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Relational versus absolute representation in categorization.

This study explores relational-like and absolute-like representations in categorization. Although there is much evidence that categorization processes can involve information about both the particular physical properties of studied instances and abstract (relational) properties, there has been little work on the factors that lead to one kind of representation as opposed to the other. We tested 370 participants in 6 experiments, in which participants had to classify new items into predefined artificial categories. In 4 experiments, we observed a predominantly relational-like mode of classification, and in 2 experiments we observed a shift toward an absolute-like mode of classification. These results suggest 3 factors that promote a relational-like mode of classification: fewer items per group, more training groups, and the presence of a time delay. Overall, we propose that less information about the distributional properties of a category or weaker memory traces for the category exemplars (induced, e.g., by having smaller categories or a time delay) can encourage relational-like categorization.