Subcategory vs category fluency: Items and networks in healthy young adults and simulation with a large language model

Abstract Category fluency tasks involve producing words constrained by a semantic field ( animals ). Subcategory fluency involves producing words from categories that are semantically related to a superordinate category but form a restricted set of items ( farm animals ). Here, we study whether people produce different patterns of words in category versus subcategory fluency by looking at differences in the total number of words produced, the properties of the words produced (e.g., frequency) and how people group words together (clusters/switches and network metrics). Forty-eight Dutch-speaking university students responded to three category fluency tasks ( animals, foods, transport ) and three subcategory fluency tasks ( farm animals, fruits, bike parts ). Also, we queried a large language model (LLM) to provide responses for 50 “pseudo-participants” for the same six categories. People in category (versus subcategory) tasks produced more words; words of higher frequency, with fewer orthographic and phonological neighbors, and shorter in length. They also produced fewer cluster switches and bigger clusters. The category and subcategory networks had different structure (e.g., number of nodes, edges, clustering coefficient). With the LLM we simulated the results regarding word properties and cluster size, but found differences regarding correct words, number of switches, and overlapping clusters between foods and fruit fluency. The differences between category and subcategory fluency may stem from differences in mental search in the lexico-semantic system. However, category and subcategory fluency tasks may be different tasks altogether. The LLM simulation provides novel insights (e.g., how words relate, task-order effects) and suggests caution when used to understand human fluency data.