SNAFU - The Semantic Network and Fluency Utility

December 08 2017
Keywords: python,fluency,semanticnetworks,memory

Overview

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 the mental organization 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 will compute several basic network measures (e.g., average node degree, others) on these representations, and allow researchers to export these networks for additional analysis.

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:

https://github.com/AusterweilLab/semnet

Currently SNAFU has limited documentation, but a good place to start is to check out one of the demo files such as make_graphs.py and e-mail us if you run into trouble.

A graphical front-end is also available, though with even less documentation (as of now). The GUI has less functionality than the Python library, but includes many of the most important bits. Find it here:

https://github.com/AusterweilLab/snafu

The easiest way to get started is to check our the GUI hosted on our servers, and click the `?’ for a brief walkthrough:

http://alab.psych.wisc.edu/snafu/


The Austerweil Lab thanks its previous and current funders.