Metaphors of Conceptual Integration
Conceptual Blending(Fauconnier & Turner)
Criticisms and concerns
Lack of clarity
Need for testing and falsification
Examination of assumptions
Assumption of at least four conceptual spaces
Examination of key metaphors
mental spaces
conceptual packets
conceptual blending
Scrutiny of metaphors vs. actual requirements of the theory
Conceptual Blending: The Model
Mental Spaces
Small conceptual packets connected to long-term schematic knowledge
Generic Space
Contains common to input spaces
Fusion of elements from input spaces
Blended Space
Elements from both input spaces
May have "emergent structure"
Additional elements from long term memory or comparison of elements
Two input spaces
Approaches to Conceptual Integration
Different ways of combining disparate concepts
Different degrees of connectedness and merging
Different neurological mechanisms and processes
Links among discrete conceptual elements
Connecting components of a stereo set
Altering the pattern of synaptic connections
Changing the level of activation in connected neuron groups
Merging of conceptual elements
Creating composite drawings of a suspect
Imprinting circuits from different products on a single chip
Synchronizing the spike trains of neuron groups
Darwinian processes in cognition
Spreading activation and lateral inhibition
Selection and reinforcement of firing patterns
Overlay or composite of distinct concepts
Criticisms and Responses
Lack of empirical testability and falsifiability
Fauconnier and Turner compare CIT to evolutionary biology and cosmology
Fauconnier and Turner claim to have falsified existing accounts of counterfactuals
The space and blending metaphors hinder precise specification and meaningful empirical tests
Computability by a human brain
Veale and O’Donoghue demonstrate computability by a computer program
Computational models of language are often metaphorical and differ from human brain processing
Semantic networks in Veale and O’Donoghue are unrealistic and difficult to represent or program
The origin and purpose of the conceptual space model
A reaction to formalist theories of meaning
A bridge to a more cognitive model of language
The problem of scope and evidence for conceptual blending theory
The alternative explanation of neural connections
The challenge of distinguishing between syntax and semantics
Metametaphors of Cognitive Processes
The metaphors used by Fauconnier and Turner to describe conceptual integration
Mental spaces as vectors, dimensions, and proximity in cognitive space
Conceptual integration as coactivation-bindings of neuronal assemblies
Mental spaces as frames, schemas, and scripts of long-term schematic knowledge
Conceptual packets, mental assemblies, and blending as conduit or container metaphors
The conflicting entailments of the metaphors
The inconsistency between a network model and a space metaphor
The creation of a new blended space as a direct entailment of the space metaphor
The use of circles and boxes to reinforce the idea of boundaries and replication
The alternative understanding of language processing
A process of selection and suppression in working memory
A connection of existing elements in a new composite pattern
A network metaphor instead of a space metaphor
The problems with the space metaphor
The conflation of physical space and conceptual space
The unnecessary creation of separate sets of activated neuronal assemblies
The confusion about levels of analysis and cultural phenomena
The comparison with meme theory
The attempt to achieve a unitary account of cultural transmission of ideas
The lack of explanation of how blended spaces are run or transmitted
The insufficiency of positing a gene-like unit of meaning
A Different Approach to Conceptual Integration
The analysis of the digging metaphors
The association of digging with the search for wealth, danger, and irony
The connection of vivid images with expectations, emotions, and expressions
The lexicalization and loss of grounding of metaphorical idioms
The association of metaphorical expressions
The loose connection between expressions with similar source domains
The preservation of the power of expressions by the remaining associations
The multiplicity of interpretations of metaphorical expressions
The alternative approach to conceptual integration
A process of activation and suppression of existing connections
A network model of language processing
A dynamic and context-sensitive view of meaning construction
Concluding Remarks
The potential and limitations of Conceptual Integration Theory
A powerful model for explaining connections among different ideas
The obscuring and contradictory effects of the space and blending metaphors
The recognition of the pitfalls of metaphorical language
The promise of the theory for understanding language processing and metaphor
The need for a formulation of the theory in terms of neural mechanisms
The criticism of the computer metaphor for cognition
The reification of the vector representation of concept meanings
The disregard of the social and cultural structure of cognition
The lack of detailed knowledge of complex concepts and their combination
The reliance on evocative metaphors and the need for rigorous criticism