Neural system interactions underlying human transitive inference

Human problem solving relies on multiple strategies supported by dynamic neural network interactions. The transitive inference (TI) problem solving task can be accomplished by the extraction of relations among stimuli or by responding to reinforcement histories of items using associative learning. Relational and associative strategies are assumed to rely on the hippocampus and caudate nucleus, respectively; which compete to control behavior. However, we found that increased recruitment of both systems in TI is correlated with greater accuracy and awareness, and reduced associative responding to single items. Contrary to prior assumptions, the hippocampus and caudate interact cooperatively to facilitate successful TI. We suggest that the dynamics of the relationship between the hippocampus and caudate depends critically upon task demands.