From: Grant Callaghan (grantc4@hotmail.com)
Date: Fri 29 Nov 2002 - 20:40:27 GMT
The following paper, of which I've extracted just the introduction and the
abstract, does not use the word meme but much of what it covers has been
speculated about on this list over the past few months and I think there may
be some relevance to what we've been talking about. Lawry, especially,
seems in a good position to check out the writers as the paper originated at
UMD.
Conceptual Integration Networks
[Expanded web version, 10 February 2001]
Gilles Fauconnier
Department of Cognitive Science
University of California, San Diego
gfauconnier@ucsd.edu
Mark Turner
Department of English and Program in Neuroscience and Cognitive Science
University of Maryland
markt@umd5.umd.edu
The web page for research on conceptual integration is
http://www.wam.umd.edu/~mturn/WWW/blending.html
Published in Cognitive Science, 22(2) 1998, 133-187.
Copyright © Cognitive Science Society, Inc. Used by permission.
Abstract
Conceptual integration—"blending"—is a general cognitive operation on a par
with analogy, recursion, mental modeling, conceptual categorization, and
framing. It serves a variety of cognitive purposes. It is dynamic, supple,
and active in the moment of thinking. It yields products that frequently
become entrenched in conceptual structure and grammar, and it often performs
new work on its previously entrenched products as inputs. Blending is easy
to detect in spectacular cases but it is for the most part a routine,
workaday process that escapes detection except on technical analysis. It is
not reserved for special purposes, and is not costly.
In blending, structure from input mental spaces is projected to a separate,
"blended" mental space. The projection is selective. Through completion
and elaboration, the blend develops structure not provided by the inputs.
Inferences, arguments, and ideas developed in the blend can have effect in
cognition, leading us to modify the initial inputs and to change our view of
the corresponding situations.
Blending operates according to a set of uniform structural and dynamic
principles. It additionally observes a set of optimality principles.
Contents
I. Introduction
II. An illustration
III. The network model
of conceptual integration
IV. Applications
V. Advanced aspects
of the network model
VI. Optimality
principles
VII. Additional
dimensions of conceptual integration
VIII. Summary and further
results
IX. Conclusion
I. Introduction
Much of the excitement about recent work on language, thought, and action
stems from the discovery that the same structural cognitive principles are
operating in areas that were once viewed as sharply distinct and technically
incommensurable. Under the old view, there were word meanings, syntactic
structures, sentence meanings (typically truth-conditional), discourse and
pragmatic principles, and then, at a higher level, figures of speech like
metaphor and metonymy, scripts and scenarios, rhetoric, forms of inductive
and deductive reasoning, argumentation, narrative structure, etc. A
recurrent finding in recent work has been that key notions, principles, and
instruments of analysis cut across all these divisions and in fact operate
in non-linguistic situations as well. Here are some of them:
Frames structure our conceptual and social life. As shown in the work of
Fillmore, Langacker, Goldberg, and others, they are also, in their most
generic, and schematic forms, a basis for grammatical constructions. Words
are themselves viewed as constructions, and lexical meaning is an intricate
web of connected frames. Furthermore, although cognitive framing is
reflected and guided by language, it is not inherently linguistic. People
manipulate many more frames than they have words and constructions for.
Analogical mapping, traditionally studied in connection with reasoning,
shows up at all levels of grammar and meaning construction, such as the
interpretation of counterfactuals and hypotheticals, category formation ,
and of course metaphor, whether creative or conventional.
Reference points, focus, viewpoints, and dominions are key notions not only
at higher levels of narrative structure, but also at the seemingly
micro-level of ordinary grammar, as shown convincingly by Langacker 1993,
Zribi-Hertz 1989, Van Hoek 1997, Cutrer 1994, among others.
Connected mental spaces account for reference and inference phenomena across
wide stretches of discourse, but also for sentence-internal multiple
readings and tense/mood distributions. Mappings at all levels operate
between such spaces, and like frames they are not specifically linguistic.
(Fauconnier 1997, Dinsmore 1991, Cutrer 1994, Fauconnier and Sweetser,
1996).
Connectors and conceptual connections also operate at all levels, linking
mental spaces and other domains for coreference, for metonymy (Nunberg
1978), and for analogy and metaphor (Turner 1991, Sweetser 1990).
There are other notions that apply uniformly at seemingly different levels,
such as figure/ground organization (Talmy 1978), profiling, or pragmatic
scales.Running through this research is the central cognitive scientific
idea of projection between structures. Projection connects frames to
specific situations, to related frames, and to conventional scenes.
Projection connects related linguistic constructions. It connects one
viewpoint to another and sets up new viewpoints partly on the basis of old.
It connects counterfactual conceptions to non-counterfactual conceptions on
which they are based. Projection is the backbone of analogy,
categorization, and grammar.
In the present study, we show that projection typically involves conceptual
integration. There is extensive previous research on varieties of
projection, but not on conceptual integration. Empirical evidence suggests
that an adequate characterization of mental projection requires a theory of
conceptual integration. We propose the basis for such a theory and argue
that conceptual integration—like framing or categorization—is a basic
cognitive operation that operates uniformly at different levels of
abstraction and under superficially divergent contextual circumstances. It
also operates along a number of interacting gradients. Conceptual
integration plays a significant role in many areas of cognition. It has
uniform, systematic properties of structure and dynamics.
The nature of mapping between domains has enjoyed sustained attention as a
central problem of cognitive science, and voluminous literatures have
developed in this area, including studies by those who call their subject
"analogy" or "similarity" (e. g., Hofstadter 1985, 1995a, Mitchell 1993,
French 1995, Keane, Ledgeway, and Duff 1994; Holyoak and Thagard, 1989,
1984; Forbus, Gentner, and Law, 1994; Gentner 1983, 1989; Holland,
Holyoak, Nesbett, and Thagard, 1986), studies by those who call their
subject "metaphor" (e.g., Lakoff and Johnson 1980; Lakoff and Turner 1989;
Sweetser 1990; Turner 1987; Indurkhya 1992; Gibbs 1994) and studies that
consider cross-domain mapping in general (e.g., Fauconnier 1997, Ortony
1979a, 1979b, Glucksberg and Keysar 1990, Turner 1991).
Our immediate goal is not to take a stand on issues and problems of
cross-space mappings. Those issues are many and the debates over them will
continue and will be further enriched, we hope, by taking blending into
consideration. What we will be suggesting is that models of cross-space
mapping do not by themselves explain the relevant data. These data involve
conceptual integration and multiple projections in ways that have typically
gone unnoticed. Cross-space mapping is only one aspect of conceptual
integration, and the existing body of research on the subject overlooks
conceptual integration, which it is our intention to foreground and analyze
here. As we move through the data that crucially involves both cross-space
mapping and conceptual integration, we will remark that much of it is
neither metaphoric nor analogical. [1]
We take it as an established and fundamental finding of cognitive science
that structure mapping and metaphorical projection play a central role in
the construction of reasoning and meaning. In fact, the data we analyze
shows that such projections are even more pervasive than previously
envisioned. Given the existence and key role of such mappings, our focus is
on the construction of additional spaces with emergent structure, not
directly available from the input domains.
We also rely on another fundamental finding of cognitive science, the
capacity for mental simulation, as demonstrated in Johnson-Laird (1983),
Kahneman (1995), Grush (1995), Schwartz and Black (1996), Barsalou (1996)
among others. In our analysis, the simulation capacity assists in the
on-line elaboration of blended spaces ("running the blend"). There is the
added twist that simulation can operate on mental spaces which need not have
potential real world reference.
Our methodology and argumentation take the following form. Since the
cognitive process of conceptual integration has been largely overlooked, it
is useful to give evidence for its operation in a wide variety of areas.
Since conceptual integration has uniform structural and dynamic properties,
it is important to reveal this uniformity behind the appearance of
observational and functional diversity. We proceed analytically and
empirically, by showing that central inferences, emotions, and
conceptualizations, not explained in currently available frameworks, are
accounted for elegantly by the conceptual integration model. The
argumentation often takes the following specific form: a particular process
of meaning construction has particular input representations; during the
process, inferences, emotions and event-integrations emerge which cannot
reside in any of the inputs; they have been constructed dynamically in a new
mental space—the blended space—linked to the inputs in systematic ways. For
example, "They dug their own financial grave" draws selectively from
different and incompatible input frames to construct a blended space that
has its own emergent structure and that provides central inferences. In
this case, the blended space has become conventional.
The diversity of our data (of which only a small sample appears in the
present paper) is necessary to support our claim for generality. (In
showing that cell division is a basic process, it is necessary to study it
for many kinds of cells. In arguing that natural selection is a general
principle, it is necessary to exemplify it for widely different organisms
and species.) In arguing that conceptual integration is a basic cognitive
operation, we must show that it operates in many different kinds of cases.
Conceptual blending is not a compositional algorithmic process and cannot be
modeled as such for even the most rudimentary cases. Blends are not
predictable solely from the structure of the inputs. Rather, they are
highly motivated by such structure, in harmony with independently available
background and contextual structure; they comply with competing optimality
constraints discussed in section VI, and with locally relevant functional
goals. In this regard, the most suitable analog for conceptual integration
is not chemical composition but biological evolution. Like analogy,
metaphor, translation, and other high-level processes of meaning
construction, integration offers a formidable challenge for explicit
computational modeling.
Special cases of conceptual blending have been discussed insightfully by
Koestler (1964), Goffman (1974), Talmy (1977), Fong (1988), Moser and
Hofstadter (ms.), and Kunda, Miller and Clare (1990). Fauconnier (1990) and
Turner (1991) also contain analyses of such phenomena. All these authors,
however, take blends to be somewhat exotic, marginal manifestations of
meaning. We will show here that the process is in fact central, uniform,
and pervasive.
The data and analysis we consider here suggest many psychological and
neuropsychological experiments (Coulson 1997), but in the present work our
emphasis is on the understanding of ecologically valid data. Research on
meaning, we suggest, requires analysis of extensive ranges of data, which
must be connected theoretically across fields and disciplines by general
cognitive principles.
We start our report with an effective but somewhat idealized example of
blending, in order to illustrate the issues and terminology. We then
outline the general process of conceptual integration and the systematic
dynamic properties of blends. We work through some case-studies in a
variety of areas. Section VI presents the competing optimality principles
under which conceptual integration operates.
Grant
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Journal of Memetics - Evolutionary Models of Information Transmission
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