 The disagreement concerning the degree to which human cognition involves symbolic processing is naturally embroiled with the innateness debate—whether higher level abilities such as language and reasoning are part of the human genetic endowment, or whether they are learned. The success of connectionist models at learning tasks starting from randomly chosen weights gives heart to empiricists, who would think that the infant brain is able to construct intelligence from perceptual input using a simple learning mechanism (Elman et al. 1996). On the other hand, nativists in the rationalist tradition argue that at least for grammar-based language, the poverty of perceptual stimulus (Chomsky 1965: 58) entails the existence of a genetically determined mechanism tailored to learning grammar. However, the alignment between connectionism and non-nativism is not so clear-cut. There is no reason that connectionist models cannot be interpreted from a nativist point of view, where the ongoing “learning” represents the process of evolutionary refinement from generation to generation of a species. The idea that the human brain has domain specific knowledge that is genetically determined can be accommodated in the connectionist paradigm by biasing the initial weights of the models to make that knowledge easy or trivial to learn. Connectionist research makes best contact with the innateness debate by providing a new strategy for disarming poverty of stimulus arguments. Nativists argue that association of ideas, the mechanism for learning proposed by the traditional empiricist, is too slender a reed to support the development of higher level cognitive abilities. They suppose that innate mechanisms are essential for learning (for example) a grammar of English from a child’s linguistic input, because the statistical regularities available to “mere association” massively underdetermine that grammar. Connectionism could support an empiricism here by providing a proof-of-concept that such structured knowledge can be learned from inputs available to humans using only learning mechanisms found in non-classical architectures. Of course it is too soon to tell whether this promise can be realized. 