THE METAPHORICAL MODELING THE METAPHORICAL MODELING

OF CONCEPTUAL SPACE:OF CONCEPTUAL SPACE:

SPECIALIZED VS. ORDINARY SPECIALIZED VS. ORDINARY KNOWLEDGEKNOWLEDGE

Oleg A. Alimuradov, Natalia S. AlimuradovaOleg A. Alimuradov, Natalia S. Alimuradova

Pyatigorsk, RussiaPyatigorsk, Russia

In cognitive linguistics where language is viewed as a “window into human nature” [Pinker 2008] modeling is an attempt to reconstruct the mental space of an individual or (a much more challenging task) of a whole language community.

As a cognitive model is always constructed, it cannot be identical to the actual array of phenomena constituting the content of our mentality.

The notion of mental space: G. Fauconnier’s view-point

Mental spaces are established by linguistic expressions:

these expressions “will typically establish new spaces, elements within them, and relations holding between the elements” [Fauconnier 1994].

Language expressions as space-builders:Max believes (space-builder for M) that

Susan hates Harry (establishes relations between the component elements of M) [Fauconnier 1994: 17].

The notion of mental space: our perspective

The mental space essentially has a non-linguistic and pre-linguistic character.

It develops before the language acquisition begins: it is the system that stores (and processes!) information for other human systems (including the language-dedicated performance systems) to access [Chomsky 2009] and process further.

The onset of speech (between 18 and 24 months) and the subsequent language development are conditioned by the child’s mental maturation and the progress of his motor coordination [Lenneberg 1967].

The correlation of the mental space with linguistic phenomena:

Language serves to reduce the complexity of the intellectual sphere by providing descriptive “labels” [Clark 2010] used to structure the mental content for verbal representation and subsequent interpretation.

Mental space 1 (non-verbal) vs. mental space 2 (verbally represented):

simultaneity vs. successive progression [Piaget];

multi-dimensionality vs. linearity; personal vs. interpersonal character; semantic complexity vs. relative semantic

simplicity; untranslatability vs. translatability to the

recipient.

Mental space 1 + mental space 2 = conceptual sphere 1.

The distinctive features of conceptual sphere 1:

dynamic; self-regulatory; open; stochastically activated; contains fuzzily determined elements that forbid

direct linguistic analysis and should be construed [Langacker 1991; Maturana et al., 2004];

highly metaphorical [Fass 1997]; multi-dimensional relations between concepts; complex relations to the outside world; recurrently represented verbally in

communication; can be abstracted as a model.

Conceptual sphere 2 is a descriptive model of our mental space as represented in the discourse process.

Fig. 1[Helbig 2006]

Metaphor in Conceptual Sphere 1 is a vital cognitive mechanism that produces new knowledge by immediately planting perceptual information within a whole network of the inter- and intra-conceptual bonds already existing in a person’s mentality:

I felt how - if I were his wife, this good man, pure as the deep sunless source, could soon kill me: without drawing from my veins a single drop of blood, or receiving on his own crystal conscience the faintest stain of crime [Ch. Brontë: Jane Eyre, P. 751]: CRIME – DIRT.

In Conceptual Sphere 1 metaphor is largely based on personal judgment [Croft & Cruse 2004] and is recurrently manifested on the verbal (linguistic) level.

This allows us to build an adequate CS2 model of CS1.

Research shows that the metaphorical modeling of conceptual sphere 1 can follow three different routes equally viable for specialized and ordinary knowledge.

The merging (blending) of two conceptual fragments (individual concepts):

She dominated by more than just her age, size and superior heritage: she had a magical, unexpected glamour [Walker 2007: 68]: BEAUTY → POWER.

Sir John Belling was no longer the dashing, blond conqueror who had wowed the dog-eat-dog political world with his mix of leggy, Andrex puppy charm and lean Doberman aggression [Walker 2007: 70]: BEAUTY → WAR.

She could see that her mother was momentarily under his spell, too. It was hard not to be. He was handsome and so nice to everyone [Steel 2007: 53]: BEAUTY → MAGIC.

A predicate-argument structure equating the information value of two concepts:

My loathings are simple: stupidity, oppression, crime, cruelty, soft music [Nabokov: http://www.quotationspage.com]: CRIME (A) CAUSES DISGUST (P).

I remembered the case well, for it was one in which Holmes had taken an interest on account of the peculiar ferocity of the crime and the wanton brutality which had marked all the actions of the assassin… [Doyle: The Hound of the Baskervilles, P. 88]: CRIME (A) IS CRUELTY (P).

It was all dim and vague, but always there is the dark shadow of crime behind it [Doyle: The Hound of the Baskervilles, P. 124]: CRIME (A) IS A SHADOW (P).

The vehicle-tenor-ground structure-based (integrated) modeling of the conceptual metaphor [Richards 1985; Alimuradov, Chursin 2009]:

E.g. [Alimuradov, Chursin 2009: 128]:

Ordinary VS. specialized knowledge

in metaphorical representation The depth of the metaphorical

representation : substantial with ordinary knowledge as this

kind of knowledge is vaguely and fuzzily structured, the conceptual links are deeply personalized and

intricately woven, the conceptual links reach deep into the conceptual

sphere. small with specialized knowledge as this

kind of knowledge is rigidly structured,

the conceptual links are conventional and predictable.

The patterns of constructing the metaphorical components of CS1:

are conditioned by personal characteristics, such as age (concept CRIME – “CRIME CAUSES DISGUST” 21,4% with English speakers aged 55-70 VS. 9,2% with English-speaking teenagers aged 15-18) and gender (concept BEAUTY – “BEAUTY → LIGHT” 39,8% with male speakers VS. 30% with females; “BEAUTY → POWER” 10,2% with men VS. 18,9% with women) when it comes to ordinary knowledge;

are conditioned by the degree of knowledge specialization, in inversely proportionate relations: e.g. [Alimuradov, Chursin 2009: 129-132]: the degree of knowledge specialization is low

the degree of knowledge specialization is relatively high:

The promising modeling patterns for The promising modeling patterns for CS2 with respect to the type of CS2 with respect to the type of

knowledge in CS1:knowledge in CS1:

Ordinary knowledge

Specialized knowledge

Integrated modelConceptual-blending

modelPredicate-argument model

Thank you!