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Feldman, Jerome A. 2006 From Molecule to

Metaphor Cambridge, MA:

Bradford MIT Books.

Short presentation by

Laura Janda and Tore Nesset

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The point of the book

• To promote linguistic models that correspond to what we know about how the brain functions– Ex: Embodied Construction Grammar, ECG

• Models based on rules do not correspond to what we know about the brain– Alternative: “neural theory of language”

• Overview of the place of linguistics in cognitive science

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Advantages of the book

• A well balanced discussion with a lot of information from various disciplines:– neurology, computer science, and linguistics

• Does not just sum up results, but also explains why they are important and the relationships between them, presents them as a story that has a plot

• Very clear and easy to read, with lots of concrete examples

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Disadvantages of the book

• Not a lot of news for people who are familiar with cognitive linguistics

• The book is often focused only on research at UC Berkeley– but it is true that many important ideas were born in

Berkeley• Searle’s ”Chinese room”• Fillmore’s frames• Lakoff’s metaphor theory• Goldberg’s construction grammar • Narayanan’s, Bergen’s, and Grady’s work with metaphors

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The structure of the book: From Amoebas to Human language

• Begins with single cells, then goes to the brain, mind, before describing increasingly complex linguistic structures

• Important questions:– What do we know about amoebas and how do they

categorize without rules?– What do we know about neurons and the brain and to

what extent can they be compared with a computer?– How can one build up linguistic models that take into

account how the brain works?– What is meaning based on, and why are humans

much more clever at using language than other beings?

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The structure of this talk: Main points

1. Category formation and modularity: Language in the brain

2. Key concepts from cognitive linguistics and the perspective of cognition

• Prototype• Image schema• Frame• Embodiment• Metaphor

3. Embodied Construction Grammar4. A question and some speculation to close with

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Category formation and modularity: Language in the

brainPART I

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Categorization• “Categorization occurs whenever a lot of data are

boiled down to a few values.” (s. 96)• Categorization is found in all living systems, not just

in language• Amoebas categorize: food vs. non-food, danger vs.

non-danger– This happens chemically, the outer membranes of the

amoeba react in different ways to food and non-food.– These reactions cause changes in the shape of the

amoeba.– It is possible to write rules for how the amoeba reacts, but

they don’t correspond to anything in the amoeba – it categorizes without such rules.

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More categorization

• Simple neural nets can categorize and react without rules– Knee-jerk reflex (just goes through the spine)

• It is possible to write a rule for it, but we know that the reflex is not stored in the body as a rule, it is just a result of how four neurons are connected to each other

• More complex systems, like dancing, can also be described as rules, but this does not mean that they exist in the brain as rules, nor that there is an autonomous ”dancing module”in the brain (p. 279-80)

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The brain• We are used to comparing the brain to a computer, but there are

many important differences– the brain has many more units than a computer – the brain’s units (neurons) work much more slowly (106) than the units in

a computer– the brian’s neurons are more connected to each other than the units in a

computer– when the brain is used, the connections between the neurons are

changed (“neurons that fire together, wire together”)– the brain is not autonomous – it is a part of the body and gets

information from the world and reacts to that information – the brain has ”mirror neurons” that fire when a person does something

or when a persons sees that someone is doing that something or when a person thinks about (simulates) doing that something

– mirror neurons give human beings a greater capacity to imitate – only humans can simulate non-actual situations and therefore

understand things from different perspectives (understand what another person is thinking)

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Linguists ignore what we know about the brain

• They claim that neurology is not sufficiently dveloped, so it is not necessary to take it into account– “neuroscience is not nearly developed enough to be taken

seriously” (Chomsky 2003)

• They assume that a grammar is a collection of formal rules• They are interested only in compentence, not in

performance (and thus they can ignore corpus data) • They assume that language is an autonomous module in

the brain• Thus they make themselves independent of other

disciplines (p. 273)• Feldman thinks this is irresponsible (p. 151)

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Trying to provide a balance

• Feldman doesn’t just criticize Chomsky and the generativists, he claims that all kinds of linguists are too focused on isolated grammatical problems without appreciating the role of meaning and language use (p. 281-2)

• Nature vs. Nurture: Formalists see only Nature, functionalists see only Nurture, but– it is a fact that the brain has a certain structure from the

beginning– it is a fact that the brain is plastic and is always changing

• That which genes specify is perhaps only a unique capacity to learn which is not further specialized for language (p. 272).

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What is learning?• Both Nature and Nurture are cumulative and

influence each other– Nature: genes are “expressed”, but all cells have the

same genes, and the parts of them that are activated change over time

– Nurture: Leaning isn’t just a matter of adding something to an already existing system; leaning changes the system itself

• Learning is a strengthening of connections between neurons.

• Learning creates categories – the world exists, but there is more than one way of categorizing it

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Key concepts

PART II

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Prototypes

• Categories can be organized around various types of prototypes:– Reference point (100, 1000, circa)– Scalar prototype (standard of measurement) – Typical member– Ideal member– Salient example

• Prototypes er compatible with what we know about the brain

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Prototypes (cont’d.)

• Neural networks have weighted connections and various grades of ”firing” (p. 97).– Gradual categorization in relation to a

prototype

• Neural systems have thresholds which have to be exceeded before they will fire at all:– Either-or categorization (classical categories)

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Image Schemas• Talmy – image schemas show how language

organizes spatial relations, with trajectors and orientation points

• Various types:– Topological (container, path, etc.)– Orientational (in relation to the body, for example, in

front of, behind)– Force dynamic (against, etc.)

• Based on genetic inheritance and universal human experience (p. 136)

• Feldman shows that image schemas can be described as feature-value structures, but doesn’t tell us exactly how the schemas are related to neural networks.

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English into combines two image schemas:

Container Source-path-goal

inside source

outside path

boundary goaltrajector

Meaning is a collection of relations among image schemas (p. 284)

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Frames• Fillmore’s frames show how knowledge is

organized and how words/concepts are connected to each other– The “commercial event frame” is activated to

understand words like ”buy” and ”sell”• Frames can be composed of scenarios

(phases):– Initial state– Exchange of money for goods– Resulting state

• Frames give us the possibility to describe series of events with characteristic elements (like buys) and options for filling in slots (like John)

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Frames (cont’d.)

• Frames are composed of simpler concepts which in turn boil down to image schemas.

• Therefore frames are based in neural networks.

• Frames are culturally conditions and thus not universal.

• Image schemas can however be universal:“universal bodily based representations of experience” (p. 136).

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Embodiment• Used in two related meanings in cognitive linguistics:

– The meaning of a word is grounded in bodily experience with situations where the word is used, what a human being does with the relevant things or events

– The meaning of a word is directly anchored in neural networks in the brain

• When small children play, they put things in containers and this establishes an image schema (container) which in turn serves as the basis for the meaning of words.

• The embodied basis for meaning also means that a person can react to actual situations (the Chinese room or a computer cannot react to questions about what color they are seeing now, or what they should do if the building is on fire)

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Embodiment and the brain: The knee-jerk reflex

• The same neural structures are active when (p. 4f.)– The doctor hits you on the knee – The doctor asks you to kick– You see someone kick– You hear about someone kicking

• Mirror neurons are activated during simulation• Understanding/meaning arises through simulation• In this way linguistic meaning is directly connected

to neural networks• Neural networks are very relevant for linguistics

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Embodiment, simulation and understanding

• Simulation semantics – understanding is the capacity to simulate a narration

• The capacity to carry out an action on the basis of linguistic description – this is the key to understanding

• Computers and programs lack this capacity

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Metaphor• How to link abstract thought with embodied experience? • “Essentially all of our cultural, abstract, and theoretical

concepts derive their meanings by mapping through metaphor, to the embodied experiential concepts we explored in earlier chapters” (p. 199)

• Grady’s 1996 primary metaphors link subjective evaluations and sensory-motor experiences:– Affection is warmth:– Subjective: Affection– Sensory-motor: Temperature– Example: “They greeted me warmly”– Experience: A child feels warmth when held by a parent

• Metaphors are a normal consequence of associative learning by means of neural networks: coactivation of neurons (“Neurons that fire together, wire together” p. 201)

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Metaphor (cont’d.)• C. Johnson shows how metaphors are learned:

– First a child learns a literal meaning: See Daddy– Then the child learns “conflations”: See what I spilled– Then the child learns metaphorical meaning: See

what I mean– “Conflation” involves coactivation.

• Complex metaphors involve the activation of several metaphors at the same time, thus involving more comprehensive neural networks.

• Narayanan 1997 developed a data program that understands metaphors in a narrative, e.g., France fell into a recession– recession is a metaphorical hole– it is linked to economic concepts– the program understands that France wasn’t in the

hole before it fell, that it is still in the hole, and that it couldn’t control its movement.

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Embodied Construction Grammar

PART III

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False assumptions1. A grammar is a collection of abstract rules2. Formal grammatical rules are expressed in the brain 3. A grammar is independent of all other structures in the

brain4. Genes provide specific grammatical information

• 2, 3, and 4 are very unlikely given what we know about the brain and genes

5. Children do not get enough input to build a grammar– But we know that children get lots of input, that all input is in

context, and that the input doesn’t come in a random order– There is no “poverty of stimulus”, but an “opulence of substrate”,

because a child has a lot of conceptual and embodied experience, plus support from other people (p. 318-19)

6. Each word has a number of set meanings, meanings are in the words, and grammatical rules are abstract and meaningless

– But we know that the meaning of a word is affected by context, by ongoing perceptions and associations

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An embodied neural theory of language• It is important to find a way to write a grammar that

corresponds to what we know about the brain and neurons

• Embodied Construction Grammar (ECG) develops a formal notation for cognitive linguistics (p. 297)

• The construction is the basic unit of speach = form + meaning – NB. Langacker’s symbolic structure, which

Feldman does not cite– Meaning can come from larger structures than

morphemes (p. 298)

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An embodied neural theory of language (cont’d.)

• Embodied

– Grounded in embodied schemas (p. 289)

• Four basic formal structures– Schemas– Constructions– Maps (which facilitate metaphor)– Mental spaces (indirect speech, etc.)

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A question and some speculation to close with

PART IV

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How did language develop?

• We don’t have any fossil languages and language changes too fast – we don’t know anything about an earlier or more primitive version of language

• Chomsky, Hauser and Fitch (2002) conclude that language (i.e. grammar) is the result of just one big genetic mutation

• Pinker and Jackendoff believe that language developed gradually

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How did language develop?• But if one believes that language is just the result of a

bigger brain with better learning capacity, there isn’t so much to explain

• According to the neural theory of language, the capacity to simulate played a major role in the development of language

• One extra step: displacement – the capacity to simulate and think about solutions which are not connected to the here and now

• All mammals show displacement when they dream, but only humans can do this willfully, when they are not asleep/dreaming

• There is a relatively small step from animals’ uncontrolled displacement to humans’ controlled displacement (p. 328)

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Let’s sum up!

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Feldman’s theory spans

• The interaction of amoebas

• The interaction of neurons

• Neural networks in the brain

• Literal meaning

• Metaphorical meaning

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Feldman shows that

• Cognitive linguistics builds upon and is congruent with what we know about the brain.

• It is possible to formalize cognitive linguistics via Embodied Construction Grammar.