Imagine verbal fluency tasks as a treasure hunt: participants search their minds for words associated with a given category. Scientists, curious about how language changes over time, often use these tasks. But analyzing the data is tricky. Traditional methods only look at the number of words produced and miss other important aspects like word order. Fortunately, researchers have turned to recurrence analysis, a tool for visualizing and analyzing complex patterns in time series data. They introduce a metric called the “distance from diagonal” (DfD). By applying this method to a dataset of individuals with Alzheimer’s disease and healthy controls, they discover that DfD can distinguish between the groups and predict diagnoses better than traditional metrics alone. What’s more, this approach allows researchers to explore other features like word order, repetitions, and intrusions beyond just counting words. These findings shed light on deficits in verbal fluency associated with various neurological conditions. If you’re fascinated by language and memory or want to dig deeper into this research’s potential implications, grab your shovels and check out the full article!

The verbal fluency task, where participants name as many instances of a specific semantic or phonemic category as possible in a certain time limit, is widely used to probe language and memory retrieval functions in research and clinical settings. More recently, interests in using longitudinal observations in verbal fluency to examine changes over the lifespan have grown, in part due to the increasing availability of such datasets, yet quantitative methods for comparing repeated measures of verbal fluency responses remain scarce. As a result, existing studies tend to focus only on the number of unique words produced and how this metric changes over time, overlooking changes in other important features in the data, such as the identity of the words and the order in which they are produced. Here, we provide an example of how the literature of recurrence analysis, which aims to visualize and analyze non-linear time series, may present useful visualization and analytical approaches for this problem. Drawing on this literature, we introduce a novel metric (the “distance from diagonal,” or DfD) to quantify semantic fluency data that incorporates analysis of the sequence order and changes between two lists. As a demonstration, we apply these methods to a longitudinal dataset of semantic fluency in people with Alzheimer’s disease and age-matched controls. We show that DfD differs significantly between healthy controls and Alzheimer’s disease patients, and that it complements common existing metrics in diagnostic prediction. Our visualization method also allows incorporation of other less common metrics—including the order that words are recalled, repetitions of words within a list, and out-of-category intrusions. Additionally, we show that these plots can be used to visualize and compare aggregate recall data at the group level. These methods can improve understanding of verbal fluency deficits observed in various neuropsychiatric and neurological disorders.

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