SNAFU - The Semantic Network and Fluency Utility

December 08 2017
Keywords: python, fluency, semantic networks, memory


The semantic fluency task (listing items within a category) is frequently used in psychology by both reseachers and clinicians. Analysis of fluency data is often done by hand, which is time-consuming and error prone. SNAFU automates the computation of many commonly used metrics, including counting perseverations, cluster size, switch count, and more.

In recent years, semantic fluency data has been used by many researchers to infer representations of semantic categories (such as animals). SNAFU implements many algorithms that use fluency data to construct semantic networks, a representation of how category members are organized within the mind.

SNAFU is intended primarily for research psychologists who wish to analyze fluency data and compare the semantic networks of different groups or individuals.

How do I use it?

SNAFU is available in several flavors. To get the most out of it, you might consider using SNAFU as a Python library, available here:

Or install directly using git (auxilliary files are not included):

pip install git+

The Github repository contains several demo files. A tutorial is also available through Zemla, Cao, Mueller, & Austerweil (2020)

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:

SNAFU 2.4.1 for Windows

SNAFU 2.4.1 for macOS

To install the Windows version, simply download and unzip the file above. To install the macOS version, download the dmg file above, and drag to your computer (e.g. your Desktop or Applications folder).

The Austerweil Lab thanks its previous and current funders.