Absence Makes the Thought Grow Stronger: Reducing Structural Overlap Can Increase Inductive Strength

Computational models of analogy have assumed that the strength of an inductive inference about the target is based directly on similarity of the analogs, and in particular on shared higher-order relations. However, in Experiment 1 we show that reducing analogical overlap by eliminating a higher-order causal relation (a preventive cause present in the source) from the target increased inductive strength even though it decreased similarity of the analogs. In Experiment 2 we extend these findings to cross-domain analogical inferences based on correspondences between higher-order causal relations. Analogical inference appears to be mediated by building and then “running” a causal model. We discuss the implications of the present findings for theories of both analogy and causal inference.