Evolution of Brain and Language II Linguistic Structure The system that had to evolve to make it possible for us to speak

Course website www.mind-study.org/evolution

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

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?

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

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

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

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”

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

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

Relationship of toy and boy to their phonemic expressions

Relationship of toy and boy to their phonemic expressions

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

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

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

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

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

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

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

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

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

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

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

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

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

Some nodes of the cortical net for fork PP P V PA

Some nodes of the cortical net for fork PP P V PA

A word network with two subnets partly shown C 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

Ignition of a word network from visual input C PR Art PA V M

Ignition of a word network from visual input C PR Art PA V M

Ignition of a word network from visual input C PR Art PA V M

Ignition of a word network from visual input C PR Art PA V M

Ignition of a word network from visual input C PR Art PA V M

Ignition of a word network from visual input C PR Art PA V M

Ignition of a word network from visual input C PR Art PA V M

Ignition of a word network from visual input C PR Art PA V M

Ignition of a word network from visual input C PR Art PA V M

Ignition of a word network from visual input C PR Art PA V M

Ignition of a word network from visual input C PR Art PA V M

Ignition of a word network from visual input C PR Art PA V M

Ignition of a word network from visual input C PR Art PA V M

Ignition of a word network from visual input C PR Art PA V M

Speaking as a response to ignition of a net C PR Art PA V M

Speaking as a response to ignition of a net C PR Art PA V M

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

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

Timing of neural pathway travel Neuron-to-neuron time in a chain (rough estimate) Neuron 1 fires (say, @ 100 Hz) Time for activation to reach ends of axon 10 mm @ 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

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

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

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

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

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

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

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.

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

Can we justify the abundance hypothesis? Number of neurons in cortex (avg.): ca. 27.4 billion Neurons beneath 1 mm2 of surface: ca. 113,000 Neurons beneath 1 cm2 of surface: ca. 11,300,000

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

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)

Strengthening of Connections: Learning B 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

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

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

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

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

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 70-100 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

Microscopic views of cortex Different stains show different features

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)

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