Application of advanced language technologies in analysis of category naming fluency task in healthy participants

Abstract Background Category naming fluency tasks have been used frequently to measure patients’ working memory and executive functions. Previous studies show that differences in scores in this task could reflect features of neurodegenerative disease. To better understand decreased performance in patients, it is critical to establish performance in healthy aging populations. Here, we investigated differences in total scores and lexical/speech features of words produced during animal fluency tasks of healthy young and older speakers which can only be derived with novel, automated, computational technologies. Method Data were collected in 52 young participants (26.9% males, mean age = 23.5±3.17) and 46 elderly participants (63.0% males, mean age = 69.9±6.77). Participants were instructed to list as many animals as possible for 60 seconds. Speech was transcribed and automatically force‐aligned. Transcripts were run through our automated Python pipeline to calculate the total number of animals listed and rate each word for the following semantic parameters: age of acquisition (AoA), ambiguity, familiarity, frequency. We also calculated phonetic and semantic distances between each word and its antecedent. Linear regression models tested the association between these automated language measures and total scores. We also examined the differences of the young and elderly groups using t‐tests. Result Elderly participants produced significantly fewer animals than young participants (p<0.001). Higher overall scores were associated with higher mean AoA (beta = 0.22, p = 0.049) and longer word duration (beta = 1.62, p = 0.037), as well as less frequent (beta = ‐0.51, p = 0.021) and familiar (beta = ‐3.96, p<0.001) animal names. We found no interaction with the age groups in any of the models. Elderly participants produced less frequent animal names than young participants (p = 0.029), along with significantly reduced phonetic distance scores (p = 0.008). Elderly participants scored significantly higher articulation rates (p = 0.007) and had a faster speech rate (p<0.001), attributable to producing more non‐animal words. Conclusion This study demonstrates the applicability of advanced language technologies in analyzing category naming fluency tasks in healthy adults. We developed an automated scoring system and identify novel semantic and phonetic measures from digitally recorded fluency tasks. We also quantified some confounding age effects which could be explained by lexical/speech differences of words produced by the groups.