Figure 155

Some of the results from Gick and Holyoak (1980, Experiment 4) showing the percentage of subjects who solved the radiation problem when they were given an analogy (general-story condition) or were just asked to solve the problem (control condition). Note that just under half of the subjects in the general-story condition had to be given a hint to use the story analogue before they solved the problem.

(isomorphic) mapping between ideas on two domains, and that the similarity of the concepts plays an important role in helping to find these correspondences.

However, the specific models instantiating this theory differ in their success at accounting for these phenomena: the Structure-Mapping Engine (SME; Falkenhainer, Forbus, & Gentner, 1986; 1989), the Incremental Analogy Machine (IAM; see Keane & Brayshaw, 1988; Keane, Ledgeway, & Duff, 1994), the Analogical Constraint Mapping Engine (ACME; Holyoak & Thagard, 1989), and LISA (Hummel & Holyoak, 1997). At present, the LISA model is probably the most adequate model, followed by IAM, SME, and ACME, at dealing with these key phenomena.

For instance, Keane (1997) has reported one key result that was predicted by the IAM model. IAM attempts to find analogies in an incremental fashion; that is, the basic proposal realised in the model is that people try to break an analogy up into chunks of knowledge and map each chunk in a consistent way one after the other. Using so-called "unnatural analogies", Keane gave subjects mapping problems that had a non-thematic or thematic base domain (see Figure 15.6 and Table 15.2). The non-thematic and thematic problems were structurally identical in their written form, but the thematic version told a vignette about a love-triangle. Using his IAM model, Keane predicted that subjects would infer causal relations for the thematic problem, and that this extra relational structure would form a chunk of knowledge that would make the problem easier to map. IAM (and LISA) predicts that causal structure in the base domain alone (i.e., A list) is sufficient to improve mapping, but all other models require relational

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