Differential Effects of Causality and Correlation on Inference

A category is a set of entities associated by specific characteristics (features). These features can have different relations between one another, including correlations and causal connections. The purpose of this study was to examine how the relations between features would affect the inference of unknown features of new entities from a given set of features. Categories and their relations were learned in a Learning Phase, whereas features were inferred in Transfer and Selection Phases. Correct inference of feature was enhanced by correlation between the features given and the features inferred. It is less clear whether causal connections further enhanced correct inference of features over and above the effect of the correlation. Future research of this topic may benefit from utilizing more difficult tasks, repeating instructions, or manipulating the participants' understanding of the relation in ways other than administration of instructions.