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,

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

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

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.