1. Evolution of Brain and LanguageII
Linguistic Structure
The system that had to evolve to
make it possible for us to speak

2. Course website

3. Outline of Topics for the Course
Introduction
How evolution works
The human brain
2. Linguistic structure: What had to evolve
3. Human evolution from 5 million BP to 1 million BP
How/why the brain grew so large
4. Early stages of language evolution:
      From 3,000,000 BP to 100,000 BP
5. Later stages: From 100,000 BP to 1,000 BP
Language spread and diversification
The Indo-European family and other families
6. The last few hundred years
The exponential progress of evolution

4. 2 – Linguistic Structure
We need to know what it is that had to evolve
What is linguistic structure?
How is it represented in our brains?

5. Major anatomical-functional dichotomies
Left hemisphere vs. Right hemisphere
Left
Analytical, linguistic, digital
Maintains existing beliefs
Right
Metaphorical, artistic, analog
Open to new data and ideas
Front (anterior) vs. Back (posterior)
Front
Action and planning of action
Process oriented
Back
Perception
Perceptual integration
Object oriented

6. Left hemisphere vs. right hemisphere
Language
Analytical thinking
Exact
Digital
Heightened contrast
Proof
Right Hemisphere
Art, music
Holistic thinking
Metaphorical
Analog
Fuzzy boundaries
Hunches, intuition

7. Cerebral dominance for language
Linguistic abilities are subserved by the left hemisphere in about 97% of people
99% of right-handed people
A majority of left-handers
But this is just a first approximation

8. How does all this complex structure work?
Representation of information in the brain is . .
quite unlike that in computers
in fact unlike anything else we have ever known
Extraordinarily complex
The cortex has billions of neurons
Trillions of interconnections
How can we make sense of it?
Brain function is revealed by linguistic structure
“Language is the window of the mind”

9. The linguistic system of the brain
It does not contain words
The brain is not a system that stores words
Rather, it is the system which
Produces words
Comprehends words (if incompletely)
Likewise, it does not contain rules (e.g., for syntax)
Rules would be linguistic products

10. Relationship of toy and boy to their phonemic expressions
Structure that produces
or recognizes /boy/
-oy t- b- a.

11. Relationship of toy and boy to their phonemic expressions

12. Relationship of toy and boy to their phonemic expressions

13. –oy: further details
toy
boy
toy
boy
-oy
-oy t- b- b- t- o -y

14. Add phonological components
toy
boy
-oy t- b- o -y Vl Ap Cl Lb Ba Vo Sv Fr

15. Alternative catalysis
toy
boy
toy
boy t- o b- -y
-oy t- Vo Fr b- Vl Ap Cl Lb Ba Sv o -y Vl Ap Cl Lb Ba Vo Sv Fr

16. Irregular past tense
PAST
TAKE
take
took -d t- -y u e -k

17. Add complex lexemes -d t- e -y u -k take over under took UNDERTAKE
OVERTAKE
TAKE
PAST
take
over
under
took -d t- e -y u -k

18. Some complex noun lexemes
NEW-AGE-MUSIC
HORSE-BACK-RIDE
RIDE
NEW-AGE
MUSIC
HORSE-BACK
NEW
AGE
HORSE
BACK
music
ride
new
age
horse
back

19. Synonymy and Polysemy synonymy polysemy person human man being
HUMAN-BEING
MALE
synonymy
polysemy
person
human
man
being

20. Syntactic Constructions: Variable complex lexemes
CONNECT-THE-DOTS
DO-SMTHG-TO-SMTHG
TRANSITIVE VERB
THING
connect
the
dots

21. More Syntax DO-SMTHG-TO-SMTHG BE-SMTHG INTRANS VERB TRANS VERB BE VERB
LOC
QUALITY
THING

22. Statements and Yes-No Questions
ASK
DECLARE
Examples:
DECLARE
Johnny can swim
ASK
Can Johnny swim?
PRED
SUBJ
FINITE

23. What are meanings? For example, DOG Conceptual properties of dogs
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

24. 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

25. The concept 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

26. Some nodes of the cortical net for forkPP P V PA

27. Some nodes of the cortical net for forkPP P V PA

28. A word network with two subnets partly shownC PP PR PA V M
C – Cardinal concept node
M – Memories
PA – Primary auditory
PP – Phonological production
PR – Phonological recognition
T – Tactile
V – Visual
Visual features

29. Ignition of a word network from visual inputC PR
Art PA V M

30. Ignition of a word network from visual inputC PR
Art PA V M

31. Ignition of a word network from visual inputC PR
Art PA V M

32. Ignition of a word network from visual inputC PR
Art PA V M

33. Ignition of a word network from visual inputC PR
Art PA V M

34. Ignition of a word network from visual inputC PR
Art PA V M

35. Ignition of a word network from visual inputC PR
Art PA V M

36. Ignition of a word network from visual inputC PR
Art PA V M

37. Ignition of a word network from visual inputC PR
Art PA V M

38. Ignition of a word network from visual inputC PR
Art PA V M

39. Ignition of a word network from visual inputC PR
Art PA V M

40. Ignition of a word network from visual inputC PR
Art PA V M

41. Ignition of a word network from visual inputC PR
Art PA V M

42. Ignition of a word network from visual inputC PR
Art PA V M

43. Speaking as a response to ignition of a netC PR
Art PA V M

44. Speaking as a response to ignition of a netC PR
Art PA V M

45. Speaking as a response to ignition of a netC PR
Art PA V M
From here (via subcortical structures) to the muscles that control the organs of speech articulation

46. An MEG study from Max Planck Institute
Levelt, Praamstra, Meyer, Helenius & Salmelin, J.Cog.Neuroscience 1998

47. Timing of neural pathway travel
Neuron-to-neuron time in a chain (rough estimate)
Neuron 1 fires 100 Hz)
Time for activation to reach ends of axon
10 10 mm/ms = 1 ms
Time to activate post-synaptic receptor – 1 ms
Neuron 2
Activation reaches firing threshold – 4 ms (??)
Hence, overall neuron-to-neuron time – ca. 6 ms
Time required for spoken identification of picture
Subject is alert and attentive
Instructions: say what animal you see as soon as you see the picture
Picture of horse is shown to subject
Subject says “horse”
This process takes about 600 ms

48. Some types of Meaning Conceptual Perceptual Processes
Concrete—CAT, CUP
Abstract—CONFLICT, PEACE, ABILITY
Qualities/Properties—HELPFUL, SHY
Perceptual
Visual—BLUE, BRIGHT
Auditory—LOUD, MUSICAL
Tactile—ROUGH, SHARP
Emotional—SCARY, WARM
Processes
Material
Low-Level—STEP, HOLD, BLINK, SEE
Mid-Level—EAT, TALK, DANCE
High-Level—NEGOTIATE, EXPLORE, ENTERTAIN
Mental
THINK, REMEMBER, DECIDE
Relations
Locational—IN, ABOVE
Abstract—ABOUT, WITH-RESPECT-TO
Represented as nodes in different areas all over the cortical network

49. The Role of RH in semantics
Conceptual information, even for a single item, is complex
Therefore, widely distributed
A network
Occupies both hemispheres
RH information is more connotative
LH information more exact

50. Some connections of the concept node CUP
HANDLE
SHORT
CERAMIC
TAPERED
MADE-OF-GLASS
WITH-SAUCER
CUP
Different lines (connections) have different strengths

51. Abstract notation and narrow notation
Bidirectional
PAST
TAKE
take
took -d
Narrow Notation
Downward
Upward
PAST
TAKE
PAST
TAKE
take
take
took -d -d
took

52. Abstract notation and narrow notation
Bidirectional ab
a b
Narrow Notation ab
Downward
Upward ab
a b
a b

53. Two speech areas Primary Oral Somato- Primary Oral Sensory Area
Motor Area
Wernicke’s
area
Broca’s area
Primary Auditory
Area

54. Variation in strength of connections
Connections (shown in graphs by lines) differ from one another in strength. A stronger connection transmits more activation than a weaker one, if both are receiving the same amount of activation.
Nodes have threshold functions, so that outgoing activation varies with amount of incoming activation; and different nodes have different threshold functions.
A connection of a given strength can carry varying degrees of activation from one moment to the next, since each node is sending out varying degrees of activation in accordance with property 2.

55. Learning Network structures have to be built for language
And other kinds of information
But they are not actually built
The neural connections are already there
Those needed for a particular language are selected
At each of multiple layers of structure
“Neural Darwinism” (Edelman)
Prerequisite: Abundance of connections in the initial state
Then learning is a process of selection
Latent nections become dedicated
Latent connecting lines become established

56. Can we justify the abundance hypothesis?
Number of neurons in cortex (avg.): ca billion
Neurons beneath 1 mm2 of surface: ca. 113,000
Neurons beneath 1 cm2 of surface: ca. 11,300,000

57. Extent of neuronal fibers in the cortex
Estimated average 10 cm of fibers per neuron (conservative estimate)
Avg. cortex has about 27 billion neurons
27 billion X 10 cm = 2.7 billion meters
Or 2.7 million kilometers
– About 1.68 million miles
– Enough to encircle the world 68 times
– 7 times the distance to the moon

58. Number of synapses in cortex
40,000 synapses per neuron (4x104)
Times 27 billion neurons (27x109)
4x104 x 27x109 = 108x1013
or about 1.1x1015 (over 1 quadrillion)

59. Strengthening of Connections: LearningB B
If connections AC and BC are active at the same time, and if their joint activation is strong enough to activate C, they both get strengthened and the threshold of C is adjusted

60. Language networks and neural networks
For this question we have to consider the narrow notation
It is less abstract
Closer to the neural substrate
Comparing Relational Networks and Neural Networks
RN lines and nodes (narrow notation) are unidirectional
Nerve fibers and cell bodies are unidirectional
RN Connections are either excitatory or inhibitory
NN Connections are either excitatory or inhibitory
RN: Inhibitory connections are to either a node or another line
NN: Inhibitory connections are to either a cell body or another axon

61. Connections in RN and NN
Relational Networks
Neural Networks
Unidirectional
Excitatory or Inhibitory
Two types of inhibitory
Different strengths
Varying activation
Unidirectional
Excitatory or Inhibitory
Two types of inhibitory
Different strengths
Varying activation

62. Thresholds In RN, every node (of narrow notation) has a threshold
Outgoing activation is a function of incoming activation
In NN, every cell body has a threshold

63. But nodes of narrow RN do not correspond to neurons
Rather, they correspond to cortical columns of neurons
The cortical column in the functional unit of the cortex
A column consists of around 100 neurons
Vertically stacked on top of one another
In a cortical column . .
All pyramidal neurons have the same response properties
Redundancy
But different pyramidal neurons project to different other areas
There are also inhibitory neurons
They turn off activation as needed
In same column or in neighboring columns

64. The node of narrow RN notation vis-à-vis neural structures
The node 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
All neurons within a column act together
When a column is activated, all of its neurons are activated

65. Microscopic views of cortex
Different stains show different features

66. Some Properties of the (mini)column
ROUGHLY CYLINDRICAL IN SHAPE
CONTAINS CELL BODIES OF 70 TO 110 NEURONS (TYPICALLY 75-80)
ABOUT 70% ARE PYRAMIDAL,
THE REST INCLUDE
OTHER EXCITATORY NEURONS (spiny stellate)
SEVERAL KINDS OF INHIBITORY NEURONS
DIAMETER IS ABOUT 30–50 M IN, SLIGHTLY LARGER THAN THE DIAMETER OF A SINGLE PYRAMIDAL CELL BODY
TWO TO FIVE MM IN LENGTH, EXTENDS THRU THE SIX CORTICAL LAYERS
IF EXPANDED BY A FACTOR OF 100, THE DIMENSIONS CORRESPOND TO A TUBE WITH DIAMETER OF 1/8 INCH AND LENGTH OF ONE FOOT
THE ENTIRE THICKNESS OF THE CORTEX (THE GREY MATTER) IS ACCOUNTED FOR BY THE COLUMNS
(BASED ON MOUNTCASTLE 1998)

67. T h a t ’ s i t f o r t o d a y