Categorization has a large impact on how people perceive theworld, especially when used to make inferences about uncer-tain features of new objects. While making these inferences,people tend to draw information from only one possible cate-gorization of a new object; in addition, people are sensitive topre-existing correlations between features. Here, we explainthese trends of feature inference using a priming-based cogni-tive process model, and show that our model is distinguishedin that it can explain not only these two main trends, but alsocases where people seem to reverse the first trend and base in-ferences on information from multiple categories.
