We will be reading the following articles:
Chater, N., and Manning, C. (2006). Probabilistic models of language processing and acquisition. Trends in Cognitive Sciences, 10(7), 335-344.
Chater, N., Tenenbaum, J., and Yuille, A. (2006). Probabilistic models of cognition: where next? Trends in Cognitive Sciences, 10(7), 292-293.
Chater, N. & Vitanyi, P. (2006). 'Ideal learning' of natural language: Positive results about learning from positive evidence. Journal of Mathematical Psychology, doi:10.1016/j.jmp.2006.10.002.
Elman, J.L. (1999). Origins of language: A conspiracy theory. In B. MacWhinney (Ed.), The emergence of language. Hillsdale, NJ: Lawrence Erlbaum Associates.
Frank, M., Goodman, N., Tenenbaum, J. (under review). Using speakers' referential intentions to model early cross-situational learning. Ms. MIT.
Gambell, T. & Yang, C. (2006). Word Segmentation: Quick but not dirty. Ms. Yale University.
Goldwater, S., Griffiths, T. L., & Johnson, M. (2007). Distributional cues to word segmentation: Context is important. Proceedings of the 31st Boston University Conference on Language Development.
Jackendoff, R. (1994). Patterns in the Mind: Language and Human Nature. USA: Basic Books. (selections from this are good background reading in the early part of the course)
Johnson, K. (2004). Gold's Theorem and Cognitive Science. Philosophy of Science, 71, 571-592.
Kam, X., Stoyneshka, I., Tornyova, L., Fodor, J. D. & Sakas, W. (2005). Statistics vs. UG in language acquisition: Does a bigram analysis predict auxiliary inversion? In Proceedings of the Second Workshop on Psycho-computational Models of Human Language Acquisition, Association of Computational Linguistics.
Kemp, C., Perfors, A., Tenenbaum, J. (2007). Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10(3), 307-321.
Landauer, T.K., & Dumais, S.T. (1997). A solution to Plato's problem: The Latent Semantic Analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 104, 211-240.
Legate, J. & Yang, C. (2002). Empirical re-assessment of stimulus poverty arguments. Linguistic Review, 19, 151-162.
Perfors, A., Tenenbaum, J., & Regier, T. (under review). The learnability of abstract syntactic principles. Ms., MIT & UChicago.
Pullum, G. & Scholz, B. (2002). Empirical assessment of stimulus poverty arguments, The Linguistic Review, 19, 9-50.
Reali, F. & Christiansen, M. (2005). Structure Dependence in Language Acquisition: Uncovering the Richness of the Stimulus: Stucture Dependence and Indirect Statistical Evidence, Cognitive Science, 29, 1007-1028.
Steyvers, M. & Griffiths, T. (2007). Probabilistic topic models. In T. Landauer, D. S. McNamara, S. Dennis, & W. Kintsch (Eds.), Handbook of Latent Semantic Analysis. Hillsdale, NJ: Erlbaum.
Xu, F., & Tenenbaum, J. (2007). Word Learning as Bayesian Inference, Psychological Review, 114(2), 245-272.
Yang, C. (2008). The Great Number Crunch. Journal of Linguistics, 44, 205-228.
Yu, C., Ballard, D. & Aslin, R. (2005). The Role of Embodied Intention in Early Lexical Acquisition, Cognitive Science, 29(6), 961-1005.
Yu, C. & Smith, B. (2007). Rapid Word Learning under Uncertainty via Cross-Situational Statistics, Psychological Science.
Chater, N., Tenenbaum, J., and Yuille, A. (2006). Probabilistic models of cognition: where next? Trends in Cognitive Sciences, 10(7), 292-293.
Chater, N. & Vitanyi, P. (2006). 'Ideal learning' of natural language: Positive results about learning from positive evidence. Journal of Mathematical Psychology, doi:10.1016/j.jmp.2006.10.002.
Elman, J.L. (1999). Origins of language: A conspiracy theory. In B. MacWhinney (Ed.), The emergence of language. Hillsdale, NJ: Lawrence Erlbaum Associates.
Frank, M., Goodman, N., Tenenbaum, J. (under review). Using speakers' referential intentions to model early cross-situational learning. Ms. MIT.
Gambell, T. & Yang, C. (2006). Word Segmentation: Quick but not dirty. Ms. Yale University.
Goldwater, S., Griffiths, T. L., & Johnson, M. (2007). Distributional cues to word segmentation: Context is important. Proceedings of the 31st Boston University Conference on Language Development.
Jackendoff, R. (1994). Patterns in the Mind: Language and Human Nature. USA: Basic Books. (selections from this are good background reading in the early part of the course)
Johnson, K. (2004). Gold's Theorem and Cognitive Science. Philosophy of Science, 71, 571-592.
Kam, X., Stoyneshka, I., Tornyova, L., Fodor, J. D. & Sakas, W. (2005). Statistics vs. UG in language acquisition: Does a bigram analysis predict auxiliary inversion? In Proceedings of the Second Workshop on Psycho-computational Models of Human Language Acquisition, Association of Computational Linguistics.
Kemp, C., Perfors, A., Tenenbaum, J. (2007). Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10(3), 307-321.
Landauer, T.K., & Dumais, S.T. (1997). A solution to Plato's problem: The Latent Semantic Analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 104, 211-240.
Legate, J. & Yang, C. (2002). Empirical re-assessment of stimulus poverty arguments. Linguistic Review, 19, 151-162.
Perfors, A., Tenenbaum, J., & Regier, T. (under review). The learnability of abstract syntactic principles. Ms., MIT & UChicago.
Pullum, G. & Scholz, B. (2002). Empirical assessment of stimulus poverty arguments, The Linguistic Review, 19, 9-50.
Reali, F. & Christiansen, M. (2005). Structure Dependence in Language Acquisition: Uncovering the Richness of the Stimulus: Stucture Dependence and Indirect Statistical Evidence, Cognitive Science, 29, 1007-1028.
Steyvers, M. & Griffiths, T. (2007). Probabilistic topic models. In T. Landauer, D. S. McNamara, S. Dennis, & W. Kintsch (Eds.), Handbook of Latent Semantic Analysis. Hillsdale, NJ: Erlbaum.
Xu, F., & Tenenbaum, J. (2007). Word Learning as Bayesian Inference, Psychological Review, 114(2), 245-272.
Yang, C. (2008). The Great Number Crunch. Journal of Linguistics, 44, 205-228.
Yu, C., Ballard, D. & Aslin, R. (2005). The Role of Embodied Intention in Early Lexical Acquisition, Cognitive Science, 29(6), 961-1005.
Yu, C. & Smith, B. (2007). Rapid Word Learning under Uncertainty via Cross-Situational Statistics, Psychological Science.