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Phonetically natural rules benefit from a learning bias: a re-examination of vowel harmony and disharmony

Published online by Cambridge University Press:  28 April 2020

Alexander Martin Open the ORCID record for Alexander Martin [Opens in a new window]  and
Sharon Peperkamp Open the ORCID record for Sharon Peperkamp [Opens in a new window]
Alexander Martin*
Affiliation:
University of Edinburgh and Laboratoire de Sciences Cognitives et Psycholinguistique (ENS, EHESS, CNRS), École Normale Supérieure – PSL University
Sharon Peperkamp*
Affiliation:
Laboratoire de Sciences Cognitives et Psycholinguistique (ENS, EHESS, CNRS), École Normale Supérieure – PSL University
*
E-mail: [email protected], [email protected].
E-mail: [email protected], [email protected].
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Abstract

Substance-based phonological theories predict that a preference for phonetically natural rules (those which reflect constraints on speech production and perception) is encoded in synchronic grammars, and translates into learning biases. Some previous work has shown evidence for such biases, but methodological concerns with these studies mean that the question warrants further investigation. We revisit this issue by focusing on the learning of palatal vowel harmony (phonetically natural) compared to disharmony (phonetically unnatural). In addition, we investigate the role of memory consolidation during sleep on rule learning. We use an artificial language learning paradigm with two test phases separated by twelve hours. We observe a robust effect of phonetic naturalness: vowel harmony is learned better than vowel disharmony. For both rules, performance remains stable after twelve hours, regardless of the presence or absence of sleep.

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Articles
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Phonology , Volume 37 , Issue 1 , February 2020 , pp. 65 - 90
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press.

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Footnotes

This work was supported by grants from the Agence Nationale de la Recherche (ANR-17-CE28-0007-01 and ANR-17-EURE-0017). We would like to thank Michel Dutat for setting up the server from which the experiment was run, and Page Piccinini for launching a number of the sleep batches. We would additionally like to thank three anonymous reviewers and the associate editor, all of whom helped us to considerably strengthen the paper.

References

Archangeli, Diana & Pulleyblank, Douglas (1994). Grounded phonology. Cambridge, Mass.: MIT Press.Google Scholar
Baer-Henney, Dinah, Kügler, Frank & van de Vijver, Ruben (2014). The interaction of language-specific and universal factors during the acquisition of morphophonemic alternations with exceptions. Cognitive Science 39. 1537–1569.CrossRefGoogle ScholarPubMed
Baer-Henney, Dinah & van de Vijver, Ruben (2012). On the role of substance, locality, and amount of exposure in the acquisition of morphophonemic alternations. Laboratory Phonology 3. 221–249.CrossRefGoogle Scholar
Bates, Douglas, Maechler, Martin, Bolker, Ben & Walker, Steven (2014). lme4: linear mixed-effects models using ‘Eigen’ and S4. R package (version 1.1-6). https://cran.r-project.org/web/packages/lme4.Google Scholar
Batterink, Laura J., Oudiette, Delphine, Reber, Paul J. & Paller, Ken A. (2014). Sleep facilitates learning a new linguistic rule. Neuropsychologia 65. 169–179.CrossRefGoogle ScholarPubMed
Beguš, Gašper (2018). Bootstrapping sound changes. Ms, University of Washington. Available at https://ling.auf.net/lingbuzz/004299.Google Scholar
Blevins, Juliette (2004). Evolutionary Phonology: the emergence of sound patterns. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Carpenter, Angela C. (2010). A naturalness bias in learning stress. Phonology 27. 345–392.CrossRefGoogle Scholar
Carpenter, Angela C. (2016). The role of a domain-specific language mechanism in learning natural and unnatural stress. Open Linguistics 2. 105–131.CrossRefGoogle Scholar
CMU pronouncing dictionary (2008). Carnegie Mellon University pronouncing dictionary. http://www.speech.cs.cmu.edu/cgi-bin/cmudict.Google Scholar
Cole, Ronald A., Jakimik, Jola & Cooper, William E. (1978). Perceptibility of phonetic features in fluent speech. JASA 64. 44–56.CrossRefGoogle ScholarPubMed
Culbertson, Jennifer & Adger, David (2014). Language learners privilege structured meaning over surface frequency. Proceedings of the National Academy of Sciences of the United States of America 111. 5842–5847.CrossRefGoogle ScholarPubMed
Culbertson, Jennifer & Kirby, Simon (2016). Simplicity and specificity in language: domain-general biases have domain-specific effects. Frontiers in Psychology 6:1964. https://doi.org/10.3389/fpsyg.2015.01964.Google Scholar
Davis, Matthew H., Di Betta, Anna Maria, Macdonald, Mark J. E. & Gareth Gaskell, M. (2009). Learning and consolidation of novel spoken words. Journal of Cognitive Neuroscience 21. 803–820.CrossRefGoogle ScholarPubMed
Diekelmann, Susanne, Wilhelm, Ines & Born, Jan (2009). The whats and whens of sleep-dependent memory consolidation. Sleep Medicine Reviews 13. 309–321.CrossRefGoogle ScholarPubMed
Docherty, Gerard J. (1992). The timing of voicing in British English obstruents. Berlin & New York: Foris.CrossRefGoogle Scholar
Donegan, Patricia J. & Stampe, David (1979). The study of natural phonology. In Dinnsen, Daniel A. (ed.) Current approaches to phonological theory. Bloomington: Indiana University Press. 126–173.Google Scholar
Dumay, Nicolas & Gaskell, M. Gareth (2007). Sleep-associated changes in the mental representation of spoken words. Psychological Science 18. 35–39.CrossRefGoogle ScholarPubMed
Earle, F. Sayako & Myers, Emily B. (2015). Sleep and native language interference affect non-native speech sound learning. Journal of Experimental Psychology: Human Perception and Performance 41. 1680–1695.Google ScholarPubMed
Ernestus, Mirjam & Mak, Willem Marinus (2004). Distinctive phonological features differ in relevance for both spoken and written word recognition. Brain and Language 90. 378–392.CrossRefGoogle ScholarPubMed
Eszkénazi, Maxine, Levow, Gina-Anne, Meng, Helen, Parent, Gabriel & Suendermann, David (2013). Crowdsourcing for speech processing: applications to data collection, transcription and assessment. Chichester: Wiley.CrossRefGoogle Scholar
Fenn, Kimberly M., Nusbaum, Howard C. & Margoliash, Daniel (2003). Consolidation during sleep of perceptual learning of spoken language. Nature 425. 614–616.CrossRefGoogle ScholarPubMed
Finley, Sara (2012). Typological asymmetries in round vowel harmony: support from artificial grammar learning. Language and Cognitive Processes 27. 1550–1562.CrossRefGoogle ScholarPubMed
Finley, Sara & Badecker, William (2008). Analytic biases for vowel harmony languages. WCCFL 27. 168–176.Google Scholar
Finley, Sara & Badecker, William (2009a). Right-to-left biases for vowel harmony: evidence from artificial grammar. NELS 38. 269–282.Google Scholar
Finley, Sara & Badecker, William (2009b). Artificial language learning and feature-based generalization. Journal of Memory and Language 61. 423–437.CrossRefGoogle Scholar
Gaskell, M. Gareth, Warker, Jill, Lindsay, Shane, Frost, Rebecca, Guest, James, Snowdon, Reza & Stackhouse, Abigail (2014). Sleep underpins the plasticity of language production. Psychological Science 25. 1457–1465.CrossRefGoogle ScholarPubMed
Grimes, Stephen M. (2010). Quantitative investigations in Hungarian phonotactics and syllable structure. PhD dissertation, Indiana University.Google Scholar
Havas, Viktória, Taylor, J. S. H., Vaquero, Lucía, de Diego-Balaguer, Ruth, Rodríguez-Fornells, Antoni & Davis, Matthew H. (2018). Semantic and phonological schema influence spoken word learning and overnight consolidation. Quarterly Journal of Experimental Psychology 71. 1469–1481.CrossRefGoogle ScholarPubMed
Hayes, Bruce & Steriade, Donca (2004). Introduction: the phonetic bases of phonological markedness. In Hayes, Bruce, Kirchner, Robert & Steriade, Donca (eds.) Phonetically based phonology. Cambridge: Cambridge University Press. 1–33.CrossRefGoogle Scholar
Hochmann, Jean-Rémy, Carey, Susan & Mehler, Jacques (2018). Infants learn a rule predicated on the relation same but fail to simultaneously learn a rule predicated on the relation different. Cognition 177. 49–57.CrossRefGoogle Scholar
Hooper, Joan B. (1976). An introduction to natural generative phonology. New York: Academic Press.Google Scholar
Kimper, Wendell (2016). Asymmetric generalisation of harmony triggers. In Hansson, Gunnar Ólafur, Farris-Trimble, Ashley, McMullin, Kevin & Pulleyblank, Douglas (eds.) Proceedings of the 2015 Annual Meeting on Phonology. http://dx.doi.org/10.3765/amp.v3i0.3662.Google Scholar
Krämer, Martin (1999). A correspondence approach to vowel harmony and disharmony. Ms, Heinrich-Heine-Universität, Düsseldorf. Available as ROA-293 from the Rutgers Optimality Archive.Google Scholar
Lahl, Olaf, Wispel, Christiane, Willigens, Bernadette & Pietrowsky, Reinhard (2008). An ultra short episode of sleep is sufficient to promote declarative memory performance. Journal of Sleep Research 17. 3–10.CrossRefGoogle ScholarPubMed
Levy, Roger (2014). Using R formulae to test for main effects in the presence of higher-order interactions. Available (January 2020) at http://arxiv.org/abs/1405.2094.Google Scholar
Martin, Alexander, Abels, Klaus, Adger, David & Culbertson, Jennifer (2019). Do learners’ word order preferences reflect hierarchical language structure? In Goel, Ashok, Seifert, Colleen & Freksa, Christian (eds.) Proceedings of the 41st Annual Meeting of the Cognitive Science Society. Montreal: Cognitive Science Society. 2303–2309.Google Scholar
Martin, Alexander & Peperkamp, Sharon (2017). Assessing the distinctiveness of phonological features in word recognition: prelexical and lexical influences. JPh 62. 1–11.Google Scholar
Moreton, Elliott (2008). Analytic bias and phonological typology. Phonology 25. 83–127.Google Scholar
Moreton, Elliott & Pater, Joe (2012a). Structure and substance in artificial-phonology learning. Part 1: Structure. Language and Linguistics Compass 6. 686–701.CrossRefGoogle Scholar
Moreton, Elliott & Pater, Joe (2012b). Structure and substance in artificial-phonology learning. Part 2: Substance. Language and Linguistics Compass 6. 702–718.CrossRefGoogle Scholar
Myers, Scott & Padgett, Jaye (2014). Domain generalisation in artificial language learning. Phonology 31. 399–433.CrossRefGoogle Scholar
Nishida, Masaki & Walker, Matthew P. (2007). Daytime naps, motor memory consolidation and regionally specific sleep spindles. PLoS One 2. https://doi.org/10.1371/journal.pone.0000341.CrossRefGoogle ScholarPubMed
Ohala, John J. (1993). The phonetics of sound change. In Jones, Charles (ed.) Historical linguistics: problems and perspectives. London & New York: Longman. 237–278.Google Scholar
Ohala, John J. (1994). Towards a universal, phonetically-based theory of vowel harmony. Proceedings of the 3rd International Conference on Spoken Language Processing (ICSLP 94). Vol. 2. Yokohama: Acoustical Society of Japan. 491–494.Google Scholar
Peperkamp, Sharon, Skoruppa, Katrin & Dupoux, Emmanuel (2006). The role of phonetic naturalness in phonological rule acquisition. In Bamman, David, Magnitskaia, Tatiana & Zaller, Colleen (eds.) Proceedings of the 30th Annual Boston University Conference on Language Development. Somerville: Cascadilla. 464–475.Google Scholar
Powell, M. J. D. (2009). The BOBYQA algorithm for bound constrained optimization without derivatives. Cambridge: Department of Applied Mathematics and Theoretical Physics, Cambridge University. Available (January 2020) at http://www.damtp.cam.ac.uk/user/na/NA_papers/NA2009_06.pdf.Google Scholar
Pycha, Anne, Nowak, Pawel, Shin, Eurie & Shosted, Ryan (2003). Phonological rule-learning and its implications for a theory of vowel harmony. WCCFL 22. 423–435.Google Scholar
Schane, Sanford A., Tranel, Bernard & Lane, Harlan (1974). On the psychological reality of a natural rule of syllable structure. Cognition 3. 351–358.CrossRefGoogle Scholar
Scullin, Michael K. & Bliwise, Donald L. (2015). Sleep, cognition, and normal aging: integrating a half century of multidisciplinary research. Perspectives on Psychological Science 10. 97–137.CrossRefGoogle ScholarPubMed
Skoruppa, Katrin, Lambrechts, Anna & Peperkamp, Sharon (2011). The role of phonetic distance in the acquisition of phonological alternations. NELS 39:2. 717–729.Google Scholar
Skoruppa, Katrin & Peperkamp, Sharon (2011). Adaptation to novel accents: feature-based learning of context-sensitive phonological regularities. Cognitive Science 35. 348–366.CrossRefGoogle ScholarPubMed
Steele, Ariana, Denby, Thomas, Chan, Chun & Goldrick, Matthew (2015). Learning non-native phonotactic constraints over the web. In The Scottish Consortium for ICPhS 2015 (ed.) Proceedings of the 18th International Congress of Phonetic Sciences. Glasgow: University of Glasgow. https://www.internationalphoneticassociation.org/icphs-proceedings/ICPhS2015/Papers/ICPHS0258.pdf.Google Scholar
Tamminen, Jakke, Payne, Jessica D., Stickgold, Robert, Wamsley, Erin J. & Gareth Gaskell, M. (2010). Sleep spindle activity is associated with the integration of new memories and existing knowledge. Journal of Neuroscience 30. 14356–14360.CrossRefGoogle ScholarPubMed
Walker, Matthew P. & Stickgold, Robert (2004). Sleep-dependent learning and memory consolidation. Neuron 44. 121–133.CrossRefGoogle ScholarPubMed
White, James (2014). Evidence for a learning bias against saltatory phonological alternations. Cognition 130. 96–115.CrossRefGoogle ScholarPubMed
Wilson, Colin (2006). Learning phonology with substantive bias: an experimental and computational study of velar palatalization. Cognitive Science 30. 945–982.CrossRefGoogle ScholarPubMed