MDLChunker: A MDL-Based Cognitive Model of Inductive Learning

Cognitive Science

Volumn 35, Issue 7, 2011, Pages 1352-1389

  Robinet, Vivien ^a   Lemaire, Benoît ^a   Gordon, Mirta B ^a  

^a UNIV GRENOBLE ALPES   (France)

Author keywords

Chunking; Computational cognitive modeling; Data compression; Information theory; Machine learning; Minimum Description Length; Simplicity; Speech segmentation

Indexed keywords

ARTICLE; BAYES THEOREM; COMPUTER SIMULATION; INFORMATION SCIENCE; LEARNING;

BAYES THEOREM; COMPUTER SIMULATION; INFORMATION THEORY; LEARNING;

EID: 80052394066     PISSN: 03640213     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1551-6709.2011.01188.x     Document Type: Article

References (53)

1. Argamon, S., Akiva, N., Amir, A., & Kapah, O. (2004). Efficient unsupervised recursive word segmentation using minimum description length. In S. Nirenburg (Ed.), Proceedings of the 20th International Conference on Computational Linguistics (Coling04) (pp. 1058-1064). Morristown, NJ: ACL.
2. Aslin, R. N., Woodward, J. Z., LaMendola, N. P., & Bever, T. G. (1996). Models of word segmentation in fluent maternal speech to infants. In J. L. Morgan & K. Demuth (Eds.), Signal to syntax (pp. 117-134). Hillsdale, NJ: Erlbaum.
3. Batchelder, E. O. (2002). Bootstrapping the lexicon: A computational model of infant speech segmentation. Cognition, 83(2), 167-206.
4. Bishop, C. M. (2006). Pattern recognition and machine learning. Berlin: Springer.
5. Brent, M. R. (1999). An efficient, probabilistically sound algorithm for segmentation and word discovery. Machine Learning, 34, 71-105.
6. Brent, M. R., & Cartwright, T. A. (1996). Distributional aregularity and phonotactic constraints are useful for segmentation. Cognition, 61(1-2), 93-125.
7. Cairns, P., Shillcock, R., Chater, N., & Levy, J. (1997). Bootstrapping word boundaries: A bottom-up corpus-based approach to speech segmentation. Cognitive Psychology, 33, 111-153.
8. Chaitin, G. J. (1966). On the length of programs for computing finite binary sequences. Journal of the ACM, 13(4), 547-569.
9. Chater, N. (1996). Reconciling simplicity and likelihood principles in perceptual organization. Psychological Review, 103, 566-581.
10. Chater, N. (1999). The search for simplicity: A fundamental cognitive principle? Quarterly Journal of Experimental Psychology, 52, 273-302.
11. Chater, N., & Vitanyi, P. M. (2003). Simplicity: A unifying principle in cognitive science? Trends in Cognitive Sciences, 7(1), 19-22.
12. Christiansen, M. H., Allen, J., & Seidenberg, M. S. (1998). Learning to segment speech using multiple cues: A connectionist model. Language and Cognitive Processes, 13(2/3), 221-268.
13. Cleeremans, A. (1997). Principles for implicit learning. In D. Berry (Ed.), How implicit is implicit learning? (pp. 195-234). Oxford, England: Oxford University Press.
14. Feigenbaum, E. A., & Simon, H. A. (1984). EPAM-like models of recognition and learning. Cognitive Science, 8(4), 305-336.
15. Fiser, J., & Aslin, R. N. (2001). Unsupervised statistical learning of higher-order spatial structures from visual scenes. Psychological Science, 12(6), 499-504.
16. French, R. M., Mareschal, D., Mermillod, M., & Quinn, P. C. (2004). The role of bottom-up processing in perceptual categorization by 3- to 4-month-old infants: Simulations and data. Journal of Experimental Psychology: General, 133(3), 382-397.
17. Giroux, I., & Rey, A. (2009). Lexical and sublexical units in speech perception. Cognitive Science, 33(2), 260-272.
18. Gobet, F. (1993). A computer model of chess memory. In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society (pp. 463-468). Hillsdale, NJ: Lawrence Erlbaum Associates.
19. Gobet, F., Lane, P., Croker, S., Cheng, P., Jones, G., Oliver, I., & Pine, J. M. (2001). Chunking mechanisms in human learning. Trends in Cognitive Sciences, 5(6), 236-243.
20. Goldsmith, J. (2001). Unsupervised learning of the morphology of a natural language. Computational Linguistics, 27(2), 153-198.
21. Grünwald, P., Myung, I. J., & Pitt, M. A. (2005). Advances in minimum description length: Theory and applications. Cambridge, MA: MIT Press.
22. Hebb, D. O. (1949). The organization of behavior. New York: Wiley.
23. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.
24. Kolmogorov, A. N. (1968). Three approaches to the quantitative definition of information. International Journal of Computer Mathematics, 2(1), 157-168.
25. Leung-Yan-Cheong, S., & Cover, T. (1978). Some equivalences between Shannon entropy and Kolmogorov complexity. IEEE Transactions on Information Theory, 24(3), 331-338.
26. Li, M., & Vitanyi, P. M. (1997). An introduction to Kolmogorov complexity and its applications. Berlin: Springer.
27. de Marcken, C. (1995). The unsupervised acquisition of a lexicon from continuous speech. A.I. Memo No. 1558, MIT Artificial Intelligence Lab.
28. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity to process information. Psychological Review, 63(2), 81-97.
29. Miller, G. A. (1958). Free recall of redundant strings of letters. Journal of Experimental Psychology, 56, 485-491.
30. Oaksford, M., & Chater, N. (1993). Reasoning theories and bounded rationality. In K. I. Manktelow & D. E. Over (Eds.), Rationality: Psychological and philosophical perspectives (pp. 31-60). London: Routledge.
31. Pearl, J. (1984). Heuristics: Intelligent search strategies for computer problem solving. Boston: Addison-Wesley.
32. Perruchet, P., & Vinter, A. (1998). PARSER: A model for word segmentation. Journal of Memory and Language, 39(2), 246-263.
33. Perruchet, P., Vinter, A., Pacteau, C., & Gallego, J. (2002). The formation of structurally relevant units in artificial grammar learning. The Quarterly Journal of Experimental Psychology Section A, 55(2), 485-503.
34. Redington, M., & Chater, N. (1996). Transfer in artificial grammar learning: A reevaluation. Journal of Experimental Psychology: General, 125, 123-138.
35. Rissanen, J. (1978). Modeling by shortest data description. Automatica, 14(5), 465-471.
36. Robinet, V. (2009). Modélisation cognitive computationnelle de l'apprentissage inductif de chunks basée sur la théorie algorithmique de l'information, PhD Thesis, Institut Polytechnique de Grenoble, Grenoble, France.
37. Robinet, V., & Lemaire, B. (2009). MDLChunker: A MDL-based model of word segmentation. In N. Taatgen & H. Van Rijn (Eds.), Proceedings of the 31th Annual Conference of the Cognitive Science Society (CogSci 2009) (pp. 2866-2871). Amsterdam, Netherlands: Cognitive Science Society.
38. van Rooij, I. (2008). The tractable cognition thesis. Cognitive Science, 32(6), 939-984.
39. Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science, 274(5294), 1926-1928.
40. Servan-Schreiber, E., & Anderson, J. R. (1990). Learning artificial grammars with competitive chunking. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16(4), 592-608.
41. Shannon, C. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423.
42. Solomonoff, R. J. (1960). A preliminary report on a general theory of inductive inference, Report V-131. Cambridge, MA: Zator Co.
43. Solomonoff, R. J. (1964). A formal theory of inductive inference. Parts I and II. Information and Control, 7(2), 224-254.
44. Sun, R. (2004). Desiderata for cognitive architectures. Philosophical Psychology, 17(3), 341-373.
45. Swingley, D. (2005). Statistical clustering and the contents of the infant vocabulary. Cognitive Psychology, 50(1), 86-132.
46. Turing, A. M. (1936). On computable numbers: With an application to the entscheidungsproblem. Proceeding of the London Mathematical Society, 2, 230-265.
47. Vapnik, V. N. (1998). The nature of statistical learning theory. Berlin: Springer.
48. Venkataraman, A. (2001). A statistical model for word discovery in transcribed speech. Computational Linguistics, 27(3), 352-372.
49. Vereshchagin, N. K., & Vitanyi, P. M. (2004). Kolmogorov's structure functions and model selection. IEEE Transactions on Information Theory, 50(12), 3265-3290.
50. Vitanyi, P. M. (2005). Algorithmic statistics and Kolmogorov's structure function. In P. D. Grünwald, J. Myung, & M. A. Pitt (Eds.), Advances in minimum description length: Theory and applications (pp. 151-174). Cambridge, MA: MIT Press.
51. Wolff, J. G. (1999). Probabilistic reasoning as information compression by multiple alignment, unification and search: An introduction and overview. Journal of Universal Computer Science, 5(7), 418-462.
52. Wolff, J. G. (2000). Syntax, parsing and production of natural language in a framework of information compression by multiple alignment, unification and search. Journal of Universal Computer Science, 6(8), 781-829.
53. Wolff, J. G. (2006). Medical diagnosis as pattern recognition in a framework of information compression by multiple alignment, unification and search. Decision Support Systems, 42(2), 608-625.