Diagnostic hypothesis-generation processes are ubiquitous in human reasoning. For example, clinicians generate disease hypotheses to explain symptoms and help guide treatment, auditors generate hypotheses for identifying sources of accounting errors, and laypeople generate hypotheses to explain patterns of information (i.e., data) in the environment. The authors introduce a general model of human judgment aimed at describing how people generate hypotheses from memory and how these hypotheses serve as the basis of probability judgment and hypothesis testing. In 3 simulation studies, the authors illustrate the properties of the model, as well as its applicability to explaining several common findings in judgment and decision making, including how errors and biases in hypothesis generation can cascade into errors and biases in judgment.

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This article introduces 2 new sources of bias in probability judgment, discrimination failure and inhibition failure, which are conceptualized as arising from an interaction between error prone memory processes and a support theory like comparison process. Both sources of bias stem from the influence of irrelevant information on participants' probability judgments, but they postulate different mechanisms for how irrelevant information affects judgment. The authors used an adaptation of the proactive interference (PI) and release from PI paradigm to test the effect of irrelevant information on judgment. The results of 2 experiments support the discrimination failure account of the effect of PI on probability judgment. In addition, the authors show that 2 commonly used measures of judgment accuracy, absolute and relative accuracy, can be dissociated. The results have broad implications for theories of judgment.

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