At root, the systematicity debate over classical versus connectionist explanations for cognitive architecture turns on quantifying the degree to which human cognition is systematic. We introduce into the debate recent psychological data that provides strong support for the purely structure-based generalizations claimed by Fodor and Pylyshyn (1988). We then show, via simulation, that two widely used connectionist models (feedforward and simple recurrent networks) do not capture the same degree of generalization as human subjects. However, we show that this limitation is overcome by tensor networks that support relational processing.

PDP model of human analogical reasoning is presented which is designed to incorporate psychologically realistic processing capacity limitations. Capacity is defined in terms of the complexity of relations that can be processed in parallel. Relations are represented in the model by computing the tensor product of vectors representing predicates and arguments. Relations in base and target are superimposed. Based on empirical evidence of capacity limitations, the model is limited to processing one quaternary relation in parallel (rank 5 tensor product). More complex relations are processed by conceptual chunking (receding to fewer arguments, but with loss of access to some relations) or segmentation (processing components of the structure serially). The model processes complex analogies, such as heat flow-water flow, and atom-solar system, while remaining within capacity limitations.