Constraints on Analogical Mapping: A Comparison of Three Models

Abstract

Three theories of analogy have been proposed which are supported by computational models and data from experiments on human analogical abilities. In this paper, we show how these theories can be unified within a common metatheoretical framework which distinguishes between levels of informational, behavioural and hardware constraints. This framework makes clear the distinctions between three computational models in the literature (the Analogical Constraint Mapping Engine, the Structure-Mapping Engine and the Incremental Analogy Machine) . The paper then goes on to develop a methodology for the comparative testing of these models. In two different manipulations of an analogical-mapping task we compare the results of computational experiments with these models against the results of psychological experiments. In the first experiment, we show that increasing the number of similar elements in two analogical domains, decreases the response time taken to reach the correct mapping for an analogy problem. In the second psychological experiment we find that the order in which the elements of the two domains are presented has significant facilitative effects on the ease of analogical mapping. Only one of the three models, that model which embodies behavioural constraints, the Incremental Analogy Machine, predicts both of these results. Finally, the immediate implications of these results for analogy research are discussed, along with the wider implications the research has for cognitive science methodology.