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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

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

Iris Berent

Florida Atlantic University


The big question
The big question

  • How to account for linguistic productivity?


The generative account chomsky 1957 pinker 1999 prince smolensky 1993
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
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  • A grammatical component is obsolete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
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
How to sort it out?(see also Marcus, 2001)

  • Scope of linguistic generalizations

  • Learnability


The scope of linguistic generalizations
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
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
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
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
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
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


How to account for the constraint on identical consonants
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
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
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
The production task

exemplar new root new word

__________________________________

CaCaC psm PaSaM

CaCaC sm ?

?


How to seat 2 c s on 3 slots
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
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


The restriction on consonant identity1
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


Predictions
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



How is identity formed
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 responses root final sm sm m s m m addition sm sm x s x m x sm
Expected vs. observed responsesroot final: sm->smm, smmaddition: sm-->smX, sXm, Xsm

Observed

?

sml

Smm

smm

sml


Expected vs observed responses root final sm sm m s m m addition sm sm x s x m x sm1
Expected vs. observed responsesroot final: sm->smm, smmaddition: sm-->smX, sXm, Xsm

sml

Smm

smm

sml


Conclusion
conclusion

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


Additional questions
Additional questions

  • Do speakers constrain root identity on-line?


Lexical decision experiments
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?


Predictions for nonwrods
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
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


Lexical decision results the representation of identity
Lexical Decision Results:The representation of identity

Exp. 1

Exp. 2

Exp. 3


Conclusions
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
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
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 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
    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
    Comparing the identity and similarity views (root finally)

    SKK>SKG

    SKK<SKG

    SKK=SKG

    Assume statistical properties

    Are matched



    Lexical decision experiments1
    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


    The materials in experiments 1 31
    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


    Predictions identity vs similarity
    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
    Are responses to identical C’s explicable by homorganicity?

    Exp. 1

    Exp. 3

    Exp. 2


    Objections
    Objections homorganicity?

    • 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
    How to measure the scope of a generalization? homorganicity?(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
    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
    Implications 2001)

    • 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
    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
    Part 3 natural language

    • 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 jj r vs r jj
    Generalization to novel phonemes natural language (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
    rationale natural language

    • 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
    Rating materials natural language

    type root transparent opaque

    ____________________________________

    initial jjr ja-jar-tem hij-ta-jar-tem

    final rjj ra-jaj-tem hit-ra-jaj-tem

    controls jkr ja-kar-tem hij-ta-kar-tem


    Ratings all roots
    ratings (all roots) natural language

    best

    worst


    Ratings only th
    ratings (only th) natural language


    Vocal lexical decision say then decide
    Vocal lexical decision natural language(say, then decide)

    Words nonwords

    ____________________________________

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

    final hit-pa-lal-tem hit-ra-jaj-tem

    controls hit-pa-lash-tem hij-ta-kar-tem


    Lexical decision all roots
    Lexical decision (all roots) natural language

    rjj

    jkr

    jjr


    Lexical decision th only
    Lexical decision (th only) natural language

    kthth

    thbk

    ththk


    Conclusion1
    conclusion natural language

    • 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
    Objection natural language

    • 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
    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


    Conclusions1
    conclusions feature space?

    • 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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