Hostname: page-component-76fb5796d-22dnz Total loading time: 0 Render date: 2024-04-29T08:48:36.831Z Has data issue: false hasContentIssue false
  • English
  • Français

Response Time Consistency Is an Indicator of Executive Control Rather than Global Cognitive Ability

Published online by Cambridge University Press:  06 December 2017

Brandon P. Vasquez ,
Malcolm A. Binns  and
Nicole D. Anderson
Brandon P. Vasquez*
Affiliation:
Rotman Research Institute, Baycrest, Toronto, Canada Department of Psychology, University of Toronto, Toronto, Canada
Malcolm A. Binns
Affiliation:
Rotman Research Institute, Baycrest, Toronto, Canada Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
Nicole D. Anderson
Affiliation:
Rotman Research Institute, Baycrest, Toronto, Canada Department of Psychology, University of Toronto, Toronto, Canada Department of Psychiatry, University of Toronto, Toronto, Canada
*
Correspondence and reprint requests to: Brandon P. Vasquez, Neuropsychology & Cognitive Health, Baycrest Health Sciences, 3560 Bathurst Street, Toronto, ON, M6A 2E1. E-mail: bvasquez@baycrest.org
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: Intraindividual variability increases with age, but the relative strength of association with cognitive domains is still unclear. The objective of this study was to examine the relation between cognitive domains and the shape and spread of response time (RT) distributions as indexed by intraindividual standard deviation (ISD), and ex-Gaussian parameters (μ, σ, τ). Methods: Healthy adults (40 young [aged 18–30 years], 40 young-old [aged 65–74 years], and 41 old-old [aged 75–85 years]) completed neuropsychological testing and a touch-screen attention task from which ISD and ex-Gaussian parameters were derived. The relation between RT performance and cognitive domains (memory, processing speed, executive functioning) was examined with structural equation modeling (SEM), and the predictive power of RT distribution indices over age was investigated with linear regression. Results:ISD, μ, and τ, but not σ, showed a linear increase with age group. An SEM showed that independent of age, τ was most strongly associated with executive functioning, while μ exhibited less critical associations. Linear regression indicated that μ and τ explained a significant portion of variance in processing speed and executive ability in addition to age group. Memory was more parsimoniously predicted by age, without any significant contribution of ex-Gaussian parameters. Conclusions: The findings suggest that exceptionally slow responses convey attention lapses through wavering of cognitive control, which strongly correspond to executive functioning tests. General slowing and extremely slow responses predicted processing speed and executive performance beyond age group, indicating that RT metrics are sensitive to differences in cognitive ability. (JINS, 2018, 24, 456–465)

Keywords

Intraindividual variabilityEx-GaussianCognitive controlExecutive functionsCognitive domainsAging
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 24 , Issue 5 , May 2018 , pp. 456 - 465
Copyright
Copyright © The International Neuropsychological Society 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bellgrove, M.A., Hester, R., & Garavan, H. (2004). The functional neuroanatomical correlates of response variability: Evidence from a response inhibition task. Neuropsychologia, 42(14), 1910–1916. http://doi.org/10.1016/j.neuropsychologia.2004.05.007.CrossRefGoogle ScholarPubMed
Bielak, A.M., Cherbuin, N., Bunce, D., & Anstey, K.J. (2014). Intraindividual variability is a fundamental phenomenon of aging: Evidence from an 8-year longitudinal study across young, middle, and older adulthood. Developmental Psychology, 50, 143–151. http://doi.org/10.1037/a0032650.CrossRefGoogle ScholarPubMed
Bielak, A.M., Hultsch, D.F., Strauss, E., MacDonald, S.W.S., & Hunter, M.A. (2010). Intraindividual variability is related to cognitive change in older adults: Evidence for within-person coupling. Psychology and Aging, 25(3), 575–586. http://doi.org/10.1037/a0019503.CrossRefGoogle ScholarPubMed
Bunce, D. (2001). Age differences in vigilance as a function of health-related physical fitness and task demands. Neuropsychologia, 39(8), 787–797. http://doi.org/10.1016/s0028-3932(01)00017-3.CrossRefGoogle ScholarPubMed
Bunce, D., Anstey, K.J., Christensen, H., Dear, K., Wen, W., & Sachdev, P. (2007). White matter hyperintensities and within-person variability in community-dwelling adults aged 60-64 years. Neuropsychologia, 45(9), 2009–2015. http://doi.org/10.1016/j.neuropsychologia.2007.02.006.CrossRefGoogle ScholarPubMed
Bunce, D., Warr, P.B., & Cochrane, T. (1993). Blocks in choice responding as a function of age and physical fitness. Psychology and Aging, 8(1), 26–33. http://doi.org/10.1037/0882-7974.8.1.26.CrossRefGoogle ScholarPubMed
Delis, D.C., Kaplan, E., & Kramer, J.H. (2001). Delis-Kaplan Executive Function System. San Antonio, TX: The Psychological Corporation.Google Scholar
Delis, D.C., Kramer, J.H., Kaplan, E., & Ober, B.A. (2000). Manual for the California Verbal Learning Test, (CVLT-II). San Antonio, TX: The Psychological Corporation.Google Scholar
Dixon, R.A., Garrett, D.D., Lentz, T.L., MacDonald, S.W.S., Strauss, E., & Hultsch, D.F. (2007). Neurocognitive markers of cognitive impairment: Exploring the roles of speed and inconsistency. Neuropsychology, 21(3), 381–399. http://doi.org/10.1037/0894-4105.21.3.381.CrossRefGoogle ScholarPubMed
Duchek, J.M., Balota, D.A., Tse, C.-S., Holtzman, D.M., Fagan, A.M., & Goate, A.M. (2009). The utility of intraindividual variability in selective attention tasks as an early marker for Alzheimer’s disease. Neuropsychology, 23(6), 746–758. http://doi.org/10.1037/a0016583.CrossRefGoogle ScholarPubMed
Dykiert, D., Der, G., Starr, J.M., & Deary, I.J. (2012). Age differences in intra-individual variability in simple and choice reaction time: Systematic review and meta-analysis. PLoS One, 7(10), e45759. http://doi.org/10.1371/journal.pone.0045759.CrossRefGoogle ScholarPubMed
Garrett, D.D., MacDonald, S.W.S., & Craik, F.I.M. (2012). Intraindividual reaction time variability is malleable: Feedback- and education-related reductions in variability with age. Frontiers in Human Neuroscience, 6(May), 1–10. http://doi.org/10.3389/fnhum.2012.00101.CrossRefGoogle ScholarPubMed
Giambra, L.M. (1993). Sustained attention in older adults: Performance and processes. In J. Cerella, J. Rybash, W. Hoyer & M. L. Commons (Eds.), Adult information processing: Limits on loss (pp. 259–272). San Diego: Academic Press.Google Scholar
Grand, J.H.G., Stawski, R.S., & MacDonald, S.W.S. (2016). Comparing individual differences in inconsistency and plasticity as predictors of cognitive function in older adults. Journal of Clinical and Experimental Neuropsychology, 3395(March), 1–17. http://doi.org/10.1080/13803395.2015.1136598.Google Scholar
Haynes, B.I., Bauermeister, S., & Bunce, D. (2017). A systematic review of longitudinal associations between reaction time intraindividual variability and age-related cognitive decline or impairment, dementia, and mortality. Journal of the International Neuropsychological Society, 23(5), 431–445. http://doi.org/10.1017/S1355617717000236.CrossRefGoogle ScholarPubMed
Heaton, R.K., Chelune, G.J., Talley, J.L., Kay, G.G., & Curtis, G. (1993). Wisconsin Card Sorting Test (WCST) manual, revised and expanded. Odessa, FL: Psychological Assessment Resources.Google Scholar
Hultsch, D.F., MacDonald, S.W.S., & Dixon, R.A. (2002). Variability in reaction time performance of younger and older adults. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 57(2), P101–P115. http://doi.org/10.1093/geronb/57.2.P101.CrossRefGoogle ScholarPubMed
Hultsch, D.F., Strauss, E., Hunter, M.A., & MacDonald, S.W.S. (2008). Intraindividual variability, cognition, and aging. In F.I.M. Craik & T.A. Salthouse (Eds.), The handbook of aging and cognition (3rd ed, pp. 491–556). New York, NY: Psychology Press.Google Scholar
Kaplan, E., Goodglass, H., & Weintraub, S. (1983). Boston Naming Test. Philadelphia: Lea & Febiger.Google Scholar
Kennedy, K.M., & Raz, N. (2015). Normal aging of the brain. In A. W. Toga (Ed.), Brain mapping: An encyclopedic reference, Vol. 3, pp. 603–617. Academic Press. Elsevier.CrossRefGoogle Scholar
Lacouture, Y., & Cousineau, D. (2008). How to use MATLAB to fit the ex-Gaussian and other probability functions to a distribution of response times. Tutorials in Quantitative Methods for Psychology, 4(1), 35–45.CrossRefGoogle Scholar
Lövdén, M., Li, S.-C., Shing, Y.L., & Lindenberger, U. (2007). Within-person trial-to-trial variability precedes and predicts cognitive decline in old and very old age: Longitudinal data from the Berlin Aging Study. Neuropsychologia, 45(12), 2827–2838. http://doi.org/10.1016/j.neuropsychologia.2007.05.005.CrossRefGoogle ScholarPubMed
MacDonald, S.W.S., Hultsch, D.F., & Bunce, D. (2006). Intraindividual variability in vigilance performance: Does degrading visual stimuli mimic age-related “neural noise”? Journal of Clinical and Experimental Neuropsychology, 28(5), 655–675. http://doi.org/10.1080/13803390590954245.CrossRefGoogle ScholarPubMed
MacDonald, S.W.S., Hultsch, D.F., & Dixon, R.A. (2003). Performance variability is related to change in cognition: Evidence from the Victoria Longitudinal Study. Psychology and Aging, 18(3), 510–523. http://doi.org/10.1037/0882-7974.18.3.510.CrossRefGoogle ScholarPubMed
Meyers, J.E., & Meyers, K.R. (1995). The Meyers Scoring System for the Rey complex figure and recognition trial: Professional manual. Odessa, FL: Psychological Assessment Resources.Google Scholar
O’Halloran, A.M., Finucane, C., Savva, G.M., Robertson, I.H., & Kenny, R.A. (2014). Sustained attention and frailty in the older adult population. The Journals of Gerontology . Series B, Psychological Sciences and Social Sciences, 69(2), 147–156. http://doi.org/10.1093/geronb/gbt009.CrossRefGoogle Scholar
Parasuraman, R., & Davies, D.R. (1977). A taxonomic analysis of vigilance performance. In R. R. Mackie (Ed.), Vigilance: Theory, operational performance, and physiological correlates (pp. 559–574). New York: Plenum.CrossRefGoogle Scholar
Salthouse, T.A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103(3), 403–428. http://doi.org/10.1037/0033-295x.103.3.403.CrossRefGoogle ScholarPubMed
Salthouse, T.A. (2004). What and when of cognitive aging. Current Directions in Psychological Science, 13(4), 140–144. http://doi.org/10.1111/j.0963-7214.2004.00293.x.CrossRefGoogle Scholar
Schmiedek, F., Oberauer, K., Wilhelm, O., Süß, H.-M., & Wittmann, W.W. (2007). Individual differences in components of reaction time distributions and their relations to working memory and intelligence. Journal of Experimental Psychology. General, 136(3), 414–429. http://doi.org/10.1037/0096-3445.136.3.414.CrossRefGoogle ScholarPubMed
Souchay, C., Isingrini, M., & Espagnet, L. (2000). Aging, episodic memory feeling-of-knowing, and frontal functioning. Neuropsychology, 14(2), 299–309. http://doi.org/10.1037/0894-4105.14.2.299.CrossRefGoogle ScholarPubMed
Spieler, D.H., Balota, D.A., & Faust, M.E. (1996). Stroop performance in healthy younger and older adults and in individuals with dementia of the Alzheimer’s type. Journal of Experimental Psychology. Human Perception and Performance, 22(2), 461–479.CrossRefGoogle ScholarPubMed
Stuss, D.T., Murphy, K.J., & Binns, M.A. (1998). The frontal lobes and performance variability: Evidence from reaction time. Journal of the International Neuropsychological Society, 5, 123.Google Scholar
Stuss, D.T., Murphy, K.J., Binns, M.A., & Alexander, M.P. (2003). Staying on the job: The frontal lobes control individual performance variability. Brain, 126(Pt, 11), 2363–2380. http://doi.org/10.1093/brain/awg237.CrossRefGoogle Scholar
Stuss, D.T., Pogue, J., Buckle, L., & Bondar, J. (1994). Characterization of stability of performance in patients with traumatic brain injury: Variability and consistency on reaction time tests. Neuropsychology, 8(3), 316–324. http://doi.org/10.1037/0894-4105.8.3.316.CrossRefGoogle Scholar
Taconnat, L., Baudouin, A., Fay, S., Clarys, D., Vanneste, S., Tournelle, L., & Isingrini, M. (2006). Aging and implementation of encoding strategies in the generation of rhymes: The role of executive functions. Neuropsychology, 20(6), 658–665. http://doi.org/10.1037/0894-4105.20.6.658.CrossRefGoogle ScholarPubMed
Tse, C.-S., Balota, D.A., Yap, M.J., Duchek, J.M., & McCabe, D.P. (2010). Effects of healthy aging and early stage dementia of the Alzheimer’s type on components of response time distributions in three attention tasks. Neuropsychology, 24(3), 300–315. http://doi.org/10.1037/a0018274.CrossRefGoogle ScholarPubMed
Vasquez, B.P., Binns, M.A., & Anderson, N.D. (2016). Staying on task: Age-related changes in the relationship between executive functioning and response time consistency. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 71(2), 189–200. http://doi.org/10.1093/geronb/gbu140.CrossRefGoogle ScholarPubMed
Wechsler, D. (1997a) Wechsler Adult Intelligence Scale Third Edition, San Antonio, TX: The Psychological Corporation.Google Scholar
Wechsler, D. (1997b) Wechsler Memory Scale Third Edition, San Antonio, TX: The Psychological Corporation.Google Scholar
Welsh, K., Breitner, J., & Magruder-Habib, K. (1993). Detection of dementia in the elderly using telephone screening of cognitive status. Neuropsychiatry, Neuropsychology and Behavioral Neurology, 6(2), 103–110.Google Scholar
West, R., Murphy, K.J., Armilio, M.L., Craik, F.I.M., & Stuss, D.T. (2002). Lapses of intention and performance variability reveal age-related increases in fluctuations of executive control. Brain and Cognition, 49(3), 402–419. http://doi.org/10.1006/brcg.2001.1507.CrossRefGoogle ScholarPubMed
Williams, B.R., Hultsch, D.F., Strauss, E.H., Hunter, M.A., & Tannock, R. (2005). Inconsistency in reaction time across the life span. Neuropsychology, 19(1), 88–96. http://doi.org/10.1037/0894-4105.19.1.88.CrossRefGoogle ScholarPubMed
Zachary, R.A. (1986). Shipley Institute of Living Scale: Revised Manual. Los Angeles, CA: Western Psychological Services.Google Scholar