Language and memes

From: Grant Callaghan (
Date: Fri 29 Nov 2002 - 20:40:27 GMT

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    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

    Mark Turner Department of English and Program in Neuroscience and Cognitive Science University of Maryland

    The web page for research on conceptual integration is

    Published in Cognitive Science, 22(2) 1998, 133-187.

    Copyright © Cognitive Science Society, Inc. Used by permission.


    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.


    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.


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