The ability to extract statistical contingencies (e.g., between cause and effect, between response and feedback) is commonly presupposed as a basic module of adaptive behavior. In reality, however, stimulus input rarely contains the complete sets of correlated attributes required to assess the actual contingencies. Instead, cognitive inferences often rely on a base-rate-driven pseudocontingency rule, which links the more (or less) frequent level of one variable to the more (or less) frequent level of the other variable. Empirical evidence shows that logically unwarranted pseudocontingency inferences override genuine contingencies across many research paradigms. Although pseudocontingencies can be severely misleading, they also provide a useful proxy that accurately predicts existing contingencies most of the time.
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