Reinforcement accounts for transitive inference performance

Transitive inference is the ability, given thatA >B andB >C, to infer thatA >C. Pigeons, rats, chimpanzees, squirrel monkeys, and humans as young as 4 years have all been shown capable of this. In this paper, simple associative learning models are explored as accounts of nonverbal transitive inference performance. A Bush-Mosteller-based model can account for transitive inference under limited conditions. A Rescorla-Wagner-based model can account for transitive inference under all conditions in the literature, but cannot account for some additional nontransitive tests. A final configural model can also account for these nontransitive data. The ability of this model to account for transitive inference formation in humans is also considered.