Random Item Generation Is Affected by Age
Publication: Journal of Speech, Language, and Hearing Research
Volume 59, Number 5
Pages 1172-1178
Abstract
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 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.
Supplemental Material
Get full access to this article
View all available purchase options and get full access to this article.
References
Albinet, C. T., Boucard, G., Bouquet, C. A., & Audiffren, M. (2012). Processing speed and executive functions in cognitive aging: How to disentangle their mutual relationship? Brain Cognition, 79, 1–11. doi:10.1016/j.bandc.2012.02.001
Albinet, C., Tomporowski, P. D., & Beasman, K. (2006). Aging and concurrent task performance: Cognitive demand and motor control. Educational Gerontology, 32, 689–706. doi:10.1080/03601270600835421
Baddeley, A. D. (1966). The capacity for generating information by randomization. The Quarterly Journal of Experimental Psychology, 18, 119–129. doi:10.1080/14640746608400019
Baddeley, A. D. (1998). The central executive: A concept and some misconceptions. Journal of the International Neuropsychological Society, 4, 523–526.
Baddeley, A. D., Emslie, H., Kolodny, J., & Duncan, J. (1998). Random generation and the executive control of working memory. The Quarterly Journal of Experimental Psychology: Human Experimental Psychology, 51(A), 819–852. doi:10.1080/713755788
Ben-David, B. M., Tewari, A., Shakuf, V., & van Lieshout, P. H. H. M. (2014). Stroop effects in Alzheimer's disease: Selective attention speed of processing, or color-naming? A meta-analysis. Journal of Alzheimer's Disease, 38, 923–938.
Bialystok, E., & Luk, G. (2012). Receptive vocabulary differences in monolingual and bilingual adults. Bilingualism: Language and Cognition, 15, 397–401.
Boucard, G. K., Albinet, C. T., Bugaiska, A., Bouquet, C. A., Clarys, D., & Audiffren, M. (2012). Impact of physical activity on executive functions in aging: A selective effect on inhibition among old adults. Journal of Sport & Exercise Psychology, 34, 808–827.
Breidt, R. (1973). Are perseverations explained by brain damage? Psychiatrica Clinica (Basel), 6, 357–369.
Brugger, P., Monsch, A. U., Salmon, D. P., & Butters, N. (1996). Random number generation in dementia of the Alzheimer type: A test of frontal executive functions. Neuropsychologia, 34, 97–103.
Dunn, L. M., & Dunn, D. M. (2007). Peabody Picture Vocabulary Test–Fourth Edition. San Antonio, TX: Pearson.
Evans, F. J. (1978). Monitoring attention deployment by random number generation: An index to measure subjective randomness. Bulletin of the Psychonomic Society, 12, 35–38.
Fisk, J. E., & Sharp, C. A. (2004). Age-related impairment in executive functioning: Updating, inhibition, shifting, and access. Journal of Clinical and Experimental Neuropsychology, 26, 874–890. doi:10.1080/13803390490510680
Gao, J., & Cai, H. (2000). On the structures and quantification of recurrence plots. Physics Letters A, 270, 75–87.
Ginsburg, N., & Karpiuk, P. (1994). Random generation: Analysis of the responses. Perceptual and Motor Skills, 79, 1059–1067.
Glisky, E. L. (2007). Changes in cognitive function in human aging. In Riddle, D. R. (Ed.), Brain aging: Models, methods, and mechanisms (pp. 3–20). Boca Raton, FL: CRC Press.
Hasher, L., & Zacks, R. T. (1988). Working memory, comprehension, and aging: A review and new view. In Bower, G. H. (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 22, pp. 193–225). New York, NY: Academic Press.
Heuer, H., Janczyk, M., & Kunde, W. (2010). Random noun generation in younger and older adults. The Quarterly Journal of Experimental Psychology, 63, 465–478. doi:10.1080/17470210902974138
Jahanshahi, M., Saleem, T., Ho, A. K., Dirnberger, G., & Fuller, R. (2006). Random number generation as an index of controlled processing. Neuropsychology, 20, 391–399.
Kiefer, M., Ahlegian, M., & Spitzer, M. (2005). Working memory capacity, indirect semantic priming, and Stroop interference: Pattern of interindividual prefrontal performance differences in healthy volunteers. Neuropsychology, 19, 332–344. doi:10.1037/0894-4105.19.3.332
LeCun, Y., Bengio, Y., & Hinton, G. (2015, May 28). Deep learning. Nature, 521, 436–444.
Little, M. A., McSharry, P. E., Roberts, S. J., Costello, D. A. E., & Moroz, I. M. (2007). Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. BioMedical Engineering OnLine, 6, 23. doi:10.1186/1475-925X-6-23
Marwan, N., Romano, M. C., Thiel, M., & Kurths, J. (2007). Recurrence plots for the analysis of complex systems. Physics Reports, 438, 237–329.
Maylor, E. A., & Wing, A. M. (1996). Age differences in postural stability are increased by additional cognitive demands. Journals of Gerontology: Series B: Psychological Sciences and Social Sciences, 51B, P143–P154.
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41, 49–100. doi:10.1006/cogp.1999.0734
Morris, R. G. (1994). Working memory in Alzheimer-type dementia. Neuropsychology, 8, 544–554.
Peters, M., Giesbrecht, T., Jelicic, M., & Merckelbach, H. (2007). The random number generation task: Psychometric properties and normative data of an executive function task in a mixed sample. Journal of the International Neuropsychological Society, 13, 626–634. doi:10.1017/S1355617707070786
Rabinowitz, F. M. (1970). Characteristic sequential dependencies in multiple-choice situations. Psychological Bulletin, 74, 141–148.
Randolph, C. (1998). Repeatable Battery for the Assessment of Neuropsychological Status. San Antonio, TX: Pearson.
Randolph, C. (2008). Repeatable Battery for the Assessment of Neuropsychological Status. Toronto, Ontario, Canada: Psychological Corporation.
Randolph, C. (2012). Repeatable Battery for the Assessment of Neuropsychological Status. San Antonio, TX: Pearson.
Randolph, C., Tierney, M. C., Mohr, E., & Chase, T. N. (1998). The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): Preliminary clinical validity. Journal of Clinical and Experimental Neuropsychology, 20, 310–319. doi:10.1076/jcen.20.3.310.823
Richmond, K., King, S., & Taylor, P. (2003). Modelling the uncertainty in recovering articulation from acoustics. Computer Speech & Language, 17, 153–172.
Rinehart, N. J., Bradshaw, J. L., Moss, S. A., Brereton, A. V., & Tonge, B. J. (2006). Pseudo-random number generation in children with high-functioning autism and Asperger's disorder: Further evidence for a dissociation in executive functioning? Autism, 10, 70–85. doi:10.1177/1362361306062011
Sexton, N. J., & Cooper, R. P. (2014). An architecturally constrained model of random number generation and its application to modeling the effect of generation rate. Frontiers in Psychology, 5, 670. doi:10.3389/fpsyg.2014.00670
Spatt, J., & Goldenberg, G. (1993). Components of random generation by normal subjects and patients with dysexecutive syndrome. Brain and Cognition, 23, 231–242. doi:10.1006/brcg.1993.1057
Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643–662.
Van der Linden, M., Beerten, A., & Pesenti, M. (1998). Age-related differences in random generation. Brain and Cognition, 38, 1–16.
Wagenaar, W. A. (1970). Subjective randomness and the capacity to generate information. Acta Psychologica, 33, 233–242.
Williams, M. A., Moss, S. A., Bradshaw, J. L., & Rinehart, N. J. (2002). Random number generation in autism. Journal of Autism and Developmental Disorders, 32, 43–47.
Information & Authors
Information
Published In
Journal of Speech, Language, and Hearing Research
Volume 59 • Number 5 • October 2016
Pages: 1172-1178
PubMed: 27681687
Copyright
Copyright © 2016 American Speech-Language-Hearing Association.
History
- Received: Feb 20, 2015
- Revised: Sep 30, 2015
- Accepted: Jan 25, 2016
- Published in issue: Oct 1, 2016
Authors
Metrics & Citations
Metrics
Article Metrics
View all metricsCitations
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
For more information or tips please see 'Downloading to a citation manager' in the Help menu.
Citing Literature
- Tim Angelike, Jochen Musch, An improved modeling approach to investigate biases in human random number generation, PLOS One, 10.1371/journal.pone.0324870, 20, 5, (e0324870), (2025).
- Han-Jun Lee, Seok-Jin Hong, Seung-Soo Kim, Young-Yon Kwon, Bong-Hwan Choi, Kyung-Mi Choi, Seo-Hyeong Sheen, Myung-Jin Lee, Sun-Young Hwang, Kyoungwan Park, Younghun Joo, Hwayoung Song, Cheol-Koo Lee, CD4+/CD8+ Ratio and Growth Differentiation Factor 8 Levels in Peripheral Blood of Large Canine Males Are Useful Parameters to Build an Age Prediction Model, The World Journal of Men's Health, 10.5534/wjmh.210003, 40, 2, (316), (2022).
View Options
Sign In Options
ASHA member? If so, log in with your ASHA website credentials for full access.
Member Login