What Drives Task Performance in Animal Fluency in Individuals Without Dementia? The SMART-MR Study
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| Title: | What Drives Task Performance in Animal Fluency in Individuals Without Dementia? The SMART-MR Study |
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| Authors: | Adrià Rofes (ORCID |
| Source: | Journal of Speech, Language, and Hearing Research. 2023 66(9):3473-3485. |
| Availability: | American Speech-Language-Hearing Association. 2200 Research Blvd #250, Rockville, MD 20850. Tel: 301-296-5700; Fax: 301-296-8580; e-mail: slhr@asha.org; Web site: http://jslhr.pubs.asha.org |
| Peer Reviewed: | Y |
| Page Count: | 13 |
| Publication Date: | 2023 |
| Sponsoring Agency: | National Institute on Aging (NIA) (DHHS/NIH) |
| Contract Number: | K99AG066934 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Indo European Languages, Language Skills, Executive Function, Animals, Cognitive Ability, Neurological Impairments, Neurological Organization, Cognitive Tests, Dementia, Word Frequency, Adults |
| DOI: | 10.1044/2023_JSLHR-22-00445 |
| ISSN: | 1092-4388 1558-9102 |
| Abstract: | Purpose: In this study, we aim to understand whether and how performance in animal fluency (i.e., total correct word count) relates to linguistic levels and/or executive functions by looking at sequence information and item-level metrics (i.e., clusters, switches, and word properties). Method: Seven hundred thirty-one Dutch-speaking individuals without dementia from the Second Manifestations of ARTerial disease-Magnetic Resonance study responded to an animal fluency task (120 s). We obtained cluster size and number of switches for the task, and eight different word properties for each correct word produced. We detected variables that determine total word count with random forests, and used conditional inference trees to assess points along the scales of such variables, at which total word count changes significantly. Results: Number of switches, average cluster size, lexical decision response times, word frequency, and concreteness determined total correct word count in animal fluency. People who produced more correct words produced more switches and bigger clusters. People who produced fewer words produced fewer switches and more frequent words. Conclusions: Concurrent with existing literature, individuals without dementia rely on language and executive functioning to produce words in animal fluency. The novelty of our work is that such results were shown based on a data-driven approach using sequence information and item-level metrics. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| Accession Number: | EJ1406913 |
| Database: | ERIC |
| Abstract: | Purpose: In this study, we aim to understand whether and how performance in animal fluency (i.e., total correct word count) relates to linguistic levels and/or executive functions by looking at sequence information and item-level metrics (i.e., clusters, switches, and word properties). Method: Seven hundred thirty-one Dutch-speaking individuals without dementia from the Second Manifestations of ARTerial disease-Magnetic Resonance study responded to an animal fluency task (120 s). We obtained cluster size and number of switches for the task, and eight different word properties for each correct word produced. We detected variables that determine total word count with random forests, and used conditional inference trees to assess points along the scales of such variables, at which total word count changes significantly. Results: Number of switches, average cluster size, lexical decision response times, word frequency, and concreteness determined total correct word count in animal fluency. People who produced more correct words produced more switches and bigger clusters. People who produced fewer words produced fewer switches and more frequent words. Conclusions: Concurrent with existing literature, individuals without dementia rely on language and executive functioning to produce words in animal fluency. The novelty of our work is that such results were shown based on a data-driven approach using sequence information and item-level metrics. |
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| ISSN: | 1092-4388 1558-9102 |
| DOI: | 10.1044/2023_JSLHR-22-00445 |
