The role of textual coherence in incremental analogical mapping

The LISA model of analogical reasoning ( Hummel & Holyoak, 1997) assumes that mapping is performed incrementally within limited-capacity working memory, and that processing is guided by principles of text coherence. Predictions of the model derived by computer simulations were tested in four experiments, using both semantically impoverished structural analogs and semantically rich story analogs. Results for college students revealed a grouping effect. Processing multiple propositions together generated more accurate mappings than did processing individual propositions, but only when propositions that jointly provided strong structural constraints were grouped together. Other experiments revealed asymmetries in mapping and inference accuracy: mappings and inferences generated from a more coherent analog to a less coherent analog were more accurate than those made in the reverse direction. Implications for computational models of analogical reasoning and for education are discussed.