How to Blend Concepts and Influence People: Computational Models of Conceptual Integration

Abstract

In this paper we explore the computational requirements of the theory of conceptual integration, and propose an algorithmic model that meets these requirements. Broadly speaking, we see three reasons for seeking a computational account of a powerful theory like conceptual integration. Firstly, consider that theoretical utility is inversely proportional to expressive power, and that overly powerful theories have little cognitive status, since scientifically, one should seek the least powerful theory that accounts for the most facts. It is important then that conceptual integration is shown not to be overly powerful. Similarly, a functional view of mind suggests that such a theory should be computationally tractable and not make infeasible processing demands. So just as cognitive theories should be falsifiable via empirical testing, such theories should also be shown to be tractable via computational modelling. This paper demonstrates the tractability of conceptual integration networks by showing how a tractable computational model, called Sapper, can accommodate the processes underlying conceptual integration.