French Language Adaptation of SNAFU (The Semantic Network and Fluency Utility)

Open Research objectives/practices

The open research practices addressed in this case study were making the outputs of research freely accessible – specifically data to adapt language research software for use in French – in addition to using open and collaborative methods and tools to increase efficiency and widen participation in the creation of this data.

Introduction

My thesis project concerned the organization of semantic networks following awake brain surgery. As part of this, I compared semantic network metrics to metrics derived from fluency tasks. Calculating these fluency metrics efficiently and reliably was the aspect of the project targeted by open research practices.

Category fluency tasks are traditionally scored based on the total number of items produced. However, more detailed analyses of semantically related clusters of items offers a rich source of additional information, reflecting semantic search behaviour and the organization of semantic associations (Troyer et al., 1997; Thiele et al., 2016; Ovando-Tellez et al., 2022). Efficiently and reliably counting clusters and switches is painstaking, sometimes with low inter-rater reliability (Ross, 2003). Because of this, the clinic I collaborated with had hundreds of fluency lists, most of which had never been analyzed at this level.

The Semantic Network and Fluency Utility (SNAFU) is a Python library developed for English (Zemla et al., 2020). Given fluency lists as input, SNAFU outputs the exact list of clusters, switches, and more. I adapted a reference file for SNAFU based on clustering rules established by Troyer and colleagues (1997), such that clusters and switches can accurately and consistently be calculated for French language fluency lists.

The reference file contains thousands of French animals, assigned to categories based on taxonomy, habitat, and human use. I created the initial file by manually adjusting and translating the original English file of Zemla and colleagues (2020), and adding further animals based on a Dutch file used by my supervisor Dr. Adrià Rofes. I tested and validated the software on French fluency lists from my own participant group, adding missing animals where needed.

In the pursuit of open research, I made this file available online, such that clinicians and researchers could both use and contribute to the project. Other contributors have included Dr. Marcela Ovando-Tellez at the Institut du Cerveau, who has been using and contributing to the adaptation in creativity research.
Motivation
To date, tools such as SNAFU have been available only for other languages, particularly English. I chose to use open research practices to benefit not only my own project, but those of others who work with French speaking populations.

The benefits of open research practices in the French adaptation of SNAFU are that (1) a more robust adaptation could be created through collaboration with other research teams, and (2) this novel tool will be more widely available for future research. The people who benefit from this project include the countless clinicians and researchers investigating linguistic and semantic patterns in French-language populations, as well as the larger neurolinguistic research community, which can benefit from potential future findings this tool may contribute to.

Lessons learned

I honestly didn’t encounter many barriers in this pursuit of open research. The barriers I encountered trying to analyse fluency lists prior to this project (“closed” science?) are what led me to undertaking this project in the first place, to reduce barriers faced by future researchers. Projects in disciplines beyond linguistics (e.g., creativity) make ample use of cluster and switch analysis, so the team I collaborated with at Lariboisière was very supportive of the work I had done. I felt very supported by my supervisors in both Paris and Groningen, who are strong proponents of open science and encouraged me pursue this.

URLs, references and further information

https://github.com/logangaudet/SNAFU-fr

Ovando-Tellez, M., Benedek, M., Kenett, Y. N., Hills, T., Bouanane, S., Bernard, M., Belo, J., Bieth, T., & Volle, E. (2022). An investigation of the cognitive and neural correlates of semantic memory search related to creative ability. Communications Biology, 5(1), 604.
https://doi.org/10.1038/s42003-022-03547-x

Ross T. P. (2003). The reliability of cluster and switch scores for the Controlled Oral Word Association Test. Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists, 18(2), 153–164.

Troyer, A. K., Moscovitch, M., & Winocur, G. (1997). Clustering and switching as two components of verbal fluency: evidence from younger and older healthy
adults. Neuropsychology, 11(1), 138–146. https://psycnet.apa.org/record/1997-08159-013?doi=1

Thiele, K., Quinting, J. M., & Stenneken, P. (2016). New ways to analyze word generation performance in brain injury: A systematic review and meta-analysis of additional performance measures. Journal of Clinical and Experimental Neuropsychology, 38(7), 764–781. https://doi.org/10.1080/13803395.2016.1163327

Zemla, J. C., Cao, K., Mueller, K. D., & Austerweil, J. L. (2020). SNAFU: The Semantic Network and fluency utility. Behavior Research Methods, 52(4), 1681–1699. https://doi.org/10.3758/s13428-019-01343-w