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.
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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 |
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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
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DOI: https://doi.org/10.1007/s41109-026-00817-z
Facts Only
* College students (N = 44) completed an assessment of receptive vocabulary knowledge and a repeated word association task.
* Participants responded to cue words with the first associated word over three list repetitions.
* Word associations were coded using cue-response similarity (word embedding, taxonomic, phonological) and word-level features (concreteness, age of acquisition, frequency).
* Participants with higher vocabulary knowledge produced lower frequency words with a later age of acquisition than those with lower vocabulary knowledge.
* Cue-response similarity decreased over list repetitions.
* Responses more often utilized lower frequency words with a later age of acquisition during repeated trials.
* Lexical metrics and vocabulary knowledge predicted edge weights (word association strength).
* Word embedding similarity and word frequency predicted stronger edge weights.
* Edge weights decreased over list repetitions, with a larger effect in the below-average vocabulary network.
* The above-average vocabulary network exhibited more clusters with shorter average distances between nodes.