Learning relations between concepts: classification and conceptual combination

People interpret noun-noun compounds like “wind power” by inferring a relational link between the compound’s two constituent concepts. Various studies have examined how people select the best relation for a compound from a set of candidate relations. However, few studies have investigated how people learn such relations in the first place. This paper describes an experiment examining how people learn which relations are possible between concepts. Participants in this experiment learned artificial, laboratory controlled relations between pairs of items and then judged how likely those relations were for new pairs of items. The results showed that people’s judgement of relation likelihood was reliably influenced by the presence of facilitating features for relations and by the diagnosticity of features for relations. A simple exemplar-based model of classification, using both diagnostic and facilitating features, was applied to people’s judgements of relation likelihood. This model accurately predicted people’s judgements of relation likelihood in the experiment, using no free parameters to fit the data.