A mathematical approach to semantic network development.

Abstract

Beginning from the notion of semantic network structure, we develop a quantitative description of how much can be learned by an animal whose developmental programme is a set of co-acting epigenetic rules. In the model considered, the activity of the rules regulates the size, connectivity and innate information content of the semantic network. The network itself is associated with a behavioral repertoire. The modeling approach shows how to begin accounting for the effects of both genetic and environmental information, in a manner that quantifies the roles of specific epigenetic rules for psychological development. In previous models the Shannon-Weaver information contentI of a semantic network follows power lawsI ∝ Nξ, withN the number of interrelated concept elements in the network and ξ a scaling exponent labeling a universality class of semantic networks. Our calculations provide evidence that epigenetic rules of the type considered, involving both innate and learned semantic network components, sustain a new universality class for which ξ = 1.