Model evaluation using grouped or individual data

Psychonomic Bulletin and Review

Volumn 15, Issue 4, 2008, Pages 692-712

  Cohen, Andrew L ^a   Sanborn, Adam N ^b   Shiffrin, Richard M ^b  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

^b INDIANA UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER GRAPHICS; COMPUTER SIMULATION; EPIDEMIOLOGY; HUMAN; INDIVIDUALITY; INFORMATION PROCESSING; LEARNING; LONG TERM MEMORY; REVIEW; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICS;

BIAS (EPIDEMIOLOGY); COMPUTER GRAPHICS; COMPUTER SIMULATION; DATA COLLECTION; DATA INTERPRETATION, STATISTICAL; HUMANS; INDIVIDUALITY; MODELS, STATISTICAL; RETENTION (PSYCHOLOGY); VERBAL LEARNING;

EID: 50949086061     PISSN: 10699384     EISSN: 15315320     Source Type: Journal    
DOI: 10.3758/PBR.15.4.692     Document Type: Review

References (49)

1. AKAIKE, H. (1973). Information theory as an extension of the maximum likelihood principle. In B. N. Petrov & F. Csaki (Eds.), Second International Symposium on Information Theory (pp. 267-281). Budapest: Akademiai Kiado.
2. ANDERSON, N. H. (1981). Foundations of information integration theory. New York: Academic Press.
3. ANDERSON, R. B., & TWENEY, R. D. (1997). Artifactual power curves in forgetting. Memory & Cognition, 25, 724-730.
4. ASHBY, F. G., & GOTT, R. E. (1988). Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, & Cognition, 14, 33-53.
5. ASHBY, F. G., & MADDOX, W. T. (1993). Relations between prototype, exemplar, and decision bound models of categorization. Journal of Mathematical Psychology, 37, 372-400.
6. ASHBY, F. G., MADDOX, W. T., & LEE, W. W. (1994). On the dangers of averaging across subjects when using multidimensional scaling or the similarity-choice model. Psychological Science, 5, 144-151.
7. BARRON, A. R., RISSANEN, J., & YU, B. (1998). The MDL principle in modeling and coding. IEEE Transactions on Information Theory, 44, 2743-2760.
8. BERGER, J. O., & PERICCHI, L. R. (1996). The intrinsic Bayes factor for model selection and prediction. Journal of the American Statistical Association, 91, 109-122.
9. BOWER, G. H. (1961). Application of a model to paired-associate learning. Psychometrika, 26, 255-280.
10. BROWNE, M. W. (2000). Cross-validation methods. Journal of Mathematical Psychology, 44, 108-132.
11. BURNHAM, K. P., & ANDERSON, D. R. (1998). Model selection and inference: A practical information-theoretic approach. New York: Springer.
12. BURNHAM, K. P., & ANDERSON, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). New York: Springer.
13. BUSEMEYER, J. R., & WANG, Y.-M. (2000). Model comparisons and model selections based on generalization criterion methodology. Journal of Mathematical Psychology, 44, 171-189.
14. COVER, T. M., & THOMAS, J. A. (1991). Elements of information theory. New York: Wiley.
15. ESTES, W. K. (1956). The problem of inference from curves based on group data. Psychological Bulletin, 53, 134-140.
16. ESTES, W. K., & MADDOX, W. T. (2005). Risks of drawing inferences about cognitive processes from model fits to individual versus average performance. Psychonomic Bulletin & Review, 12, 403-408.
17. GRÜNWALD, P. [D.] (2000). Model selection based on minimum description length. Journal of Mathematical Psychology, 44, 133-152.
18. GRÜNWALD, P. D. (2005). Minimum description length tutorial. In P. D. Grünwald, I. J. Myung, & M. A. Pitt (Eds.), Advances in minimum description length: Theory and applications (pp. 23-80). Cambridge, MA: MIT Press.
19. GRÜNWALD, P. D. (2007). The minimum description length principle. Cambridge, MA: MIT Press.
20. HASTIE, T., TIBSHIRANI, R., & FRIEDMAN, J. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.
21. HAYES, K. J. (1953). The backward curve: A method for the study of learning. Psychological Review, 60, 269-275.
22. JEFFREYS, H. (1935). Some tests of significance, treated by the theory of probability. Proceedings of the Cambridge Philosophical Society, 31, 203-222.
23. JEFFREYS, H. (1961). Theory of probability (3rd ed.). Oxford: Oxford University Press.
24. KARABATSOS, G. (2006). Bayesian nonparametric model selection and model testing. Journal of Mathematical Psychology, 50, 123-148.
25. KASS, R. E., & RAFTERY, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773-795.
26. LAGARIAS, J. C., REEDS, J. A., WRIGHT, M. H., & WRIGHT, P. E. (1998). Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM Journal of Optimization, 9, 112-147.
27. LEE, M. D., & WEBB, M. R. (2005). Modeling individual differences in cognition. Psychonomic Bulletin & Review, 12, 605-621.
28. MACK, A., & ROCK, I. (1998). Inattentional blindness. Cambridge, MA: MIT Press.
29. MACMILLAN, N. A., & CREELMAN, C. D. (2005). Detection theory: A user's guide (2nd ed.). Mahwah, NJ: Erlbaum.
30. MASSARO, D. W. (1998). Perceiving talking faces: From speech perception to a behavioral principle. Cambridge, MA: MIT Press.
31. MEDIN, D. L., & SCHAFFER, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207-238.
32. MINDA, J. P., & SMITH, J. D. (2002). Comparing prototype-based and exemplar-based accounts of category learning and attentional allocation. Journal of Experimental Psychology: Learning, Memory, & Cognition, 28, 275-292.
33. MYUNG, I. J., KIM, C., & PITT, M. A. (2000). Toward an explanation of the power law artifact: Insights from response surface analysis. Memory & Cognition, 28, 832-840.
34. NAVARRO, D. J., GRIFFITHS, T. L., STEYVERS, M., & LEE, M. D. (2006). Modeling individual differences using Dirichlet processes. Journal of Mathematical Psychology, 50, 101-122.
35. NAVARRO, D. J., PITT, M. A., & MYUNG, I. J. (2004). Assessing the distinguishability of models and the informativeness of data. Cognitive Psychology, 49, 47-84.
36. NOSOFSKY, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39-57.
37. NOSOFSKY, R. M., & ZAKI, S. R. (2002). Exemplar and prototype models revisited: Response strategies, selective attention, and stimulus generalization. Journal of Experimental Psychology: Learning, Memory, & Cognition, 28, 924-940.
38. ODEN, G. C., & MASSARO, D. W. (1978). Integration of featural information in speech perception. Psychological Review, 85, 172-191.
39. REED, S. K. (1972). Pattern recognition and categorization. Cognitive Psychology, 3, 382-407.
40. RISSANEN, J. (1978). Modeling by the shortest data description. Automatica, 14, 465-471.
41. RISSANEN, J. (1987). Stochastic complexity. Journal of the Royal Statistical Society B, 49, 223-239, 252-265.
42. RISSANEN, J. (1996). Fisher information and stochastic complexity. IEEE Transactions on Information Theory, 42, 40-47.
43. RISSANEN, J. (2001). Strong optimality of the normalized ML models as universal codes and information in data. IEEE Transactions on Information Theory, 47, 1712-1717.
44. SCHWARZ, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
45. SHEPARD, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1317-1323.
46. SIDMAN, M. (1952). A note on functional relations obtained from group data. Psychological Bulletin, 49, 263-269.
47. SIEGLER, R. S. (1987). The perils of averaging data over strategies: An example from children's addition. Journal of Experimental Psychology: General, 116, 250-264.
48. WAGENMAKERS, E.-J., RATCLIFF, R., GOMEZ, P., & IVERSON, G. J. (2004). Assessing model mimicry using the parametric bootstrap. Journal of Mathematical Psychology, 48, 28-50.
49. WIXTED, J. T., & EBBESEN, E. B. (1991). On the form of forgetting. Psychological Science, 2, 409-415.