Vanessa Taler1,2, Brendan T Johns3, Michael N Jones4. 1. School of Psychology, University of Ottawa, Canada. 2. Bruyère Research Institute, Ottawa, Canada. 3. Department of Communicative Disorders and Sciences, University at Buffalo, New York. 4. Department of Psychological and Brain Sciences, Indiana University, Bloomington.
Abstract
OBJECTIVES: The present study aimed to characterize changes in verbal fluency performance across the lifespan using data from the Canadian Longitudinal Study on Aging (CLSA). METHODS: We examined verbal fluency performance in a large sample of adults aged 45-85 (n = 12,686). Data are from the Tracking cohort of the CLSA. Participants completed a computer-assisted telephone interview that included an animal fluency task, in which they were asked to name as many animals as they could in 1 min. We employed a computational modeling approach to examine the factors driving performance on this task. RESULTS: We found that the sequence of items produced was best predicted by their semantic neighborhood, and that pairwise similarity accounted for most of the variance in participant analyses. Moreover, the total number of items produced declined slightly with age, and older participants produced items of higher frequency and denser semantic neighborhood than younger adults. DISCUSSION: These findings indicate subtle changes in the way people perform this task as they age. The use of computational models allowed for a large increase in the amount of variance accounted for in this data set over standard assessment types, providing important theoretical insights into the aging process.
OBJECTIVES: The present study aimed to characterize changes in verbal fluency performance across the lifespan using data from the Canadian Longitudinal Study on Aging (CLSA). METHODS: We examined verbal fluency performance in a large sample of adults aged 45-85 (n = 12,686). Data are from the Tracking cohort of the CLSA. Participants completed a computer-assisted telephone interview that included an animal fluency task, in which they were asked to name as many animals as they could in 1 min. We employed a computational modeling approach to examine the factors driving performance on this task. RESULTS: We found that the sequence of items produced was best predicted by their semantic neighborhood, and that pairwise similarity accounted for most of the variance in participant analyses. Moreover, the total number of items produced declined slightly with age, and older participants produced items of higher frequency and denser semantic neighborhood than younger adults. DISCUSSION: These findings indicate subtle changes in the way people perform this task as they age. The use of computational models allowed for a large increase in the amount of variance accounted for in this data set over standard assessment types, providing important theoretical insights into the aging process.
Authors: Ian J Deary; Janie Corley; Alan J Gow; Sarah E Harris; Lorna M Houlihan; Riccardo E Marioni; Lars Penke; Snorri B Rafnsson; John M Starr Journal: Br Med Bull Date: 2009 Impact factor: 4.291