Random Item Generation Is Affected by Age

• Published in: Journal of speech, language, and hearing research language, and hearing research, 2016-10, Vol.59 (5), p.1172-1178
• Main Authors: Multani, Namita, Rudzicz, Frank, Wong, Wing Yiu Stephanie, Namasivayam, Aravind Kumar, van Lieshout, Pascal
• Format: Article
• Language: English
• Subjects: Adult Learning; Age; Age Differences; Aged; Aging (Individuals); Aging - psychology; Alphabets; Alzheimer's disease; Alzheimers Disease; Attention; Autism; Cognitive Ability; Cognitive Measurement; Color; Comparative Analysis; Control Groups; Correlation; Data collection; Executive Function; Executive function (Psychology); Female; Females; Humans; Inhibition; Inhibition (Psychology); Intelligence Tests; Interference (Learning); Machine Learning; Male; Males; Measurement; Memory; Neural networks; Neural Networks (Computer); Neuropsychological Tests; Nonlinear Dynamics; Numbers; Older Adults; Older people; Outcome Measures; Peabody Picture Vocabulary Test; Pilot Projects; Psychological aspects; Reaction Time; Regression Analysis; Semantics; Semiotics; Short Term Memory; Statistical Analysis; Stroop Color Word Test; Stroop task; Studies; Verbal Ability; Visual Stimuli; Vocabulary; Young Adult; Young Adults
• ISSN: 1092-4388; 1558-9102
• DOI: 10.1044/2016_JSLHR-L-15-0077

Purpose: Random item generation (RIG) involves central executive functioning. Measuring aspects of random sequences can therefore provide a simple method to complement other tools for cognitive assessment. We examine the extent to which RIG relates to specific measures of cognitive function, and whether those measures can be estimated using RIG only. Method: Twelve healthy older adults (age: M = 70.3 years, SD = 4.9; 8 women and 4 men) and 20 healthy young adults (age: M = 24 years, SD = 4.0; 12 women and 8 men) participated in this pilot study. Each completed an RIG task, along with the color Stroop test, the Repeatable Battery for the Assessment of Neuropsychological Status, and the Peabody Picture Vocabulary Test--Fourth Edition (Dunn & Dunn, 2007). Several statistical features extracted from RIG sequences, including recurrence quantification, were found to be related to the other measures through correlation, regression, and a neural-network model. Results: The authors found significant effects of age in RIG and demonstrate that nonlinear machine learning can use measures of RIG to accurately predict outcomes from other tools. Conclusions: These results suggest that RIG can be used as a relatively simple predictor for other tools and in particular seems promising as a potential screening tool for selective attention in healthy aging.