Towards a theory of conceptual integration
In attempting to account for examples like the SURGEON AS BUTCHER metaphor, Fauconnier and Turner took aspects of the two frameworks they had developed and produced a theory of integration networks. An integration network is a mechanism for modelling how emergent meaning might come about. Fauconnier and Turner suggest that an integration network consists of inputs in which elements in each input are linked by mappings (see Figure 12.1). In this respect, Blending Theory draws upon Conceptual Metaphor Theory. Recall that Conceptual Metaphor Theory represents a two-domain model in which domains are linked by conventional mappings relating comparable elements.
From Mental Spaces Theory, Fauconnier and Turner took the idea that the conceptual units that populate an integration network should be Mental Spaces rather than domains of knowledge, as in Conceptual Metaphor Theory. As we have seen in previous chapters, the difference between the two is that domains of knowledge are relatively stable pre-existing knowledge structures, while mental spaces are temporary structures created during the on-line process of meaning construction. Therefore, the initial focus in Blending Theory was to account for local and dynamic meaning construction, a focus that is inherited from Mental Spaces Theory.
Moreover, integration networks in Blending Theory are not simply two space entities. Because these networks represent an attempt to account for the dynamic aspects of meaning construction, they are multiple-space entities, just like mental space lattices. One of the ways in which this model gives rise to complex networks is by linking two (or more) input spaces by means of a generic space. The generic space provides information that is abstract enough to be common to both (or all) the inputs. Indeed, Fauconnier and Turner hypothesise that integration networks are in part licensed by interlocutors identifying the structure common to both inputs that licenses integration. Elements in the generic space are mapped onto counterparts in each of the input spaces, which motivates the identification of cross-space counterparts in the input spaces. This is illustrated in Figure 12.2.
A further distinguishing feature of an integration network is that it consists of a fourth blended space or blend. This is the space that contains new or emergent structure: information that is not contained in either of the inputs. This is represented by the blended space in Figure 12.3. The blend takes elements from both inputs, as indicated by the broken lines, but goes further in pro viding additional structure that distinguishes the blend from either of its inputs. In other words, the blend derives structure that is contained in neither input. In Figure 12.3, this emergent structure or ‘novel’ meaning is represented by the elements in the blended space that are not connected to either of the inputs.
That surgeon is a butcher: the blending theory account
Having set out the basic architecture of the Blending Theory model, we outline an analysis of the SURGEON AS BUTCHER metaphor from a Blending Theory perspective. As noted by Grady, Oakley and Coulson (1999), Blending Theory is able to account for the negative assessment associated with this utterance by allowing for emergent structure. This follows from the fact that, while a blend contains structure projected from both inputs, it also contains additional structure projected from neither. In the input space for BUTCHER, we have a highly skilled professional. However, in the blend, these skills are inappropriate for performing surgery on human patients. While surgeons attempt to save lives, butchers perform their work on dead animals. While the activity performed by butchers is dismembering, the activity performed by surgeons typically involves repair and reconstruction, and so on. The consequence of these contrasts is that in the blend a surgeon who is assessed as a butcher brings inappropriate skills and indeed goals to the task at hand and is therefore incompetent. This emergent meaning of incompetence represents the additional structure provided by the blend.
The emergent structure provided by the blend includes the structure copied from the input spaces, together with the emergent structure relating to a surgeon who performs an operation using the skills of butchery and is therefore incompetent. This individual does not exist in either of the input spaces. The structure in the blend is ‘emergent’ because it emerges from ‘adding together’ structure from the inputs to produce an entity unique to the blend. Furthermore, it is precisely by virtue of the mismatch between goal (healing) and means (butchery), which exists only in the blend, that the inference of incompetence arises. This means that all the structure in the blend can be described as emergent, even though its ‘ingredients’ are provided by the input spaces. Finally, we address the role of the generic space in this integration network. As we noted earlier, the generic space contains highly schematic information which serves as a basis for establishing cross-space mappings between the two input spaces. In other words, the generic space facilitates the identification of counterparts in the input spaces by serving as a ‘template’ for shared structure. It is these counterparts that can then be projected to the blend. The integration network for this blend is illustrated in Figure 12.4.
While metaphors of this kind originally motivated Fauconnier and Turner’s development of Blending Theory, this approach applies equally to non-metaphorical instances of meaning construction. Consider the counterfactual example (2), which we discussed in Chapter 5.
As with the SURGEON AS BUTCHER metaphor, this counterfactual prompts for a complex conceptualisation that is more than the sum of its parts. In particular, it involves the conceptual blending of counterparts in order to produce a blend in which Clinton is not politically harmed by his relationship with Lewinsky, an emergent meaning that does not exist in either of the inputs that give rise to it.
