Metaphor as Categorization: A Connectionist Implementation

A key issue for models of metaphor comprehension is to explain how, in some metaphorical comparison "A is B," only some features of B are transferred to A. The features of B that are transferred to A depend both on A and on B. This is the central thrust of Black's (1979) well-known interaction theory of metaphor comprehension. However, this theory is somewhat abstract, and it is not obvious how it may be implemented in terms of mental representations and processes. In this article, we describe a simple computational model of online metaphor comprehension that combines Black's interaction theory with the idea that metaphor comprehension is a type of categorization process (Glucksberg & Keysar, 1990, 1993). The model is based on a distributed connectionist network depicting semantic memory (McClelland & Rumelhart, 1986). The network learns feature-based information about various concepts. A metaphor is comprehended by applying a representation of the first term (A) to the network storing knowledge of the second term (B), in an attempt to categorize it as an exemplar of B. The output of this network is a representation of A transformed by the knowledge of B. We explain how this process embodies an interaction of knowledge between the 2 terms of the metaphor, how it accords with the contemporary theory of metaphor stating that comprehension for literal and metaphorical comparisons is carried out by identical mechanisms (Gibbs, 1994), and how it accounts for existing empirical evidence (Glucksberg, McGlone, & Manfredi, 1997) and generates new predictions. In this model, the distinction between literal and metaphorical language is one of degree, not of kind.