Subcategory vs category fluency: Items and networks in healthy young adults and simulation with a large language model.
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 di...
| Published in: | Memory & Cognition Vol. 54; no. 6; pp. 2270 - 2289 |
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| Main Authors: | , , |
| Format: | Article |
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Springer Nature
Aug2026
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=196093710&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 196093710 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0090502X MEG jtl: Memory & Cognition issn: 0090502X maglogo: N pubinfo: dt: Aug2026 vid: 54 iid: 6 pid: 237 pub: Springer Nature artinfo: ui: 196093710 10.3758/s13421-026-01869-3 ppf: 2270 ppct: 19 formats: tig: atl: Subcategory vs category fluency: Items and networks in healthy young adults and simulation with a large language model. aug: au: Rofes, Adrià van Dijk, Demi Zemla, Jeffrey C. affil: https://ror.org/012p63287 Center for Language and Cognition Groningen (CLCG), Faculty of Arts—Neurolinguistics and Language Development, University of Groningen, Oude Kijk in 't Jatstraat 26, 9712EK, Groningen, The Netherlands https://ror.org/012p63287 Research School of Behavioural and Cognitive Neurosciences, University of Groningen, Groningen, The Netherlands https://ror.org/0175ya539 Koninklijke Kentalis, Sint-Michielsgestel, The Netherlands https://ror.org/025r5qe02 Department of Psychology, Syracuse University, Syracuse, NY, USA su: Netherlands Communicative competence Task performance Analysis of variance Semantics College students Phonetics Research funding Phonological awareness Natural language processing Descriptive statistics Simulation methods in education Phonology Thought & thinking sug: subj: Communicative competence Task performance Analysis of variance Semantics College students Phonetics Netherlands Research funding Phonological awareness Natural language processing Descriptive statistics Simulation methods in education Phonology Thought & thinking keyword: Category Clusters Fluency LLM Networks Subcategory Word properties Category Clusters Fluency LLM Networks Subcategory Word properties ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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