Response times seen as decompression times in Boolean concept use

Psychological Research

Volumn 72, Issue 2, 2008, Pages 211-234

  Bradmetz, Joël ^a   Mathy, Fabien ^b  

^a UNIVERSITÉ DE REIMS CHAMPAGNE ARDENNE   (France)

^b UNIVERSITÉ DE FRANCHE COMTÉ   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ADOLESCENT; ADULT; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; ATTENTION; COLOR VISION; CONCEPT FORMATION; DECISION MAKING; DEPTH PERCEPTION; FEMALE; HUMAN; LEARNING; MALE; ORIENTATION; PATTERN RECOGNITION; PERCEPTION; PROBLEM SOLVING; PSYCHOLOGICAL MODEL; PSYCHOPHYSICS; REACTION TIME; SHORT TERM MEMORY;

ADOLESCENT; ADULT; ALGORITHMS; ATTENTION; COLOR PERCEPTION; CONCEPT FORMATION; DECISION MAKING; DEPTH PERCEPTION; FEMALE; GENERALIZATION (PSYCHOLOGY); HUMANS; MALE; MEMORY, SHORT-TERM; MODELS, PSYCHOLOGICAL; NEURAL NETWORKS (COMPUTER); ORIENTATION; PATTERN RECOGNITION, VISUAL; PROBLEM SOLVING; PSYCHOPHYSICS; REACTION TIME; SERIAL LEARNING; SIZE PERCEPTION;

EID: 38349085245     PISSN: 03400727     EISSN: 14302772     Source Type: Journal    
DOI: 10.1007/s00426-006-0098-7     Document Type: Article

References (86)

1. Anthony, M., & Biggs, N. (1992). Computational learning theory. Cambridge, MA: Cambridge University Press.
2. Arnauld, A., & Nicole, P. (1996). Logic or the art of thinking. Cambridge, UK: Cambridge university press (Original work published 1662).
3. Ashby, F. G., Alfonso-Reese, L. A., Turken, A. U., & Waldron, E. M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 105, 442-481.
4. Ashby, F. G., Boyton, G., & Lee, W. W. (1994). Categorization response time with multidimensional stimuli. Perception and Psychophysics, 55, 11-27.
5. Axelrod, R. (1997). The complexity of cooperation: Agent-based models of competition and collaboration. Princeton, NJ: Princeton University Press.
6. Bennett, C. H. (1986). On the nature and origin of complexity in discrete, homogeneous, locally-interacting systems. Foundations of Physics, 16(6), 585-592.
7. Bourne, L. E. Jr. (1970). Knowing and using concepts. Psychological Review, 77, 546-556.
8. Bruner, J. S., Goodnow, J. J., & Austin, G. A. (1956). A study of thinking. New York: Wiley.
9. Bryant, R. E. (1986). Graph-based algorithms for Boolean function manipulation. IEEE Transactions on Compilers, 35(8), 677-691.
10. Chaitin, G. J. (1974). Information-theoretic computational complexity. IEEE Transactions on Information Theory, 20(1), 1015.
11. Chaitin, G. J. (1987). Information, randomness and incompleteness: papers on algorithmic information theory. New York: World Scientific.
12. Chaitin, G. J. (1990). Algorithmic information theory. Cambridge, MA: Cambridge University Press.
13. Chater, N., Vitányi, P. (2003). Simplicity: A unifying principle in cognitive science? Trends in Cognitive Science, 7(1), 19-22.
14. Davies, P. C. W. (1989). Why is the physical world so comprehensible? In W. H. Zurek (Ed.), Complexity, entropy, and the physics of information, pp. 61-70. New York: Addison-Wesley.
15. De Raedt, L. (1997). Logical settings for concept-learning. Artificial Intelligence, 95, 187-201.
16. Delahaye, J. P. (1993). Complexités. In J. P. Delahaye (Ed.), Logique, informatique et paradoxe. Paris: Pour la science.
17. Delahaye, J. P. (1994). Information, complexité et hasard. Paris: Hermès.
18. Duda, R. O., Hart, E., & Stork, D. G. (2001). Pattern classification (2nd ed.). New York: Wiley.
19. Estes, W. K. (1994). Classification and cognition. Oxford: Oxford University Press.
20. Fahlman, S. E., & Lebiere, C. (1990). The cascade-correlation learning architecture. Technical report. CMU-CS-90-100. Canergy-Mellon University.
21. Fass, D., & Feldman, J. (2002). Categorization under complexity: A unified MDL account of human learning of regular and irregular categories. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems, Proceedings of the 2002 conference (Vol. 15). Cambridge, MA: MIT Press.
22. Feldman, J. (2000). Minimization of Boolean complexity in human concept learning. Nature, 407, 630-633.
23. Feldman, J. (2003a). A catalog of Boolean concepts. Journal of Mathematical Psychology, 47, 75-89.
24. Feldman, J. (2003b). The simplicity principle in human concept learning. Cognition, 93, 199-224.
25. Feldman, J. (2004). How surprising is a simple pattern? Quantifying "Eureka!". Current Directions in Psychological Science, 12(6), 227-232.
26. Ferber, J. (1999). Multi-agent systems, an introduction to distributed artificial intelligence. Harlow: Addison-Wesley.
27. Gell-Mann, M. (1994). The quark and the jaguar: Adventures in the simple and the complex. New York: Freeman and Company.
28. Gluck, M. A., & Bower, G. H. (1988a). Evaluating an adaptive network model of human learning. Journal of Memory and Language, 27, 166-195.
29. Gluck, M. A., & Bower, G. H. (1988b). From conditioning to category learning: an adaptive network model. Journal of Experimental Psychology: General, 117, 227-247.
30. Gold, E. M. (1967). Language identification in the limit. Information and Control, 10, 447-474.
31. Gosselin, F., & Schyns, P. G. (2001). Why do we SLIP to the basic level? Computational constraints and their implementation. Psychological Review, 108(4), 735-758.
32. Hahn, U., & Chater, N. (1997). Concepts and similarity. In K. Lamberts & D. Shanks (Eds.), Knowledge, concepts and categories (pp. 43-92). Cambridge, MA: MIT Press.
33. Hanson, S. J., Drastal, G. A., & Rivest, R. L. (1994a). Computational learning theory and natural learning systems (Vol. 1). Cambridge, MA: MIT Press.
34. Hanson, S. J., Petsche, T., Kearns, M., & Rivest, R. L. (1994b). Computational learning theory and natural learning systems (Vol. 2). Cambridge, MA: MIT Press.
35. Hovland, C. L. (1966). A communication analysis of concept learning. Psychological Review, 59, 461-472.
36. Hromkovič, J. (1997). Communication complexity and parallel computing. New York: Springer.
37. Huber, L. (2001). Visual categorization in pigeons. In R. G. Cook (Ed.), Avian visual cognition [On-line]. Available: www.pigeon.psy.tufts.edu/avc/huber/ .
38. Hunt, E. B., Marin, J., & Stone, P. J. (1966). Experiments in induction. New York: Academic.
39. Huth, M., & Ryan, M. (2000). Logic in computer science: Modeling and reasoning about systems. London: Cambridge University Press.
40. Keanrs, M. J., Vazirani, U. V. (1994). An introduction to computational learning theory. Cambridge, MA: MIT Press.
41. Kolmogorov, A. N. (1965). Three approaches for defining the concept of information quantity. Information Transmission, 1, 3-11.
42. Kruschke, J. K. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22-44.
43. Lamberts, K. (1997). Process models of categorization. In K. Lamberts & D. Shanks (Eds.), Knowledge, concepts and categories (pp. 43-92). Cambridge, MA: MIT Press.
44. Lamberts, K. (2000). Information-accumulation theory of speeded categorization. Psychological Review, 107, 227-260.
45. Levine, M. (1966). Hypothesis behavior by humans during discrimination learning. Journal of Experimental Psychology, 71, 331-338.
46. Li, M., & Vitányi, P. (1997). An introduction to Kolmogorov complexity and its applications. New York: Springer Verlag.
47. Lockhead, G., & Pomerantz, J. R. (Eds.). (1991). The perception of structure. Washington: American Psychological Association.
48. Love, B. C., & Markman, A. B. (2003). The nonindependence of stimulus properties in human concept learning. Memory and Cognition, 31(5), 790-799.
49. Love, B. C., Medin, D. L., & Gureckis, T. M. (2004a). SUSTAIN: A network model of category learning. Psychological Review, 111, 309-332.
50. Love, B. C., Medin, D. L., & Gureckis, T. M. (2004b). SUSTAIN: A network model of category learning. Psychological Review, 111(2), 309-332.
51. Luce, R. D. (1963). Detection and recognition. In R. D. Luce, R. R. Bush, E. Galanter (Eds.), Handbook of mathematical psychology (pp. 103-189). New York: Wiley.
52. Mathy, F. (2002). L'apprenabilité des concepts évaluée au moyen d'un modèle multi-agent de la complexité des communications en mémoire de travail. Unpublished Doctoral Dissertation. Université de Reims, France.
53. Mathy, F., Bradmetz, J. (1999). Un modèle multi-agent de la complexité conceptuelle. In M. P. Gleizes, P. Marcenac (Eds.), Ingénierie des systèmes multi-agents: acte des 7e Journées Francophones d'Intelligence Artificielle et Systèmes Multi-Agents. Paris: Hermès. (pp. 343-345).
54. Mathy, F., & Bradmetz, J. (2004). A theory of the graceful complexification of concepts and their learnability. Current Psychology of Cognition, 22(1), 41-82.
55. Medin, D. L., & Schaffer, M. M. (1978). A context theory of classification learning, Psychological Review, 85, 207-238.
56. Minsky, M. L. (1985). The society of mind. New York, NY: Simon and Schuster (French translation: La société de l'esprit. Paris: Inter Editions, 1988).
57. Mitchell, T. (1997). Machine learning. New York, NY: McGraw-Hill.
58. Myung, I. J. (1994). Maximum entropy interpretation of decision bound and context models of categorization. Journal of Mathematical Psychology, 38, 335-365.
59. Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39-57.
60. Nosofsky, R. M., & Alfonso-Reese, L. A. (1999). Effects of similarity and practice on speeded classification response times and accuracies: Further tests of an exemplar-retrieval model. Memory and Cognition, 27, 78-93.
61. Nosofsky, R. M., Gluck, M. A., Palmeri, T. J., McKinley, S. C., & Gauthier, P. (1994a). Comparing models of rules-based classification learning: A replication and extension of Shepard, Hovland, and Jenkins (1961). Memory and Cognition, 22, 352-369.
62. Nosofsky, R. M., Kruschke, J. K., & McKinley, S. C. (1992). Combining exemplar-based category representations and connectionist learning rules. Journal of experimental Psychology: Learning, Memory, and Cognition, 18, 211-233.
63. Nosofsky, R. M., Palmeri, T. J. (1997). An exemplar-based random walk model of speeded classification. Psychological Review, 104, 266-300.
64. Nosofsky, R. M., Palmeri, T. J., & McKinley, S. C. (1994b). Rule-plus-exception model of classification learning. Psychological Review, 101(1), 53-79.
65. Osherson, D. N., & Smith, E. E. (1981). On the adequacy of prototype theory as a theory of concepts. Cognition, 9, 35-58.
66. Osherson, D. Stob, M., & Weistein, S. (1986). Systems that learn: an introduction to learning theory for cognitive and computer scientists. Cambridge, MA: MIT Press.
67. Pothos, E. M., & Chater, N. (2002). A simplicity principle in unsupervised human categorization. Cognitive Science, 26, 303-343.
68. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81-106.
69. Richards, W., & Bobick, A. (1988). Playing twenty questions with nature. In Z. W. Pylyshyn (Ed.), Computational processes in human vision: an interdisciplinary perspective (pp. 3-26). Norwood, NJ: Ablex.
70. Richards, W., McKay, B. D., & Richards, D. (2002). The probability of collective choice with shared knowledge structures. Journal of Mathematical Psychology, 46, 338-351.
71. Rips, L. J., Shoben, E. J., Smith, E. E. (1973). Semantic distance and the verification of semantic relations. Journal of Verbal Learning and Verbal Behavior, 12, 1-20.
72. Roberts, S., & Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107, 358-367.
73. Rosch, E. (1973). Natural categories. Cognitive Psychology, 4, 328-350.
74. Rosch, E., & Mervis, C. B. (1975). Family resemblances: studies in the internal structure of categories. Cognitive Psychology, 7, 573-605.
75. Selfridge, O. (1959). Pandemonium: a paradigm for learning. In D. V. Blake, A. M. Uttley (Eds.), Proceedings of the symposium on mechanisation of thought processes (pp 511-529). London: H. M. Stationary Office.
76. Shavlik, J. W., & Dietterich, T. G. (Eds.). (1990). Readings in machine learning. San Mateo, CA: Morgan Kaufmann.
77. Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1317-1323.
78. Shepard, R. N., Hovland, C. L., & Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs, 75 (13, whole No. 517).
79. Shin, H. J., & Nosofsky, R. M. (1992). Similarity-scaling studies of dot-pattern classification and recognition. Journal of Experimental Psychology: General, 121, 278-304.
80. Siegler, R. S. (1977). The twenty questions game as a form of problem solving. Child Development, 48 (2), 395-403.
81. Smith, E. E., & Medin, D. L. (1981). Categories and concepts. Cambridge, MA: Harvard University Press.
82. Townsend, J. T., & Wenger, M. J. (2004). The serial-parallel dilemma: A case study in a linkage of theory and method. Psychonomic Bulletin & Review, 11, 391-418.
83. Valiant, L. G. (1984). A theory of the learnable. Communication of the Association for Computing Machinery, 27, 1134-1142.
84. Wolfram, S. (2002). A new kind of science. Champaign, IL: Wolfram Media.
85. Yamauchi, T., Love, B. C., & Markman, A. B. (2002). Learning nonlinearly separable categories by inference and classification. Journal of Experimental Psychology: Learning, Memory and Cognition, 28, 585-593.
86. Zucchini, W. (2000). An introduction to model selection. Journal of Mathematical Psychology, 44, 41-61.