Decision-Tree Models of Categorization Response Times, Choice Proportions, and Typicality Judgments

Psychological Review

Volumn 116, Issue 4, 2009, Pages 833-855

  Lafond, Daniel ^a   Lacouture, Yves ^a   Cohen, Andrew L ^b  

^a UNIVERSITÉ LAVAL   (Canada)

^b UNIVERSITY OF MASSACHUSETTS   (United States)

Author keywords

concept formation; decision trees; rule based categorization

Indexed keywords

ARTICLE; DECISION MAKING; DECISION TREE; HUMAN; LEARNING; PSYCHOLOGICAL MODEL; REACTION TIME;

ASSOCIATION LEARNING; CHOICE BEHAVIOR; DECISION TREES; HUMANS; JUDGMENT; MODELS, PSYCHOLOGICAL; PROBABILITY LEARNING; REACTION TIME;

EID: 70350434319     PISSN: 0033295X     EISSN: None     Source Type: Journal    
DOI: 10.1037/a0017188     Document Type: Article

References (75)

1. Aha D.W., and Goldstone R.L. Concept learning and flexible weighting. Proceedings of the 14th Annual Conference of the Cognitive Science Society (1992), Erlbaum, Hillsdale, NJ 534-539
2. Armstrong S.L., Gleitman L.R., and Gleitman H. What some concepts might not be. Cognition 13 (1983) 263-308
3. Ashby F.G., and Maddox T.W. A response time theory of separability and integrality in speeded classification. Journal of Mathematical Psychology 38 (1994) 423-466
4. Ashby F.G., and Townsend J.T. Varieties of perceptual independence. Psychological Review 93 (1986) 154-179
5. Bergert F.B., and Nosofsky R.M. A response-time approach to comparing generalized rational and take-the-best models of decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition 33 (2007) 107-129
6. Bradmetz J., and Mathy F. Response times seen as decompression time in Boolean concept use. Psychological Research 72 (2008) 211-234
7. Briscoe E., and Feldman J. Conceptual complexity and the bias-variance tradeoff. Proceedings of the 28th Annual Conference of the Cognitive Science Society (2006), Erlbaum, Mahwah, NJ 1038-1043
8. Browne M. Cross-validation methods. Journal of Mathematical Psychology 44 (2000) 108-132
9. Bruner J.S., Goodnow J.J., and Austin G.A. A study of thinking (1956), Wiley, New York, NY
10. Busemeyer J.R. Decision making under uncertainty: Simple scalability, fixed sample, and sequential sampling models. Journal of Experimental Psychology: Learning, Memory, and Cognition 11 (1985) 538-564
11. Busemeyer J.R., and Wang Y. Model comparisons and model selections based on the generalization criterion methodology. Journal of Mathematical Psychology 44 (2000) 171-189
12. Cohen A.L., and Nosofsky R.M. An extension of the exemplar-based random-walk model to separable-dimension stimuli. Journal of Mathematical Psychology 47 (2003) 150-165
13. Dijkstra S., and Dekker P.H. Inference processes in learning well-defined concepts. Acta Psychologica 51 (1982) 181-205
14. Einhorn H.J., Kleinmuntz D.N., and Kleinmuntz B. Linear regression and process-tracing models of judgment. Psychological Review 86 (1979) 465-485
15. Estes W.K., and Maddox W.T. Risks of drawing inferences about cognitive processes from model fits to individual versus average performance. Psychonomic Bulletin & Review 12 (2005) 403-408
16. Fano R.M. The transmission of information: I (Technical Report No. 65) (1949), MIT Research Laboratory of Electronics, Cambridge, MA
17. Feldman J. Minimization of Boolean complexity in human concept learning. Nature 407 (2000, October 5) 630-633
18. Feldman J. A catalog of Boolean concepts. Journal of Mathematical Psychology 47 (2003) 98-112
19. Feldman J. An algebra of human concept learning. Journal of Mathematical Psychology 50 (2006) 339-368
20. Ford J.K., Schmitt N., Schechtman S.L., Hults B.M., and Doherty M.L. Process tracing methods: Contributions, problems, and neglected research questions. Organizational Behavior and Human Decision Processes 43 (1989) 75-117
21. Garner W.R. The processing of information and structure (1974), Wiley, New York, NY
22. Gigerenzer G. Rationality for mortals: How people cope with uncertainty (2008), Oxford University Press, New York, NY
23. Gigerenzer G., and Goldstein D.G. Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review 103 (1996) 650-669
24. Goodman N.D., Tenenbaum J.B., Feldman J., and Griffiths T.L. A rational analysis of rule-based concept learning. Cognitive Science 32 (2008) 108-154
25. Goodman R.M., and Smyth P. Decision tree design from a communication theory standpoint. IEEE Transactions on Information Theory 34 (1988) 979-994
26. Hahn U., and Chater N. Similarity and rules: Distinct? Exhaustive? Empirically distinguishable?. Cognition 65 (1998) 197-230
27. Haygood R., and Bourne Jr. L.E. Attribute and rule learning aspects of conceptual behavior. Psychological Review 72 (1965) 175-195
28. Hunt E. Concept learning: An information processing approach (1962), Wiley, New York, NY
29. Hunt E., Marin J., and Stone P. Experiments in induction (1966), Academic Press, New York, NY
30. Kruschke J.K. Toward a unified model of attention in associative learning. Journal of Mathematical Psychology 45 (2001) 812-863
31. Lafond D., Lacouture Y., and Mineau G. Complexity minimization in rule-based category learning: Revising the catalog of Boolean concepts and evidence for non-minimal rules. Journal of Mathematical Psychology 51 (2007) 57-74
32. Lamberts K. Categorization under time pressure. Journal of Experimental Psychology: General 124 (1995) 161-180
33. Lamberts K. Information-accumulation theory of speeded categorization. Psychological Review 107 (2000) 227-260
34. Lamberts K., and Freeman R.P.J. Building object representations from parts: Tests of a stochastic sampling model. Journal of Experimental Psychology: Human Perception and Performance 25 (1999) 904-926
35. Lee M.D., and Webb M.R. Modeling individual differences in cognition. Psychonomic Bulletin & Review 12 (2005) 605-621
36. Link S.W. The wave theory of difference and similarity (1992), Erlbaum, Hillsdale, NJ
37. Logan G.D. Toward an instance theory of automatization. Psychological Review 95 (1988) 492-527
38. Luce R.D., and Raiffa H. Games and decisions: Introduction and critical survey (1957), Wiley, New York, NY
39. Marley A.A.J., and Colonius H. The "horse race" random utility model for choice probabilities and response times, and its competing risks interpretation. Journal of Mathematical Psychology 35 (1992) 1-20
40. Martignon L., Katsikopoulos K.V., and Woikeb J.K. Categorization with limited resources: A family of simple heuristics. Journal of Mathematical Psychology 52 (2008) 352-361
41. Martignon L., Vitouch O., Takezawa M., and Forster M. Naïve and yet enlightened: From natural frequencies to fast and frugal decision trees. In: Hardman D., and Macchi L. (Eds). Thinking: Psychological perspectives on reasoning, judgment, and decision making (2003), Wiley, Chichester, United Kingdom 189-211
42. Martin R.C., and Caramazza A. Classification in well-defined and ill-defined categories: Evidence for common processing strategies. Journal of Experimental Psychology: General 109 (1980) 320-353
43. Matsuka T., and Corter J.E. Observed attention allocation processes in category learning. Quarterly Journal of Experimental Psychology 61 (2008) 1067-1097
44. Medin D.L., and Schaffer M.M. Context theory of classification learning. Psychological Review 85 (1978) 207-238
45. Medin D.L., and Smith E.E. Concepts and concept formation. Annual Review of Psychology 35 (1984) 113-138
46. Medin D.L., Wattenmaker W.D., and Michalski R.S. Constraints and preferences in inductive learning: An experimental study of human and machine performance. Cognitive Science 11 (1987) 299-339
47. Mosier C.I. Symposium: The need and means of cross-validation: I. Problems and designs of cross-validation. Educational and Psychological Measurement 11 (1951) 5-11
48. Myung I.J. The importance of complexity in model selection. Journal of Mathematical Psychology 44 (2000) 190-204
49. Myung I.J., Pitt M.A., and Kim W. Model evaluation, testing and selection. In: Lambert K., and Goldstone R. (Eds). The handbook of cognition (2005), Sage, Thousand Oaks, CA 422-436
50. Navarro D.J., and Myung I.J. Model evaluation. In: Everitt B., and Howel D. (Eds). Encyclopedia of statistics in behavioral science 3 (2005), Wiley, New York, NY 1239-1242
51. Nosofsky R.M. Attention, similarity, and the identification categorization relationship. Journal of Experimental Psychology: General 115 (1986) 39-57
52. Nosofsky R.M. Typicality in logically defined categories: Exemplar-similarity versus rule instantiation. Memory & Cognition 19 (1991) 131-150
53. Nosofsky R.M., and Palmeri T.J. An exemplar-based random walk model of speeded classification. Psychological Review 104 (1997) 266-300
54. Nosofsky R.M., Palmeri T.J., and McKinley S.C. Rule-plus-exception model of classification learning. Psychological Review 101 (1994) 53-79
55. Palmeri T.J., and Nosofsky R.M. A rule-plus-exception model for classifying objects in continuous-dimension spaces. Psychonomic Bulletin & Review 5 (1998) 345-369
56. Peebles, D. (1997). The effect of stimulus frequency on classification accuracy and response time (Unpublished doctoral thesis). University of Birmingham, Birmingham, United Kingdom.
57. Peebles D., and Lamberts K. A connectionist model of categorization response times. In: Heinke D., Humphreys G.W., and Olson A. (Eds). Connectionist models in cognitive neuroscience (1999), Springer-Verlag, London, England 228-239
58. Pitt M.A., and Myung I.J. When a good fit can be bad. Trends in Cognitive Sciences 6 (2002) 421-425
59. Quinlan J.R. Induction of decision trees. Machine Learning 1 (1986) 81-106
60. Ratcliff R. A theory of memory retrieval. Psychological Review 85 (1978) 59-108
61. Roberts S., and Pashler H. How persuasive is a good fit? A comment on theory testing. Psychological Review 107 (2000) 358-367
62. Rosch E. On the internal structure of perceptual and semantic categories. In: Moore T.E. (Ed). Cognitive development and the acquisition of language (1973), Academic Press, New York, NY 111-144
63. Sakamoto Y., Matsuka T., and Love B.C. Dimension-wide vs. exemplar-specific attention in category learning and recognition. In: Lovett M., Schunn C., Lebiere C., and Munro P. (Eds). Proceedings of the 6th International Conference on Cognitive Modeling (2004), Erlbaum, Mahwah, NJ 261-266
64. Sayeki Y. Information seeking for object identification. Organizational Behavior and Human Performance 4 (1969) 267-283
65. Shannon C.E. A mathematical theory of communication. Bell System Technical Journal 27 (1948) 379-423
66. Shepard R.N. Attention and the metric structure of the stimulus space. Journal of Mathematical Psychology 1 (1964) 54-87
67. Shepard R.N., Hovland C.I., and Jenkins H.M. Learning and memorization of classifications. Psychological Monographs: General and Applied 75 (1961) 1-42
68. Smith E.E., Patalano A.L., and Jonides J. Alternative strategies of categorization. Cognition 65 (1998) 167-196
69. Stone M. Cross-validatory choice and assessment of statistical predictions (with discussion). Journal of the Royal Statistical Society: Series B 36 (1974) 111-147
70. Townsend J.T., and Ashby F.G. Stochastic modeling of elementary psychological processes (1983), Cambridge University Press, New York, NY
71. Trabasso T., Rollins H., and Shaughnessy E. Storage and verification stages in processing concepts. Cognitive Psychology 2 (1971) 239-289
72. Vandierendonck A. Typicality gradient in well-defined artificial categories. Acta Psychologica 69 (1988) 61-81
73. Vigo R. A note on the complexity of Boolean concepts. Journal of Mathematical Psychology 50 (2006) 501-510
74. Voorspoels W., Vanpaemel W., and Storms G. Exemplars and prototypes in natural language concepts: A typicality-based evaluation. Psychonomic Bulletin and Review 15 (2008) 630-637
75. Wedell D.H., and Senter S.M. Looking and weighting in judgment and choice. Organizational Behavior and Human Decision Processes 70 (1997) 41-64