On the Generalization of Simple Alternating Category Structures

A fundamental question in the study of human cognition is how people learn to predict the category membership of an example from its properties. Leading approaches account for a wide range of data in terms of comparison to stored examples, abstractions capturing statistical regularities, or logical rules. Across three experiments, participants learned a category structure in a low-dimension, continuous-valued space consisting of regularly alternating regions of class membership (A B A B). The dependent measure was generalization performance for novel items outside the range of the training space. Human learners often extended the alternation pattern--a finding of critical interest given that leading theories of categorization based on similarity or dimensional rules fail to predict this behavior. In addition, we provide novel theoretical interpretations of the observed phenomenon.