Large-scale network representations of semantics in the mental lexicon

Abstract

The mental lexicon contains the knowledge about words acquired over a lifetime. A central question is how this knowledge is structured and changes over time. Here we propose to represent this lexicon as a network consisting of nodes that correspond to words and links reflecting associative relations between two nodes, based on free association data. A network view of the mental lexicon is inherent to many cognitive theories, but the predictions of a working model strongly depend on a realistic scale, covering most words used in daily communication. Combining a large network with recent methods from network science allows us to answer questions about its organization at different scales simul..