Attention, Memory, and Judgment

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A fundamental component of human decision making is how people generate, assess, and test diagnostic hypotheses. Take the task of a clinical diagnostician as an example. The clinician's task is similar to that of a detective in that he or she is compelled to search for clues (i.e., data) that can be used to generate and test possible explanations of the presenting symptoms. The clinician presumably generates likely diagnoses (i.e., disease hypotheses) and actively seeks information to evaluate the generated diagnoses. The clinician's search for information in the environment likely is not random, but is instead guided by those diagnostic hypotheses he or she is presently entertaining. The information or data newly revealed through the search process is used to both evaluate the diagnoses currently under consideration as well as generate new diagnoses. The generation of new diagnoses may, in turn, lead to fresh information search threads where new hypotheses might be brought to mind. At some point, either the search space is exhausted, the clinician continually fails to generate additional plausible hypotheses, or one particular hypothesis gains enough evidential support that the clinician can render a diagnosis with confidence. The goal of the proposed research is two fold. One goal is to better understand the cognitive constraints that govern hypothesis generation, probability judgment, and information search in humans. Understanding these cognitive constraints hopefully will enable us to developed methodologies or technologies to improve diagnostic decision making in real-world decision tasks such as medical diagnosis or intelligence analysis. The second goal is to develop a cognitive model of human judgment that can take as input a piece of data (e.g., a symptom) and generate a set of diagnostic hypotheses to explain that datum, provide probability estimates of the generated hypotheses, and revise both the generated hypotheses and the probability judgments iteratively as new data are experienced. This type of system has natural implications both for understanding human decision making in dynamic tasks and for developing artificial intelligence systems that can outperform human decision makers.