SNAFU: The Semantic Network and Fluency Utility.

Area of Science:

• Cognitive Psychology
• Computational Neuroscience
• Psycholinguistics

Background:

• Verbal fluency tasks are crucial for diagnosing memory impairments and understanding knowledge retrieval.
• Current manual analysis of fluency data is time-consuming and prone to errors.
• Existing computational methods for semantic network estimation lack standardization and are difficult to implement.

Purpose of the Study:

• To introduce SNAFU (Semantic Network and Fluency Utility), a novel computational tool.
• To automate traditional verbal fluency analyses and estimate semantic networks from fluency data.
• To provide a user-friendly utility for researchers in cognitive science and neuroscience.

Main Methods:

• SNAFU automates the counting of cluster switches, cluster sizes, intrusions, perseverations, and word frequencies.
• The tool estimates semantic networks, representing latent structures of semantic memory.
• The manuscript includes a primer, an illustrative example (food semantic network), and validation against human coders.

Main Results:

• SNAFU successfully automates complex fluency analyses, reducing manual effort and potential errors.
• The tool generates reliable semantic network estimations comparable to trained human coders.
• Validation across multiple datasets confirms the utility and accuracy of SNAFU.

Conclusions:

• SNAFU offers a standardized, efficient, and accurate method for analyzing verbal fluency data.
• The tool facilitates the estimation of semantic networks, advancing our understanding of semantic memory.
• Widespread adoption of SNAFU is expected to accelerate research in cognitive and computational linguistics.