What is it about?
As people age, many notice that finding the right words—especially action-related ones like “run” or “clean”—gets harder. This study looked at how younger and older adults retrieve such words during a one-minute challenge where they had to say as many action verbs as possible. We analyzed not just how many words they said, but how they searched for them—whether they jumped between different types of actions or stayed within one category. We used a method called hierarchical clustering to group the words based on shared features like movement or sensation. The results showed that older adults used fewer strategies and produced fewer varied words. Those with better working memory skills, however, tended to perform better. These findings help us understand how language and memory change with age and may one day help with earlier screening of cognitive decline, such as in dementia.
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Why is it important?
To understand how people search for and say action words, we need to go beyond just counting how many words they say—we need to look at the patterns in how they think. This kind of deeper, qualitative analysis requires grouping similar words together. But action verbs are complex—they often involve multiple senses and body parts, which makes them hard to classify. Our study introduced a new way to organize action verbs based on 11 different features, such as hand movement, sound, and even internal sensations, using something called the Lancaster sensorimotor norms. We applied a method called hierarchical clustering to automatically group these verbs and tested how these groupings reflected real differences between younger and older adults. This is one of the first studies to create and validate such a system for verbs. It not only provides a better way to study language and memory in aging but may also help us spot early signs of cognitive changes using a simple task.
Perspectives
While working on this study, I often felt the limitations of not having a well-established sensorimotor norm for Korean. At the same time, I was deeply grateful for the tools and resources available in English that made this kind of analysis possible—even with the extra challenge of translation. Collaborating with researchers from different fields also opened my eyes to methods not typically used in speech-language pathology, like computational clustering methods. It was a learning experience that helped me grow both intellectually and professionally. I hope this paper not only broadens the scientific understanding of language and aging, but also offers readers the same kind of inspiration and expanded perspective that I gained while writing it.
Yae Rin Yoo
Ewha Womans University
Read the Original
This page is a summary of: Aging-Related Changes in Switching and Cluster Diversity in the Action Verbal Fluency Task Using Hierarchical Clustering Analysis, American Journal of Speech-Language Pathology, July 2025, American Speech-Language-Hearing Association (ASHA),
DOI: 10.1044/2025_ajslp-24-00399.
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