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References

  • Albright, A. (2009). Feature-based generalisation as a source of gradient acceptibility. Phonology, 26(1), 941. doi: 10.1017/S0952675709001705
  • Archangeli, D., & Pulleyblank, D. (1994). Grounded phonology. Cambridge, MA: The MIT Press.
  • Booij, G. (1984). Neutral vowels and the autosegmental analysis of Hungarian vowel harmony. Linguistics, 22, 629641.
  • Cairns, P., Shillcock, R., Chater, N., & Levy, J. (1997). Bootstrapping word boundaries: A bottom-up corpus-based approach to speech segmentation. Cognitive Psychology, 22, 111153.
  • Chelba, C., & Jelinek, F. (2000). Structured language modeling. Computer Speech and Language, 14(4), 283332.
  • Chomsky, N. (1981). Lectures on government and binding. Dordrecht, The Netherlands: Foris.
  • Clements, G. N. (1976). The autosegmental treatment of vowel harmony. Phonologica, 19, 111119.
  • Clements, G. N. (1977). Neutral vowels in Hungarian vowel harmony: An autosegmental interpretation. NELS, 7, 4964.
  • Cohen, J. D., MacWhinney, B., Flatt, M., & Provost, J. (1993). PsyScope: A new graphic interactive environment for designing psychology experiments. Behavioral Research Methods, Instruments and Computers, 25, 257271.
  • Culicover, P., & Wilkins, W. (1984). Locality in linguistic theory. New York: Academic Press.
  • Finley, S. (2011). The privileged status of locality in consonant harmony. Journal of Memory and Language, 65, 7483.
  • Finley, S., & Badecker, W. (2008). Analytic biases for vowel harmony languages. WCCFL, 27, 168176.
  • Finley, S., & Badecker, W. (2009a). Artificial grammar learning, and feature-based generalization. Journal of Memory and Language, 61, 423437.
  • Finley, S., & Badecker, W. (2009b). Right-to-left biases for vowel harmony: Evidence from artificial grammar. In A. Shardl, M. Walkow, & M. Abdurrahman (Eds.), Proceedings of the 38th North East Linguistic Society Annual Meeting (Vol. 1, pp. 269282) Amherst: University of Massachusetts.
  • Finley, S., & Badecker, W. (in press). Towards a substantively biased theory of learning. Proceedings of the 33rd Annual Meeting of the Berkeley Linguistics Society. Berkeley, CA: Berkeley Linguistics Society.
  • Goldsmith, J. (1975). Tone melodies and the autosegment. Paper presented at the the 6th Conference on African Linguistics. Ohio State University, Columbus, OH.
  • Gomez, R. L. (2002). Variability and detection of invariant structure. Psychological Science, 13(5), 431436.
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  • Hansson, G. O. (2001). Theoretical and typological issues in consonant harmony. PhD dissertation, Berkeley, University of California.
  • Hayes, B., & Londe, Z. C. (2006). Stochastic phonological knowledge: The case of Hungarian vowel harmony. Phonology, 23, 59104.
  • Hayes, B., & Wilson, C. (2008). A maximum entropy model of phonotactics and phonotactic learning. Linguistic Inquiry, 39, 379440.
  • Heinz, J. (2007). Inductive learning of phonotactic patterns. PhD dissertation, UCLA.
  • Heinz, J. (2010). Learning long-distance phonotactics. Linguistic Inquiry, 41(4), 623661.
  • Heinz, J., & Rogers, J. (2010). Estimating strictly piecewise distributions. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Uppsala, Sweden: Association for Computational Linguistics.
  • Jurafsky, D., & Martin, J. H. (2008). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition. Upper Saddle River, NJ: Prentice Hall.
  • Keating, P. (1988). Underspecification in phonetics. Phonology, 5(2), 275292.
  • McDonough, J. (1990). The Navajo sound system. Dordrecht, The Netherlands: Kluwer Academic Publishers.
  • Misyak, J. B., & Christiansen, M. H. (2007). Extending statistical learning farther and further: Long-distance dependencies, and individual differences in statistical language learning. Proceedings of the 29th Annual Conference of the Cognitive Science Society (pp. 13071312) Austin, TX: Cognitive Science Society.
  • Misyak, J. B., Christiansen, M. H., & Tomblin, J. B. (2009). Statistical learning of nonadjacent dependencies predicts on-line processing of long-distance dependencies in natural language. Proceedings of the 31st Annual Conference of the Cognitive Science Society, Austin, TX: Cognitive Science Society.
  • Newport, E., & Aslin, R. N. (2004). Learning at a distance I Statistical learning of non-adjacent dependencies. Cognitive Psychology, 48, 127162.
  • Onnis, L., Christiansen, M. H., Chater, N., & Gomez, R. L. (2003). Reduction of uncertainty in human sequential learning: Evidence from artificial grammar learning. Proceedings of the 25th Annual Conference of the Cognitive Science Society (pp. 970975) Austin, TX: Cognitive Science Society.
  • Onnis, L., Monaghan, P., Christiansen, M. H., & Chater, N. (2004). Variability is the spice of learning, and a crucial ingredient for detection and generalizing in nonadjacent dependencies. Proceedings of the 26th Annual Conference of the Cognitive Science Society (pp. 270275) Austin, TX: Cognitive Science Society.
  • Pycha, A., Nowak, P., Shin, E., & Shosted, R. (2003). Phonological rule-learning and its implications for a theory of vowel harmony. WCCFL, 22, 101113.
  • Rose, S., & Walker, R. (2004). A typology of consonant agreement as correspondence. Language, 3, 475531.
  • Sapir, E., & Hojier, H. (1967). The phonology and morphology of the Navajo language (Vol. 50). Berkeley: University of California Publications.
  • St. Clair, M., Monaghan, P., & Ramscar, M. (2009). Relationship between language structure and language learning: The suffixing preference and grammatical categorization. Cognitive Science, 33, 13171329.
  • Wilson, C. (2003). Experimental investigations of phonological naturalness. WCCFL, 22, 101114.
  • Wilson, C. (2006). Learning phonology with substantive bias: An experimental and computational study of velar palatalization. Cognitive Science, 30, 945982.