Department of Psychology, University of Wisconsin-Madison, 1202 West Johnson Street, Madison, WI, 53706, USA.
Department of Computer Sciences, University of Wisconsin-Madison, 1210 West Dayton Street, Madison, WI, 53706, USA.
Behav Res Methods. 2020 Aug;52(4):1681-1699. doi: 10.3758/s13428-019-01343-w.
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.
词语流畅性任务——从一个类别中列出单词或以特定字母开头的单词——是一种常用的实验范式，用于诊断记忆障碍，并了解我们如何存储和检索知识。词语流畅性任务的数据以许多不同的方式进行分析，通常需要耗费大量时间且容易出错的手动编码。研究人员还使用来自群体或个体的流畅性数据来估计语义网络——语义记忆的潜在表示，描述概念之间的关系——进一步了解知识是如何编码的。然而，用于估计网络的计算方法没有标准化，并且难以实现，这阻碍了其广泛采用。我们提出了 SNAFU：语义网络和流畅性实用程序，这是一种从流畅性数据中估计网络并实现传统流畅性分析自动化的工具，包括计算簇转换和簇大小、侵入、持续和单词频率。在本文中，我们提供了一个关于使用该工具的入门指南，通过为食品创建语义网络来说明其应用，并使用多个数据集通过比较结果与经过训练的人工编码员来验证该工具。
