2. Experimental evidence for product- oriented and source-oriented generalizations Vsevolod Kapatsinski Indiana University Dept. of Linguistics Cognitive Science Program Speech Research Laboratory vkapatsi@indiana.edu http://mypage.iu.edu/~vkapatsi/

3. Product-oriented vs. source-oriented generalizations Bybee (2001:126) “Generative rules express source-oriented generalizations. That is, they act on a specific input to change it in well-defined ways into an output of a certain form. Many, if not all, schemas are product- oriented rather than source-oriented. A product- oriented schema generalizes over forms of a specific category, but does not specify how to derive that category from some other.” Source oriented: k] sg  t  i] pl Product-oriented: ‘plurals must end in t  i’

4. Present study Given a lexicon and a particular training paradigm what generalizations do the learners extract?

5. The paradigm (Bybee & Newman 1995)

10. The artificial languages BLUERED {k;g}  {t  ;d  }i 100% 30 {t;d;p;b}  {t;d;p;b}i25% 8 75% 24 {t;d;p;b}  {t;d;p;b}a75% 24 25% 8 Two plural suffixes –i and -a If –i attached to a velar ({k;g}), the velar changes to an alveopalatal This is velar palatalization

11. Velar palatalization The process: k  t  /_i Productivity: p(k  t  i) / ( p(k  t  i) + p(k  ki) ) Coding scheme: BLUE – velar palatalization applies RED – velar palatalization fails

12. Research question Does the productivity of velar palatalization differ in the BLUE language and the RED language? Depends on your model of grammar.

13. Research question BLUERED {k;g}  {t  ;d  }i 100% 30 {t;d;p;b}  {t;d;p;b}i25% 8 75% 24 {t;d;p;b}  {t;d;p;b}a75% 24 25% 8 /62

14. Non-competing rules BLUERED {k;g}  {t  ;d  }i 100% 30 {t;d;p;b}  {t;d;p;b}i25% 8 75% 24 {t;d;p;b}  {t;d;p;b}a75% 24 25% 8 Triggers velar palatalization Does not compete with anything Equally supported in both languages BLUE = RED e.g., Hale and Reiss 2008, Plag 2003

15. Constraints BLUERED {k;g}  {t  ;d  }i 100% 30 *ki0 {t;d;p;b}  {t;d;p;b}i25% 8 75% 24 Ci3854 {t;d;p;b}  {t;d;p;b}a75% 24 25% 8 Triggers velar palatalization /ki/ less expected in the blue language  its absence is less notable BLUE < RED

16. Positive product-oriented generalizations BLUERED {k;g}  {t  ;d  }i 100% 30 i3854 a248 {t;d;p;b}  {t;d;p;b}i824 {t;d;p;b}  {t;d;p;b}a248 BLUE > RED Bybee & Slobin 1982, Bybee & Moder 1983, Bybee 2001 Triggers vel.pal. Attaches –i with or without vel.pal.

17. Competing weighted rules BLUERED {k;g}  {t  ;d  }i 100% 30 C  Ci25% 8 75% 24 C  Ca75% 24 25% 8 Triggers velar palatalization BLUE > RED Competes with Competition stronger in red Albright & Hayes 2003 Iff the choice between the rules is stochastic.

18. Results BLUE RED

19. Results * 100% 30 BLUE RED Non-competing rules Constraints Positive product-oriented Competing weighted rules

20. Individual subject data

21. Competing weighted rules Positive product-oriented BLUERED {k;g}  {t  ;d  }i 100% 30 C  Ci25% 8 75% 24 C  Ca75% 24 25% 8 Albright & Hayes 2003 {p;b;t;d}

22. 21 Results *** ANCOVA: This correlation is significant F(1,27)=14.23, p<.001, while Language is not, F(1,27)=.082, p>.5). The predicted explanatory variable accounts for all the variance in velar palatalization rate attributable to the artificial language

23. Natural language data (Kapatsinski, in press) In Russian loanword adaptation (English  Russian on the web not in dictionary), vel.pal is fully productive before the suffixes –-ek, -ok but only partially productive before --ik, -i. Why? - Despite no exceptions in the dictionary - Despite /i/ being a more natural trigger of vel.pal than /o/, /e/ (Bhat 1974, Guion 1998, Wilson 2006)

24. Natural language data (Kapatsinski, in press) In Russian loanword adaptation (English  Russian on the web not in dictionary), vel.pal is fully productive before the suffixes –-ek, -ok but only partially productive before --ik, -i. Why? -ik, –i tend not to attach to velars -ok, -ek mostly attach to velars

25. Positive product-oriented generalizations vs. competing weighted rules

26. BLUERED {k;g}  {t  ;d  }i Support vel.pal Support vel.pal {t  ;d  }  {t  ;d  }i {t;d;p;b}  {t;d;p;b}i8/3824/54 {t;d;p;b}  {t;d;p;b}a11 Product-oriented generalizations

27. Competing weighted rules BLUERED {k;g}  {t  ;d  }i 100% 30 {t  ;d  }  {t  ;d  }i Oppose vel.pal Oppose vel.pal C  Ci C  Ca75% 24 25% 8

28. The addition of t   t  i hurts palatalization t(33)=2.88, p=.007 Competing weighted rules Product-oriented

29. Perception / rating

30. Competing weighted rules Product-oriented Perception: The addition of t   t  i helps palatalization * {t;d}  {t  ;d ӡ }i vs. {t;d}  {t;d}i

31. Competing weighted rules Product-oriented Perception: The addition of t   t  i helps palatalization * Because ratings of  {t  ;d ӡ }i increase (t(51)=2.245, p<.05)

32. Is perception purely product- oriented? No. Listeners know at least that singular-final velars are the changeable segments: –{k;g}  {t  ;d ӡ }i better than {t;d}  {t  ;d ӡ }i (p<.001) in every language –{k;g}  {k;g}V better than {t  ;d ӡ }  {k;g}V (p<.00001) in every language

33. 32 Learners extract competing rules, which they use to derive a word from a morphologically related word (Albright & Hayes 2003) The outcome of competition between rules is influenced by reliability or type frequency (Albright and Hayes 2003, Pierrehumbert 2006) The choice between rules is stochastic Learners also learn about what a typical plural sounds like (product-oriented generalizations, Bybee 2001) Product-oriented generalizations are used more by the listener to evaluate the goodness of a paradigmatic mapping than by the speaker to form a word from a morphologically related word (perception is more liberal than production) Caveat: source-oriented paradigm Summary

34. References Albright, A., and B. Hayes. 2003. Rules vs. analogy in English past tenses: A computational/experimental study. Cognition, 90, 119-61. Bhat, D. N. S. 1974. A general study of palatalization. Working Papers on Language Universals 14: 17-58. Bybee, J. L. 2001. Phonology and language use. CUP. Bybee, J. L., & C. L. Moder. 1983. Morphological classes as natural categories. Language, 59, 251-70. Bybee, J. L., & J. E. Newman. 1995. Are stem changes as natural as affixes? Linguistics, 33, 633-54. Bybee, J. L., & D. I. Slobin. 1982. Rules and schemas in the development and use of the English past. Language 58: 265-89. Guion, S. G. 1998. The role of perception in the sound change of velar palatalization. Phonetica 55: 18-52. Hale, M., & C. Reiss. 2008. The phonological enterprise. OUP. Kapatsinski, V. M. In press. Rule reliability and productivity: Velar palatalization in Russian and artificial grammar. Proceedings of LabPhon 11. Köpcke, K.-M. 1988. Schemas in German plural formation. Lingua, 74, 303-35. Lobben, M. 1991. Pluralization of Hausa nouns, viewed from psycholinguistic experiments and child language data. M.Phil Thesis, University of Oslo. Pierrehumbert, J. B. 2006. The statistical basis of an unnatural alternation. In Laboratory Phonology 8, 81-107. Mouton de Gruyter. Plag, I. 1999. Word formation in English. Mouton de Gruyter. Wilson, C. 2006. Learning phonology with substantive bias: An experimental and computational study of velar palatalization. Cognitive Science 30: 945-82.