| Abstrakt: |
Introduction: Category and letter fluency tasks are commonly used neuropsychological tasks to evaluate lexical retrieval. Methods: This study used validated automated methods, which allow for more expansive investigation, to analyze speech production of both category ("Animal") and letter ("F") fluency tasks produced by healthy participants (n = 36) on an online platform. Recordings were transcribed and analyzed through automated pipelines, which utilized natural language processing and automatic acoustic processing tools. Automated pipelines calculated overall performance scores, mean inter- word response time, and word start time; errors were excluded from analysis. Each word was rated for age of acquisition (AoA), ambiguity, concreteness, frequency, familiarity, word length, word duration, and phonetic and semantic distance from its previous word. Results: Participants produced significantly more words on the category fluency task relative to the letter fluency task (p < 0.001), which is in line with previous studies. Wilcoxon tests also showed tasks differed on several mean speech measures of words, and category fluency was associated with lower mean AoA (p<0.001), lower frequency (p < 0.001), lower semantic ambiguity (p < 0.001), lower semantic distance (p < 0.001), lower mean inter-word RT (p = 0.03), higher concreteness (p < 0.001), and higher familiarity (p = 0.02), compared to letter fluency. ANOVAs significant interactions for fluency task on total score and lexical measures showed that lower category fluency scores were significantly related to lower AoA and higher prevalence, and this was not observed for letter fluency scores. Finally, word-characteristics changed over time and significant interactions were noted between the tasks, including word familiarity (p = 0.019), semantic ambiguity (p = 0.002), semantic distance (p=0.001), and word duration (p<0.001). Discussion: These findings showed that certain lexical measures such as AoA, word familiarity, and semantic ambiguity were important for understanding how these tasks differ. Additionally, it found that acoustic measures such as inter-word RT and word duration are also imperative to analyze when comparing the two tasks. By implementing these automated techniques, which are reproducible and scalable, to analyze fluency tasks we were able to quickly detect these differences. In future clinical settings, we expect these methods to expand our knowledge on speech feature differences that impact not only total scores, but many other speech measures among clinical populations. [ABSTRACT FROM AUTHOR] |
