The present study explores learning phonological alternations that contain exceptions. Participants were exposed to a back/round vowel harmony pattern in which a regular suffix obeyed a vowel harmony rule, varying between /e/ and /o/ depending on the back/round phonetic features of the stem, and a non-alternating suffix that was always /o/ regardless of the features of the stem vowel. Participants in Experiment 1 learned the behavior of both suffixes, but correct performance for the non-alternating suffix was higher when the suffix happened to be in harmony with the stem. Participants in Experiment 2 were exposed to the non-alternating affix in harmonic contexts only, and continued to show a bias towards harmony. Experiment 3 replicated Experiment 2 with minimal training on disharmonic cases of the non-alternating morpheme. However, participants were less likely to learn the alternating affix without exposure to morphological stem, stem + suffix alternations in Experiment 4, suggesting a bias towards morphophonological alternations in learning vowel harmony patterns.
AlbrightA. (2002). Islands of reliability for regular morphology: Evidence from Italian. Language, 78(4), 684–709. https://doi.org/10.1353/lan.2003.0002
AnttilaA. (2002). Morphologically conditioned phonological alternations. Natural Language & Linguistic Theory, 20(1), 1–42. https://doi.org/10.1023/A:1014245408622
4.
Baer-HenneyD.KüglerF.van de VijverR. (2015). The interaction of language-specific and universal factors during the acquisition of morphophonemic alternations with exceptions. Cognitive Science, 39(7), 1537–1569. https://doi.org/10.1111/cogs.12209
5.
BakovićE. (2000). Harmony, dominance and control. [PhD dissertation, Rutgers University.]
6.
BakovićE. (2003). Vowel harmony and stem identity. San Diego Linguistic Papers, 1, 1–42.
7.
BarrD. J.LevyR.ScheepersC.TilyH. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3), 255–278. https://doi.org/10.1016/j.jml.2012.11.001
8.
BatesD.MächlerM.BolkerB.WalkerS. (2015). Fitting linear mixed-effects models using {lme4}. Journal of Statistical Software, 67, 1–48. https://doi.org/10.18637/jss.v067.i01
BoersmaP.WeeninkD. (2019). Praat: Doing phonetics by computer [computer program]. Version 6.1.06. http://www.praat.org
11.
BowmanS. R.LokshinB. (2014). Idiosyncratically transparent vowels in Kazakh. In KingstonJ.Moore-CantwellC.PaterJ.StaubsR. (Eds.), Proceedings of the 2013 Meeting on Phonology, Linguistic Society of America, Washington, DC, 1–12.
ChoM. H. (1994). Vowel harmony in Korean: A grounded phonology approach. [PhD dissertation, Indiana University.]
14.
ChomskyN.HalleM. (1968). The sound pattern of English. Harper & Row.
15.
ChongA. J. (2017). On the relation between phonotactic learning and alternation learning. [PhD dissertation, University of California at Los Angeles.]
16.
ClementsG. N. (1976). The autosegmental treatment of vowel harmony. In DressierW.PfeifferO. E. (Eds.), Phonologica (pp. 111–119). Innsbruck: Institut fur Sprachwissenschaft.
17.
ClementsG. N.SezerE. (1982). Vowel and consonant disharmony in Turkish. In H. van der Hulst & N. Smith (Eds.), The structure of phonological representations (2nd ed., pp. 213–255). Dordrecht: Foris.
CoetzeeA. W. (2010). Gradient well-formedness in Harmonic Grammar: Phonological performance as a window on phonological competence. Journal of the Phonetic Society of Japan, 14, 13–23.
20.
CoetzeeA. W. (2016). A comprehensive model of phonological variation: Grammatical and non-grammatical factors in variable nasal place assimilation. Phonology, 33(2), 211–246. https://doi.org/10.1017/S0952675716000117
21.
CoetzeeA. W.PaterJ. (2011). The place of variation in phonological theory. In GoldsmithJ. A.RiggleJ.YuA. (Eds.), The handbook of phonological theory (2nd ed.) (pp. 401–431). Blackwell.
22.
CohenJ. D.MacWhinneyB.FlattM.ProvostJ. (1993). PsyScope: A new graphic interactive environment for designing psychology experiments. Behavioral Research Methods, Instruments and Computers, 25, 257–271. https://doi.org/10.3758/BF03204507
23.
DasingerL. (1997). Issues in the acquisition of Estonian, Finnish, and Hungarian: A crosslinguistic comparison. In SlobinD. I. (Ed.), The crosslinguistic study of language acquisition, Vol. 4 (pp. 1–86). Lawrence Erlbaum.
24.
DienesP. (1997). Hungarian neutral vowels. The Odd Yearbook, 151–180.
FinleyS. (2013). Generalization to unfamiliar talkers in artificial language learning. Psychonomic Bulletin & Review, 20(4), 780–789. https://doi.org/10.3758/s13423-013-0402-7
27.
FinleyS. (2015a). Learning exceptions in phonological alternations. In Proceedings of the 37th Annual Conference of the Cognitive Science Society (Vol. 37, pp. 698–703). Cognitive Science Society.
28.
FinleyS. (2015b). Learning nonadjacent dependencies in phonology: Transparent vowels in vowel harmony. Language, 91(1), 48–72. https://doi.org/10.1353/lan.2015.0010
29.
FinleyS.BadeckerW. (2009). Right-to-left biases for vowel harmony: Evidence from artificial grammar. In ShardlA.WalkowM.AbdurrahmanM. (Eds.), Proceedings of the 38th North East Linguistic Society Annual Meeting (Vol. 1, pp. 269–282). GLSA.
30.
HautK. (2020). Conspiratorial exceptionality: A case study of Mushunguli. [PhD dissertation, University of California San Diego.]
31.
HayJ. B.BaayenR. H. (2005). Shifting paradigms: Gradient structure in morphology. Trends in Cognitive Sciences, 9(7), 342–348. https://doi.org/10.1016/j.tics.2005.04.002
32.
HayesB.LondeZ. C. (2006). Stochastic phonological knowledge: The case of Hungarian vowel harmony. Phonology, 23(1), 59–104. https://doi.org/10.1017/S0952675706000765
33.
HayesB.ZurawK.SiptárP.LondeZ. (2009). Natural and unnatural constraints in Hungarian vowel harmony. Language, 85, 822–863. https://doi.org/10.1353/lan.0.0169
34.
HughtoC.LamontA.PrickettB.JaroszG. E. (2019). Learning exceptionality and variation with lexically scaled MaxEnt. Proceedings of the Society for Computation in Linguistics, 2(1), 91–101.
LegendreG.MiyataY.SmolenskyP. (1990). Harmonic grammar: A formal multi-level connectionist theory of linguistic well-formedness: Theoretical foundations. In Proceedings of the twelfth annual conference of the Cognitive Science Society (pp. 388–395). Lawrence Erlbaum.
38.
LegendreG.SoraceA.SmolenskyP. (2006). The Optimality Theory–Harmonic Grammar connection. In SmolenskyP.LegendreG. (Eds.), The Harmonic Mind: From Neural Computation to Optimality Theoretic Grammar (pp. 339–409). MIT Press.
LinzenT.KasyanenkoS.GouskovaM. (2013). Lexical and phonological variation in Russian prepositions. Phonology, 30(3), 453–515.
42.
MartinA.WhiteJ. C. (in press). Vowel harmony and disharmony are not equivalent in learning. Linguistic Inquiry, 1–13. https://doi.org/10.1162/ling_a_00375
43.
Moore-CantwellC.PaterJ. (2016). Gradient exceptionality in maximum entropy grammar with lexically specific constraints. Catalan Journal of Linguistics, 15, 53–66.
MorleyR. L. (2018). Is phonological consonant epenthesis possible? A series of artificial grammar learning experiments. Phonology, 35(4), 649–688. https://doi.org/10.1017/S0952675718000234
46.
NevinsA. I. (2010). Locality in vowel harmony. MIT Press.
47.
PaterJ. (2007). The locus of exceptionality: Morpheme-specific phonology as constraint indexation. In BatemanL.O’KeefeM.ReillyE.WerleA. (Eds.), University of Massachusetts Occasional Papers 32: Papers in Optimality Theory III (pp. 259–296). GLSA.
48.
PaterJ. (2010). Morpheme-specific phonology: Constraint indexation and inconsistency resolution. In ParkerS. (Ed.), Phonological argumentation: Essays on evidence and motivation (pp. 123–154.). Equinox.
PrinceA.SmolenskyP. (2004). Optimality Theory: Constraint interaction in generative grammar. Blackwell.
51.
PychaA.NowakP.ShinE.ShostedR. (2003). Phonological rule-learning and its implications for a theory of vowel harmony. In West Coast Conference of Formal Linguistics 22 (pp. 101–113).
52.
R Development Core Team. (2018). R: A language and environment for statistical computing. In R Development Core Team, R Foundation for Statistical Computing (Vol. 1, Issue 2.11.1, p. 409). R Foundation for Statistical Computing.
RingenC. O.HeinämäkiO. (1999). Variation in Finnish vowel harmony: An OT account. Natural Language & Linguistic Theory, 17, 303–337. https://doi.org/10.1023/A:1006158818498
55.
RingenC. O.VagoR. M. (1998). Hungarian vowel harmony in Optimality Theory. Phonology, 15(3), 393–416. www.jstor.org/stable/4420136
ShihS. S. (2018). Learning lexical classes from variable phonology. In SeoY.OgawaH. (Eds.), Selected Papers from Asian Junior Linguists Conference (pp. 1–15). ICUWPL.
58.
StaveM.SmolekA.KapatsinskiV. (2013). Inductive bias against stem changes as perseveration: Experimental evidence for an articulatory approach to output-output faithfulness. In Proceedings of the Cognitive Science Society, 35 (pp. 3454–3459). Cognitive Science Society.
59.
VagoR. M. (1976). Theoretical implications of Hungarian vowel harmony. Linguistic Inquiry, 7(2), 243–263.
60.
Van der HulstH. G. (1985). Vowel harmony in Hungarian: A comparsion of segmental and autosegmental analyses. In van der HulstH. G.SmithN. (Eds.), Advances in nonlinear phonology (pp. 267–303). Foris.
WhiteJ. C.KagerR.LinzenT.MarkopoulosG.MartinA.NevinsA.PeperkampS.PolgárdiK.TopintziN.VijverR. van de. (2018). Preference for locality is affected by the prefix/suffix asymmetry: Evidence from artificial language learning. In HucklebridgeS.NelsonM. (Eds.), NELS 48: Proceedings of the Forty-Eighth Annual Meeting of the North East Linguistic Society: Vol. 3, 207–220. GLSA
63.
WilsonC. (2006). Learning phonology with substantive bias: An experimental and computational study of velar palatalization. Cognitive Science, 30, 945–982. https://doi.org/10.1207/s15516709cog0000_89
64.
ZonnefeldW. (1978). A formal theory of exceptions in generative phonology. Peter de Ridder Press.
65.
ZurawK. (2000). Patterned exceptions in phonology. [PhD dissertation, University of California, Los Angeles.]
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
