A simple, biologically motivated neural network solves the transitive inference problem

Configural learning problems can be resolved by both rats and humans if they are not too difficult. The configural learning problem which we explore here is transitive inference. Transitive inference (learn the four pairs A>B, B>C, C>D, D>E, then test with the novel pair B?D) was once viewed as a logical problem. However; it is now acknowledged that when the stimuli are appropriate even three year old humans can solve this problem and, as well, so can pigeons and rats. Thus, even though the problem is a simple exercise in logic, there is reason to suspect that mammals, or for that matter neural networks, will solve such a problem without recourse to any explicit syllogistic reasoning. In fact, by casting the input stimuli in a form appropriate for a sequence learning neural network a hippocampal-like network can solve the transitive inference problem. Furthermore, performance is appropriately disrupted by turning the linear sequence of relationships into a nonlinear (circular) relationship