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Does a theory of language need a grammar? Evidence from the Obligatory Contour Principle

Does a theory of language need a grammar? Evidence from the Obligatory Contour Principle. Iris Berent Florida Atlantic University. The big question. How to account for linguistic productivity?. The generative account (Chomsky, 1957, Pinker, 1999, Prince & Smolensky, 1993 ).

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Does a theory of language need a grammar? Evidence from the Obligatory Contour Principle

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  1. Does a theory of language need a grammar?Evidence from the Obligatory Contour Principle Iris Berent Florida Atlantic University

  2. The big question • How to account for linguistic productivity?

  3. The generative account(Chomsky, 1957, Pinker, 1999, Prince & Smolensky, 1993) • Grammar: A symbolic computational mechanism that operates over variables • abstract placeholders • Noun, verb • Hallmarks of operations on variables • Blind to specific instances • Generalizes across the board, irrespective of item properties, familiarity • Dog + s-->dogs • Ktiv + s-->ktivs • Appeal to variables is critical to explain productivity Noun + S

  4. An associative account (Rumelhart & McClelland, 1986; Elman et al. 1996) • A grammatical component is obsolete • Speakers generalize by analogizing novel forms to similar lexical instances • Hallmark of associative processes: • generalizations are constrained by statistical properties of lexical instances • Similarity • Familiarity • Such generalizations are inexplicable by a grammatical operations on variables (blind to instance properties) gog Dog-dogs Log-logs

  5. Examples of instance based generalizations • Generalizations in natural and artificial languages are guided by the co-occurrence of instances at various grain sizes: • morpheme (de Jong, Schreuder & Baayen, 2000) • Syllables (Saffran, Aslin, & Newport, 1996) • Subsyllabic units (Frisch et al., 2000) • Segments: (Dell, Reed, Adams & Meyer, 2000) • Features: (Goldrick, 2002)

  6. AgreementSpeakers are equipped with a powerful associative mechanism of statistical learning that generalizes from lexical instances gog dog debate • Is an associative lexicon sufficient to account for linguistic productivity? • Do some linguistic generalizations appeal to variables? • Does a theory of language need a grammar (a mechanism that operates on variables)? Noun +S

  7. How to sort it out?(see also Marcus, 2001) • Scope of linguistic generalizations • Learnability

  8. The scope of linguistic generalizations • Agreement (all accounts): people can generalize • Debate: scope of generalizations • Associative accounts: instance based generalizations are sensitive to similarfamiliarinstances (gog-dog) • Symbolic account: operations over variables allow for generalizations across the board, irrespective of similarity of novel items to familiar items • Do people generalize in such a manner?

  9. Do speakers generalize across the board? • No (strong associationist view): • the symbolic hypothesis has the empirical facts wrong: Speakers don’t generalize across the board • Yes (weak associationist view): • Speakers can generalize across the board (operate over variables) • Symbolic view is wrong about the innateness of the learning mechanism: • Symbolic view: Prior to learning, speakers have the (innate) capacity to operate over variables • associationist alternative: operations over variables are an emergent property of associative systems (does not come equipped with operations over variables)

  10. The learnability issue • Is the ability to operate over variables learnable by an associative system? • Associationist system: has no capacity to operate on variables prior to learning

  11. What is not relevant to this debate • The contents of the grammar • Rules vs. constraints • What is constrained (articulatory vs. acoustic entities) • Domain specificity • Innateness of specific constraints • The debate:Is a grammar required? • Grammar: a computational mechanism that is innately equipped with operations over variables

  12. Does a theory of language need a grammar? • Most research: inflectional morphology • Current focus: (morph)phonology • Phonology: an interface between the grammar and perceptual system • Many phonological processes are governed by similarity--prone to an associative explanation • E.g., assimilation • The success of connectionist accounts of phonology in reading • Question: Does phonological knowledge appeal to variables?

  13. Case study: Constraint on Hebrew root structure • Hebrew word formation root word pattern Outcome smm CiCeC SiMeM • Restriction on the position of identical consonants: • Identity is frequent root finally: smm • Identity is rare root initially: ssm • Speakers generalize the constraint on root structure to novel roots

  14. How to account for the constraint on identical consonants? • Symbolic account: • Speakers constrain identity (OCP, McCarthy, 1986) *bbg • Identity is represented by a variable: XX • A constraint on identity implicates a grammatical operation on variables • Associative account (strong): • Variables are eliminated • Root structure knowledge does not appeal to identity (variables)--explicable in terms of the statistical structure of root tokens and their constituents (phonemes, features) • bbg • bb=rare root initially

  15. Does a constraint on identical C’s require a grammar: An overview • The distinction between identical and nonidentical consonants is inexplicable by statistical knowledge • segment co-occurrence (Part 1) • feature co-occurrence (Part 2) • The constraint on identical C’s is observed in the absence of relevant statistical knowledge(Part 3): • novel phonemes with novel feature values • Such generalizations may be unlearnable in the absence of innate operations over variables • The restriction in identity implicates a grammar • a computational mechanism that is innately equipped with operates on variables

  16. Part 1 • Speakers’ sensitivity to root identity is inexplicable by the co-occurrence of segments? • Production Berent, I., Everett, D. & Shimron, J. (2001). Cognitive Psychology, 42(1),1-60. • Lexical decision Berent, I., Shimron, J. & Vaknin, V. (2001). Journal of Memory and Language, 44(4),644-665

  17. The production task exemplar new root new word __________________________________ CaCaC psm PaSaM CaCaC sm ? ?

  18. How to seat 2 C’s on 3 slots? • An additional root segment is needed • Two possible solutions: • new segment: SaMaL • Identical segments: • final: SaMaM • initial: SaSaM • McCarthy (1986) • Speakers solve this problem routinely • Opt for root final identity

  19. The restriction on consonant identity • McCarthy (1986) • OCP: adjacent identical elements are prohibited • The root SMM is prohibited • Verbs like SaMaM are stored as SM • Root identity emerges during word formation by rightwards spreading s m c v c v c a

  20. The restriction on consonant identity • McCarthy (1986) • OCP: adjacent identical elements are prohibited • The root SMM is prohibited • Verbs like SaMaM are stored as SM • Root identity emerges during word formation by rightwards spreading s m c v c v c a • Outcome: identity is well formed only root finally • Reduplication: Sm-->smm

  21. predictions • Speakers productively form identity from a biconsonantal input by “reduplication” • The location of identity is constrained: • Smm • *ssm • The domain of the constraint is the root: root initial identity is avoided irrespective of word position • CaCaC • maCCiCim • hitCaCaCtem

  22. The location of identical consonants in the root % of total responses

  23. How is identity formed? • Symbolic view: Reduplication--operation on variables • X-->XX • Associationist view (strong): • Variables are eliminated--identity is not represented • All new segments (identical or not) are inserted by a single process: segment addition • sm--> smm • sm-->sml • The selection of added segment reflects its frequency • Question: is the production of identical consonants explicable by segement co-occurrence?

  24. Expected vs. observed responsesroot final: sm->smm, smmaddition: sm-->smX, sXm, Xsm Observed ? sml Smm smm sml

  25. Expected vs. observed responsesroot final: sm->smm, smmaddition: sm-->smX, sXm, Xsm sml Smm smm sml

  26. conclusion • The formation of identical consonants is inexplicable by their expected lexical frequency: a grammatical mechanism

  27. Additional questions • Do speakers constrain root identity on-line?

  28. Lexical decision experiments • Words Final DiMuM (bleeding) No DiShuN (fertilization) • Nonwords: Novel roots in existing word patterns Initial KiKuS Final SiKuK No NiKuS • Are speakers sensitive to the location of identity?

  29. Predictions for nonwrods • ssm type roots are ill formed-->easier to reject (classify as nonword) than smm • The representation of identity: SMM vs. PSM (freuqency matched) • Associative account (strong): no distinction between root types when statisical properties are controlled for • Symbolic view: • speakers distinguish between identity and nonidentity • If identity is formed by the grammar--may be more wordlike--difficult to reject than no identity • The domain of the constraint: root or word

  30. The materials in Experiments 1-3 Exp. 1 Exp. 2 Exp. 3 Nonwords Initial Ki-KuS Ki-KaS-tem hit-Ka-KaS-ti Final Si-KuK Si-KaK-tem hiS-ta-KaK-ti No Ni-KuS Ni-KaS-tem hit-Na-KaS-ti Words Final Di-MuM Si-NaN-tem hit-Ba-SaS-ti No Di-ShuN Si-MaN-tem hit-Ba-LaT-ti • Word vs. word: • Word domain: no consistency across word patterns • Root domain: consistent performance despite differences in word pattern

  31. Lexical Decision Results:The representation of identity Exp. 1 Exp. 2 Exp. 3

  32. Conclusions • Speakers constrain the location of identical consonants in the roots • The constraint is inexplicable by the statisical co-occurrence of segments • Inconsistent with a strong associative account

  33. Part 2 • Is the constraint on identical root consonant explicable by statistical properties of features? • Is the constraint on identity due to similarity? • Rating experiments • Berent, I. & Shimron, I. (2003). Journal of Linguistics, 39.1. • Lexical decision experiments • Berent,Vaknin & Shimron, (in preparation)

  34. The similarity explanation • General claim: (e.g.,Pierrehumbert, 1993): • Similarity among adjacent segments is undesirable • Identical consonants are maximally similar • The ban on identical consonants is due to their similarity: full segment identity is independently not constrained • Symbolic version (degree of feature overlap): • Similar segments are undesirable because the grammar constrains identical features • Appeals to variables:“Any feature”, “identity” • Associationist version (freq. of similar segments): • Similar segments are desirable because they are rare • Appeals to specific instances (e.g., bb, labial) not variables • Either way: a single restriction on identical and similar consonants

  35. The identity account (McCarthy, 1986; 1994) • The constraint on full segment identity is irreducible to the restriction on similarity (homorganicity: same place of articulation) • A shared principle: adjacent identical elements are prohibited (OCP) • Different domains of application • Identity: full segment (root node) • Homorganicity: place • Different potential for violation

  36. Predicted dissociations *[velar] [velar] S k g C V C V C a S k C V C V C a Homorganic: violation Identical: No violation SKK SKG

  37. Comparing the identity and similarity views (root finally) SKK>SKG SKK<SKG SKK=SKG Assume statistical properties Are matched

  38. Acceptability ratings good bad

  39. Lexical decision experiments • Nonwords(novel roots +existing word patterns) Homorganicity SiGuK Identity RiGuG Control GiDuN • Control for statistical properties: • All trio members matched for • bigram frequency • Word pattern • Identical and homorganic members are matched for • Place of articulation • Co-occurrence of • Segments (bigrams) • homorganic features • At the feature level: (iden, homor)<controls

  40. The materials in Experiments 1-3 Exp. 1 Exp. 2 Exp. 3 nouns verbs (Suf) verbs (Pre+suf) _______________________________________________________ Nonwords(novel roots +existing word patterns) Homorganicity SiGuK SiGaKtem hiStaGaKtem Identity RiGuG RiGaGtem hitRaGaGtem Control GiDuN GiDaNtem hitGaDaNtem Words Identity: KiDuD LiKaKtem hitLaKaKtem No Identity: KiShuT LiMaDtem hitLaMaDtem

  41. Predictions (identity vs. similarity) RT: SKK>SKG RT: SKK>SKG RT: SKK<SKG RT: SKK=SKG Assume statistical properties Are matched

  42. Are responses to identical C’s explicable by homorganicity? Exp. 1 Exp. 3 Exp. 2

  43. Objections • Do speakers generalize across the board? • The absence of a statistical explanation is due to an inaccurate estimate of statistical properties • Type • Token • How far can speakers generalize? • Is a grammar implicated? • Suppose people can generalize “across the board” • Are such generalizations learnable by associative systems that are not innately equipped with operations over variables?

  44. How to measure the scope of a generalization? (Marcus, 1998, 2001) • The training space: space used to representtraining items • Classification of novel items: • Within training space: described exhaustively by using values of trained features • Outside the training space: • represented by some untrained feature values xog xog Dog Log gog Dog Gog

  45. Network’s architecture determines scope (Marcus, 1998, 2001) • Generalizations of byconnectionist networks that lack innate operations on variables (FF networks, SRN) • an identity mapping: X-->X • A dog is a dog • Outside the training space: • No systematic generalizations! • A xog is a ? • Within training space: • Successful generalizations • A gog is a gog Dog Gog Dog Log gog xog Critics:: Altmann & Dienes, 1999; Christiansen & Curtin, 1999; Christiansen, Conway & Curtin, 2000; Eimas, 1999; McClelland & Plaut, 1999; Negishi, 1999; Seidenberg & Elman, 1999; 1999b; Shastri, 1999

  46. Implications • Generalizations over variables cannot be learned from training on instances • If speakers can generalize beyond their training space, then they possess a grammar (a mechanism operating on variables) • Question: do speakers generalize in such a fashion?

  47. Existing evidence for exceeding the training space in natural language • Phonotactic restrictions extend to unattested clusters (Moreton, 2002): bw>dl • Inexplicable by segment-co-occurrence • Are they explained by feature-co-occurrence? • Regular inflection generalizes to strange novel items (Prasada & Pinker 1993; Berent, Pinker & Shimron, 1999) • Are “strange” words outside speakers’ space?

  48. Part 3 • Does the constraint on root structure generalize beyond the phonological space of Hebrew? • Berent, I., Marcus, G., Shimron, J., & Gafos, A. (2002). Cognition, 83, 113-139.

  49. Generalization to novel phonemes (e.g., jjr vs. rjj) Tongue tip Constriction Area:wide(Gafos, 1999) th Ch J w Hebrew phonemes TTCA narrow (s, z, ts) TTCA mid (sh) Hebrew features

  50. rationale • identical novel phonemes never co-occure • Root initially • Root finally • A restriction on novel identical phonemes is inexplicable by • Statistical knowledge of phonemeco-occurrence • Statistical knowledge of feature co-occurrence th (novel place value) • question: Can speakers generalize in the absence of relevant statistical knowledge?

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