Characterising Innateness in Artificial and Natural Learning

The purpose of this paper is to propose a refinement of the notion of innateness. If we merely identify innateness with bias, then we obtain a poor characterisation of this notion, since any learning device relies on a bias that makes it choose a given hypothesis instead of another. We show that our intuition of innateness is better captured by a characteristic of bias, related to isotropy. Generalist models of learning are shown to rely on an “isotropic” bias, whereas the bias of specialised models, which include some specific a priori knowledge about what is to be learned, is necessarily “anisotropic”. The so-called generalist models, however, turn out to be specialised in some way: they learn “symmetrical” forms preferentially, and have strictly no deficiencies in their learning ability. Because some learning beings do not always show these two properties, such generalist models may be sometimes ruled out as bad candidates for cognitive modelling.