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Brain Function I: Evidence from Linguistics

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1 Brain Function I: Evidence from Linguistics
Brain, Mind, and Belief: The Quest for Truth Brain Function I: Evidence from Linguistics The Neurological Basis of Language and Thought Language, for all its seeming complexity, is more amenable to analysis than other cognitive structures, so that investigation of language is one of the best ways to proceed to an understanding of human mind and nature. Tim Pulju, 1990

2 Agenda for Today The Problem: How does the brain work?
History of the study of brain function Common errors to be avoided Tool-driven inquiry The misapplied metaphor Help from the study of linguistic structure Claim: As language works in the brain, so the brain works in general Therefore, If we can understand how language works, we will know how the brain works Relational networks Cortical columns of neurons

3 History of the study of brain function I
Early investigators in the 19th century came up with the idea of locationism: Local areas of the cortex have specific functions Franz Joseph Gall ( ) promoted the idea His followers took it too far As a result, the idea of locationism was widely discredited

4 History of the study of brain function II
Locationism: Local areas of the cortex have specific functions Paul Pierre Broca ( ) Stroke patient with impaired speech Autopsy after patient’s death Damage in lower left frontal lobe Area now known as Broca’s area Responsible for articulation of speech

5 History of the study of brain function III
The origin of Connectionism Like locationism but more sophisticated: Local areas of the cortex have highly specific simple functions, and complex functions are carried out by multiple interconnected areas Carl Wernicke ( ) Stroke patient unable to comprehend speech Damage in upper left temporal lobe Area now known as Wernicke’s area Responsible for speech comprehension

6 Connectionism Connectionism includes a version of locationism
But is more sophisticated Each local area performs a very specific simple function Complex functions require multiple local areas acting together They can act together because they are interconnected CONNECTIONS RULE!

7 Two basic language areas
Primary Somato- sensory Area Leg Primary Motor Area Trunk Arm Hand Wernicke’s area Fingers Mouth Phonological Production Phonological Recognition Broca’s area Primary Auditory Area Primary Visual Area

8 Arcuate Fasciculus (from langbrain website)
Connects Wernicke’s area to Broca’s area

9 History of the study of brain function IV
The decline and revival of Connectionism As with Gall, followers of Wernicke were too speculative and went too far, and the whole idea was discredited for several decades Finally revived in the 1960’s by Norman Geschwind ( ) Now widely accepted by neurologists (but criticized by some psychologists)

10 History of the study of brain function V
Two major methods of investigation Lesion studies E.g., Broca and Wernicke and Geschwind If area A is damaged and function F is impaired, then A must have function F (or at least contribute to F) Functional Brain Imaging A recent innovation Made possible by technological advances Now very widely used Location-based but sometimes without the sophistication of connectionism

11 Functional Brain Imaging
Electro-Encephalography (EEG) Excellent temporal resolution Very poor spatial resolution Positron Emission Tomography (PET) Poor spatial resolution Very poor temporal resolution Functional Magnetic Resonance Imaging (fMRI) Spatial resolution better than PET Temporal resolution a little better than PET Magneto-Encephalography (MEG) Spatial resolution not so good

12 Positron emission tomography (PET)
PET shows areas of increased cortical metabolism Spatial resolution: 5-10 mm How good is that? Under one sq mm of cortical surface, 130,000 neurons Temporal resolution: “…on the order of minutes…” (A. Papanicolaou, Fundamentals of Functional Brain Imaging (1998), p. 14)

13 Functional Magnetic Resonance Imaging (fMRI)
Measures the amount of oxygenated blood supplied to different areas of the brain Common abbreviation: rCBF (regional cerebral blood flow) When a group of neurons increases its signaling rate, its metabolic rate increases

14 An fMRI example Areas of the brain used in working memory

15 Properties of fMRI Temporal resolution: Not very good
Image reflects the increase in oxygenated blood that occurs 5 to 8 seconds after the neurons fire Spatial resolution: Better than PET But it is unclear whether the imaged area is precisely the area involved in the activity The flow of oxygenated blood into the depleted area may also flow into neighboring vessels in areas where neural firing did not occur

16 Magnetoencephalography (MEG)
MEG (MagnetoEncephaloGraphy) measures the magnetic field around the head Magnetoencephalography magnetic brain picture production of

17 How MEG works An electric current is always accompanied by a magnetic field perpendicular to its direction MEG records the magnetic flux or the magnetic fields that arise from electric currents in neurons Magnetic flux lines are not distorted as they pass through the brain tissue because biological tissues offer practically no resistance to them Therefore, MEG is more accurate than EEG

18 Magnetic flux from source currents
Magnetometer Source current

19 Recording of Magnetic Signals

20 Temporal Resolution of MEG
Excellent – unlike fMRI and PET Therefore, it is possible to discern the temporal order of activation of cortical areas MEG has potential to detect the activation of several brain regions as they become active from moment to moment during a complex function such as recognition

21 A major challenge of MEG
The cortex is a parallel processor Hundreds or thousands of dipoles can be active simultaneously Multiple dipoles make comprehensive inverse dipole modeling virtually impossible Hence, compromises are necessary Sample larger time spans (up to 500 ms) Sample larger areas (up to several sq cm)

22 Some MEG results: Speech recognition
Hemispheric Asymmetry Wernicke's Area

23 Wernicke’s area in Spanish-English bilinguals
From MEG lab, UT Houston

24 Spatial Resolution How accurately is location determined? EEG: Poor
PET: Fair – 4-5 mm fMRI: Fair – 4-5 mm MEG: Fairly good – 3-4 mm or less Under good conditions How good are these figures? under 1 sq mm of cortical surface,140,000 neurons

25 Temporal resolution Temporal resolution
Key neural events can occur within 5 ms Terrible: PET 40 seconds and up Pretty bad: fMRI 10 seconds or more Excellent: MEG and EEG Instantaneous Theoretically it is possible to do ms by ms tracking, to follow time course of activation But such tracking is usually difficult or impossible The inverse problem Too many dipoles at each point in time

26 Sensitivity of Imaging Methods
All of the methods have limited sensitivity MEG 10,000 dendrites in close proximity have to be active to detect signal PET and fMRI Similar limitations Any activation that involves fewer numbers goes undetected

27 Faulty thinking in neuroscience I: Tool-driven inquiry
Tool-driven inquiry: letting the available tools shape the investigation Like looking for the lost car keys under the street light instead of where they got lost The available tools: Brain imaging machines What they are good for: determining locations of brain activity Therefore, what do they investigate? Locations of brain activity The question being asked: Where? The questions not being asked: What?, How? What is going on? How does the brain work?

28 More on the lack of interest in what/how
There are no available machines for investigating the what/how question Experiments have not been devised for investigating the what/how question It is necessary to rely on thinking Scientists believe that doing science is conducting experiments and using high-tech machinery Thinking is done by theoreticians Akin to philosophers and poets

29 A mitigating circumstance?
Many have not realized that the what/how question is important They may assume it is already known: The brain is assumed to work like a computer A symbol-manipulating device This assumption is unwarranted

30 Faulty thinking in neuroscience II: The misapplied metaphor
The brain is assumed to work like a computer This assumption is unwarranted The computer is a symbol-manipulating device Not a connectionist system An example of faulty thinking: The misapplied metaphor The brain works by means of connectivity and operations upon its connectivity

31 The Cortex is a Network Entirely different structure than that of computers
Connectivity as key property of brain structure Symbol-manipulation is the key property of computers The cortex operates by means of connections Transmission of activation along neural pathways Changes in connection strengths

32 Computers and Brains: Different Structures, Different Skills
Flexible, fault tolerant Slow processing Association Intuition Adaptability, plasticity Self-driven activity Unpredictable Self-driven learning Computers Exact, literal Rapid calculation Rapid sorting Rapid searching Faultless memory Do what they are told Predictable

33 Things that brains but not computers can do
Acquire information to varying degrees “Entrenchment” How does it work? Variable connection strength Connections get stronger with repeated use Perform at varying skill levels Degrees of alertness, attentiveness Variation in reaction time Mechanisms: Global neurotransmitters Variation in blood flow Variation in available nutrients Presence or absence of fatigue Presence or absence of intoxication

34 How to study the what/how question
You have to think harder Also, we can make use of findings from structural linguistics Language as the key to unlock mysteries of the mind If cortical structures for language are like those for other high-level skills Then if we figure out language, we also have the answer to how other high-level intellectual processing works

35 Thinking harder Avoid metaphorical thinking
The brain is not a computer Not like a human being with paper & pencil & books In fact it is not like anything else It is itself: the brain

36 How does your brain tell your fingers what to do?
Question to daughter (age 6): How does your brain tell your finger to hit that key on the piano? Sarah: Well my brain writes a little note and sends it down to my finger, … What really happens? Neurons in the motor cortex send activation (nerve impulses) down (through subcortical structures and the spinal cord) to neurons that activate the muscles that make my finger move

37 Auditory Imagery Auditory images of words, music, etc.
We can hear things in our heads What is an auditory image? What does it consist of? Sound? There is no air inside the head to vibrate What hears it? There are no little ears inside the head

38 Visual Imagery Visual images of people, buildings, etc.
What is a visual image? What does it consist of? Is it a little picture? If so, where are the eyes to see it? What is it drawn on? Where is the visual perception system to interpret it? If not, what?

39 Vision When you see something.. A picture on your retina?
Something in your brain looks at it? Are there a couple of little eyes inside? A picture somewhere inside your brain? Same problems: no eyes inside And if there were, they would have to be supported by a visual perception system

40 Compare the TV set Does it have little people inside?
Similarly, no pictures inside the brain No sounds inside the brain No words or other symbols inside the brain

41 How vision really works
There is no picture on the retina, just neurons that get activated by light Some of them get activated by color The visual perception system learns during early childhood to integrate configurations of these little dots into larger units Next higher level: larger configurations Many levels up, recognition of objects The brain goes through a long process of learning, to build these many levels More on this next week!

42 The Nature of Language Some history Louis Hjelmslev
Prolegomena to a Theory of Language (1943/60) Linguistic structure is a system of relationships ”The postulation of objects as something different from the terms of relationships is a superfluous axiom and con-sequently a metaphysical hypothesis from which linguistic science will have to be freed.”

43 Understanding linguistic units as purely relational I
dog d - o - g Symbols? Objects?

44 Understanding linguistic units as purely relational II
Seems to be one unit Three phonemes (or graphemes), in sequence dog d o g

45 Understanding linguistic units as purely relational III
Grammatical properties DOG Noun The meaning of dog – a concept dog The object we are considering d o g

46 Understanding linguistic units as purely relational IV
DOG Noun dog d o g

47 Understanding linguistic units as purely relational V
DOG Noun We can remove the symbol with no loss of information. Therefore, it is a connection, not an object d o g

48 Another way of looking at it
DOG Noun dog d o g

49 Another way of looking at it
DOG Noun d o g

50 The phonological (or graphemic) segments
DOG Noun What about these segments? Are they objects? d o g

51 The phonological (or graphemic) segments
What about these segments? Are they objects? d o g /d/ as a phoneme has components: Articulation: closure of mouth (so also /g/) Position: Tongue tip against alveolar ridge Voiced (compare /t/) (Also auditory components: more complex)

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

53 Relations all the way Perhaps 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

54 What is at the bottom? 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

55 What is at the top? Somehow at the top there must be meaning

56 What are meanings? In the Mind For example, DOG The World Outside DOG
C DOG Perceptual properties of dogs All those dogs out there and their properties

57 What are meanings? In the Mind For example, DOG The World Outside
Conceptual properties of dogs Perceptual properties of dogs All those dogs out there and their properties

58 What are meanings? In the Mind For example, DOG The World Outside
Conceptual properties of dogs Perceptual properties of dogs All those dogs out there and their properties Also relational network structure

59 The concept DOG We know what a dog looks like
A visual subnetwork, in occipital lobe We know what its bark sounds like An auditory subnetwork, in temporal lobe We know what its fur feels like A somatosensory subnetwork, in parietal lobe All of the above.. constitute perceptual information are subnetworks with many nodes each Are interconnected into a larger network

60 The concept of DOG as a network
A – Auditory C – Conceptual M – Memories P – Phonological T – Tactile V - Visual T P A C V M Each node in this diagram connects to a subnetwork of properties

61 Objects in the mind? When the relationships are fully identified, the objects as such disappear, as 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)

62 How the mind operates Example: language
People are able to use their languages Speaking Writing Comprehension Such operation takes the form of activation of lines and nodes Activation travels from node to node Along connecting lines Compare the highway system A network Operation: vehicles move along the roads

63 Two different network notations
ab Abstract notation Bidirectional a b Upward Downward ab b a b f a b Narrow notation

64 Narrow relational network notation
Represents network structures in greater detail internal structures of the lines and nodes of the more abstract notation Closer to neurological structure Each node represents a bundle of neurons Links represent neural fibers (or bundles of fibers)

65 Abstract and narrow network notation
The lines and nodes of the abstract notation are abbreviations for more complex structures Compare the representation of a divided highway on a highway map In a more abstract notation it is shown as a single line In a narrow notation it is shown as two parallel lines of opposite direction

66 AND vs. OR 27 AND OR twenty seven 12 twelve dozen

67 AND vs. OR: Internal Structure (narrow notation)
2 1

68 Thresholds in Narrow Notation
OR AND 1 2 3 4 – You can have intermediate degrees, between AND and OR – The AND/OR distinction was a simplification anyway — doesn’t always work!

69 Levels of precision: Add another
Abstract relational network notation Narrow relational network notation Neural structures

70 Narrow RN notation and neural structures
Question: Are relational networks related in any way to neural networks? We can find out Relational networks were devised as a means of accounting for linguistic structure Their properties depend on properties of language Evidence for them comes from language, not from the brain Narrow RN notation can be viewed as a set of hypotheses about brain structure and function Properties of narrow RN notation can be tested for neurological plausibility

71 Some properties of narrow RN notation
Lines have direction (they are one-way) But they tend to come in pairs of opposite direction (“upward” and “downward”) Connections are either excitatory or inhibitory Nerve fibers carry activation in just one direction Cortico-cortical connections are generally reciprocal Connections are either excitatory or inhibitory (from different types of neurons, with two different neurotransmitters)

72 More properties as hypotheses
Neurons have different thresholds of activation Inhibitory connections are of two kinds (Type 2: “axo-axonal”) All are verified Nodes have differing thresholds of activation Inhibitory connections are of two kinds Additional properties – (too technical for this presentation) Type 1 Type 2

73 The node of narrow RN notation vis-à-vis neural structures
The node (of narrow RN notation) corresponds (not to a single neuron but) to a bundle of neurons The cortical column A column consists of neurons stacked on top of one another More on this next week!

74 T h a t ‘ s i t f o r t o d a y !

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