Common and distinctive features in stimulus similarity: A modified version of the contrast model

Psychonomic Bulletin and Review

Volumn 11, Issue 6, 2004, Pages 961-974

  Navarro, Daniel J ^a   Lee, Michael D ^a  

^a UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 21944444843     PISSN: 10699384     EISSN: None     Source Type: Journal    
DOI: 10.3758/BF03196728     Document Type: Article

References (52)

1. AKAIKE, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716-723.
2. ARABIE, P., & CARROLL, J. D. (1980). MAPCLUS: A mathematical programming approach to fitting the ADCLUS model. Psychometrika, 45, 211-235.
3. ARABIE, P., CARROLL, J. D., & DESARBO, W. S. (1987). Three-way scaling and clustering. Newbury Park, CA: Sage.
4. BALASUBRAMANIAN, V. (1997). Statistical inference, Occam's razor and statistical mechanics on the space of probability distributions. Neural Computation, 9, 349-368.
5. BROOKS, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47, 139-159.
6. CARROLL, J. D. (1976). Spatial, nonspatial and hybrid models for scaling. Psychometrika, 41, 439-463.
7. CHATURVEDI, A., & CARROLL, J. D. (1994). An alternating combinatorial optimization approach to fitting the INDCLUS and generalized INDCLUS models. Journal of Classification, 11, 155-170.
8. CHEESEMAN, P. (1999). Foundations of probability. In R. A. Wilson & F. C. Keil (Eds.), MIT encyclopedia of the cognitive sciences (pp. 673-674). Cambridge, MA: MIT Press.
9. CLOUSE, D. S., & COTTRELL, G. W. (1996). Discrete multidimensional scaling. Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society (pp. 290-294). Mahwah, NJ: Erlbaum.
10. GATI, I., & TVERSKY, A. (1984). Weighting common and distinctive features in perceptual and conceptual judgments. Cognitive Psychology, 16, 341-370.
11. GILKS, W. R., RICHARDSON, S., & SPIEGELHALTER, D. J. (1996). Markov chain Monte Carlo in practice. London: Chapman & Hall.
12. GLUSHKO, R. J. (1975). Pattern goodness and redundancy revisited: Multidimensional scaling and hierarchical cluster analyses. Perception & Psychophysics, 17, 158-162.
13. GOLDSTONE, R. L. (1999). Similarity. In R. A. Wilson & F. C. Keil (Eds.), MIT encyclopedia of the cognitive sciences (pp. 763-765). Cambridge, MA: MIT Press.
14. KASS, R. E., & RAFTERY, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773-795.
15. KOMATSU, L. K. (1992). Recent views of conceptual structure. Psychological Bulletin, 112, 500-526.
16. KRUSCHKE, J. K. (1993). Human category learning: Implications for backpropagation models. Connection Science, 5, 3-36.
17. LEE, M. D. (1998). Neural feature abstraction from judgments of similarity. Neural Computation, 10, 1815-1830.
18. LEE, M. D. (2001a). Determining the dimensionality of multidimensional scaling representations for cognitive modeling. Journal of Mathematical Psychology, 45, 149-166.
19. LEE, M. D. (2001b). On the complexity of additive clustering models. Journal of Mathematical Psychology, 45, 131-148.
20. LEE, M. D. (2002a). Generating additive clustering models with limited stochastic complexity. Journal of Classification, 19, 69-85.
21. LEE, M. D. (2002b). A simple method for generating additive clustering models with limited complexity. Machine Learning, 49, 39-58.
22. LEE, M. D., & NAVARRO, D. J. (2002). Extending the ALCOVE model of category learning to featural stimulus domains. Psychonomic Bulletin & Review, 9, 43-58.
23. LEE, M. D., & POPE, K. J. (2003). Avoiding the dangers of averaging across subjects when using multidimensional scaling. Journal of Mathematical Psychology, 47, 32-46.
24. MIRKIN, B. G. (1987). Additive clustering and qualitative factor analysis methods for similarity matrices. Journal of Classification, 4, 7-31.
25. MYUNG, I. J., BALASUBRAMANIAN, V., & PITT, M. A. (2000). Counting probability distributions: Differential geometry and model selection. Proceedings of the National Academy of Sciences, 97, 11170-11175.
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. NAVARRO, D. J. (2003). Regarding the complexity of additive clustering models: Comment on Lee (2001). Journal of Mathematical Psychology, 47, 241-243.
28. NAVARRO, D. J., & LEE, M. D. (2001). Clustering using the contrast model. In J. D. Moore & K. Stenning (Eds.), Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society (pp. 686-691). Mahwah, NJ: Erlbaum.
29. NAVARRO, D. J., & LEE, M. D. (2003). Combining dimensions and features in similarity-based representations. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems (Vol. 15, pp. 59-66). Cambridge, MA: MIT Press.
30. NOSOFSKY, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39-57.
31. NOSOFSKY, R. M. (1992). Similarity scaling and cognitive process models. Annual Review of Psychology, 43, 25-53.
32. PINKER, S. (1998). How the mind works. U.K.: Softback Preview.
33. PITT, M. A., MYUNG, I. J., & ZHANG, S. (2002). Toward a method of selecting among computational models of cognition. Psychological Review, 109, 472-491.
34. POTTS, B. C., MELARA, R. D., & MARKS, L. E. (1998). Circle size and diameter tilt: A new look at integrality and separability. Perception & Psychophysics, 60, 101-112.
35. RESTLE, F. (1959). A metric and an ordering on sets. Psychometrika, 24, 207-220.
36. RISSANEN, J. (1996). Fisher information and stochastic complexity. IEEE Transactions on Information Theory, 42, 40-47.
37. RITOV, I., GATI, I., & TVERSKY, A. (1990). Differential weighting of common and distinctive components. Journal of Experimental Psychology: General, 119, 30-41.
38. ROHDE, D. L. T. (2002). Methods for binary multidimensional scaling. Neural Computation, 14, 1195-1232.
39. ROSENBERG, S., & KIM, M. P. (1975). The method of sorting as a datagenerating procedure in multivariate research. Multivariate Behavioral Research, 10, 489-502.
40. SATTATH, S., & TVERSKY, A. (1987). On the relation between common and distinctive feature models. Psychological Review, 94, 16-22.
41. SCHERVISH, M. J. (1995). Theory of statistics. New York: Springer-Verlag.
42. SCHWARZ, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
43. SHEPARD, R. N. (1972). Psychological representation of speech sounds. In E. E. David & P. B. Denes (Eds.), Human communication: A unified view (pp. 67-113). New York: McGraw-Hill.
44. SHEPARD, R. N. (1974). Representation of structure in similarity data: Problems and prospects. Psychometrika, 39, 373-422.
45. SHEPARD, R. N. (1980). Multidimensional scaling, tree-fitting, and clustering. Science, 210, 390-398.
46. SHEPARD, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1317-1323.
47. SHEPARD, R. N. (1991). Integrality versus separability of stimulus dimensions: From an early convergence of evidence to a proposed theoretical basis. In J. R. Pomerantz & G. L. Lockhead (Eds.), The perception of structure: Essays in honor of Wendell R. Garner (pp. 53-71). Washington, DC: American Psychological Association.
48. SHEPARD, R. N., & ARABIE, P. (1979). Additive clustering representations of similarities as combinations of discrete overlapping properties. Psychological Review, 86, 87-123.
49. TENENBAUM, J. B. (1996). Learning the structure of similarity. In D. S. Touretzky, M. C. Mozer, & M. E. Hasselmo (Eds.), Advances in neural information processing systems (Vol. 8, pp. 3-9). Cambridge, MA: MIT Press.
50. TENENBAUM, J. B., & GRIFFITHS, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral & Brain Sciences, 24, 629-640.
51. TVERSKY, A. (1977). Features of similarity. Psychological Review, 84, 327-352.
52. TVERSKY, A., & HUTCHINSON, J. W. (1986). Nearest neighbor analysis of psychological spaces. Psychological Review, 93, 3-22.