Abstract
Objectives: This study aims to specify the scoring criteria for the animal fluency task (AFT)
and establish Korean-adjusted animal clustering criteria. Additionally, we aim to develop
an automated analysis approach for the AFT and make this tool accessible for practical use
following validation. To validate this approach, we seek to identify the relationship be-
tween automated and manual analysis methods and examine the differences in correct re-
sponses, switchings, and mean cluster size between younger and older adults. Methods:
275 healthy younger adults and 374 healthy older adults participated in the study. The AFT
was conducted within a 1-minute time window, and data were analyzed for the number of
correct responses, switchings, and mean cluster size. The study proposed five sub-criteria
for scoring correct responses and specified animal clustering criteria into two upper-level
and 14 lower-level categories. Additionally, a comprehensive list of animal examples corre-
sponding to each criterion. Manual analysis was conducted by 14 graduate students, and
the automated analysis is publicly available on GitHub. Results: The findings revealed sig-
nificant positive correlations between automated and manual analyses for correct re-
sponses, switchings, and mean cluster size. Younger adults also produced more correct re-
sponses, exhibited more switching behaviors, and had larger mean cluster sizes than older
adults. Conclusion: This study establishes integrated scoring and clustering standards for
the objective analysis of Korean participants’ performance on the AFT. Furthermore, by
providing an automated analysis approach, we contribute to the development of practical
resource use in both clinical settings and research areas.
Keywords
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Animal fluency task
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Automated analysis
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Scoring
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Clustering criteria
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