Keane, M.T., Ledgeway T. & Duff S.
Constraints on Analogical Mapping: A Comparison of Three Models
(Substantial revision of TCD-CS_92-08; Also to appear
in Artificial Intelligence & Cognitive Science VI, Belfast:
Queens University Press).
Three theories of analogy have been proposed that are supported by
computational models and data from experiments on human analogical
abilities. In this article we show how these theories can be unified
within a common metatheoretical framework that distinguishes among
levels of informational, behavioral, and hardware constraints. This
framework clarifies the distinctions among three computational models in
the literature: the Analogical Constraint Mapping Engine (ACME), the
Structure-Mapping Engine (SME), and the Incremental Analogy Machine
(IAM). We then go 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 a 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. Of the three models, only
IAM embodies behavioral constraints and 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.
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