On Order Effects in Analogical Mapping: Predicting Human Error Using IAM

Abstract

The Incremental Analogy Machine (IAM) predicts that the order in which parts of an analogy are processed can affect the ease of analogical mapping. In this paper, the predictions of this model are tested in two experiments. Previous work has shown that such order effects can be found in attribute-mapping problems. In the first experiment, it is shown that these effects generalise to relational-mapping problems, when subjects' error performance (incorrect mappings) is considered. It is also found that relational-mapping problems are significantly harder than attribute-mapping problems. In the second experiment, it is shown using relational-mapping problems, that order effects can be demonstrated for doubles (two sentences about two indiviudals) in these problems. Throughout the paper it is shown that these results are best approximated by IAM's measure of the complexity of global mappings (the remaps-complexity measure), and not as has been found previously, by a measure using frequency of remaps (the re-maps measure). The empirical and theoretical significance of these results are discussed.