Sequential Diagnostic Reasoning with Verbal Information


In sequential diagnostic reasoning, the goal is to infer the probability of a cause event from sequentially observed effects. Typically, studies investigating such tasks provide subjects with precise quantitative information regarding the strength of the relations between causes and effects. By contrast, we examined people’s performance when this information is communicated through qualitative, rather vague verbal terms (e.g., “X occasionally causes symptom A”). We conducted an experiment in which we compared subjects’ judgments with a Bayesian model whose predictions were derived using numeric equivalents of various verbal terms from an unrelated study with different subjects. We found a remarkably close correspondence between subjects’ diagnostic judgments based on verbal information and the model’s predictions, as well as compared to a matched control condition in which information was presented numerically. Additionally, we observed interindividual differences regarding the temporal weighting of evidence.

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