Analogy
Dario D. Salvucci
John R. Anderson
Abstract
In this chapter we propose an approach to developing models of
analogical reasoning within production system architectures. Though
existing theories of analogy have succeeded in capturing many aspects
of analogical behavior, they have several limitations. First, the
theories use empirical support that focuses almost exclusively on
high-level data that illustrate the results of analogy, ignoring
low-level data that illustrate step-by-step processes during analogy.
Second, the theories cannot fully account for variability prevalent in
analogical behavior, nor can they account for the adaptation of
analogical strategies in learning. Third, some of the theories cannot
be readily incorporated into a unified theory of cognition. We show
how production rule models of analogy can address and to some extent
overcome these limitations. As our exemplar, we describe empirical
and modeling results for a task in which subjects solved simple
physics problems by analogy. The empirical results, which include both
high- and low-level data, give evidence that subjects use multiple
analogical strategies and shift between strategies. The modeling
results show that both a declarative and procedural ACT-R model of the
task can account for much of subjects' observed behavior. We also
present a model for a similar analogical task involving picture
analogies (Sternberg, 1977) to illustrate how the simple physics model
can generalize to other tasks.
Models
Simple Physics Task
People-Piece Task