Inferring correlations: from exemplars to categories.

Research and theorizing suggest a processing advantage of category-level correlations over exemplar-level correlations. That research has also shown that category-level correlations serve as a proxy for inferring exemplar-level correlations. For example, an individual may learn that the demand for a product category, like cheese, in one store predicts the demand for this category in another. The individual could then draw the unwarranted conclusion that the demand for an exemplar, like cheddar, would also predict the demand for this exemplar in the other store. This notion is supported by previous experiments demonstrating that the subjective exemplar-level correlation follows the implication of the category-level correlation. However, in virtually all previous experiments suggesting a processing advantage for category-level over exemplar-level correlations, the stimulus correlation at the category level was substantial, whereas the correlation at the exemplar level was weak. Here, we tested the hypothesis that individuals process the level that is most informative, either the exemplar or the category level. We presented participants with a zero correlation at the category level, but varied the correlation at the exemplar level. Participants presented with a zero correlation across exemplar products correctly reproduced a zero correlation across product categories. When presented with a substantial correlation at the exemplar level, however, they erroneously reproduced a similar correlation at the category level. These findings therefore imply that there is no general processing advantage for correlations at higher aggregation levels. Instead, individuals seemingly attend to the level that holds the most regular information. Findings are discussed regarding the role of covariation strength in correlation detection and use.