1. Linguistic Evidence for Relational Networks Ling 411 – 15

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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)

8. Morpheme as item and its phonemic representation boy b - o - y Symbols? Objects? What are these?

9. Morpheme and phoneme as objects How related? Morpheme Phoneme Problem: the morpheme “has” a meaning; the phoneme doesn’t

10. Alternative view: morpheme and phoneme on different levels boy As a morpheme, it is just one unit Three phonemes, in sequence b o y

11. This “morphemic unit” also has meaning and grammatical function BOY Noun b o y Morpheme

12. 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

13. Another way of looking at it BOY Noun b o y

14. Another way of looking at it BOY Noun b o y

15. A closer look at the segments b boy y Phonological features o The phonological segments also are just locations in the network – not objects (Bob) (toy)

16. Structure vs. labels BOY Noun b o y boy Just labels – not part of the structure

17. 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

18. 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?

19. Objects in the mind? When the relationships are fully identified, the objects as such disappear, since they have no existence apart from those relationships

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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

27. 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

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

29. Syntax: Linked constructions CL Nom DO--SMTHG Vt Nom Material process (type 2) TOPIC-COMMENT

30. Add another type of process CL DO-TO-SMTHG THING-DESCR BE-SMTHG be Nom Vt Adj Loc

31. More of the English Clause DO-TO-SMTHG BE-SMTHG be Vt Vi to -ing CL Subj Pred Conc Past Mod Predicator FINITE

32. 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

33. 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

34. Optionality Sometimes the unmarked choice is nothing b unmarked choice marked choice In other words, the marked choice is an optional constituent

35. 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

36. 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

37. 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

38. Lines and Nodes in Abstract and Narrow 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

39. Two different network notations Narrow notation ab b Abstract notation  Bidirectional ab f Upward Downward

40. Example: A syllable and its demisyllables: narrow notation, upward direction kin ki- -in Node for syllable Nodes for demisyllables Auditory features

41. Local Representation: kin (narrow notation, upward direction) ki- -is -in shi- kin shin kiss This node is unique to kin

42. The Two Directions 1 2 w w

43. w w Two Questions: 1. Are they really next to each other? 2. How do they “communicate” with each other? 1 2

44. Separate but in touch w w 1 2 Down Up In phonology, we know from aphasiology and neuroscience that they are in different parts of the cerebral cortex

45. Phonological nodes in the cortex w w 1 2 Arcuate fasciculus Frontal lobe Temporal lobe

46. The ‘Wait’ Element w Keeps the activation alive AB Activation continues to B after A has been activated Downward AND, downward direction a b

47. Structure of the ‘Wait’ Element W 1 2 www.ruf.rice.edu/~lngbrain/neel

48. Paradigmatic contrast: Competition a b 2 2 For example, /p/ vs. /k/ A structural detail not shown in abstract notation

49. Paradigmatic contrast: Competition a b abab

50. Paradigmatic contrast: Competition a b 2 2 abab

51. 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

52. Levels of precision  (E.g.) Systemic networks (Halliday)  Abstract relational network notation  Narrow relational network notation

53. Three levels of precision a b 2 2 abab Systemic Relational Networks Networks Abstract Narrow (downward)

54. 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

55. Different Levels of Precision: The Study of Living Beings  Systems Biology  Cellular Biology  Molecular Biology  Chemistry  Physics

56. 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.

57. 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

58. 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

59. 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.

60. 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

61. Complex lexemes b o w l f u l BOWLFUL BOWL - FUL concepts phonemes

62. 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

63. 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”

64. hotdog HOT HOTDOG DOG hot dog

65. ZhongGuo MIDDLE CHINA KINGDOM zhong guo

66. Alternative analyses hamburger — ham - burger or hamburg - er ? Which is the correct analysis?

67. hamburger as ham - burger hamburger burger cheese burg -er ham

68. hamburger as hamburg - er hamburger burg -er ham Hamburg

69. Coexisting Parallel Structures hamburger burger cheese 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 )

70. 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

71. 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

72. 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

73. 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 www.ruf.rice.edu/~lngbrain/struan

74. 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?

75. Polysemy Lexeme Meanings

76. Polysemy: e.g., attack attack Meanings

77. Denotation and connotation attack Connotation The denotation in this context CORRODE The acid attacked the pipe

78. Denotation and connotation Lexeme Connotation The denotation in this context

79. Denotation and connotation Broadcasting and integration Lexeme Broadcasting Integration

80. 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

81. More Linguistic Evidence: Recurring semantic components DIE as a component/feature of the meanings of die kill murder assassinate terminally ill wither etc.

82. How do you describe the situation without using network structure? die kill murder assassinate DIE DIE DIE DIE CAUSE CAUSE HUMAN PAT. HUMAN PAT. POLITICALLY IMPORTANT (etc., etc.) But isn’t it all the same element DIE ?

83. With network DIE KILL CAUSE MURDER PATIENT HUMAN PATIENT POLITICALLY IMPORTANT ASSASSINATE diekillmurder assassinate

84. 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

85. 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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