Does a theory of language need a grammar evidence from the obligatory contour principle
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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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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)

  • 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


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  • A grammatical component is obsolete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


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)


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


How to sort it out?(see also Marcus, 2001)

  • Scope of linguistic generalizations

  • Learnability


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?


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)


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


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


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?


Case study: Constraint on Hebrew root structure

  • Hebrew word formation

    rootword patternOutcome

    smmCiCeCSiMeM

  • 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


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


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


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


The production task

exemplarnew rootnew word

__________________________________

CaCaCpsmPaSaM

CaCaCsm?

?


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


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

      sm

      cvcvc

      a


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

      sm

      cvcvc

      a

  • Outcome: identity is well formed only root finally

    • Reduplication: Sm-->smm


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


The location of identical consonants in the root

% of total responses


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?


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

Observed

?

sml

Smm

smm

sml


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

sml

Smm

smm

sml


conclusion

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


Additional questions

  • Do speakers constrain root identity on-line?


Lexical decision experiments

  • Words

    FinalDiMuM(bleeding)

    NoDiShuN(fertilization)

  • Nonwords: Novel roots in existing word patterns

    InitialKiKuS

    FinalSiKuK

    NoNiKuS

  • Are speakers sensitive to the location of identity?


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


The materials in Experiments 1-3

Exp. 1Exp. 2Exp. 3

Nonwords

InitialKi-KuSKi-KaS-temhit-Ka-KaS-ti

FinalSi-KuKSi-KaK-temhiS-ta-KaK-ti

NoNi-KuSNi-KaS-temhit-Na-KaS-ti

Words

FinalDi-MuMSi-NaN-temhit-Ba-SaS-ti

NoDi-ShuNSi-MaN-temhit-Ba-LaT-ti

  • Word vs. word:

    • Word domain: no consistency across word patterns

    • Root domain: consistent performance despite differences in word pattern


Lexical Decision Results:The representation of identity

Exp. 1

Exp. 2

Exp. 3


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


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)


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


  • 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


    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


    Comparing the identity and similarity views (root finally)

    SKK>SKG

    SKK<SKG

    SKK=SKG

    Assume statistical properties

    Are matched


    Acceptability ratings

    good

    bad


    Lexical decision experiments

    • Nonwords(novel roots +existing word patterns)

      HomorganicitySiGuK

      IdentityRiGuG

      ControlGiDuN

    • 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


    The materials in Experiments 1-3

    Exp. 1Exp. 2Exp. 3

    nounsverbs (Suf)verbs (Pre+suf)

    _______________________________________________________

    Nonwords(novel roots +existing word patterns)

    HomorganicitySiGuKSiGaKtemhiStaGaKtem

    IdentityRiGuGRiGaGtemhitRaGaGtem

    ControlGiDuNGiDaNtemhitGaDaNtem

    Words

    Identity:KiDuD LiKaKtemhitLaKaKtem

    No Identity: KiShuT LiMaDtemhitLaMaDtem


    Predictions (identity vs. similarity)

    RT: SKK>SKG

    RT: SKK>SKG

    RT: SKK<SKG

    RT: SKK=SKG

    Assume statistical properties

    Are matched


    Are responses to identical C’s explicable by homorganicity?

    Exp. 1

    Exp. 3

    Exp. 2


    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?


  • 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


    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


    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?


    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?


    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.


    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


    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?


    Rating materials

    typeroottransparentopaque

    ____________________________________

    initialjjrja-jar-temhij-ta-jar-tem

    finalrjjra-jaj-temhit-ra-jaj-tem

    controlsjkrja-kar-temhij-ta-kar-tem


    ratings (all roots)

    best

    worst


    ratings (only th)


    Vocal lexical decision(say, then decide)

    Wordsnonwords

    ____________________________________

    Initial-----hij-ta-jar-tem

    finalhit-pa-lal-temhit-ra-jaj-tem

    controlshit-pa-lash-temhij-ta-kar-tem


    Lexical decision (all roots)

    rjj

    jkr

    jjr


    Lexical decision (th only)

    kthth

    thbk

    ththk


    conclusion

    • The constraint on the location of identical root consonants generalizes to

      • Novel phonemes

      • Novel feature variables

    • Speakers can extend phonological generalizations beyond the space of phonemes and feature values of their language


    Objection

    • Must such generalizations exceed the training space?

    • Problem: generalization outside the feature space is unattainable

    • Solution: change the feature space to accommodate the novel phonemes


    Can the novel phonemes be accommodated within the Hebrew feature space?

    • Probably yes!

    • Are these solutions motivated

      • Th is “more foreign”

        • Borrowings into Hebrew

          Many phonemes are maintained (job, check)

          Th is not (termometer, terapya)

        • Roots with th are rated lower than the other foreign phonemes

    • Will these solutions work?

      • The constraint on identical consonants is inexplicable by feature co-occurrence

      • It is unlikely that a model formulated at the feature level could capture the facts


    conclusions

    • Hebrew speakers generalize the constraint on root structure across the board

      • Irrespective of the statistical properties of novel items

      • Despite having no relevant statistical knowledge

    • Such generalizations may not be learnable by an associative system from the statistical properties of the lexicon (so far…)

    • An account of language, in general, and phonology, in particular must incorporate a grammar--a mechanism innately equipped with operations on variables-- that is irreducible to an associative lexicon.


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