How vocabulary knowledge influences word associations: applications of lexical metrics and latent space network models

Abstract This study examines how word association networks differ as a function of vocabulary knowledge using two methods: lexical metrics and latent space modeling. College students (N = 44) completed a standardized assessment of receptive vocabulary knowledge and a repeated word association task, where they responded to cue words with the first word that came to mind over three list repetitions. Word associations were coded for cue-response similarity (word embedding, taxonomic, phonological) and word-level features (concreteness, age of acquisition, frequency). Participants with higher vocabulary knowledge more often produced lower frequency words with a later age of acquisition than their counterparts with lower vocabulary knowledge. Over list repetitions, cue-response similarity decreased and responses more often utilized lower frequency words with a later age of acquisition. We pooled word associations to construct a latent space model, and used lexical metrics and vocabulary knowledge (above-average vs. below-average) to predict edge weights (i.e., word association strength). Both word embedding similarity and word frequency predicted stronger edge weights. Over list repetitions, edge weights decreased with a larger effect in the below-average vocabulary network. The above-average vocabulary network exhibited more clusters with shorter average distances between nodes, suggesting greater differentiation within the lexicon. Taken together, the results indicate minimal differences in cue-response similarities of word associations of adults varying in their vocabulary knowledge, but more diverse word associations among those with above-average vocabularies. Growing one’s vocabulary over the lifespan may influence the organization of the mental lexicon by altering proximities between neighboring words. Acknowledgements This work builds on work presented at the 14th International Conference on Complex Networks and their Applications to appear in Complex Networks & Their Applications XIV (Gravelle & Brooks, 2026b). A previous version of this manuscript was submitted as a chapter of the first author‘s dissertation (Gravelle 2026). The authors would like to thank Alexandria Garzone, Fabienne Geara, and Fiza Akram for their assistance in collecting and transcribing the word association data, and Martin Chodorow for his feedback on our analytic approach. Author information Authors and Affiliations Corresponding author Ethics declarations Conflict of Interests The authors declare no competing interests. Additional information Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary Information Appendix A. Cue Words Listed in Accordance with Dominant Part of Speech Appendix A. Cue Words Listed in Accordance with Dominant Part of Speech Noun | Verb | |---|---| Bridge | Carry | Broom | Clap | Cow | Count | Desk | Crawl | Dog | Cry | Drawer | Dive | Duck | Drive | Feather | Eat | Foot | Give | Fox | Hide | Frog | Kick | Goat | Kneel | Gun | Lick | Hat | Push | Kite | Read | Pillow | Run | Saddle | Sing | Snake | Sit | Sock | Smile | Spoon | Squeeze | Tree | Sweep | Turtle | Swim | Window | Whisper | Zipper | Yawn | Rights and permissions Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. About this article Cite this article Gravelle, C., Brooks, P.J. How vocabulary knowledge influences word associations: applications of lexical metrics and latent space network models. Appl Netw Sci (2026). https://doi.org/10.1007/s41109-026-00817-z Received: Accepted: Published: DOI: https://doi.org/10.1007/s41109-026-00817-z