We will refer to selections from the following books, in addition to the articles below:
Blanchard, D., Heinz, J., & Golinkoff, R. (2010). Modeling the contribution of phonotactic cues to the problem of word segmentation. Journal of Child Language, 27, 487-511.
Chemla, E., Mintz, T., Bernal, S., & Christophe, A. (2009). Categorizing Words Using "Frequent Frames": What Cross-Linguistic Analyses Reveal About Distributional Acquisition Strategies. Developmental Science.
Crain, C. & Pietroski, P. (2002). Why language acquisition is a snap. The Linguistic Review, 19, 163-183.
Dietrich, C., Swingley, D., & Werker, J.F. (2007). Native language governs interpretation of salient speech sound differences at 18 months. Proceedings of the National Academy of Sciences of the US, 16027-16031.
Dillon, B., Dunbar, E., & Idsardi, B. (2010 ms). A single stage approach to learning phonological categories: Insights from Inuktitut. University of Maryland, College Park.
Feldman, N., Griffiths, T., and Morgan, J. (2009). Learning phonetic categories by learning a lexicon. Proceedings of the 31st Annual Conference on Cognitive Science.
Finn, A. & Hudson Kam, C. (2008). The curse of knowledge: First language knowledge impairs adult learners’ use of novel statistics for word segmentation. Cognition, 108, 477-499.
Foraker, S., Regier, T., Kheterpal, N., Perfors, A., and Tenenbaum, J. (2009). Indirect Evidence and the Poverty of the Stimulus: The Case of Anaphoric One. Cognitive Science, 33, 287-300.
Frank, M., Arnon, I., Tily, H., & Goldwater, S. (2010). Beyond Transitional Probabilities: Human Learners Impose a Parsimony Bias in Statistical Word Segmentation. Proceedings of the 32nd Annual Meeting of the Cognitive Science Society.
Frank, M., Goldwater, S., Griffiths, T., & Tenenbaum, J. (2010 forthcoming). Modeling human performance in statistical word segmentation. Cognition.
Frank, M., Goodman, N., & Tenenbaum, J. (2009). Using Speakers' Referential Intentions to Model Early Cross-Situational Word Learning. Psychological Science, 20(5), 578-585.
Freudenthal, D., Pine, J., & Gobet, F. (2010). Explaining quantitative variation in the rate of Optional Infinitive errors across languages: A comparison of MOSAIC and the Variational Learning Model. Journal of Child Language, 37(3), 643-669.
Gambell, T. & Yang, C. (2006). Word Segmentation: Quick but not dirty. Manuscript, Yale University.
Gerken, L. (2006). Decisions, decisions: infant language learning when multiple generalizations are possible. Cognition, 98, B67-B74.
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.
Goldwater, S., Griffiths, T. L., & Johnson, M. (2009). A Bayesian Framework for Word Segmentation: Exploring the Effects of Context. Cognition, 112(1), 21-54. Gomez, R. & Gerken, L. (2000). Infant artificial language learning and language acquisition.Trends in Cognitive Sciences, 4(5), 178-186.
Goodluck, H. (2010). First language acquisition. Wiley Interdisciplinary Reviews: Cognitive Science, n/a. doi: 10.1002/wcs.95.
Hsu, A. & Chater, N. (2010). The Logical Problem of Language Acquisition: A Probabilistic Perspective. Cognitive Science, 34, 972-1016.
Johnson, M. & Goldwater, S. (2009). Improving nonparametric Bayesian inference: experiments on unsupervised word segmentation with adaptor grammars. Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the ACL, Boulder, Colorado, 317-325.
Johnson, E. & Tyler, M. (2010). Testing the limits of statistical learning forwWord segmentation. Developmental Science, 13(2),339-345.
Kam, X., Stoyneshka, I., Tornyova, L., Fodor, J., & Sakas, W. (2008). Bigrams and the Richness of the Stimulus. Cognitive Science, 32, 771-787.
Kemp, C., Perfors, A., Tenenbaum, J. (2007). Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10(3), 307-321.
Kidd, C., Piantadosi, S., & Aslin, R. (2010). The Goldilocks Effect: Infants’ preference for stimuli that are neither too predictable nor too surprising. Proceedings of the 32nd Annual Meeting of the Cognitive Science Society.
Lasnik, H. and Lohndal, T. (2010). Government–binding/principles and parameters theory. Wiley Interdisciplinary Reviews: Cognitive Science, 1, 40–50. doi: 10.1002/wcs.35.
Legate, J. & Yang, C. (2002). Empirical re-assessment of stimulus poverty arguments. The Linguistic Review, 19, 151-162.
Legate, J. & Yang, C. (2007). Morphosyntactic learning and the development of tense. Language Acquisition, 14(3), 315-344.
Liebbrandt, R. & Powers, D. (2010). Frequent Frames as Cues to Part-of-Speech in Dutch: Why Filler Frequency Matters. Proceedings of the 32nd Annual Meeting of the Cognitive Science Society.
Lightfoot, D. (2010). Language acquisition and language change. Wiley Interdisciplinary Reviews: Cognitive Science, 1, 677–684. doi: 10.1002/wcs.39.
Marr, D. (1982). Vision. San Francisco: W.H. Freeman, pp. 3-43.
Mintz, T. (2003). Frequent frames as a cue for grammatical categories in child directed speech.Cognition, 90, 91-117.
Onnis, L., Monaghan, P., Richmond, K., & Chater, N. (2005). Phonology impacts segmentation in online speech processing. Journal of Memory and Language, 53, 225-237.
Newport, E. (2010). Plus or Minus 30 Years in Language Sciences. Topics in Cognitive Science, 2, 367-373.
Pearl, L. (2008). Putting the Emphasis on Unambiguous: The Feasibility of Data Filtering for Learning English Metrical Phonology. In Chan, H., Jacob, H., and Kapia, E (eds.), BUCLD 32: Proceedings of the 32nd annual Boston University Conference on Child Language Development, Somerville, MA: Cascadilla Press, 390-401.
Pearl, L. (2009). Learning English Metrical Phonology: When Probability Distributions Are Not Enough. In Jean Crawford, Koichi Otaki, and Masahiko Takahashi (eds.), Proceedings of the 3rd Conference on Generative Approaches to Language Acquisition North America (GALANA 2008), Somerville, MA: Cascadilla Press, 200-211.
Pearl, L. (2010). Computational Models of Language Acquisition. In E. Blom & S. Unsworth (eds), Experimental Methods in Language Acquisition Research, John Benjamins.
Pearl, L. (2010 ms). When unbiased probabilistic learning is not enough: Acquiring a parametric system of metrical phonology. University of California, Irvine.
Pearl, L. & Goldwater, S. (2010 ms). Statistical Learning, Inductive Bias, and Bayesian Inference in Language Acquisition. University of California, Irvine and University of Edinburgh.
Pearl, L., Goldwater, S., & Steyvers, M. (2010). How Ideal Are We? Incorporating Human Limitations into Bayesian Models of Word Segmentation. BUCLD 34: Proceedings of the 34th annual Boston University Conference on Child Language Development, Somerville, MA: Cascadilla Press, 315-326.
Pearl, L., Goldwater, S., & Steyvers, M. (2010 ms.) Online Learning Mechanisms for Bayesian Models of Word Segmentation. University of California, Irvine & University of Edinburgh.
Pearl, L. & Lidz, J. (2009). When domain-general learning fails and when it succeeds: Identifying the contribution of domain-specificity. Language Learning & Development, 5(4), 235-265.
Pearl, L. & Lidz, J. (2010 ms.) Parameters in Language Acquisition. University of California, Irvine & University of Maryland, College Park.
Perfors, A., Tenenbaum, J., Griffiths, T., & Xu, F., (2010 ms). A tutorial introduction to Bayesian models of cognitive development. University of Adelaide, Massachusetts Institute for Technology, and University of California, Berkeley.
Perfors, A., Tenenbaum, J., & Regier, T. (2006). Poverty of the Stimulus? A Rational Approach. In R. Sun & N. Miyake (eds.) Proceedings of the 28th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society: 663-668.
Perfors, A., Tenenbaum, J., & Regier, T. (2010 ms). The learnability of abstract syntactic principles. University of Adelaide, Massachusetts Institute for Technology, & University of California, Berkeley.
Pinker, S. (2004). Clarifying the logical problem of language acquisition. Journal of Child Language, 31, 949-953.
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.
Regier, T., and Gahl, S. (2004). Learning the unlearnable: the role of missing evidence. Cognition, 93, 147-155.
Saffran, J.R., Aslin, R.N., & Newport, E.L. (1996). Statistical learning by 8-month old infants. Science, 274, 1926-1928.
Smith, L. & Yu, C. (2008). Infants rapidly learn word-referent mappings via cross-situational statistics. Cognition, 106, , 1558-1568.
Swingley, D. (2005). Statistical clustering and the contents of the infant vocabulary. Cognitive Psychology, 50, 86-132.
Valian, V. (2009). Input and innateness: Controversies in language acquisition. Cambridge, MA: Bradford books/M.I.T. Press., Chapter 2 draft.
Vallabha, G., McClelland, J., Pons, F., Werker, J., & Amano, S. (2007). Unsupervised learning of vowel categories from infant-directed speech. Proceedings of the National Academy of Sciences of the U.S., 104(33), 13273-13278.
Wang, H. & Mintz, T. (2008). A Dynamic Learning Model for Categorizing Words Using Frames. In H. Chan, H. Jacob, & E. Kapia (eds.), BUCLD 32 Proceedings, 525-536.
Werker, J. (1995). Exploring Developmental Changes in Cross-language Speech Perception, Chapter 4 (pp.87-106) in Gleitman, L. & Liberman, M., Language. Cambridge, MA: The MIT Press.
Willits, J., Seidenberg, M., & Saffran, J. (2009). Verbs are LookING Good in Language Acquisition. Proceedings of the Annual Meeting of the Cognitive Science Society, VU University, Amsterdam, Netherlands.
Xu, F., & Tenenbaum, J. (2007). Word Learning as Bayesian Inference, Psychological Review, 114(2), 245-272.
Yang, C. (2004). Universal Grammar, statistics, or both? Trends in Cognitive Sciences, 8(10), 451-456.
Yu, C. & Smith, L. (2007). Rapid Word Learning Under Uncertainty via Cross-Situational Statistics. Psychological Science, 18(5), 414-420.
Chemla, E., Mintz, T., Bernal, S., & Christophe, A. (2009). Categorizing Words Using "Frequent Frames": What Cross-Linguistic Analyses Reveal About Distributional Acquisition Strategies. Developmental Science.
Crain, C. & Pietroski, P. (2002). Why language acquisition is a snap. The Linguistic Review, 19, 163-183.
Dietrich, C., Swingley, D., & Werker, J.F. (2007). Native language governs interpretation of salient speech sound differences at 18 months. Proceedings of the National Academy of Sciences of the US, 16027-16031.
Dillon, B., Dunbar, E., & Idsardi, B. (2010 ms). A single stage approach to learning phonological categories: Insights from Inuktitut. University of Maryland, College Park.
Feldman, N., Griffiths, T., and Morgan, J. (2009). Learning phonetic categories by learning a lexicon. Proceedings of the 31st Annual Conference on Cognitive Science.
Finn, A. & Hudson Kam, C. (2008). The curse of knowledge: First language knowledge impairs adult learners’ use of novel statistics for word segmentation. Cognition, 108, 477-499.
Foraker, S., Regier, T., Kheterpal, N., Perfors, A., and Tenenbaum, J. (2009). Indirect Evidence and the Poverty of the Stimulus: The Case of Anaphoric One. Cognitive Science, 33, 287-300.
Frank, M., Arnon, I., Tily, H., & Goldwater, S. (2010). Beyond Transitional Probabilities: Human Learners Impose a Parsimony Bias in Statistical Word Segmentation. Proceedings of the 32nd Annual Meeting of the Cognitive Science Society.
Frank, M., Goldwater, S., Griffiths, T., & Tenenbaum, J. (2010 forthcoming). Modeling human performance in statistical word segmentation. Cognition.
Frank, M., Goodman, N., & Tenenbaum, J. (2009). Using Speakers' Referential Intentions to Model Early Cross-Situational Word Learning. Psychological Science, 20(5), 578-585.
Freudenthal, D., Pine, J., & Gobet, F. (2010). Explaining quantitative variation in the rate of Optional Infinitive errors across languages: A comparison of MOSAIC and the Variational Learning Model. Journal of Child Language, 37(3), 643-669.
Gambell, T. & Yang, C. (2006). Word Segmentation: Quick but not dirty. Manuscript, Yale University.
Gerken, L. (2006). Decisions, decisions: infant language learning when multiple generalizations are possible. Cognition, 98, B67-B74.
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.
Goldwater, S., Griffiths, T. L., & Johnson, M. (2009). A Bayesian Framework for Word Segmentation: Exploring the Effects of Context. Cognition, 112(1), 21-54. Gomez, R. & Gerken, L. (2000). Infant artificial language learning and language acquisition.Trends in Cognitive Sciences, 4(5), 178-186.
Goodluck, H. (2010). First language acquisition. Wiley Interdisciplinary Reviews: Cognitive Science, n/a. doi: 10.1002/wcs.95.
Hsu, A. & Chater, N. (2010). The Logical Problem of Language Acquisition: A Probabilistic Perspective. Cognitive Science, 34, 972-1016.
Johnson, M. & Goldwater, S. (2009). Improving nonparametric Bayesian inference: experiments on unsupervised word segmentation with adaptor grammars. Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the ACL, Boulder, Colorado, 317-325.
Johnson, E. & Tyler, M. (2010). Testing the limits of statistical learning forwWord segmentation. Developmental Science, 13(2),339-345.
Kam, X., Stoyneshka, I., Tornyova, L., Fodor, J., & Sakas, W. (2008). Bigrams and the Richness of the Stimulus. Cognitive Science, 32, 771-787.
Kemp, C., Perfors, A., Tenenbaum, J. (2007). Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10(3), 307-321.
Kidd, C., Piantadosi, S., & Aslin, R. (2010). The Goldilocks Effect: Infants’ preference for stimuli that are neither too predictable nor too surprising. Proceedings of the 32nd Annual Meeting of the Cognitive Science Society.
Lasnik, H. and Lohndal, T. (2010). Government–binding/principles and parameters theory. Wiley Interdisciplinary Reviews: Cognitive Science, 1, 40–50. doi: 10.1002/wcs.35.
Legate, J. & Yang, C. (2002). Empirical re-assessment of stimulus poverty arguments. The Linguistic Review, 19, 151-162.
Legate, J. & Yang, C. (2007). Morphosyntactic learning and the development of tense. Language Acquisition, 14(3), 315-344.
Liebbrandt, R. & Powers, D. (2010). Frequent Frames as Cues to Part-of-Speech in Dutch: Why Filler Frequency Matters. Proceedings of the 32nd Annual Meeting of the Cognitive Science Society.
Lightfoot, D. (2010). Language acquisition and language change. Wiley Interdisciplinary Reviews: Cognitive Science, 1, 677–684. doi: 10.1002/wcs.39.
Marr, D. (1982). Vision. San Francisco: W.H. Freeman, pp. 3-43.
Mintz, T. (2003). Frequent frames as a cue for grammatical categories in child directed speech.Cognition, 90, 91-117.
Onnis, L., Monaghan, P., Richmond, K., & Chater, N. (2005). Phonology impacts segmentation in online speech processing. Journal of Memory and Language, 53, 225-237.
Newport, E. (2010). Plus or Minus 30 Years in Language Sciences. Topics in Cognitive Science, 2, 367-373.
Pearl, L. (2008). Putting the Emphasis on Unambiguous: The Feasibility of Data Filtering for Learning English Metrical Phonology. In Chan, H., Jacob, H., and Kapia, E (eds.), BUCLD 32: Proceedings of the 32nd annual Boston University Conference on Child Language Development, Somerville, MA: Cascadilla Press, 390-401.
Pearl, L. (2009). Learning English Metrical Phonology: When Probability Distributions Are Not Enough. In Jean Crawford, Koichi Otaki, and Masahiko Takahashi (eds.), Proceedings of the 3rd Conference on Generative Approaches to Language Acquisition North America (GALANA 2008), Somerville, MA: Cascadilla Press, 200-211.
Pearl, L. (2010). Computational Models of Language Acquisition. In E. Blom & S. Unsworth (eds), Experimental Methods in Language Acquisition Research, John Benjamins.
Pearl, L. (2010 ms). When unbiased probabilistic learning is not enough: Acquiring a parametric system of metrical phonology. University of California, Irvine.
Pearl, L. & Goldwater, S. (2010 ms). Statistical Learning, Inductive Bias, and Bayesian Inference in Language Acquisition. University of California, Irvine and University of Edinburgh.
Pearl, L., Goldwater, S., & Steyvers, M. (2010). How Ideal Are We? Incorporating Human Limitations into Bayesian Models of Word Segmentation. BUCLD 34: Proceedings of the 34th annual Boston University Conference on Child Language Development, Somerville, MA: Cascadilla Press, 315-326.
Pearl, L., Goldwater, S., & Steyvers, M. (2010 ms.) Online Learning Mechanisms for Bayesian Models of Word Segmentation. University of California, Irvine & University of Edinburgh.
Pearl, L. & Lidz, J. (2009). When domain-general learning fails and when it succeeds: Identifying the contribution of domain-specificity. Language Learning & Development, 5(4), 235-265.
Pearl, L. & Lidz, J. (2010 ms.) Parameters in Language Acquisition. University of California, Irvine & University of Maryland, College Park.
Perfors, A., Tenenbaum, J., Griffiths, T., & Xu, F., (2010 ms). A tutorial introduction to Bayesian models of cognitive development. University of Adelaide, Massachusetts Institute for Technology, and University of California, Berkeley.
Perfors, A., Tenenbaum, J., & Regier, T. (2006). Poverty of the Stimulus? A Rational Approach. In R. Sun & N. Miyake (eds.) Proceedings of the 28th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society: 663-668.
Perfors, A., Tenenbaum, J., & Regier, T. (2010 ms). The learnability of abstract syntactic principles. University of Adelaide, Massachusetts Institute for Technology, & University of California, Berkeley.
Pinker, S. (2004). Clarifying the logical problem of language acquisition. Journal of Child Language, 31, 949-953.
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.
Regier, T., and Gahl, S. (2004). Learning the unlearnable: the role of missing evidence. Cognition, 93, 147-155.
Saffran, J.R., Aslin, R.N., & Newport, E.L. (1996). Statistical learning by 8-month old infants. Science, 274, 1926-1928.
Smith, L. & Yu, C. (2008). Infants rapidly learn word-referent mappings via cross-situational statistics. Cognition, 106, , 1558-1568.
Swingley, D. (2005). Statistical clustering and the contents of the infant vocabulary. Cognitive Psychology, 50, 86-132.
Valian, V. (2009). Input and innateness: Controversies in language acquisition. Cambridge, MA: Bradford books/M.I.T. Press., Chapter 2 draft.
Vallabha, G., McClelland, J., Pons, F., Werker, J., & Amano, S. (2007). Unsupervised learning of vowel categories from infant-directed speech. Proceedings of the National Academy of Sciences of the U.S., 104(33), 13273-13278.
Wang, H. & Mintz, T. (2008). A Dynamic Learning Model for Categorizing Words Using Frames. In H. Chan, H. Jacob, & E. Kapia (eds.), BUCLD 32 Proceedings, 525-536.
Werker, J. (1995). Exploring Developmental Changes in Cross-language Speech Perception, Chapter 4 (pp.87-106) in Gleitman, L. & Liberman, M., Language. Cambridge, MA: The MIT Press.
Willits, J., Seidenberg, M., & Saffran, J. (2009). Verbs are LookING Good in Language Acquisition. Proceedings of the Annual Meeting of the Cognitive Science Society, VU University, Amsterdam, Netherlands.
Xu, F., & Tenenbaum, J. (2007). Word Learning as Bayesian Inference, Psychological Review, 114(2), 245-272.
Yang, C. (2004). Universal Grammar, statistics, or both? Trends in Cognitive Sciences, 8(10), 451-456.
Yu, C. & Smith, L. (2007). Rapid Word Learning Under Uncertainty via Cross-Situational Statistics. Psychological Science, 18(5), 414-420.
2. O'Grady, W. (2005). How Children Learn Language. Cambridge: Cambridge University Press.