amari S.-I. Network information criterion: determining the number of hidden units for an artificial neural network model IEEE Transactions on Neural Networks 5 (1994) 865-872 doi:10.1109/72.329683 http://dx.doi.org/10.1109/72.329683
34. Myung I. J., Balasubramanian V., Pitt M. A. Counting probability distributions: differential geometry and model selection Proceedings of the National Academy of Sciences of the USA 97 (2000) 11170-11175 doi:10.1073/pnas.170283897 http://dx.doi.org/10.1073/pnas.170283897
35. Myung I. J., Forster M., Browne M. W. Special issue on model selection Journal of Mathematical Psychology 44 (2000) 1-2 doi:10.1006/jmps.1999.1273 http://dx.doi.org/10.1006/jmps.1999.1273
36. Myung, I. J., & Pitt, M. A. (2001). Mathematical modeling. In J. Wixted (ed.), Stevens’ handbook of experimental psychology: Vol. IV. Methodology (3rd ed., pp. 429-459). New York: Wiley.
37. Nosofsky R. M. Attention, similarity and the identification-categorization relationship Journal of Experimental Psychology: General 115 (1986) 39-57 doi:10.1037/0096-3445.115.1.39 http://dx.doi.org/10.1037/0096-3445.115.1.39
38. Nosofsky R. M., Palmeri T. J. An exemplarbased random walk model of speeded classification Psychological Review 104 (1997) 266-300 doi:10.1037/0033-295X.104.2.266 http://dx.doi.org/10.1037/0033-295X.104.2.266
39. Nosofsky R. M., Zaki S. Dissociation between categorization and recognition in amnesic and normal individuals: an exemplar-based interpretation Psychological Science 9 (1998) 247-255 doi:10.1111/1467-9280.00051 http://dx.doi.org/10.1111/1467-9280.00051
40. Pitt M. A., Myung I. J., Zhang S. Toward a method of selecting among computational models of cognition Psychological Review 109 (2002) 472-491 doi:10.1037/0033-295X.109.3.472 http://dx.doi.org/10.1037/0033-295X.109.3.472
41. Popper, K. R. (1959). The logic of scientific discovery. New York: Basic Books.
42. Ratcliff R., Spieler D., McKoon G. Explicitly modeling the effects of aging on response time Psychonomic Bulletin and Review 7 (2000) 1-25 doi:10.3758/BF03210723 http://dx.doi.org/10.3758/BF03210723
43. Reed S. K. Pattern recognition and categorization Cognitive Psychology 3 (1972) 382-407 doi:10.1016/0010-0285(72)90014-X http://dx.doi.org/10.1016/0010-0285%2872%2990014-X
44. Rissanen J. A universal prior for integers and estimation by minimum description length Annals of Statistics 11 (1983) 416-431 doi:10.1214/aos/1176346150 http://dx.doi.org/10.1214/aos/1176346150
45. Rissanen, J. (1989). Stochastic complexity in statistical inquiries. Singapore: World Scientific.
46. Rissanen J. Fisher information and stochastic complexity IEEE Transactions on Information Theory 42 (1996) 40-47 doi:10.1109/18.481776 http://dx.doi.org/10.1109/18.481776
47. Roberts S., Pashler H. How persuasive is a good fit? A comment on theory testing in psychology Psychological Review 107 (2000) 358-367 doi:10.1037/0033-295X.107.2.358 http://dx.doi.org/10.1037/0033-295X.107.2.358
48. Schwarz G. Estimating the dimension of a model Annals of Statistics 6 (1978) 461-464 doi:10.1214/aos/1176344136 http://dx.doi.org/10.1214/aos/1176344136
49. Shiffrin R. M., Nobel P. A. The art of model development and testing Behavioral Research Methods, Instruments and Computers 29 (1997) 6-14 doi:10.3758/BF03200560 http://dx.doi.org/10.3758/BF03200560
50. Shiffrin R. M., Steyvers M. A model for recognition memory: REM - retrieving effectively from memory Psychonomic Bulletin and Review 4 (1997) 145-166 doi:10.3758/BF03209391 http://dx.doi.org/10.3758/BF03209391
51. Shin H. J., Nosofsky R. M. Similarity-scaling studies of dot-pattern classification and recognition Journal of Experimental Psychology: General 121 (1992) 278-304 doi:10.1037/0096-3445.121.3.278 http://dx.doi.org/10.1037/0096-3445.121.3.278
52. Stone M. Cross-validatory choice and assessment of statistical predictions (with discussion) Journal of Royal Statistical Society, Series B 36 (1974) 111-147
53. Wixted J. T., Ebbesen E. B. On the form of forgetting Psychological Science 2 (1991) 409-415 doi:10.1111/j.1467-9280.1991.tb00175.x http://dx.doi.org/10.1111/j.1467-9280.1991.tb00175.x