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Natural and unnatural constraints in Hungarian vowel harmony
Open accessAbstract
Phonological constraints can, in principle, be classified according to whether they are natural (founded in principles of universal grammar (UG)) or unnatural (arbitrary, learned inductively from the language data). Recent work has used this distinction as the basis for arguments about the role of UG in learning. Some languages have phonological patterns that arguably reflect unnatural constraints. With experimental testing, one can assess whether such patterns are actually learned by native speakers. Becker, Ketrez, and Nevins (2007), testing speakers of Turkish, suggest that they do indeed go unlearned. They interpret this result with a strong UG position: humans are unable to learn data patterns not backed by UG principles. This article pursues the same research line, locating similarly unnatural data patterns in the vowel harmony system of Hungarian, such as the tendency (among certain stem types) for a final bilabial stop to favor front harmony. Our own test leads to the opposite conclusion of Becker and colleagues': Hungarians evidently do learn the unnatural patterns. To conclude we consider a bias account—that speakers are able to learn unnatural environments, but devalue them relative to natural ones. We outline a method for testing the strength of constraints as learned by speakers against the strength of the corresponding patterns in the lexicon, and show that it offers tentative support for the hypothesis that unnatural constraints are disfavored by language learners.
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R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
Numerical recipes in C: the art of scientific computing
(1993) · Choice Reviews Online · doi:10.5860/choice.30-5638 · Cited by 17,933
The Proceedings of the National Academy of Sciences
Richards, J. W. (1915) · Science · doi:10.1126/science.42.1074.161 · Cited by 13,983
Numerical recipes in Pascal: the art of scientific computing
(1990) · Choice Reviews Online · doi:10.5860/choice.27-5158 · Cited by 11,811
Metropolis, N., & Ulam, S. (1949) · Journal of the American Statistical Association · doi:10.1080/01621459.1949.10483310 · Cited by 6,474
Chomsky, N., & Halle, M. (1968) · Cited by 5,162
Optimality Theory: Constraint Interaction in Generative Grammar
Prince, A., & Smolensky, P. (2004) · Optimality Theory in Phonology · doi:10.1002/9780470756171.ch1 · Cited by 4,683
A learning algorithm for Boltzmann machines
Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985) · Cognitive Science · doi:10.1207/s15516709cog0901_7 · Cited by 3,402
A maximum entropy approach to natural language processing
Berger, A. L., Pietra, V. J. D., & Pietra, S. A. D. (1996) · doi:10.5555/234285.234289 · Cited by 3,065
Smith, T. L., & Chomsky, N. (1982) · The Philosophical Review · doi:10.2307/2184681 · Cited by 2,808
Metadata
- Authors
- Hayes, B., Zuraw, K., Siptár, P., & Londe, Z.
- Venue
- Language
- Publisher
- Cambridge University Press (CUP)
- Published
- 2009-12
- Volume
- 85
- Issue
- 4
- Pages
- 822-863
- ISSN
- 0097-8507 (ISSN-L)
- 1535-0665
- OpenAlex
- W2051820083
- Cited by
- 391
- References
- 76
- FWCI
- 20.49
- Type
- journal article
- Language
- en
- Open access
- bronze
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