Skip to main content Accessibility help
Hostname: page-component-cd4964975-ppllx Total loading time: 0 Render date: 2023-03-31T22:26:43.077Z Has data issue: true Feature Flags: { "useRatesEcommerce": false } hasContentIssue true

Chapter 12 - Alterations in Executive Functions with Aging

Published online by Cambridge University Press:  30 November 2019

Kenneth M. Heilman
University of Florida
Stephen E. Nadeau
University of Florida
Get access


It is frequently reported that processing speed slows and executive functions (EFs) become less effective in the course of healthy aging. This chapter highlights research supporting these claims in three areas of investigation: cognitive aging research, the neuropsychological perspective, and studies evaluating the association of EF with structural and functional imaging measures. Several themes emerge in this review. For example, diminished processing speed with aging appears to reflect aging-related changes in the anterior cingulate/superior medial frontal cortex, as well as perceptuomotor slowing. The definition of EF varies between different publications and there is a need for more precise operational definitions. There is also a need to decompose EFs into their component processes. Impairments of EF are strongly related to damage in prefrontal regions, but disorders of EF also occur with injury to nonfrontal regions, indicating that complex networks are involved in EF. Additionally, domain-specific changes beyond the changes in EF are important considerations in network analyses. We propose a method to advance future research on EF by using focal frontal lesion studies and neural network principles as frameworks to expand our understanding of aging-related changes in EF and processing speed.

Publisher: Cambridge University Press
Print publication year: 2019

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.)


Lezak, MD. Neuropsychological assessment, 3rd ed. New York: Oxford University Press; 1995.Google Scholar
Park, DC, Reuter-Lorenz, P. The adaptive brain: Aging and neurocognitive scaffoldingAnnu Rev Psychol 2009;60: 173–96.CrossRefGoogle ScholarPubMed
Zacks, RT, Hasher, L, Li, KZH. Human memory. In: Craik, FIM, Salthouse, TA, editors. The handbook of aging and cognitionMahwah, NJ: Lawrence Erlbaum; 2000, pp. 293357.Google Scholar
Craik, FIM, Bialystok, E. Cognition through the lifespan: Mechanisms of change. Trends Cogn Sci 2006;10: 131–8.CrossRefGoogle ScholarPubMed
Verhaeghen, P, Cerella, J. Aging, executive control, and attention, a review of meta-analyses. Neurosci Biobehav Rev 2002;26: 849–57.CrossRefGoogle ScholarPubMed
Reimers, S, Maylor, EA. Task switching across the life span: Effects of age on general and specific switch costsDev Psychol 2005;41: 661–71.CrossRefGoogle ScholarPubMed
Miyake, A, Friedman, NP, Emerson, MJ, Witzki, AH, Howerter, A, Wager, TD. The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysisCogn Psychol 2000;41: 49100.CrossRefGoogle ScholarPubMed
Jacoby, LL. A process dissociation framework: Separating automatic from intentional uses of memoryJ Mem Lang 1991;30: 513–41.CrossRefGoogle Scholar
Zelazo, PD, Craik, FIM, Booth, L. Executive function across the life span. Acta Psychol 2004;115: 167–83.CrossRefGoogle ScholarPubMed
Cepeda, NJ, Kramer, AF, Gonzalez De Sather, JC. Changes in executive control across the life span: Examination of task-switching performanceDev Psychol 2001;37: 715–30.CrossRefGoogle ScholarPubMed
Hasher, L, Zacks, RT, May, CP. Inhibitory control, circadian arousal, and age. In: Gopher, D, Koriat, A, editors. Attention and performance, vol. 17. Cambridge, MA: MIT Press; 1999, pp. 653–75.Google Scholar
Hasher, L, Zacks, RT. Working memory, comprehension, and aging: A review and a new view. In: Bower, GH, editor. The psychology of learning and motivation, vol. 22. San Diego, CA: Academic Press; 1988, pp. 193225.Google Scholar
Lhermitte, F. Human autonomy and the frontal lobes. 2. Patient behavior in complex and social situations – The environmental dependency syndrome. Ann Neurol 1986;19: 335–43.CrossRefGoogle Scholar
West, R, Alain, C. Age-related decline in inhibitory control contributes to the increased Stroop effect observed in older adultsPsychophysiol 2000;37: 179–89.CrossRefGoogle ScholarPubMed
Verhaeghen, P, De Meersman, L. Aging and the Stroop effect: A meta-analysisPsychol Aging 1998;13: 120–6.Google ScholarPubMed
Braver, TS, West, R. Working memory, executive control, and aging. In: Craik, FIM, Salthouse, TA, editors. The handbook of aging and cognition, 3rd ed. New York: Psychology Press; 2008, pp. 311–72.Google Scholar
De Beni, R, Palladino, P. Decline in working memory updating through ageing: Intrusion error analysesMemory 2004;12: 7589.CrossRefGoogle ScholarPubMed
Salthouse, TA, Atkinson, TM, Berish, DE. Executive functioning as a potential mediator of age-related cognitive decline in normal adults. J Exp Psychol General 2003;132: 566–94.CrossRefGoogle ScholarPubMed
Baddeley, AD, Hitch, G. Working memory. In: Bower, GH, editor. The psychology of learning and motivation, vol. 8. New York: Academic Press; 1974, pp. 4789.Google Scholar
Salthouse, TA. Individual differences in working memory and aging. In: Logie, RH, Morris, RG, editors. Working memory and ageing. New York: Psychology Press; 2015, pp. 120.Google Scholar
Craik, FIM, Bialystok, E, Gillingham, S, Stuss, DT. Alpha Span: A measure of working memoryCan J Exp Psychol 2018 Sep;72(3): 141–52.CrossRefGoogle ScholarPubMed
Mayr, U, Kliegl, R. Sequential and coordinative complexity: Age-based processing limitations in figural transformationsJ Exp Psychol Learn Mem Cogn 1993;19:1297–320.CrossRefGoogle ScholarPubMed
Craik, FIM. Age differences in human memory. In: Birren, JE, Schaie, KW, editors. Handbook of the psychology of aging. New York: Van Nostrand Reinhold; 1977, pp. 384420.Google Scholar
Braver, TS, Cohen, JD. Working memory, cognitive control, and the prefrontal cortex: Computational and empirical studiesCog Processing 2001;2: 2555.Google Scholar
Macpherson, SE, Phillips, LH, Della Sala, S. Age, executive function and social decision making: A dorsolateral prefrontal theory of cognitive agingPsychol Aging 2002;17: 598609.CrossRefGoogle ScholarPubMed
D’Esposito, M, Postle, BR. The cognitive neuroscience of working memory. Ann Rev Psychol 2015;66:115–42.Google ScholarPubMed
Jennings, JM, Jacoby, LL. Automatic versus intentional uses of memory: Aging, attention, and control. Psychol Aging 1993;8: 283–93.CrossRefGoogle Scholar
Hay, JF, Jacoby, LL. Separating habit and recollection in young and older adults: Effects of elaborative processing and distinctivenessPsychol Aging 1999;14: 122–34.CrossRefGoogle Scholar
Braver, TS, Barch, DM. A theory of cognitive control, aging cognition, and neuromodulationNeurosci Biobehav Rev 2002;26: 809–17.CrossRefGoogle ScholarPubMed
Braver, TS, Barch, DM, Keys, BA, Carter, CS, Cohen, JD, Kaye, JA, et al. Context processing in older adults: Evidence for a theory relating cognitive control to neurobiology in healthy agingJ Exp Psychol Gen 2001;130: 746–63.CrossRefGoogle ScholarPubMed
Koen, JD, Yonelinas, AP. Recollection, not familiarity, decreases in healthy aging: Converging evidence from four estimation methods. Memory 2016;24: 7588.CrossRefGoogle Scholar
Koen, JD, Yonelinas, AP. The effects of healthy aging, amnestic MCI and Alzheimer’s disease on recollection and familiarity: A meta-analytic review. Neuropsychol Rev 2014;24: 332–54.CrossRefGoogle Scholar
Salthouse, TA. The processing-speed theory of adult age differences in cognition. Psychol Rev 1996;103: 403–28.CrossRefGoogle ScholarPubMed
Salthouse, TA. Influence of processing speed on adult age differences in working memoryActa Psychol 1992;79: 155–70.CrossRefGoogle ScholarPubMed
Craik, FIM, Byrd, M. Aging and cognitive deficits: The role of attentional resources. In: Craik, FIM, Trehub, SE, editors. Aging and cognitive processes. New York: Plenum Press; 1982, pp. 191211.CrossRefGoogle Scholar
Anderson, ND, Craik, FIM, Naveh-Benjamin, M. The attentional demands of encoding and retrieval in younger and older adults: I. Evidence from divided attention costs. Psychol Aging 1998;13: 405–23.CrossRefGoogle ScholarPubMed
Craik, FIM. On the transfer of information from temporary to permanent memory. Philos Trans R Soc Lond B 1983;302: 341–59.Google Scholar
Craik, FIM. A functional account of age differences in memory. In: Klix, F, Hagendorf, H, editors. Human memory and cognitive capabilities. Amsterdam: North‑Holland; 1986, pp. 409–22.Google Scholar
Verhaeghen, P, Salthouse, TA. Meta-analyses of age–cognition relations in adulthood: Estimates of linear and non-linear age effects and structural models. Psychol Bull 1997;122: 231–49.CrossRefGoogle Scholar
Waugh, NC, Vyas, S. Expectancy and choice reaction time in early and late adulthood. Exp Aging Res 1980;6: 563–7.CrossRefGoogle ScholarPubMed
Der, G, Deary, IJ. Age and sex differences in reaction time in adulthood: Results from the United Kingdom Health and Lifestyle Survey. Psychol Aging 2006;21: 6273.CrossRefGoogle ScholarPubMed
Godefroy, O, Roussel, M, Despretz, P, Quaglino, V, Booucart, M. Age-related slowing: Perceptuomotor, decision, or attention decline? Exp Aging Res 2010;36: 169–89.CrossRefGoogle ScholarPubMed
Bielak, AA, Cherbuin, N, Bunce, D, Anstey, KJ. Intraindividual variability is a fundamental phenomenon of aging: Evidence from an 8-year longitudinal study across young, middle, and older adulthood. Dev Psychol 2014;50: 143–51.CrossRefGoogle ScholarPubMed
Hultsch, DF, MacDonald, SWS, Dixon, RA. Variability in reaction time performance of younger and older adults. J Gerontol Psychol Sci 2002;57B: P101P115.CrossRefGoogle Scholar
Shammi, P, Bosman, E, Stuss, DT. Aging and variability in performance. Aging Neuropsychol Cogn 1998;5: 113.CrossRefGoogle Scholar
West, R, Murphy, KJ, Armilio, ML, Craik, FIM, Stuss, DT. Lapses of intention and performance variability reveal age related increases in fluctuations of executive control. Brain Cogn 2002;49: 402–19.CrossRefGoogle ScholarPubMed
Dykiert, D, Der, G, Starr, JM, Deary, IJ. Age differences in intra-individual variability in simple and choice reaction time: Systematic review and meta-analysis. PLoS ONE 2012;7(10): e45759.CrossRefGoogle ScholarPubMed
Stuss, DT, Pogue, J, Buckle, L, Bondar, J. Characterization of stability of performance in patients with traumatic brain injury: Variability and consistency on reaction time tests. Neuropsychol 1994;8: 316–24.CrossRefGoogle Scholar
Stuss, DT, Murphy, KJ, Binns, MA, Alexander, MP. Staying on the job: The frontal lobes control individual performance variability. Brain 2003;126: 2363–80.CrossRefGoogle ScholarPubMed
Murphy, KJ, West, R, Armilio, ML, Craik, FIM, Stuss, DT. Word list learning performance in younger and older adults: Intra-individual performance variability and false memory. Aging Neuropsychol Cogn 2007;14: 7094.CrossRefGoogle ScholarPubMed
Iskandar, S, Murphy, J, Baird, AD, West, R, Armilio, J, Craik, FIM, et al. Interacting effects of age and time of day on verbal fluency performance and intraindividual variability. Aging Neuropsychol Cogn 2016;23: 117.CrossRefGoogle ScholarPubMed
Garrett, DD, MacDonald, SWS, Craik, FIM. Intraindividual reaction time variability is malleable: Feedback- and education-related reductions in variability with age. Front Hum Neurosci 2012;6: article 101, 110.CrossRefGoogle ScholarPubMed
Axelrod, BN, Henry, RR. Age-related performance on the Wisconsin card sorting, similarities, and controlled oral word association tests. Clin Neuropsychol 1992;6: 1626.CrossRefGoogle Scholar
Rhodes, MG. Age-related differences in performance on the Wisconsin Card Sorting Test: A meta-analytic review. Psychol Aging 2004;19: 482–94.CrossRefGoogle ScholarPubMed
Fristoe, NM, Salthouse, TA, Woodard, JL. Examination of age-related deficits on the Wisconsin Card Sorting Test. Neuropsychol 1997;11: 428–36.CrossRefGoogle ScholarPubMed
Hartman, M, Bolton, E, Fehnel, SE. Accounting for age differences on the Wisconsin Card Sorting Test: Decreased working memory, not inflexibility. Psychol Aging 2001;16: 385–99.CrossRefGoogle Scholar
Ridderinkhof, KR, Span, MM, van der Molen, MW. Perseverative behavior and adaptive control in older adults: Performance monitoring, rule induction, and set shifting. Brain Cogn 2002;49: 382401.CrossRefGoogle ScholarPubMed
Gamboz, N, Borella, E, Brandimonte, MA. The role of switching, inhibition and working memory in older adults’ performance in the Wisconsin Card Sorting Test. Aging Neuropsychol Cogn 2009;16: 260–84.CrossRefGoogle ScholarPubMed
Ashendorf, L, McCaffrey, RJ. Exploring age-related decline on the Wisconsin Card Sorting Test. Clin Neuropsychol 2008;22: 262–72.CrossRefGoogle ScholarPubMed
Belleville, S, Rouleau, N, Van der Linden, M. Use of the Hayling task to measure inhibition of prepotent responses in normal aging and Alzheimer’s disease. Brain Cogn 2006;62: 113–19.CrossRefGoogle ScholarPubMed
Houx, PJ, Holles, J, Vreeling, FW. Stroop interference: Aging effects assessed with the Stroop Color-Word test. Exp Aging Res 1993;19: 209–24.CrossRefGoogle ScholarPubMed
Uttl, B, Graf, P. Color-Word Stroop test performance across the adult life span. J Clin Exp Neuropsychol 1997;19: 405–20.CrossRefGoogle ScholarPubMed
Van der Elst, W, Van Boxten, MPJ, Van Breukelen, GJP, Jolles, J. The Stroop Color-Word Test. Influence of age, sex, and education; and normative data for a large sample across the adult age range. Assessment 2006;13: 6279.CrossRefGoogle Scholar
Ludwig, C, Borella, E, Tettamanti, M, de Ribaupierre, A. Adult age differences in the Color Stroop Test: A comparison between an item-by-item and a blocked version. Arch Gerontol Geriatr 2010;51: 135–42.CrossRefGoogle Scholar
Bélanger, S, Belleville, S, Gauthier, S. Inhibition impairments in Alzheimer’s disease, mild cognitive impairment and health aging: Effect of congruency proportion in a Stroop task. Neuropsychologia 2010;48: 581–90.CrossRefGoogle Scholar
Lindsey, BA, Coppinger, NW. Age-related deficits in sample capabilities and their consequences for Trail Making performance. J Clin Psychol 1969;25: 156–9.3.0.CO;2-H>CrossRefGoogle Scholar
Salthouse, TA, Fristoe, NM. A process analysis of adult age effects on a computer-administered trail making test. Neuropsychology 1995;9: 518–28.CrossRefGoogle Scholar
Wecker, NS, Kramer, JH, Wisniewski, A, Delis, DC, Kaplan, E. Age effects on executive ability. Neuropsychology 2000;14: 409–14.CrossRefGoogle ScholarPubMed
Bugg, JM, DeLosh, EL, Davalos, DB, Davis, HP. Age differences in Stroop interference: Contributions of general slowing and task-specific deficits. Aging Neuropsychol Cogn 2007;14: 155–67.CrossRefGoogle ScholarPubMed
Lin, H, Chan, RCK, Zheng, L, Yang, JT, Wang, Y. Executive functioning in healthy elderly Chinese people. Arch Clin Neuropsychol 2007;22: 501–11.CrossRefGoogle ScholarPubMed
Raz, N, Gunning, FM, Head, D, Dupuis, JH, McQuain, J, Briggs, SD, et al. Selective aging of the human cerebral cortex observed in vivo: Differential vulnerability of the prefrontal gray matter. Cereb Cortex 1997;7: 268–82.CrossRefGoogle ScholarPubMed
Salat, DH, Buckner, RL, Snyder, AZ, Greve, DN, Desikan, RSR, Busa, E, et al. Thinning of the cerebral cortex in aging. Cereb Cortex 2004;14: 721–30.CrossRefGoogle Scholar
Haier, RJ, Jung, RE, Yeo, RA, Head, K, Alkire, MT. Structural brain variation, age, and response time. Cogn Affect Behav Neurosci 2005;5: 246–51.CrossRefGoogle ScholarPubMed
Head, D, Kennedy, KM, Rodrigue, KM, Raz, N. Age differences in perseveration: Cognitive and neuroanatomical mediators of performance on the Wisconsin Card Sorting Test. Neuropsychologia 2009;47: 1200–3.CrossRefGoogle ScholarPubMed
Bettcher, BM, Mungas, D, Patel, N, Elofson, J, Dutt, S, Wynn, M, et al. Neuroanatomical substrates of executive functions: Beyond prefrontal structures. Neuropsychologia 2016;85: 100–9.CrossRefGoogle ScholarPubMed
Lockhart, SN, DeCarli, C. Structural imaging measures of brain aging. Neuropsychol Rev 2014;24: 271–89.CrossRefGoogle ScholarPubMed
Yuan, P, Raz, N. Prefrontal cortex and executive functions in healthy adults: A meta-analysis of structural neuroimaging studies. Neurosci Biobehav Rev 2014;42: 180–92.CrossRefGoogle ScholarPubMed
Makris, N, Papadimitriou, GM, van der Kouwe, A, Kennedy, DN, Hodge, SM, Dale, AM, et al. Frontal connections and cognitive changes in normal aging rhesus monkeys: A DTI study. Neurobiol Aging 2007;28: 1556–67.CrossRefGoogle ScholarPubMed
Brickman, AM, Schupf, N, Manly, JJ, Stern, Y, Luchsinger, JA, Provenzano, FA, et al. APOE epsilon4 and risk for Alzheimer’s disease: Do regionally distributed white matter hyperintensities play a role? Alzheimers Dement 2014;10: 619–29.CrossRefGoogle ScholarPubMed
Daselaar, SM, Iyengar, V, Davis, SW, Eklund, K, Hayes, SM, Cabeza, RE. Less wiring, more firing: Low-performing older adults compensate for impaired white matter with greater neural activity. Cereb Cortex 2015;25: 983–90.CrossRefGoogle ScholarPubMed
Hirsiger, S, Koppelmans, V, Merillat, S, Erdin, C, Narkhede, A, Brickman, AM, et al. Executive functions in healthy older adults are differentially related to macro- and microstructural white matter characteristics of the cerebral lobes. Front Aging Neurosci 2017 Nov 30;9:373. doi: 10.3389/fnagi.2017.00373. eCollection 2017.CrossRefGoogle ScholarPubMed
Yang, Y, Bender, AR, Raz, N. Age related differences in reaction time components and diffusion properties of normal-appearing white matter in healthy adults. Neuropsychologia 2015;66: 246–58.CrossRefGoogle ScholarPubMed
Bunce, D, Anstey, KJ, Cherbuin, N, Burns, R, Christensen, H, Wen, W, et al. Cognitive deficits are associated with frontal and temporal lobe white matter lesions in middle-aged adults living in the community. PLoS ONE 2010;5(10). ScholarPubMed
Haynes, BI, Bunce, D, Kochan, NA, Wen, W, Brodaty, H, Sachdev, PS. Associations between reaction time measures and white matter hyperintensities in very old age. Neuropsychologia 2017;96: 249–55.CrossRefGoogle ScholarPubMed
Jackson, JD, Balota, DA, Duchek, JM, Head, D., White matter integrity and reaction time intraindividual variability in healthy aging and early-stage Alzheimer disease. Neuropsychologia 2012;50: 357–66.CrossRefGoogle ScholarPubMed
Lovden, M, Schmiedek, F, Kennedy, KM, Rodrigue, KM, Lindenberger, U, Raz, N. Does variability in cognitive performance correlate with frontal brain volume? Neuroimage 2013;64: 209–15.CrossRefGoogle ScholarPubMed
Deary, IJ, Bastin, ME, Pattie, A, Clayden, JD, Whalley, LJ, Starr, JM, et al. White matter integrity and cognition in childhood and old age. Neurology 2006;66: 505–12.CrossRefGoogle ScholarPubMed
Kennedy, KM, Raz, N. Aging white matter and cognition: Differential effects of regional variations in diffusion properties on memory, executive functions, and speed. Neuropsychologia 2009;47: 916–27.CrossRefGoogle ScholarPubMed
Dennis, NA, Cabeza, R. Neuroimaging of healthy cognitive aging. In: Craik, FIM, Salthouse, TA, editors. Handbook of aging and cognition, 3rd ed. Mahwah, NJ: Erlbaum; 2008, pp. 154.Google Scholar
Maillet, D, Rajah, MN. Association between prefrontal activity and volume change in prefrontal and medial temporal lobes in aging and dementia: A review. Ageing Res Rev 2013;12: 479–89.CrossRefGoogle ScholarPubMed
Spreng, RN, Wojtowicz, M, Grady, CL. Reliable differences in brain activity between young and old adults: A quantitative meta-analysis across multiple cognitive domains. Neurosci Biobehav Rev 2010;34: 1178–94.CrossRefGoogle Scholar
Langenecker, SA, Nielson, KA, Rao, SM. fMRI of health older adults during Stroop interference. NeuroImage 2004;21: 192200.CrossRefGoogle Scholar
Coull, JT, Frackowiak, RSJ, Frith, CD. Monitoring for target objects: Activation of right frontal and parietal cortices with increasing time on task. Neuropsychologia 1998;36: 1325–34.CrossRefGoogle ScholarPubMed
Goh, JO, Beason-Held, LL, An, Y, Kraut, MA, Resnick, SM. Frontal function and executive processing in older adults: Process and region specific age-related longitudinal functional changes. NeuroImage 2013;69: 4350.CrossRefGoogle ScholarPubMed
Kalpouzos, G, Persson, J, Nyberg, L. Local brain atrophy accounts for functional activity differences in normal aging. Neurobiol Aging 2012;33: 62.e1–623c13.CrossRefGoogle ScholarPubMed
Eyler, LT, Sherzai, A, Kaup, AR, Jeste, DV. A review of functional brain imaging correlates of successful cognitive aging. Biol Psychiatry 2011;70: 115–22.CrossRefGoogle ScholarPubMed
Turner, GR, Spreng, RN. Prefrontal engagement and reduced default network suppression co-occur and are dynamically coupled in older adults: The default-executive coupling hypothesis of aging. J Cogn Neurosci 2015;27: 2462–76.CrossRefGoogle Scholar
Robertson, IH. A right hemisphere role in cognitive reserve. Neurobiol Aging 2014;35: 1375–85.CrossRefGoogle ScholarPubMed
Vallesi, A, McIntosh, AR, Stuss, DT. Temporal preparation in aging: A functional MRI study. Neuropsychologia 2009;47: 2876–81.CrossRefGoogle ScholarPubMed
Nebes, RD, Madden, DJ, Berg, WD. The effect of age on hemispheric asymmetry in visual and auditory identification. Exp Aging Res 1983;9: 8791.CrossRefGoogle ScholarPubMed
Rajah, MN, D’Esposito, M. Region-specific changes in prefrontal function with age: A review of PET and fMRI studies on working and episodic memory. Brain 2005;128: 1964–83.CrossRefGoogle ScholarPubMed
Shallice, T, Stuss, DT, Picton, TW, Alexander, MP, Gillingham, S. Multiple effects of prefrontal lesions on task-switching. Philos Trans R Soc Lond B Biol Sci. 2008;1: 112.Google ScholarPubMed
Raz, N, Gunning-Dixon, F, Head, D, Rodrigue, KM, Williamson, A, Acker, JD. Aging, sexual dimorphism, and hemispheric asymmetry of the cerebral cortex: Replicability of regional differences in volume. Neurobiol Aging 2004;25: 377–96.CrossRefGoogle Scholar
Stern, Y. What is cognitive reserve? Theory and research application of the reserve conceptJ Int Neuropsychol Soc 2002;8: 448–60.CrossRefGoogle ScholarPubMed
Bialystok, E, Craik, FIM, Luk, G. Bilingualism: Consequences for mind and brain. Trends Cogn Sci 2012;16: 240–50.CrossRefGoogle ScholarPubMed
Raz, N, Rodrigue, KM, Acker, JD. Hypertension and the brain: Vulnerability of the prefrontal regions and executive functions. Behav Neurosci 2003;117: 1169–80.CrossRefGoogle ScholarPubMed
Brickman, AM, Schupf, N, Manly, JJ, Luchsinger, JA, Andrews, H, Tang, MX, et al. Brain morphology in older African Americans, Caribbean Hispanics, and whites from northern Manhattan. Arch Neurol 2008;65: 1053–61.CrossRefGoogle ScholarPubMed
DeCarli, C, Massaro, J, Harvey, D, Hald, J, Tullberg, M, Au, R, et al. Measures of brain morphology and infarction in the Framingham heart study: Establishing what is normal. Neurobiol Aging 2005;26: 491510.CrossRefGoogle Scholar
Vasan, RS, Beiser, A, Seshadri, S, Larson, MG, Kannel, WB, D’Agostino, RB, et al. Residual lifetime risk for developing hypertension in middle-aged women and men: The Framingham Heart Study. JAMA 2002;287: 1003–10.CrossRefGoogle ScholarPubMed
Bucur, B, Madden, DJ. Effects of adult age and blood pressure on executive function and speed of processing. Exp Aging Res 2010;36: 153–68.CrossRefGoogle ScholarPubMed
Nyberg, L, Lövdén, M, Riklund, K, Lindenberger, U, Bäckman, L. Memory aging and brain maintenance. Trends Cogn Sci 2012;16: 292305.CrossRefGoogle ScholarPubMed
Stuss, DT, Shallice, T, Alexander, MP, Picton, TW. A multidisciplinary approach to anterior attentional functions. Ann N Y Acad Sci 1995;769: 191212.CrossRefGoogle ScholarPubMed
Stuss, DT, Alexander, MP. Is there a dysexecutive syndrome? Philos Trans R Soc Lond B Biol Sci 2007;362: 901–15.Google Scholar
Alexander, MP, Stuss, DT, Shallice, T, Picton, TW, Gillingham, S. Impaired concentration due to frontal lobe damage from two distinct lesion sites. Neurology 2005;65: 572–9.CrossRefGoogle ScholarPubMed
Picton, TW, Stuss, DT, Shallice, T, Alexander, MP, Gillingham, S. Keeping time: Effects of focal frontal lesions. Neuropsychologia 2006;44: 1195–209.CrossRefGoogle ScholarPubMed
Stuss, DT, Binns, MA, Murphy, KJ, Alexander, MP. Dissociations within the anterior attentional system: Effects of task complexity and irrelevant information on reaction time speed and accuracy. Neuropsychology 2002;16: 500–13.CrossRefGoogle ScholarPubMed
Stuss, DT, Alexander, MP, Shallice, T, Picton, TW, Binns, MA, MacDonald, R, et al. Multiple frontal systems controlling response speed. Neuropsychologia 2005;43: 396417.CrossRefGoogle ScholarPubMed
Stuss, DT. Functions of the frontal lobes: Relation to executive functions. J Int Neuropsychol Soc 2011;17: 17.CrossRefGoogle ScholarPubMed
Alexander, GE, Delong, MR, Strick, PI. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Ann Rev Neurosci 1986;9: 357–81.CrossRefGoogle ScholarPubMed
Floden, D, Vallesi, A, Stuss, DT. Task context and frontal lobe activation in the Stroop task. J Cogn Neurosci 2011;23: 867–79.CrossRefGoogle ScholarPubMed
Stuss, DT, Floden, D, Alexander, MP, Levine, B, Katz, D. Stroop performance in focal lesion patients: Dissociation of processes and frontal lobe lesion location. Neuropsychologia 2001;39: 771–86.CrossRefGoogle ScholarPubMed
Alexander, MP, Stuss, DT, Picton, T, Shallice, T, Gillingham, S. Regional frontal injuries cause distinct impairments in cognitive control. Neurology 2007;68: 1515–23.CrossRefGoogle ScholarPubMed
Pandya, DN, Barnes, CL. Architecture and connections of the frontal lobe. In: Perecman, E, editor. The frontal lobes revisited. New York: IRBN Press; 1987, pp. 41–72.Google Scholar
Stuss, DT. Frontal lobes and attention: Processes and networks, fractionation and integration. J Int Neuropsychol Soc 2006;12: 261–71.CrossRefGoogle Scholar
Alexander, MP. Impairments of procedures for implementing complex language are due to disruption of frontal attention processes. J Int Neuropsychol Soc 2006;12: 236–47.CrossRefGoogle ScholarPubMed
Stuss, DT, Craik, FIM, Sayer, L, Franchi, D, Alexander, MP. Comparison of older people and patients with frontal lobe lesions: Evidence from word list learning. Psychol Aging 1996;11: 387–95.CrossRefGoogle Scholar
Persson, J, Nyberg, L, Lind, J, Larsson, A, Nillson, LG, Ingvar, M, et al. Structure–function correlates of cognitive decline in aging. Cereb Cortex 2006;16: 907–15.CrossRefGoogle Scholar
Buchsbaum, BR, Greer, S, Chang, WL, Berman, KF. Meta-analysis of neuroimaging studies of the Wisconsin card-sorting task and component processes. Hum Brain Mapp 2005;25: 3545.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the or variations. ‘’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats