JSLHR Research Note Random Item Generation Is Affected by Age Namita Multani, a Frank Rudzicz, b,c,d Wing Yiu Stephanie Wong, a Aravind Kumar Namasivayam, a,d and Pascal van Lieshout a,b,c,d,e 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. T he ability to generate sequences randomly has been used to predict important aspects of central executive functioning, because it requires active generation of new strategies and inhibition of stereotypical responses (Baddeley, 1966). For random item generation (RIG), a participant is typically asked to produce a random sequence of items such as letters of the alphabet, numbers, or keystrokes. In general, humans demonstrate greater diffi- culty in producing random series of items, compared with computer-generated random series (Ginsburg & Karpiuk, 1994; Rabinowitz, 1970; Spatt & Goldenberg, 1993). When associated with highly overlearned associations (e.g., ascend- ing sequences of numbers or letters), this capacity for ran- domness may depend on two main factors: (a) the ability to inhibit competing or habitual responses (i.e., suppressing stereotyped sequences) and (b) the ability to allocate atten- tion and working-memory resources to monitoring and updating responses according to a perceived concept of ran- domness (Miyake et al., 2000). Both of these factors place demands on executive function, which may relate to pre- frontal cortical functions (Baddeley, Emslie, Kolodny, & Duncan, 1998; Miyake et al., 2000). Thus, producing random sequences of items relates to central executive function, because generating long random sequences is associated with neither short-term memory deficits nor misunder- stood notions of randomness (Baddeley, 1998; Wagenaar, 1970). RIG tasks have been applied in various clinical populations, including people with autism, Asperger’s dis- order, brain injury, and Alzheimer’s disease (Breidt, 1973; Brugger, Monsch, Salmon, & Butters, 1996; Rinehart, Bradshaw, Moss, Brereton, & Tonge, 2006). Compared with healthy adults, people with neuropsychological deficits display decreased performance in generating random pat- terns of numbers during the execution of such tasks. In a study by Brugger et al. (1996), individuals with Alzheimer’s disease were compared with a healthy control group on their ability to generate random numbers. That analysis showed that people with Alzheimer’s disease were more likely to generate items outside of the allowable set or vocabulary, and with a higher number of consecutive digit pairs (Evans, 1978), which indicates a more stereotypical response pattern (Brugger et al., 1996). This evidence relates a Oral Dynamics Lab, University of Toronto, Ontario, Canada b University of Toronto, Ontario, Canada c Rehabilitation Sciences Institute, University of Toronto, Ontario, Canada d Toronto Rehabilitation Institute—University Health Network, Ontario, Canada e Institute of Biomaterials and Biomedical Engineering, University of Toronto, Ontario, Canada Correspondence to Frank Rudzicz: frank@cs.toronto.edu Editor: Rhea Paul Associate Editor: Swathi Kiran Received February 20, 2015 Revision received September 30, 2015 Accepted January 25, 2016 DOI: 10.1044/2016_JSLHR-L-15-0077 Disclosure: The authors have declared that no competing interests existed at the time of publication. Journal of Speech, Language, and Hearing Research • Vol. 59 • 1172–1178 • October 2016 • Copyright © 2016 American Speech-Language-Hearing Association 1172 Downloaded From: http://jslhr.pubs.asha.org/ by a Humboldt Univ Zu Berlin User on 12/21/2016 Terms of Use: http://pubs.asha.org/ss/rights_and_permissions.aspx