A fundamental component of intelligent behavior is how people make inferences about novel objects, people, and situations. In category-based induction, people use their knowledge of categories to make such inferences about new items from those categories (Will that dog attack? Which treatment will help this patient?). The present research investigates how people make such predictions in a common situation, when the categories are uncertain. For example, if medical treatment is required before a final diagnosis is possible, how do people take account of the various possible diagnoses (categories)? Past research has shown people often make inductions poorly in such situations, focusing on the most likely category and ignoring other possibilities.

The proposed research examines the basis for these errors in explicit, conscious predictions and whether reasoning can be improved by including information from implicit, fast, unconscious predictions. Pilot work suggests that implicit predictions can be more accurate than explicit ones. The proposed research attempts to confirm this finding and explores the psychological mechanisms that underlie the improved reasoning.

This research will have two important contributions. First, it will help in understanding the differences and similarities between conscious and unconscious processes. This distinction is crucial in much research on mind and behavior, and the present research will help us understand why these differences exist. Second, the results may lead to specific proposals for ways to improve predictions in medical, personal, and economic decisions. When categories are uncertain, people often do not make good predictions, but decision aids that help include implicit, unconscious processes may improve performance.

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New York University
New York
United States
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