The semantic fluency task is frequently used in psychology by both reseachers and clinicians. SNAFU is tool that helps you analyze fluency data. It can help with:
- Counting cluster switches and cluster sizes
- Counting perseverations
- Detecting intrusions
- Calculating average age-of-acquisition and word frequency
- ...more!
SNAFU also implements multiple network estimation methods which allow you to perform network analysis on your data (see Zemla & Austerweil, 2018). These methods are implemented:
- U-INVITE networks
- Pathfinder networks
- Correlation-based networks
- Naive random walk network
- Conceptual networks
- First Edge networks
SNAFU can be used as a Python library or as a stand-alone GUI. ThePython library is available here:
https://github.com/AusterweilLab/snafu-py
Or install directly using git (auxilliary files are not included):
pip install git+https://github.com/AusterweilLab/snafu-py
The Github repository contains several demo files, and a tutorial covering some basic usage is available in Zemla, Cao, Mueller, & Austerweil, 2020
If you plan to use the correlationBasedNetwork() function, you will need to install the planarity package separately using pip install planarity
A graphical front-end is also available, though it does not contain as many features as the Python library. You can download it for macOS or Windows. Find it here:
| Mac | ||
|---|---|---|
| SNAFU 2.4.1 for macOS (latest version) | ||
| Windows | ||
| SNAFU 2.2.0 for Windows | ||
The primary citation for SNAFU is:
Zemla, J. C., Cao, K., Mueller, K. D., & Austerweil, J. L. (2020). SNAFU: The semantic network and fluency utility. Behavior Research Methods, 52, 1681-1699.
If using the English-language animal scheme (animals_snafu_scheme.csv), also cite:
Troyer, A. K. (2000). Normative data for clustering and switching on verbal fluency tasks. Journal of Clinical and Experimental Neuropsychology, 22, 370-378.
Hills, T. T., Jones, M. N., & Todd, P. M. (2012). Optimal foraging in semantic memory. Psychological Review, 119, 431-440.
The English-language foods scheme (foods_snafu_scheme.csv) should also cite the primary SNAFU publication and Troyer et al. (2000)
If using the English-language age-of-aquisition norms (kuperman.csv):
Kuperman, V., Stadthagen-Gonzalez, H., & Brysbaert, M. (2012). Age-of-acquisition ratings for 30,000 English words. Behavior Research Methods, 44, 978-990.
If using the English-language word frequency norms (subtlex-us.csv):
Brysbaert, M., & New, B. (2009). Moving beyond Kucˇera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior Research Methods, 41, 977– 990.
If using the Dutch-language animal scheme (Dutch_animals_snafu_scheme.csv):
Rofes, A., Beran, M., Jonkers, R., Geerlings, M.I., Vonk, J.M.J. (2023). What drives task performance in animal fluency in individuals without dementia? The SMART-MR study. Journal of Speech, Language, and Hearing Research (ASHA). Retrieved from: https://github.com/jmjvonk/2022_Rofes_SMART-MR/blob/main/Dutch_animals_snafu_scheme.csv
If using the Greek-language animal scheme (animals_Scheme_Greek_Karousou_v.01.2.csv):
Karousou, A., Economacou, D., & Makris, N. (2023). 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, 11(11), 209. https://doi.org/10.3390/jintelligence11110209
If using the Spanish-language animal scheme (animals_ESnoaccent_scheme.csv):
Neergaard, K. D., Zemla, J. C., Lubrini, G., Periañez, J. A., Bernabéu, E., Ríos-Lago, M., ... & Ayesa-Arriola, R. (2025). Novel computational measure of semantic fluency performance associated with first-episode of psychosis. Psychiatry Research, 348, 116462.
The Mexican Spanish-language animal scheme (animals_snafu_mexican_spanish.csv) was adapted from the Spanish-language scheme above and provided by Yamilka Garcia Avila and Yaira Chamorro (Universidad of Guadalajara)
The Dutch-language schemes for bike parts, fruits, foods, transportation, and farm animals were provided by Adria Rofes (University of Groningen).
The Italian-language animal scheme (snafu_Italian_scheme_Costantini.csv) was provided by Sabia Costantini (Universität Potsdam)
If would like to contribute additional files to this repository, and/or would like to change the list citations associated with your work, please contact us.
Check out our Google Group that will be used for troubleshooting. If you have question or comment, e-mail the list at snafu-fluency [at] googlegroups [dot] com