Large-scale network representations of semantics in the mental lexicon

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 simultaneously, such as: How efficient and robust is lexical knowledge represented considering the global network architecture? What are the organization principles of words in the mental lexicon (i.e. thematic versus taxonomic)? How does the local connectivity with neighboring words explain why certain words are processed more efficiently than others? Networks built from word associations are specifically suited to address prominent psychological phenomena such as developmental shifts, individual differences in creativity, or clinical states like schizophrenia. While these phenomena can be studied using these networks, various future challenges and ways in which this proposal complements other perspectives are also discussed.