Jakobson's Grand Unified Theory of Linguistic Cognition

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Jakobson's Grand Unified Theory of Linguistic
Cognition

• Paul Smolensky
• Cognitive Science Department
• Johns Hopkins University

with
Géraldine Legendre Alan Prince Peter Jusczyk
Suzanne Stevenson
Elliott Moreton Karen Arnold Donald
Mathis Melanie Soderstrom
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Grammar and Cognition

• 1. What is the system of knowledge?
• 2. How does this system of knowledge arise in
  the mind/brain?
• 3. How is this knowledge put to use?
• 4. What are the physical mechanisms that serve
  as the material basis for this system of
  knowledge and for the use of this knowledge?
• (Chomsky 88 p. 3)

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Advertisement

• The complete story, forthcoming (2003) Blackwell
• The harmonic mind From neural computation to
  optimality-theoretic grammar
• Smolensky Legendre

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Jakobsons Program

• A Grand Unified Theory for the cognitive science
  of language is enabled by Markedness
• Avoid a
• ? Structure
• Alternations eliminate a
• Typology Inventories lack a
• ? Acquisition
• a is acquired late
• ? Processing
• a is processed poorly
• ? Neural
• Brain damage most easily disrupts a

Formalize through OT?
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Structure Acquisition Use Neural Realization

• ? Theoretical. OT (Prince Smolensky 91, 93)
  
• Construct formal grammars directly from
  markedness principles
• General formalism/ framework for grammars
  phonology, syntax, semantics GB/LFG/
• Strongly universalist inherent typology

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Structure Acquisition Use Neural Realization

• Theoretical Formal structure enables OT-general
• Learning algorithms
• Constraint Demotion Provably correct and
  efficient (when part of a general decomposition
  of the grammar learning problem)
• Tesar 1995 et seq.
• Tesar Smolensky 1993, , 2000
• Gradual Learning Algorithm
• Boersma 1998 et seq.

• ? Empirical
• Initial state predictions explored through
  behavioral experiments with infants

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Structure Acquisition Use Neural Realization

• Theoretical
• Theorems regarding the computational complexity
  of algorithms for processing with OT grammars
• Tesar 94 et seq.
• Ellison 94
• Eisner 97 et seq.
• Frank Satta 98
• Karttunen 98

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Structure Acquisition Use Neural Realization

• Theoretical OT derives from the theory of
  abstract neural (connectionist) networks
• via Harmonic Grammar (Legendre, Miyata, Smolensky
  90)
• For moderate complexity, now have general
  formalisms for realizing
• complex symbol structures as distributed patterns
  of activity over abstract neurons
• structure-sensitive constraints/rules as
  distributed patterns of strengths of abstract
  synaptic connections
• optimization of Harmony

• Empirical

? Construction of a miniature, concrete LAD
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Program

• Structure
• ? OT
• Constructs formal grammars directly from
  markedness principles
• Strongly universalist inherent typology
• ? OT allows completely formal markedness-based
  explanation of highly complex data
• Acquisition
• ? Initial state predictions explored through
  behavioral experiments with infants
• Neural Realization
• ? Construction of a miniature, concrete LAD

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? The Great Dialectic

• Phonological representations serve two masters

FAITHFULNESS
MARKEDNESS
Locked in conflict
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OT from Markedness Theory

• MARKEDNESS constraints a No a
• FAITHFULNESS constraints
• Fa demands that /input/ ? output leave a
  unchanged (McCarthy Prince 95)
• Fa controls when a is avoided (and how)
• Interaction of violable constraints Ranking
• a is avoided when a Fa
• a is tolerated when Fa a
• M1 M2 combines multiple markedness dimensions

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OT from Markedness Theory

• MARKEDNESS constraints a
• FAITHFULNESS constraints Fa
• Interaction of violable constraints Ranking
• a is avoided when a Fa
• a is tolerated when Fa a
• M1 M2 combines multiple markedness dimensions
• Typology All cross-linguistic variation results
  from differences in ranking in how the
  dialectic is resolved (and in how multiple
  markedness dimensions are combined)

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OT from Markedness Theory

• MARKEDNESS constraints
• FAITHFULNESS constraints
• Interaction of violable constraints Ranking
• Typology All cross-linguistic variation results
  from differences in ranking in resolution of
  the dialectic
• Harmony MARKEDNESS FAITHFULNESS
• A formally viable successor to Minimize
  Markedness is OTs Maximize Harmony (among
  competitors)

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? Structure

• Explanatory goals achieved by OT
• Individual grammars are literally and formally
  constructed directly from universal markedness
  principles
• Inherent Typology
• Within the analysis of phenomenon F in language
  L is inherent a typology of F across all languages

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Program

• Structure
• ? OT
• Constructs formal grammars directly from
  markedness principles
• Strongly universalist inherent typology
• ? OT allows completely formal markedness-based
  explanation of highly complex data --- Friday
• Acquisition
• ? Initial state predictions explored through
  behavioral experiments with infants
• Neural Realization
• ? Construction of a miniature, concrete LAD

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? Structure Summary

• OT builds formal grammars directly from
  markedness MARK, with FAITH
• Friday
• Inventories consistent with markedness relations
  are formally the result of OT with local
  conjunction
• Even highly complex patterns can be explained
  purely with simple markedness constraints all
  complexity is in constraints interaction through
  ranking and conjunction Lango ATR vowel harmony

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Program

• Structure
• ? OT
• Constructs formal grammars directly from
  markedness principles
• Strongly universalist inherent typology
• ? OT allows completely formal markedness-based
  explanation of highly complex data
• Acquisition
• ? Initial state predictions explored through
  behavioral experiments with infants
• Neural Realization
• ? Construction of a miniature, concrete LAD

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Nativism I Learnability

• Learning algorithm
• Provably correct and efficient (under strong
  assumptions)
• Sources
• Tesar 1995 et seq.
• Tesar Smolensky 1993, , 2000
• If you hear A when you expected to hear E,
  increase the Harmony of A above that of E by
  minimally demoting each constraint violated by A
  below a constraint violated by E

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Constraint Demotion Learning
If you hear A when you expected to hear E,
increase the Harmony of A above that of E by
minimally demoting each constraint violated by A
below a constraint violated by E
Correctly handles difficult case multiple
violations in E
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Nativism I Learnability

• M F is learnable with /inpossible/?impossible
• not in- except when followed by
• exception that proves the rule, M NPA
• M F is not learnable from data if there are no
  exceptions (alternations) of this sort, e.g.,
  if lexicon produces only inputs with mp, never
  np then ?M and ?F, no M vs. F conflict, no
  evidence for their ranking
• Thus must have M F in the initial state, H0

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The Initial State

• OT-general MARKEDNESS FAITHFULNESS
• Learnability demands (Richness of the Base)
• (Alan Prince, p.c., 93 Smolensky 96a)
• ? Child production restricted to the unmarked
• ? Child comprehension not so restricted
• (Smolensky 96b)

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Nativism II Experimental Test

• Collaborators
• Peter Jusczyk
• Theresa Allocco
• Language Acquisition (2002)

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Nativism II Experimental Test

• Linking hypothesis
• More harmonic phonological stimuli ? Longer
  listening time
• More harmonic
• ?M ? M, when equal on F
• ?F ? F, when equal on M
• When must chose one or the other, more harmonic
  to satisfy M M F
• M Nasal Place Assimilation (NPA)

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Experimental Paradigm

• Headturn Preference Procedure (Kemler Nelson et
  al. 95 Jusczyk 97)

• X/Y/XY paradigm (P. Jusczyk)
• un...b?...umb?
• un...b?...umb?

FNP
R
p .006
?FAITH

• Highly general paradigm Main result

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4.5 Months (NPA)
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4.5 Months (NPA)
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4.5 Months (NPA)
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4.5 Months (NPA)
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Program

• Structure
• ? OT
• Constructs formal grammars directly from
  markedness principles
• Strongly universalist inherent typology
• ? OT allows completely formal markedness-based
  explanation of highly complex data
• Acquisition
• ? Initial state predictions explored through
  behavioral experiments with infants
• Neural Realization
• ? Construction of a miniature, concrete LAD

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The question

• The nativist hypothesis, central to generative
  linguistic theory
• Grammatical principles respected by all human
  languages are encoded in the genome.
• Questions
• Evolutionary theory How could this happen?
• Empirical question Did this happen?
• Today What concretely could it mean for a
  genome to encode innate knowledge of universal
  grammar?

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UGenomics

• The game Take a first shot at a concrete example
  of a genetic encoding of UG in a Language
  Acquisition Device
• Proteins ? Universal grammatical principles ?

Time to willingly suspend disbelief
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UGenomics

• The game Take a first shot at a concrete example
  of a genetic encoding of UG in a Language
  Acquisition Device
• Proteins ? Universal grammatical principles ?

• Case study Basic CV Syllable Theory (Prince
  Smolensky 93)
• Innovation Introduce a new level, an abstract
  genome notion parallel to and encoding
  abstract neural network

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Approach Multiple Levels of Encoding
Biological Genome
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UGenome for CV Theory

• Three levels
• Abstract symbolic Basic CV Theory
• Abstract neural CVNet
• Abstract genomic CVGenome

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UGenomics Symbolic Level

• Three levels
• Abstract symbolic Basic CV Theory
• Abstract neural CVNet
• Abstract genomic CVGenome

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Approach Multiple Levels of Encoding
Biological Genome
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Basic syllabification Function

• Basic CV Syllable Structure Theory
• Basic No more than one segment per syllable
  position .(C)V(C).
• /underlying form/ ? surface form
• /CVCC/ ? .CV.C V C. /pædd/?pæd?d
• Correspondence Theory
• McCarthy Prince 1995 (MP)
• /C1V2C3C4/ ? .C1V2.C3 V C4

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Why basic CV syllabification?

• underlying ? surface linguistic forms
• Forms simple but combinatorially productive
• Well-known universals typical typology
• Mini-component of real natural language grammars
• A (perhaps the) canonical model of universal
  grammar in OT

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Syllabification Constraints (Con)

• PARSE Every element in the input corresponds to
  an element in the output
• ONSET No V without a preceding C
• etc.

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UGenomics Neural Level

• Three levels
• Abstract symbolic Basic CV Theory
• Abstract neural CVNet
• Abstract genomic CVGenome

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Approach Multiple Levels of Encoding
Biological Genome
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CVNet Architecture

• /C1 C2/ ? C1 V C2

/ C1 C2 /
C1 V C2
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Connection substructure
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PARSE

• All connection coefficients are 2

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ONSET

• All connection coefficients are ?1

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Crucial Open Question(Truth in Advertising)

• Relation between strict domination and neural
  networks?

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CVNet Dynamics

• Boltzmann machine/Harmony network
• Hinton Sejnowski 83 et seq. Smolensky 83 et
  seq.
• stochastic activation-spreading algorithm higher
  Harmony ? more probable
• CVNet innovation connections realize fixed
  symbol-level constraints with variable strengths
• learning modification of Boltzmann machine
  algorithm to new architecture

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Learning Behavior

• A simplified system can be solved analytically
• Learning algorithm turns out to
• Dsi(?) e violations of constrainti P?

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UGenomics Genome Level

• Three levels
• Abstract symbolic Basic CV Theory
• Abstract neural CVNet
• Abstract genomic CVGenome

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Approach Multiple Levels of Encoding
Biological Genome
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Connectivity geometry

• Assume 3-d grid geometry

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ONSET
x0 segment S S VO
N S x0

• VO segment NS S VO

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Connectivity PARSE

• Correspondence units grow north west and
  connect with input output units.

• Output units grow east and connect

• Input units grow south and connect

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To be encoded

• How many different kinds of units are there?
• What information is necessary (from the source
  units point of view) to identify the location of
  a target unit, and the strength of the connection
  with it?
• How are constraints initially specified?
• How are they maintained through the learning
  process?

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Unit types

• Input units C V
• Output units C V x
• Correspondence units C V
• 7 distinct unit types
• Each represented in a distinct sub-region of the
  abstract genome
• Help ourselves to implicit machinery to spell
  out these sub-regions as distinct cell types,
  located in grid as illustrated

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Direction of projection growth

• Topographic organizations widely attested
  throughout neural structures
• Activity-dependent growth a possible alternative
• Orientation information (axes)
• Chemical gradients during development
• Cell age a possible alternative

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Projection parameters

• Direction
• Extent
• Local
• Non-local
• Target unit type
• Strength of connections encoded separately

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Connectivity Genome

• Contributions from ONSET and PARSE

• Key

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CVGenome Connectivity
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Encoding connection strength

• Network-level specification

• For each constraint ?i , need to embody
• Constraint strength si
• Connection coefficients (F ? ? cell
  types)
• Product of these is contribution of ?i to the
  F ? ? connection weight

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Processing
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Development
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Learning
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CVGenome Connection Coefficients
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Abstract Gene Map
General Developmental Machinery
Connectivity
Constraint Coefficients
C-I
V-I
C-C
direction
extent
target
CORRESPOND
RESPOND
COVx B 1
CCVC B ?2
CC CICO 1
VC VIVO 1
G??
G??
?
?
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UGenomics

• Realization of processing and learning algorithms
  in abstract molecular biology, using the types
  of interactions known to be biologically possible
  and genetically encodable

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UGenomics

• Host of questions to address
• Will this really work?
• Can it be generalized to distributed nets?
• Is the number of genes 770.26 plausible?
• Are the mechanisms truly biologically plausible?
• Is it evolvable?

? How is strict domination to be handled?
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Hopeful Conclusion

• Progress is possible toward a Grand Unified
  Theory of the cognitive science of language
• addressing the structure, acquisition, use, and
  neural realization of knowledge of language
• strongly governed by universal grammar
• with markedness as the unifying principle
• as formalized in Optimality Theory at the
  symbolic level
• and realized via Harmony Theory in abstract
  neural nets which are potentially encodable
  genetically

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Hopeful Conclusion

• Progress is possible toward a Grand Unified
  Theory of the cognitive science of language

Thank you for your attention (and indulgence)
Still lots of promissory notes, but all in a
common currency Harmony unmarkedness
hopefully this will promote further progress by
facilitating integration of the sub-disciplines
of cognitive science