SEARCH

SEARCH BY CITATION

References

  • Anttila, A., & Yu Cho, Y. (1998). Variation and change in optimality theory. Lingua, 104, 3156.
  • Bhat, D. N. S. (1978). A general study of palatalization. In J. Greenberg (Ed.), Universals of human language (pp. 4792). Stanford, CA: Stanford University Press, Volume 3.
  • Boersma, P., & Hayes, B. (2001). Empirical tests of the gradual learning algorithm. Linguistic Inquiry, 32, 4586.
  • Bybee, J. (2008). Formal universals as emergent phenomena: The origins of structure preservation. In J. Good (Ed.), Linguistic universals and language change (pp. 108121). New York: Oxford University Press.
  • Chater, N., & Manning, C. D. (2006). Probabilistic models of language processing and acquisition. Trends in Cognitive Sciences, 10, 335344.
  • Chomsky, N. (1988). Language and problems of knowledge: The Managua lectures. Cambridge, MA: MIT Press.
  • Clark, A., & Roberts, I. (1993). A computational model of language learnability and language change. Linguistic Inquiry, 24, 299345.
  • Culbertson, J. (2010). Learning biases, regularization, and the emergence of typological universals in syntax. Ph.D. thesis, Johns Hopkins University, Baltimore, MD.
  • Culbertson, J., Smolensky, P., & Legendre, G. (2012). Learning biases predict a word order universal. Cognition, 122, 306329.
  • Dryer, M. (2008a). Order of adjective and noun. In M. Haspelmath, M. S. Dryer, D. Gil, and B. Comrie (Eds.), The world atlas of language structures online chapter 87. Munich: Max Planck Digital Library.
  • Dryer, M. (2008b). Order of numeral and noun. In M. Haspelmath, M. S. Dryer, D. Gil, and B. Comrie (Eds.), The world atlas of language structures online chapter 89. Munich: Max Planck Digital Library.
  • Evans, N., & Levinson, S. C. (2009). The myth of language universals: Language diversity and its importance for cognitive science. Behavioral and Brain Sciences, 32, 429448.
  • Geman, S., Bienenstock, E., & Doursat, R. (1992). Neural networks and the bias/variance dilemma. Neural Computation, 4, 158.
  • Good, P. I. (2005). Permutation, parametric and bootstrap tests of hypotheses. New York: Springer.
  • Greenberg, J. (1963). Some universals of grammar with particular reference to the order of meaningful elements. In J. Greenberg (Ed.), Universals of language (pp. 73113). Cambridge, MA: MIT Press.
  • Griffiths, T., & Kalish, M. (2007). Language evolution by iterated learning with bayesian agents. Psychonomic Bulletin and Review, 31, 441480.
  • Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010). Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences, 14, 357364.
  • Hoff, P. D. (2009). A first course in Bayesian statistical methods. Berlin: Springer.
  • Hsu, A., & Griffiths, T. L. (2009). Differential use of implicit negative evidence in generative and discriminative language learning. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams & A. Culotta (Eds.), Advances in Neural Information Processing Systems, 22, 754762. Red Hook, NY: Curran,
  • Hudson Kam, C., & Newport, E. (2005). Regularizing unpredictable variation. Language Learning and Development, 1, 151195.
  • Hudson Kam, C., & Newport, E. (2009). Getting it right by getting it wrong: When learners change languages. Cognitive Psychology, 59, 3066.
  • Iglewicz, B., & Hoaglin, D. (1993). How to detect and handle outliers . Milwaukee, WI: American Society for Quality Control.
  • Kirby, S. (1999). Function, selection, and innateness. Oxford, England: Oxford University Press.
  • Kroch, A. (2000). Syntactic change. In M. Baltin & C. Collins (Eds.) Handbook of syntax (pp. 699729). New York: Blackwell.
  • Lehmann, E. L. (1986). Testing statistical hypotheses (2nd ed.). New York: Wiley.
  • Levinson, S. C., & Evans, N. (2010). Time for a sea-change in linguistics: Response to comments on ‘the myth of language universals’. Lingua, 120, 27332758.
  • Lightfoot, D. (2006). How new languages emerge. New York: Cambridge University Press.
  • Mitchell, T. M. 1997. Machine learning. New York: McGraw-Hill.
  • Nettle, D. (1999). Linguistic diversity. New York: Oxford University Press.
  • Niyogi, P. (2006). The computational nature of language learning and evolution. Cambridge, MA: MIT Press.
  • Perfors, A., Tenenbaum, J., & Wonnacott, E. (2010). Variability, negative evidence, and the acquisition of verb argument constructions. Journal of Child Language, 37, 607642.
  • R Development Core Team (2010). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
  • Reali, F., & Griffiths, T. L. (2009). The evolution of frequency distributions: Relating regularization to inductive biases through iterated learning. Cognition, 111, 317328.
  • Schwarz, G. (1977). Estimating the dimension of a model. Annals of Statistics, 6, 461464.
  • Tomasello, M. (2009). Universal grammar is dead. Behavioral and Brain Sciences, 32, 470471.
  • Wilson, C. (2006). An experimental and computational study of velar palatalization. Cognitive Science, 30, 945982.
  • Wonnacott, E., Newport, E. L., & Tanenhaus, M. K. (2008). Acquiring and processing verb argument structure: Distributional learning in a miniature language. Cognitive Psychology, 56, 165209.
  • Yang, C. (2002). Knowledge and Learning in Natural Language. Oxford, England: Oxford University Press.
  • Zhu, C., Byrd, R., Lu, P., & Nocedal, J. (1994). L-BFGS-B: A limited memory FORTRAN code for solving bound constrained optimization problems. Evanston, IL: Technical Report EECS Department, Northwestern University.