Humans are evidently able to learn contingencies from the co-occurrence of cuesand outcomes. But how do humans judge contingencies when observations of cue andoutcome are learned on different occasions? The pseudocontingency frameworkproposes that humans rely on base-rate correlations across contexts, that is,whether outcome base rates increase or decrease with cue base rates. Here, weelaborate on an alternative mechanism for pseudocontingencies that exploits baserate information within contexts. In two experiments, cue and outcome base ratesvaried across four contexts, but the correlation by base rates was kept constantat zero. In some contexts, cue and outcome base rates were aligned (e.g., cueand outcome base rates were both high). In other contexts, cue and outcome baserates were misaligned (e.g., cue base rate was high, but outcome base rate waslow). Judged contingencies were more positive for contexts in which cue andoutcome base rates were aligned than in contexts in which cue and outcome baserates were misaligned. Our findings indicate that people use the alignment ofbase rates to infer contingencies conditional on the context. As such, they lendsupport to the pseudocontingency framework, which predicts that decision makersrely on base rates to approximate contingencies. However, they challengeprevious conceptions of pseudocontingencies as a uniform inference fromcorrelated base rates. Instead, they suggest that people possess a repertoire ofmultiple contingency inferences that differ with regard to informationalrequirements and areas of applicability.

We have been oversold on the base rate fallacy in probabilistic judgment from an empirical, normative, and methodological standpoint. At the empirical level, a thorough examination of the base rate literature (including the famous lawyer–engineer problem) does not support the conventional wisdom that people routinely ignore base rates. Quite the contrary, the literature shows that base rates are almost always used and that their degree of use depends on task structure and representation. Specifically, base rates play a relatively larger role in tasks where base rates are implicitly learned or can be represented in frequentist terms. Base rates are also used more when they are reliable and relatively more diagnostic than available individuating information. At the normative level, the base rate fallacy should be rejected because few tasks map unambiguously into the narrow framework that is held up as the standard of good decision making. Mechanical applications of Bayes's theorem to identify performance errors are inappropriate when (1) key assumptions of the model are either unchecked or grossly violated, and (2) no attempt is made to identify the decision maker's goals, values, and task assumptions. Methodologically, the current approach is criticized for its failure to consider how the ambiguous, unreliable, and unstable base rates of the real world are and should be used. Where decision makers' assumptions and goals vary, and where performance criteria are complex, the traditional Bayesian standard is insufficient. Even where predictive accuracy is the goal in commonly defined problems, there may be situations (e.g., informationally redundant environments) in which base rates can be ignored with impunity. A more ecologically valid research program is called for. This program should emphasize the development of prescriptive theory in rich, realistic decision environments.