We examined how decision makers generate and evaluate hypotheses when data are presented sequentially. In the first 2 experiments, participants learned the relationship between data and possible causes of the data in a virtual environment. Data were then presented iteratively, and participants either generated hypotheses they thought caused the data or rated the probability of possible causes of the data. In a 3rd experiment, participants generated hypotheses and made probability judgments on the basis of previously stored general knowledge. Findings suggest that both the hypotheses one generates and the judged probability of those hypotheses are heavily influenced by the most recent evidence observed and by the diagnosticity of the evidence. Specifically, participants generated a narrow set of possible explanations when the presented evidence was diagnostic compared with when it was nondiagnostic, suggesting that nondiagnostic evidence entices participants to cast a wider net when generating hypotheses.
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