ArticleBehavior research methods2020
SNAFU: The Semantic Network and Fluency Utility.
Article in Behavior research methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Subcategory vs category fluency: Items and networks in healthy young adults and simulation with a large language model.Memory & cognition · 2026Article
- Lexical Search and Retrieval in Aphasia: A Computational Analysis of Semantic Fluency.Journal of speech, language, and hearing research : JSLHR · 2026Article
- Investigating category transitions and interresponse times in fluency tasks.Memory & cognition · 2026Article
- Context-dependent motor-semantic interactions: evidence from dual-task paradigms in semantic fluency.Psychological research · 2026Article
- Animal fluency in people with Parkinson's disease: Item-based performance before and after deep brain stimulation surgery.Journal of neuropsychology · 2026Article
- Probabilistic modeling of the semantic fluency task with extended Markov networks.Behavior research methods · 2026Article
- Category fluency and creative potential in semantic aphasia.Journal of neuropsychology · 2026Article
- Development and Piloting of Co.Ge.: A Web-Based Digital Platform for Generative and Clinical Cognitive Assessment.Journal of personalized medicine · 2025Article
- Emotion word production tasks grant insight into the development of emotion word organization and accessibility.Emotion (Washington, D.C.) · 2025Article
- A Japanese LDA model for automatic clustering analysis of semantic verbal fluency tests.Behavior research methods · 2025Article
- Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements, and future challenges.Psychonomic bulletin & review · 2024Review
- The association of sociodemographic factors with total and item-level semantic fluency metrics.Neuropsychology · 2024Article
- forager: a Python package and web interface for modeling mental search.Behavior research methods · 2024Article
- Mapping the Memory Structure of High-Knowledge Students: A Longitudinal Semantic Network Analysis.Journal of Intelligence · 2024Article
- Spontaneous eye movements reflect the representational geometries of conceptual spaces.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- Clustering and Switching in Semantic Verbal Fluency: Their Development and Relationship with Word Productivity in Typically Developing Greek-Speaking Children and Adolescents.Journal of Intelligence · 2023Article
- Divergent semantic integration (DSI): Extracting creativity from narratives with distributional semantic modeling.Behavior research methods · 2023Article
- Article
- What Drives Task Performance in Animal Fluency in Individuals Without Dementia? The SMART-MR Study.Journal of speech, language, and hearing research : JSLHR · 2023Article
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4 authors.
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Abstract
The verbal fluency task-listing words from a category or words that begin with a specific letter-is a common experimental paradigm that is used to diagnose memory impairments and to understand how we store and retrieve knowledge. Data from the verbal fluency task are analyzed in many different ways, often requiring manual coding that is time intensive and error-prone. Researchers have also used fluency data from groups or individuals to estimate semantic networks-latent representations of semantic memory that describe the relations between concepts-that further our understanding of how knowledge is encoded. However computational methods used to estimate networks are not standardized and can be difficult to implement, which has hindered widespread adoption. We present SNAFU: the Semantic Network and Fluency Utility, a tool for estimating networks from fluency data and automatizing traditional fluency analyses, including counting cluster switches and cluster sizes, intrusions, perseverations, and word frequencies. In this manuscript, we provide a primer on using the tool, illustrate its application by creating a semantic network for foods, and validate the tool by comparing results to trained human coders using multiple datasets.
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