Feature- vs. Relation-Defined Categories: Probab(alistic)ly Not the Same

Relational categories underlie many uniquely human cognitive processes including analogy, problem solving, and scientific discovery. Despite their ubiquity and importance, the field of category learning has focused almost exclusively on categories based on features. Classification of feature-based categories is typically modeled by calculating similarity to stored representations, an approach that successfully models the learning of both probabilistic and deterministic category structures. In contrast, we hypothesize that relational category learning is analogous to schema induction, and relies on finding common relational structures. This hypothesis predicts that relational category acquisition should function well for deterministic categories but suffer catastrophically when faced with probabilistic categories, which contain no constant relations. We report support for this prediction, along with evidence that the schemas induced in the deterministic condition drive categorization of novel and even category-ambiguous exemplars.