SNAFU: The Semantic Network and Fluency Utility

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.

Automating creativity assessment with SemDis: An open platform for computing semantic distance

“…The present work contributes to the growing study of creativity in the context of semantic networks (Christensen & Kenett, 2019;Kenett & Faust, 2019;Zemla, Cao, Mueller, & Austerweil, 2020). Kenett and colleagues have published several recent papers empirically validating the longstanding associative theory of creativity (Mednick, 1962), which posits that creative thinking involves making connections between remote concepts in semantic memory.…”

Evidence against a relation between bilingualism and creativity

“…Participants were given this task three times during the course of the study, with at least one other task between each iteration of the fluency task. Following current best practices for estimating semantic networks of individual participants, we used U-INVITE within the SNAFU toolkit to estimate a semantic network for each individual from their fluency lists [30,43,52].…”

“…U-INVITE conducts a stochastic search to find the network that maximizes the posterior PLOS ONE probability of the data. See Zemla and Austerweil (2018, 2019) and Zemla et al (2020) for more details on this technique and the SNAFU toolkit [30,43,52]. For each participant network, we calculated various network characteristics like ASPL, optimal modularity, connectivity and small-world index.…”

Semantic Network Analysis (SemNA): A Tutorial on Preprocessing, Estimating, and Analyzing Semantic Networks

“…Based on their results, they recommended the Community Network method for groups where the network may not be fully connected and the goal is to minimize non-edge (absent edge) similarities; the Pathfinder Network method was recommended for groups where the network is fully connected and the goal is to maximize edge similarities whereas the Naïve Random Walk method was recommended when the goal is to minimize non-edge similarities. Finally, two methods that are not available in SemNeT but are available in SNAFU (Zemla et al, 2020), U-INVITE and Hierarchical U-INVITE, were recommended for groups when the network is fully connected and the goal is to minimize non-edge similarities and when the network may not be fully connecting and the goal is to maximize edge similarity, respectively. Correlation-based methods were not recommended for any of these conditions.…”

“…Finally, the convert2snafu function will save the data in a format that is compatible with the SNAFU (Semantic Network and Fluency Utility) library (Zemla, Cao, Mueller, & Austerweil, 2020) in Python:…”