Linguistic Evidence for Relational Networks

Ling 411 – 15

Linguistic Evidence: Relational Networks

As we have seen, evidence from neuroscience shows that linguistic structure is a network

Since the whole human information system is a network Evidence from

• Neuroanatomy• Perceptual neuroscience (Mountcastle)

And the linguistic system is part of the overall information system

The same conclusion can be reached from purely linguistic evidence

Language vs linguistic system

What is a language?• Set of texts?• A system underlying texts?• A set or system of processes?• A propensity for learning to speak? Language vs. dialect vs. idiolect Conclusion: the term language is too

abstract to allow for a clear definition

Alternative: The linguistic system

Easily definable (in contrast to language) Must be defined in terms of the individual The linguistic system of an individual

• An information system• A neurological system, since it is contained in the brain• Hence, a physical system• Varies from one individual to the next• Can include multiple registers, dialects, languages

Linguistic science and neuroscience

Adopting the view that a linguistic system is a neurological system allows us to build bridges• From neuroscience to linguistic science

We can use the findings of Mountcastle And findings from neuroanatomy, aphasiology, etc.

• From linguistic science to neuroscience We can provide hypotheses of how the brain works

more generally for information processing

Starting from purely linguistic evidence

The structure of the linguistic system of an individual The system is able to operate

• Hence, a fundamental requirement for any theory of linguistic structure: Operational plausibility

• For example, it is obvious that the system can process, e.g., words Comprehension: from speech sounds to meaning Production: from meaning to speech sounds Learning: new words can be learned

Operational Plausibility

To understand how language operates, we need to have the linguistic information represented in such a way that it can be used for speaking and understanding

(A “competence model” that is not competence to perform is unrealistic)

Morpheme as item and its phonemic representation

boy

b - o - y

Symbols?Objects?

What are these?

Morpheme and phoneme as objectsHow related?

Morpheme

Phoneme

Problem: the morpheme “has” a meaning; the phoneme doesn’t

Alternative view: morpheme and phoneme on different levels

boy As a morpheme, it is just one unit

Three phonemes, in sequence

b o y

This “morphemic unit” also has meaning and grammatical function

BOY Noun

b o y

boy Morpheme

The morpheme as purely relational

BOY Noun

b o y

We can remove the symbol with no loss of information. Therefore, it is a connection, not an object

boy

Another way of looking at it

BOY Noun

b o y

boy

Another way of looking at it

BOY Noun

b o y

A closer look at the segments

b

boy

y

Phonologicalfeatures

o The phonological segments also are just locations in the network – not objects

(Bob) (toy)

Structure vs. labels

BOY Noun

b o y

boy Just labels – not part of the structure

Objection I

If there are no symbols, how does the system distinguish this morpheme from others?

Answer: Other morphemes necessarily have different connections

Another node with the same connections would be another (redundant) representation of the same morpheme

Objection II

If there are no symbols, how does the system know which morpheme it is?

Answer: If there were symbols, what would read them? Miniature eyes inside the brain?

Objects in the mind?

When the relationships are fully identified, the objects as such disappear, since they have no existence apart from those relationships

The postulation of objects as some- thing different from the terms of relationships is a superfluous axiom and consequently a metaphysical hypothesis from which linguistic science will have to be freed.

Louis Hjelmslev (1943/61)

Quotation from Hjelmslev

Upward and Downward

Expression (phonetic or graphic) is at the bottom

Therefore, downward is toward expression

Upward is toward meaning (or other function) – more abstract

network

meaning

expression

Neurological interpretation of up/down

At the bottom are the interfaces to the world outside the brain:• Sense organs on the input side• Muscles on the output side

‘Up’ is more abstract

Syntax is also purely relational:Example: The Actor-Goal Construcion

CLAUSE DO-SMTHG

Vt Nom

Material process (type 2)

Syntactic function

Semantic function

Variable expression

Syntax is also purely relational:Example: The Actor-Goal Construcion

CLAUSE DO-SMTHG

Vt Nom

Material process (type 2)

Syntactic function

Semantic function

For example, eat an apple

Narrow and abstract network notation

Narrow notation Closer to neurological structure Nodes represent cortical columns Links represent neural fibers (or

bundles of fibers) Uni-directional

Abstract notation Nodes show type of relationship (OR,

AND) Easier for representing linguistic

relationships Bidirectional Not as close to neurological

structure

eat apple

eat apple

eat apple

eat apple

Narrow and abstract network notation

Narrow notation Closer to neurological structure Nodes represent cortical columns Links represent neural fibers (or

bundles of fibers) Uni-directional

Abstract notation Nodes show type of relationship (OR,

AND) Easier for representing linguistic

relationships Bidirectional Not as close to neurological

structure

pin

pi- -in

pin

pi- -in

More on the two network notations

The lines and nodes of the abstract notation represent abbreviations – hence the designation ‘abstract’

Compare the representation of a divided highway on a highway map• In a more compact notation it is

shown as a single line• In a narrow notation it is shown as

two parallel lines of opposite direction

Abstract and narrow notation

Having two notations available is like being able to draw a highway map to different scales

Narrow notation shows greater detail and greater precision

Narrow notation is closer to the actual neural structures

www.ruf.rice.edu/~lngbrain/shipman

Syntax: Linked constructions

CL

Nom

DO--SMTHG

Vt Nom

Material process (type 2)

TOPIC-COMMENT

Add another type of process

CL

DO-TO-SMTHG

THING-DESCR

BE-SMTHG

be

Nom

Vt

AdjLoc

More of the English Clause

DO-TO-SMTHGBE-SMTHG

be Vt

Vi

to

<V>-ing

CL

Subj Pred

Conc

Past Mod

Predicator

FINITE

The downward ordered or

a b

marked choice unmarked choice (a.k.a. default )

The unmarked choice is the line that goes right through. The marked choice is off to the side – either side

The downward ordered or

a b

unmarked choice marked choice(a.k.a. default )

The unmarked choice is the one that goes right through. The marked choice is off to the side – either side

OptionalitySometimes the unmarked choice is nothing

b

unmarked choice marked choice

In other words, the marked choice is an optional constituent

Relations all the way

Claim: all of linguistic structure is relational

It’s not relationships among linguistic items; it is relations to other relations to other relations, all the way to the top – at one end – and to the bottom – at the other

In that case the linguistic system is a network of interconnected nodes

Relationships all the way to..What is at the bottom?

Introductory view: it is phonetics In the system of the speaker, we have

relational network structure all the way down to the points at which muscles of the speech-producing mechanism are activated• At that interface we leave the purely relational

system and send activation to a different kind of physical system

For the hearer, the bottom is the cochlea, which receives activation from the sound waves of the speech hitting the ear

Relational networks and operational plausibility

Language users are able to use their languages. Such operation takes the form of activation of

lines and nodes The nodes can be defined on the basis of how

they treat incoming activation

Lines and Nodes in Abstract andNarrow Network Notation

As each line of abstract notation is bidirectional – it can be analyzed into a pair of one-way lines

Likewise, the simple nodes of abstract notation can be analyzed as pairs of one-way nodes

Two different network notations

Narrow notation

ab

a b

b

a b

Abstract notation Bidirectional

ab

a b f

Upward Downward

Example: A syllable and its demisyllables:narrow notation, upward direction

kin

ki- -in

Node for syllable

Nodes for demisyllables

Auditory features

Local Representation: kin(narrow notation, upward direction)

ki- -is -in shi-

kin shin kiss

This node is unique to kin

The Two Directions

1

2

ww

The Two Directions

ww

Two Questions:

1. Are they really next to each other?

2. How do they “communicate” with each other?

1

2

Separate but in touch

ww

1

2

Down UpIn phonology, we know from aphasiology and neuroscience that they are in different parts of the cerebral cortex

Phonological nodes in the cortex

ww

1

2

Arcuate fasciculus

Frontal lobe

Temporal lobe

The ‘Wait’ Element

wKeeps the activation alive

A B

Activation continues to B after A has been activated

Downward AND, downward direction

a b

Structure of the ‘Wait’ Element

W

1

2

www.ruf.rice.edu/~lngbrain/neel

Paradigmatic contrast: Competition

a b2 2

For example, /p/ vs. /k/

A structural detail not shown in abstract notation

Paradigmatic contrast: Competition

a b

a

b

Paradigmatic contrast: Competition

a b2 2

a

b

Levels of precision in network notation:How related?

They operate at different levels of precision Compare chemistry and physics

• Chemistry for molecules• Physics for atoms

Both are valuable for their purposes

Levels of precision

(E.g.) Systemic networks (Halliday) Abstract relational network notation Narrow relational network notation

Three levels of precision

a b2 2

a

b

Systemic Relational Networks Networks

Abstract Narrow (downward)

Levels of Precision

Advantages of description at a level of greater precision:• Greater precision• Shows relationships to other areas

Disadvantages of description at a level of greater precision:• More difficult to accomplish

Therefore, can’t cover as much ground• More difficult for consumer to grasp

Too many trees, not enough forest

Different Levels of Precision: The Study of Living Beings

Systems Biology Cellular Biology Molecular Biology Chemistry Physics

Levels of precision

Systemic networks (Halliday) Abstract relational network notation Narrow relational network notation Cortical columns and neural fibers Neurons, axons, dendrites, neurotransmitters Intraneural structures

• Pre-/post-synaptic terminals• Microtubules• Ion channels• Etc.

Levels of precision

Informal functional descriptions Semi-formal functional descriptions Systemic networks Abstract relational network notation Narrow relational network notation Cortical columns and neural fibers Neurons, axons, dendrites Intraneural structures and processes

Precision vis-à-vis variability

Description at a level of greater precision encourages observation of variability

At the level of the forest, we are aware of the trees, but we tend to overlook the differences among them

At the level of the trees we clearly see the differences among them

But describing the forest at the level of detail used in describing trees would be very cumbersome

At the level of the trees we tend to overlook the differences among the leaves

At the level of the leaves we tend to overlook the differences among their component cells

Linguistic examples

At the cognitive level we clearly see that every person’s linguistic system is different from that of everyone else

We also see variation within the single person’s system from day to day

At the level of narrow notation we can treat • Variation in connection strengths• Variation in threshold strength• Variation in levels of activation

We are thus able to explain• prototypicality phenomena• learning• etc.

More linguistic evidence for network structure: Complex lexemes

m r s i l e s

MERCILESS MERCY -LESS concepts*

phonemes*

* Actually, the diagram shows just labels for cardinal nodes

Complex lexemes

b o w l f u l

BOWLFUL BOWL -FUL concepts

phonemes

Question: do we get representations for all words?

Rephrase the question:• Do we get cardinal nodes for all words?

Answer: • No – only for those that have been learned• i.e., for words that have occurred often enough to get

their own distinctive representations Words and phases that have been learned as units:

• merciless, hamburger, unfinished, underprivileged• Rice University, after dinner, over my dead body

Words that most people have not learned as units:• undeconstructable, overprivileged

Shadow meanings

hotdog• Shadow meaning: “hot dog”• Not a hot dog, but:

It is typically hot Has the body shape of a dachshund

zhongguo “China”• Shadow meaning: “middle kingdom”

zhong “middle” guo “kingdom”

hotdog

HOTHOTDOG

DOG

hot dog

ZhongGuo

MIDDLE

CHINAKINGDOM

zhong guo

Alternative analyses

hamburger —ham - burger or hamburg - er ?

Which is the correct analysis?

hamburger as ham - burger

hamburger

burger cheese

burg-er

ham

hamburger as hamburg - er

hamburger

burg-er

ham

Hamburg

Coexisting Parallel Structures

hamburger

burgercheese

burg-er

ham

Hamburg

N.B. : Heavier lines for more entrenched

The network allows the two analyses to exist together and to operate in parallel (Lamb 1999: 233ff)

Degrees of entrenchment

Accounted for as varying strengths of connections Similarly, the gradualness of learning is accounted for by

gradual strengthening of connections with repeated use

Variation in Connection Strength

Connections get stronger with use• Every time the linguistic system is used,

it changes Can be indicated roughly by

• Thickness of connecting lines in diagrams or by• Little numbers written next to lines

The representation of words:Functional webs and cardinal nodes

hamburger

burger cheese

burg-er

ham

Hamburg

(label for) cardinal node for hamburger

Functional web for hamburger

Operations in relational networks

Relational networks are dynamic Activation moves along lines and through nodes The difference between AND and OR

• The AND requires activation on both or all incoming lines

• The OR requires activation on just one line www.ruf.rice.edu/~lngbrain/struan

Denotation and Connotation

Alternative statements• The acid corroded the pipe• The acid attacked the pipe• The acid ate the pipe

Same denotation, different connotations How to account for the difference in connotation?

Polysemy

Lexeme

Meanings

Polysemy: e.g., attack

attack

Meanings

Denotation and connotation

attack

Connotation

The denotation in this context

CORRODE

The acid attacked the pipe

Denotation and connotation

Lexeme

Connotation

The denotation in this context

Denotation and connotationBroadcasting and integration

Lexeme

Broadcasting

Integration

The pun: Both meanings supported by context

A talking duck goes into a bar, orders a drink, and says, “Put it on my bill”.

bill

BILL-1 BILL-2

More Linguistic Evidence: Recurring semantic components

DIE as a component/feature of the meanings of

diekillmurderassassinateterminally illwitheretc.

How do you describe the situation without using network structure?

die kill murder assassinate

DIE DIE DIE DIE CAUSE CAUSE CAUSE

HUMAN PAT. HUMAN PAT. POLITICALLY IMPORTANT

(etc., etc.)But isn’t it all the same element DIE?

With network

DIE

KILL

CAUSE

MURDER

PATIENTHUMAN

PATIENTPOLITICALLYIMPORTANT

ASSASSINATE

die kill murderassassinate

Quantitative evidence:How many columns in Wernicke’s area?

Size of area: about 20 sq cm (3 x 7)• Temporal plane• Superior temporal gyrus• Superior temporal sulcus

Minicolumns per sq cm: 140,000 Maxicolumns per sq cm: 1,400 Minicolumns in Wernicke’s area: 2,800,000 Maxicolumns in Wernicke’s area: 28,000 Functional columns: say, about 280,000

Quantitative evidence:Capacity of Wernicke’s area

Requirement• About 50,000 nodes for native language• Thousands more for each additional language

Capacity• Size of area: about 20 sq cm (3 cm x 7 cm)• Minicolumns in Wernicke’s area: 2,800,000• Maxicolumns in Wernicke’s area: 28,000• Hypothetical functional columns: 280,000

At avg 10 minicolumns per functional column, 10 functional columns per maxicolumn

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