A review of empirical work suggests that the lexical representations of a bilingual's two languages are independent (Smith, 1991), but may also be sensitive to between language similarity patterns (e.g. Cristoffanini, Kirsner, and Mi lech, 1986). Some researchers hold that infant bilinguals do not initially differentiate between their two languages (e.g. Redlinger & Park, 1980). Yet by the age of two they appear to have acquired separate linguistic systems for each language (Lanza, 1992). This paper explores the hypothesis that the separation of lexical representations in biUnguals is a functional rather than an architectural one. It suggests that the separation may be driven by differences in the structure of the input to a comm o n architectural system. Connectionist simulations are presented modelling the representation of two sets of lexical information. These simulations explore the conditions required to create functionally independent lexical representations in a single neural network. It is shown that a single network may acquire a second language after learning a first (avoiding the traditional problem of catastrophic interference in these networks). Further it is shown that in a single network, the functional independence of representations is dependent on inter-language similarity patterns. The latter finding is difficult to account for in a model that postulates architecturally separate lexical representations.

In this paper we present a simple process model (based on connectionist pattern completion) of A is B metaphor comprehension. The Metaphor by Pattern Completion (MPC) model capitalizes on an existing semantic memory mechanism. Metaphorical enhancement is produced by presenting a semantic vector representation of the target word (A) to a connectionist network storing the knowledge base (B). Effects found in human data such as meaning enhancement, asymmetric processing, context sensitivity and compound indexing all fall naturally out of the pattern completion mechanism. The MPC model suggests a simple way of separating literal from metaphorical statements. It provides a means of predicting when a metaphor will appear to fail. Moreover, we suggest that the mechanism can form the basis of a comparison procedure that supports analogy. The MPC mechanism avoids the problem of identifying which features of a concept are relevant for similarity matching in analogies, because the prior metaphor stage naturally enhances relevant features and suppresses the irrelevant features. The M P C model is both domain general (in that it does not depend on the structure of the metaphor domain) and parsimonious (in that it does not posit metaphor-specific mechanisms).