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Random Item Generation (Multani et al., 2016)
journal contribution
posted on 2022-02-23, 05:04 authored by Namita Multani, Frank Rudzicz, Wing Yiu Stephanie Wong, Aravind Kumar Namasivayam, Pascal van LieshoutPurpose: 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 a 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.
