Word Class-Based Clustering and Switching Analyses of Phonemic Fluency in Alzheimer’s Disease

EasyChair Preprint no. 6392

Abstract

Verbal fluency tasks are well known to sensitively detect cognitive-linguistic declines in Alzheimer’s disease(AD)(Murphy et al., 2006). The aim of this study was to investigate whether the word class dissociations emerged in the phonemic fluency task and to explore the best predictors to account for the number of correct responses among word class-based clustering and switching behaviors in addition to demographic variables of AD.

Participants were 58 individuals with probable AD from the dementia bank project, Pitt Corpus(Becker et al., 1994). Participants generated words beginning with f for 60 seconds. We categorized the word class for each item and analyzed word class-based mean cluster size and number of switching. As a result, nouns were the most frequently generated word class, consisting of 71% of the total words, followed by verbs(15%) and adjectives(13%). For multiple regression analyses to examine the best predictors for the number of correct responses, the number of switches was the most influential predictor for correct responses, accounting for 52.5% of the total variance. Furthermore, the most influential predictor for the number of switches was the number of verbs, which explained 52.2% of the variance.

In conclusion, current results revealed a strong advantage for nouns in line with previous findings showing that individuals with AD have more difficulties in retrieving verbs than nouns(Cotelli et al., 2006). Switching contributed most to increasing the correct responses. Although the nouns are the most frequently generated word class, verbs turned out to be the most crucial factor for facilitating switching, indicating that the abilities to generate more verbs are related to eliciting more switching behaviors. The results suggest individuals with AD who can activate a diverse linguistic word class can successfully generate more numbers of correct responses with more frequent switching behaviors.

Keyphrases: Alzheimer’s disease, fluency, word class

@Booklet{EasyChair:6392,
  author = {Eunha Jo and Se Jin Oh and Sujin Choi and Jee Eun Sung},
  title = {Word Class-Based Clustering and Switching Analyses of Phonemic Fluency in Alzheimer’s Disease},
  howpublished = {EasyChair Preprint no. 6392},

  year = {EasyChair, 2021}}