Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Evidence that ageing yields improvements as well as declines across attention and executive functions

Nature Human Behaviour volume 6, pages 97–110 (2022)Cite this article

Subjects

A Publisher Correction to this article was published on 27 August 2021

This article has been updated

Abstract

Many but not all cognitive abilities decline during ageing. Some even improve due to lifelong experience. The critical capacities of attention and executive functions have been widely posited to decline. However, these capacities are composed of multiple components, so multifaceted ageing outcomes might be expected. Indeed, prior findings suggest that whereas certain attention/executive functions clearly decline, others do not, with hints that some might even improve. We tested ageing effects on the alerting, orienting and executive (inhibitory) networks posited by Posner and Petersen’s influential theory of attention, in a cross-sectional study of a large sample (N = 702) of participants aged 58–98. Linear and nonlinear analyses revealed that whereas the efficiency of the alerting network decreased with age, orienting and executive inhibitory efficiency increased, at least until the mid-to-late 70s. Sensitivity analyses indicated that the patterns were robust. The results suggest variability in age-related changes across attention/executive functions, with some declining while others improve.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Linear effects of age on the efficiencies of the three attentional networks.
Fig. 2: The nonlinear effect of age on the efficiency of the executive network.
Fig. 3: Comparison between a recent qualitative review of age effects on the three attentional networks and the findings from the present study.
Fig. 4: A neurocognitive account of age effects on the three attentional networks.

Similar content being viewed by others

Data availability

The anonymized data (with accompanying documentation) have been uploaded to the Open Science Framework. They can be found at https://osf.io/59er2/.

Code availability

Commented analysis scripts (in the R programming language) for all statistical models reported in this paper have been uploaded to the Open Science Framework. They can be found at https://osf.io/59er2/.

Change history

References

  1. Nyberg, L., Lövdén, M., Riklund, K., Lindenberger, U. & Bäckman, L. Memory aging and brain maintenance. Trends Cogn. Sci. 16, 292–305 (2012).

    Article  PubMed  Google Scholar 

  2. Old, S. R. & Naveh-Benjamin, M. Differential effects of age on item and associative measures of memory: a meta-analysis. Psychol. Aging 23, 104–118 (2008).

    Article  PubMed  Google Scholar 

  3. Rönnlund, M., Nyberg, L., Bäckman, L. & Nilsson, L.-G. Stability, growth, and decline in adult life span development of declarative memory: cross-sectional and longitudinal data from a population-based study. Psychol. Aging 20, 3–18 (2005).

    Article  PubMed  Google Scholar 

  4. Ratcliff, R., Thapar, A., Gomez, P. & McKoon, G. A diffusion model analysis of the effects of aging in the lexical-decision task. Psychol. Aging 19, 278–289 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Wierenga, C. E. et al. Age-related changes in word retrieval: role of bilateral frontal and subcortical networks. Neurobiol. Aging 29, 436–451 (2008).

    Article  PubMed  Google Scholar 

  6. Greve, A., Cooper, E. & Henson, R. N. No evidence that ‘fast-mapping’ benefits novel learning in healthy older adults. Neuropsychologia 60, 52–59 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Whiting, E., Chenery, H. J. & Copland, D. A. Effect of aging on learning new names and descriptions for objects. Aging Neuropsychol. Cogn. 18, 594–619 (2011).

    Article  Google Scholar 

  8. Howard, D. V. et al. Implicit sequence learning: effects of level of structure, adult age, and extended practice. Psychol. Aging 19, 79–92 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Howard, J. H. & Howard, D. V. Aging mind and brain: is implicit learning spared in healthy aging?. Front. Psychol. 4, 817 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Shea, C. H., Park, J.-H. & Braden, H. W. Age-related effects in sequential motor learning. Phys. Ther. 86, 478–488 (2006).

    Article  PubMed  Google Scholar 

  11. Bo, J., Borza, V. & Seidler, R. D. Age-related declines in visuospatial working memory correlate with deficits in explicit motor sequence learning. J. Neurophysiol. 102, 2744–2754 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Iachini, T., Iavarone, A., Senese, V., Ruotolo, F. & Ruggiero, G. Visuospatial memory in healthy elderly, AD and MCI: a review. Curr. Aging Sci. 2, 43–59 (2009).

    Article  PubMed  Google Scholar 

  13. Monge, Z. A. & Madden, D. J. Linking cognitive and visual perceptual decline in healthy aging: the information degradation hypothesis. Neurosci. Biobehav. Rev. 69, 166–173 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Naveh-Benjamin, M. & Kilb, A. Age-related differences in associative memory: the role of sensory decline. Psychol. Aging 29, 672–683 (2014).

    Article  PubMed  Google Scholar 

  15. Salthouse, T. A. The processing-speed theory of adult age differences in cognition. Psychol. Rev. 103, 403–428 (1996).

    Article  CAS  PubMed  Google Scholar 

  16. Jenkins, L., Myerson, J., Joerding, J. A. & Hale, S. Converging evidence that visuospatial cognition is more age-sensitive than verbal cognition. Psychol. Aging 15, 157–175 (2000).

    Article  CAS  PubMed  Google Scholar 

  17. Lindenberger, U., Scherer, H. & Baltes, P. B. The strong connection between sensory and cognitive performance in old age: not due to sensory acuity reductions operating during cognitive assessment. Psychol. Aging 16, 196–205 (2001).

    Article  CAS  PubMed  Google Scholar 

  18. Park, D. C. et al. Mediators of long-term memory performance across the life span. Psychol. Aging 11, 621–637 (1996).

    Article  CAS  PubMed  Google Scholar 

  19. Verhaeghen, P. & Salthouse, T. A. Meta-analyses of age–cognition relations in adulthood: estimates of linear and nonlinear age effects and structural models. Psychol. Bull. 122, 231–249 (1997).

    Article  CAS  PubMed  Google Scholar 

  20. Cabeza, R., Nyberg, L. & Park, D. C. Cognitive Neuroscience of Aging: Linking Cognitive and Cerebral Aging (Oxford Univ. Press, 2016).

  21. Carstensen, L. L. et al. Emotional experience improves with age: evidence based on over 10 years of experience sampling. Psychol. Aging 26, 21–33 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Schaie, K. W. & Willis, S. L. The Seattle Longitudinal Study of adult cognitive development. ISSBD Bull. 2010(1), 24–29 (2010).

    Google Scholar 

  23. Anderson, N. D. & Craik, F. I. M. 50 years of cognitive aging theory. J. Gerontol. B 72, 1–6 (2017).

    Article  Google Scholar 

  24. Hartshorne, J. K. & Germine, L. T. When does cognitive functioning peak? The asynchronous rise and fall of different cognitive abilities across the life span. Psychol. Sci. 26, 433–443 (2015).

    Article  PubMed  Google Scholar 

  25. Hedden, T. & Gabrieli, J. D. E. Insights into the ageing mind: a view from cognitive neuroscience. Nat. Rev. Neurosci. 5, 87–96 (2004).

    Article  CAS  PubMed  Google Scholar 

  26. Park, D. C. et al. Models of visuospatial and verbal memory across the adult life span. Psychol. Aging 17, 299–320 (2002).

    Article  PubMed  Google Scholar 

  27. Piolino, P., Desgranges, B., Benali, K. & Eustache, F. Episodic and semantic remote autobiographical memory in ageing. Memory 10, 239–257 (2002).

    Article  PubMed  Google Scholar 

  28. Schaie, K. W. Intellectual Development in Adulthood: The Seattle Longitudinal Study (Cambridge Univ. Press, 1996).

  29. Cansino, S. et al. The decline of verbal and visuospatial working memory across the adult life span. Age (Dordr.) 35, 2283–2302 (2013).

    Article  Google Scholar 

  30. Ikier, S., Yang, L. & Hasher, L. Implicit proactive interference, age, and automatic versus controlled retrieval strategies. Psychol. Sci. 19, 456–461 (2008).

    Article  PubMed  Google Scholar 

  31. Jacoby, L. L. Ironic effects of repetition: measuring age-related differences in memory. J. Exp. Psychol. Learn. Mem. Cogn. 25, 3–22 (1999).

    Article  CAS  PubMed  Google Scholar 

  32. Laver, G. D. Adult aging effects on semantic and episodic priming in word recognition. Psychol. Aging 24, 28–39 (2009).

    Article  PubMed  Google Scholar 

  33. Campbell, K. L. et al. Robust resilience of the frontotemporal syntax system to aging. J. Neurosci. 36, 5214–5227 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Cohen-Shikora, E. R. & Balota, D. A. Visual word recognition across the adult lifespan. Psychol. Aging 31, 488–502 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Reifegerste, J., Elin, K. & Clahsen, H. Persistent differences between native speakers and late bilinguals: evidence from inflectional and derivational processing in older speakers. Biling. Lang. Cogn. 22, 425–440 (2019).

    Article  Google Scholar 

  36. Royle, P., Steinhauer, K., Dessureault, É., Herbay, A. C. & Brambati, S. M. Aging and language: maintenance of morphological representations in older adults. Front. Commun. 4, 16 (2019).

    Article  Google Scholar 

  37. Shafto, M. A. & Tyler, L. K. Language in the aging brain: the network dynamics of cognitive decline and preservation. Science 346, 583–587 (2014).

    Article  CAS  PubMed  Google Scholar 

  38. Cabeza, R. et al. Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing. Nat. Rev. Neurosci. 19, 701–710 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Horton, S., Baker, J. & Schorer, J. Expertise and aging: maintaining skills through the lifespan. Eur. Rev. Aging Phys. Act. 5, 89–96 (2008).

    Article  Google Scholar 

  40. Horn, J. L. in Aging and Cognitive Processes (eds Craik, F. I. M. & Trehub, S.) 237–278 (Plenum, 1982).

  41. Horn, J. L. & Cattell, R. B. Age differences in fluid and crystallized intelligence. Acta Psychol. (Amst.) 26, 107–129 (1967).

    Article  CAS  Google Scholar 

  42. Li, S.-C. in International Encyclopedia of the Social and Behavioral Sciences (eds Smelser, N. J. & Baltes, P. B.) 310–317 (Elsevier, 2001).

  43. Baltes, P. B. & Kliegl, R. in Neurology (eds. Poeck, K. et al.) 1–17 (Springer, 1986).

  44. Ben-David, B. M., Erel, H., Goy, H. & Schneider, B. A. “Older is always better”: age-related differences in vocabulary scores across 16 years. Psychol. Aging 30, 856–862 (2015).

    Article  PubMed  Google Scholar 

  45. Cattell, R. B. Abilities: Their Structure, Growth, and Action (Houghton Mifflin, 1971).

  46. Goral, M., Spiro, A., Albert, M. L., Obler, L. K. & Connor, L. T. Change in lexical retrieval skills in adulthood. Ment. Lex. 2, 215–238 (2007).

    Article  Google Scholar 

  47. Horn, J. L. & Donaldson, G. On the myth of intellectual decline in adulthood. Am. Psychol. 31, 701–719 (1976).

    Article  CAS  PubMed  Google Scholar 

  48. Spreng, R. N. & Turner, G. R. The shifting architecture of cognition and brain function in older adulthood. Perspect. Psychol. Sci. 14, 523–542 (2019).

    Article  PubMed  Google Scholar 

  49. Verhaeghen, P. Aging and vocabulary score: a meta-analysis. Psychol. Aging 18, 332–339 (2003).

    Article  PubMed  Google Scholar 

  50. Baltes, P. B. & Smith, J. The fascination of wisdom: its nature, ontogeny, and function. Perspect. Psychol. Sci. 3, 56–64 (2008).

    Article  PubMed  Google Scholar 

  51. Lim, K. T. K. & Yu, R. Aging and wisdom: age-related changes in economic and social decision making. Front. Aging Neurosci. 7, 120 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Happé, F. G. E., Winner, E. & Brownell, H. The getting of wisdom: theory of mind in old age. Dev. Psychol. 34, 358–362 (1998).

    Article  PubMed  Google Scholar 

  53. Burr, D. A., Castrellon, J. J., Zald, D. H. & Samanez-Larkin, G. R. Emotion dynamics across adulthood in everyday life: older adults are more emotionally stable and better at regulating desires. Emotion https://doi.org/10/gg3kg2 (2020).

  54. Carstensen, L. L., Pasupathi, M., Mayr, U. & Nesselroade, J. R. Emotional experience in everyday life across the adult life span. J. Pers. Soc. Psychol. 79, 644–655 (2000).

    Article  CAS  PubMed  Google Scholar 

  55. Carstensen, L. L., Fung, H. H. & Charles, S. T. Socioemotional selectivity theory and the regulation of emotion in the second half of life. Motiv. Emot. 27, 103–123 (2003).

    Article  Google Scholar 

  56. Urry, H. L. & Gross, J. J. Emotion regulation in older age. Curr. Dir. Psychol. Sci. 19, 352–357 (2010).

    Article  Google Scholar 

  57. Li, Y., Baldassi, M., Johnson, E. J. & Weber, E. U. Complementary cognitive capabilities, economic decision making, and aging. Psychol. Aging 28, 595–613 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Mata, R., Schooler, L. J. & Rieskamp, J. The aging decision maker: cognitive aging and the adaptive selection of decision strategies. Psychol. Aging 22, 796–810 (2007).

    Article  PubMed  Google Scholar 

  59. Strough, J., Schlosnagle, L. & DiDonato, L. Understanding decisions about sunk costs from older and younger adults’ perspectives. J. Gerontol. B 66B, 681–686 (2011).

    Article  Google Scholar 

  60. Strough, J., Mehta, C. M., McFall, J. P. & Schuller, K. L. Are older adults less subject to the sunk-cost fallacy than younger adults? Psychol. Sci. 19, 650–652 (2008).

    Article  PubMed  Google Scholar 

  61. Kanfer, R., Beier, M. E. & Ackerman, P. L. Goals and motivation related to work in later adulthood: an organizing framework. Eur. J. Work Organ. Psychol. 22, 253–264 (2013).

    Article  Google Scholar 

  62. Kooij, D. T. A. M., De Lange, A. H., Jansen, P. G. W., Kanfer, R. & Dikkers, J. S. E. Age and work-related motives: results of a meta-analysis. J. Organ. Behav. 32, 197–225 (2011).

    Article  Google Scholar 

  63. Donnellan, M. B. & Lucas, R. E. Age differences in the big five across the life span: evidence from two national samples. Psychol. Aging 23, 558–566 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Ibanez, A. et al. Empathy, sex and fluid intelligence as predictors of theory of mind. Pers. Individ. Differ. 54, 616–621 (2013).

    Article  Google Scholar 

  65. Opitz, P. C., Lee, I. A., Gross, J. J. & Urry, H. L. Fluid cognitive ability is a resource for successful emotion regulation in older and younger adults. Front. Psychol. 5, 609 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Kanfer, R. & Ackerman, P. L. Aging, adult development, and work motivation. Acad. Manage. Rev. 29, 440–458 (2004).

    Article  Google Scholar 

  67. Lövdén, M., Ghisletta, P. & Lindenberger, U. Cognition in the Berlin Aging Study (BASE): the first 10 years. Aging Neuropsychol. Cogn. 11, 104–133 (2004).

    Article  Google Scholar 

  68. Ng, T. W. H. & Feldman, D. C. The relationship of age to ten dimensions of job performance. J. Appl. Psychol. 93, 392–423 (2008).

    Article  PubMed  Google Scholar 

  69. Ardelt, M. Wisdom as expert knowledge system: a critical review of a contemporary operationalization of an ancient concept. Hum. Dev. 47, 257–285 (2004).

    Article  Google Scholar 

  70. Buchler, N. E. G. & Reder, L. M. Modeling age-related memory deficits: a two-parameter solution. Psychol. Aging 22, 104–121 (2007).

    Article  PubMed  Google Scholar 

  71. Ramscar, M., Hendrix, P., Shaoul, C., Milin, P. & Baayen, R. H. The myth of cognitive decline: non-linear dynamics of lifelong learning. Top. Cogn. Sci. 6, 5–42 (2014).

    Article  PubMed  Google Scholar 

  72. Ramscar, M., Sun, C. C., Hendrix, P. & Baayen, R. H. The mismeasurement of mind: life-span changes in paired-associate-learning scores reflect the ‘cost’ of learning, not cognitive decline. Psychol. Sci. 28, 1171–1179 (2017).

    Article  PubMed  Google Scholar 

  73. Reifegerste, J. et al. Early-life education may help bolster declarative memory in old age, especially for women. Aging Neuropsychol. Cogn. 28, 218–252 (2021).

    Article  Google Scholar 

  74. Stine-Morrow, E. A. L. The Dumbledore hypothesis of cognitive aging. Curr. Dir. Psychol. Sci. 16, 295–299 (2007).

    Article  Google Scholar 

  75. Umanath, S. & Marsh, E. J. Understanding how prior knowledge influences memory in older adults. Perspect. Psychol. Sci. 9, 408–426 (2014).

    Article  PubMed  Google Scholar 

  76. Zaval, L., Li, Y., Johnson, E. J. & Weber, E. U. in Aging and Decision Making (eds Hess, T. M. et al.) 149–168 (Elsevier, 2015).

  77. Fernandez-Duque, D. & Posner, M. I. Relating the mechanisms of orienting and alerting. Neuropsychologia 35, 477–486 (1997).

    Article  CAS  PubMed  Google Scholar 

  78. Miyake, A. et al. The unity and diversity of executive functions and their contributions to complex ‘frontal lobe’ tasks: a latent variable analysis. Cogn. Psychol. 41, 49–100 (2000).

    Article  CAS  PubMed  Google Scholar 

  79. Zanto, T. P. & Gazzaley, A. in The Oxford Handbook of Attention (eds Nobre, A. C. & Kastner, S.) 927–971 (Oxford Univ. Press, 2014).

  80. Kinsella, G., Storey, E. & Crawford, J. R. in Neurology and Clinical Neuroscience (ed. Schapira, A. H. V.) 83–95 (Elsevier, 1998).

  81. Vaughan, L. & Giovanello, K. Executive function in daily life: age-related influences of executive processes on instrumental activities of daily living. Psychol. Aging 25, 343–355 (2010).

    Article  PubMed  Google Scholar 

  82. Del Missier, F., Mäntylä, T. & Bruine de Bruin, W. Executive functions in decision making: an individual differences approach. Think. Reason. 16, 69–97 (2010).

    Article  Google Scholar 

  83. García-Madruga, J. A., Gómez-Veiga, I. & Vila, J. Ó. Executive functions and the improvement of thinking abilities: the intervention in reading comprehension. Front. Psychol. 7, 58 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Martin, R. & Allen, C. A disorder of executive function and its role in language processing. Semin. Speech Lang. 29, 201–210 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Mazuka, R., Jincho, N. & Oishi, H. Development of executive control and language processing. Lang. Linguist. Compass 3, 59–89 (2009).

    Article  Google Scholar 

  86. Schiebener, J. et al. Among three different executive functions, general executive control ability is a key predictor of decision making under objective risk. Front. Psychol. 5, 1386 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Braver, T. S. & Barch, D. M. A theory of cognitive control, aging cognition, and neuromodulation. Neurosci. Biobehav. Rev. 26, 809–817 (2002).

    Article  PubMed  Google Scholar 

  88. Buckner, R. L. Memory and executive function in aging and AD. Neuron 44, 195–208 (2004).

    Article  CAS  PubMed  Google Scholar 

  89. Fjell, A. M., Sneve, M. H., Grydeland, H., Storsve, A. B. & Walhovd, K. B. The disconnected brain and executive function decline in aging. Cereb. Cortex 27, 2303–2317 (2016).

    Google Scholar 

  90. Hasher, L., Lustig, C. & Zacks, R. T. in Variation in Working Memory (eds Conway, A. R. A. et al.) 227–249 (Oxford Univ. Press, 2007).

  91. Hasher, L. & Zacks, R. T. in The Psychology of Learning and Motivation (ed. Bower, G. H.) Vol. 22, 193–225 (Academic, 1988).

  92. Hasher, L., Stoltzfus, E. R., Zacks, R. T. & Rypma, B. Age and inhibition. J. Exp. Psychol. Learn. Mem. Cogn. 17, 163–169 (1991).

    Article  CAS  PubMed  Google Scholar 

  93. MacPherson, S. E., Phillips, L. H. & Della Sala, S. Age, executive function and social decision making: a dorsolateral prefrontal theory of cognitive aging. Psychol. Aging 17, 598–609 (2002).

    Article  PubMed  Google Scholar 

  94. Posner, M. I., Rothbart, M. K. & Ghassemzadeh, H. Restoring attention networks. Yale J. Biol. Med. 92, 139–143 (2019).

    PubMed  PubMed Central  Google Scholar 

  95. West, R. L. An application of prefrontal cortex function theory to cognitive aging. Psychol. Bull. 120, 272–292 (1996).

    Article  CAS  PubMed  Google Scholar 

  96. West, R. L. In defense of the frontal lobe hypothesis of cognitive aging. J. Int. Neuropsychol. Soc. 6, 727–729 (2000).

    Article  CAS  PubMed  Google Scholar 

  97. Goh, J. O., Beason-Held, L. L., An, Y., Kraut, M. A. & Resnick, S. M. Frontal function and executive processing in older adults: process and region specific age-related longitudinal functional changes. NeuroImage 69, 43–50 (2013).

    Article  PubMed  Google Scholar 

  98. Kennedy, K. M. & Raz, N. Aging white matter and cognition: differential effects of regional variations in diffusion properties on memory, executive functions, and speed. Neuropsychologia 47, 916–927 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  99. Mather, M. & Harley, C. W. The locus coeruleus: essential for maintaining cognitive function and the aging brain. Trends Cogn. Sci. 20, 214–226 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Pardo, J. V. et al. Where the brain grows old: decline in anterior cingulate and medial prefrontal function with normal aging. NeuroImage 35, 1231–1237 (2007).

    Article  PubMed  Google Scholar 

  101. Raz, N., Ghisletta, P., Rodrigue, K. M., Kennedy, K. M. & Lindenberger, U. Trajectories of brain aging in middle-aged and older adults: regional and individual differences. NeuroImage 51, 501–511 (2010).

    Article  PubMed  Google Scholar 

  102. Raz, N. & Rodrigue, K. M. Differential aging of the brain: patterns, cognitive correlates and modifiers. Neurosci. Biobehav. Rev. 30, 730–748 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  103. Bopp, K. L. & Verhaeghen, P. Aging and verbal memory span: a meta-analysis. J. Gerontol. B. 60, P223–P233 (2005).

    Article  Google Scholar 

  104. Bopp, K. L. & Verhaeghen, P. Aging and n-back performance: a meta-analysis. J. Gerontol. B https://doi.org/10/ggjwwt (2020)

  105. Burke, D. M. & Osborne, G. in Inhibition in Cognition (eds Gorfein, D. S. & MacLeod, C. M.) 163–183 (American Psychological Association, 2007).

  106. Glisky, E. L. in Brain Aging: Models, Methods, and Mechanisms (ed. Riddle, D. R.) Ch. 1 (Taylor & Francis, 2007).

  107. McDonough, I. M., Wood, M. M. & Miller, W. S. A review on the trajectory of attentional mechanisms in aging and the Alzheimer’s disease continuum through the Attention Network Test. Yale J. Biol. Med. 92, 37–51 (2019).

    PubMed  PubMed Central  Google Scholar 

  108. McDowd, J. M. & Shaw, R. J. in The Handbook of Aging and Cognition (eds Craik, F. I. M. & Salthouse, T. A.) 221–292 (Lawrence Erlbaum, 2000).

  109. Pliatsikas, C. et al. Working memory in older adults declines with age, but is modulated by sex and education. Q. J. Exp. Psychol. 72, 1308–1327 (2019).

    Article  Google Scholar 

  110. Rey-Mermet, A. & Gade, M. Inhibition in aging: what is preserved? What declines? A meta-analysis. Psychon. Bull. Rev. 25, 1695–1716 (2018).

    Article  PubMed  Google Scholar 

  111. Verhaeghen, P. Aging and executive control: reports of a demise greatly exaggerated. Curr. Dir. Psychol. Sci. 20, 174–180 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  112. Verhaeghen, P. The Elements of Cognitive Aging: Meta-analyses of Age-Related Differences in Processing Speed and Their Consequences (Oxford Univ. Press, 2013).

  113. Verhaeghen, P. & Cerella, J. Aging, executive control, and attention: a review of meta-analyses. Neurosci. Biobehav. Rev. 26, 849–857 (2002).

    Article  PubMed  Google Scholar 

  114. Friedman, N. P. & Miyake, A. Unity and diversity of executive functions: individual differences as a window on cognitive structure. Cortex 86, 186–204 (2017).

    Article  PubMed  Google Scholar 

  115. Karr, J. E. et al. The unity and diversity of executive functions: a systematic review and re-analysis of latent variable studies. Psychol. Bull. 144, 1147–1185 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  116. Miyake, A. & Friedman, N. P. The nature and organization of individual differences in executive functions: four general conclusions. Curr. Dir. Psychol. Sci. 21, 8–14 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  117. Petersen, S. E. & Posner, M. I. The attention system of the human brain: 20 years after. Annu. Rev. Neurosci. 35, 73–89 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Salthouse, T. A. Is flanker-based inhibition related to age? Identifying specific influences of individual differences on neurocognitive variables. Brain Cogn. 73, 51–61 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  119. Uttl, B. & Graf, P. Color–Word Stroop test performance across the adult life span. J. Clin. Exp. Neuropsychol. 19, 405–420 (1997).

    Article  CAS  PubMed  Google Scholar 

  120. Posner, M. I. & Petersen, S. E. The attention system of the human brain. Annu. Rev. Neurosci. 13, 25–42 (1990).

    Article  CAS  PubMed  Google Scholar 

  121. Staub, B., Doignon-Camus, N., Després, O. & Bonnefond, A. Sustained attention in the elderly: what do we know and what does it tell us about cognitive aging? Ageing Res. Rev. 12, 459–468 (2013).

    Article  PubMed  Google Scholar 

  122. Fan, J., McCandliss, B. D., Sommer, T., Raz, A. & Posner, M. I. Testing the efficiency and independence of attentional networks. J. Cogn. Neurosci. 14, 340–347 (2002).

    Article  PubMed  Google Scholar 

  123. Fan, J. et al. Testing the behavioral interaction and integration of attentional networks. Brain Cogn. 70, 209–220 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  124. Posner, M. I., Sheese, B. E., Odludaş, Y. & Tang, Y. Analyzing and shaping human attentional networks. Neural Netw. 19, 1422–1429 (2006).

    Article  PubMed  Google Scholar 

  125. Wang, H., Fan, J. & Johnson, T. R. A symbolic model of human attentional networks. Cogn. Syst. Res. 5, 119–134 (2004).

    Article  Google Scholar 

  126. Fan, J., Fossella, J., Sommer, T., Wu, Y. & Posner, M. I. Mapping the genetic variation of executive attention onto brain activity. Proc. Natl Acad. Sci. USA 100, 7406–7411 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Fan, J., Mccandliss, B., Fossella, J., Flombaum, J. & Posner, M. I. The activation of attentional networks. NeuroImage 26, 471–479 (2005).

    Article  PubMed  Google Scholar 

  128. Konrad, K., Neufang, S., Hanisch, C., Fink, G. R. & Herpertz-Dahlmann, B. Dysfunctional attentional networks in children with attention deficit/hyperactivity disorder: evidence from an event-related functional magnetic resonance imaging study. Biol. Psychiatry 59, 643–651 (2006).

    Article  PubMed  Google Scholar 

  129. Niogi, S., Mukherjee, P., Ghajar, J. & McCandliss, B. D. Individual differences in distinct components of attention are linked to anatomical variations in distinct white matter tracts. Front. Neuroanat. https://doi.org/10/cghhr4 (2010).

  130. Beane, M. & Marrocco, R. T. in Cognitive Neuroscience of Attention (ed. Posner, M. I.) 313–325 (Guilford, 2004).

  131. Posner, M. I. & Rothbart, M. K. Research on attention networks as a model for the integration of psychological science. Annu. Rev. Psychol. 58, 1–23 (2007).

    Article  PubMed  Google Scholar 

  132. Witte, E. A., Davidson, M. C. & Marrocco, R. T. Effects of altering brain cholinergic activity on covert orienting of attention: comparison of monkey and human performance. Psychopharmacol. (Berl.) 132, 324–334 (1997).

    Article  CAS  Google Scholar 

  133. Witte, E. A. & Marrocco, R. T. Alteration of brain noradrenergic activity in rhesus monkeys affects the alerting component of covert orienting. Psychopharmacol. (Berl.) 132, 315–323 (1997).

    Article  CAS  Google Scholar 

  134. Fossella, J. et al. Assessing the molecular genetics of attention networks. BMC Neurosci. 3, 14 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  135. Pozuelos, J. P., Paz-Alonso, P. M., Castillo, A., Fuentes, L. J. & Rueda, M. R. Development of attention networks and their interactions in childhood. Dev. Psychol. 50, 2405–2415 (2014).

    Article  PubMed  Google Scholar 

  136. Posner, M. I. Orienting of attention. Q. J. Exp. Psychol. 32, 3–25 (1980).

    Article  CAS  PubMed  Google Scholar 

  137. Eriksen, B. A. & Eriksen, C. W. Effects of noise letters upon the identification of a target letter in a nonsearch task. Percept. Psychophys. 16, 143–149 (1974).

    Article  Google Scholar 

  138. Ishigami, Y. et al. The Attention Network Test-Interaction (ANT-I): reliability and validity in healthy older adults. Exp. Brain Res. 234, 815–827 (2016).

    Article  PubMed  Google Scholar 

  139. Ishigami, Y. & Klein, R. M. Repeated measurement of the components of attention of older adults using the two versions of the Attention Network Test: stability, isolability, robustness, and reliability. Front. Aging Neurosci. 3, 17 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  140. Mahoney, J. R., Verghese, J., Goldin, Y., Lipton, R. & Holtzer, R. Alerting, orienting, and executive attention in older adults. J. Int. Neuropsychol. Soc. 16, 877–889 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  141. MacLeod, J. W. et al. Appraising the ANT: psychometric and theoretical considerations of the Attention Network Test. Neuropsychology 24, 637–651 (2010).

    Article  PubMed  Google Scholar 

  142. Dash, T., Berroir, P., Joanette, Y. & Ansaldo, A. I. Alerting, orienting, and executive control: the effect of bilingualism and age on the subcomponents of attention. Front. Neurol. 10, 1122 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  143. Deiber, M.-P., Ibañez, V., Missonnier, P., Rodriguez, C. & Giannakopoulos, P. Age-associated modulations of cerebral oscillatory patterns related to attention control. NeuroImage 82, 531–546 (2013).

    Article  PubMed  Google Scholar 

  144. Fernandez-Duque, D. & Black, S. E. Attentional networks in normal aging and Alzheimer’s disease. Neuropsychology 20, 133–143 (2006).

    Article  PubMed  Google Scholar 

  145. Gamboz, N., Zamarian, S. & Cavallero, C. Age-related differences in the Attention Network Test (ANT). Exp. Aging Res. 36, 287–305 (2010).

    Article  PubMed  Google Scholar 

  146. Jennings, J. M., Dagenbach, D., Engle, C. M. & Funke, L. J. Age-related changes and the Attention Network Task: an examination of alerting, orienting, and executive function. Aging Neuropsychol. Cogn. 14, 353–369 (2007).

    Article  Google Scholar 

  147. Kaufman, D. A. S., Sozda, C. N., Dotson, V. M. & Perlstein, W. M. An event-related potential investigation of the effects of age on alerting, orienting, and executive function. Front. Aging Neurosci. 8, 99 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  148. Knight, M. & Mather, M. Look out—it’s your off-peak time of day! Time of day matters more for alerting than for orienting or executive attention. Exp. Aging Res. 39, 305–321 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  149. Lu, H., Fung, A. W. T., Chan, S. S. M. & Lam, L. C. W. Disturbance of attention network functions in Chinese healthy older adults: an intra-individual perspective. Int. Psychogeriatr. 28, 291–301 (2016).

    Article  PubMed  Google Scholar 

  150. Westlye, L. T., Grydeland, H., Walhovd, K. B. & Fjell, A. M. Associations between regional cortical thickness and attentional networks as measured by the Attention Network Test. Cereb. Cortex 21, 345–356 (2011).

    Article  PubMed  Google Scholar 

  151. Williams, R. S. et al. Age differences in the attention network test: evidence from behavior and event-related potentials. Brain Cogn. 102, 65–79 (2016).

    Article  PubMed  Google Scholar 

  152. Young-Bernier, M., Tanguay, A. N., Tremblay, F. & Davidson, P. S. R. Age differences in reaction times and a neurophysiological marker of cholinergic activity. Can. J. Aging 34, 471–480 (2015).

    Article  PubMed  Google Scholar 

  153. Zhou, S., Fan, J., Lee, T. M. C., Wang, C. & Wang, K. Age-related differences in attentional networks of alerting and executive control in young, middle-aged, and older Chinese adults. Brain Cogn. 75, 205–210 (2011).

    Article  PubMed  Google Scholar 

  154. Baayen, R. H. A real experiment is a factorial experiment? Ment. Lex. 5, 149–157 (2010).

    Article  Google Scholar 

  155. Jacobsen, G. M., de Mello, C. M., Kochhann, R. & Fonseca, R. P. Executive functions in school-age children: influence of age, gender, school type and parental education. Appl. Cogn. Psychol. 31, 404–413 (2017).

    Article  Google Scholar 

  156. Kamkar, N. H. & Morton, J. B. CanDiD: a framework for linking executive function and education. Front. Psychol. 8, 1187 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  157. Liu, G., Hu, P., Fan, J. & Wang, K. Gender differences associated with orienting attentional networks in healthy subjects. Chin. Med. J. (Engl.) 126, 2308–2312 (2013).

    Google Scholar 

  158. Faust, M. E., Balota, D. A., Spieler, D. H. & Ferraro, F. R. Individual differences in information-processing rate and amount: implications for group differences in response latency. Psychol. Bull. 125, 777–799 (1999).

    Article  CAS  PubMed  Google Scholar 

  159. Friedman, L. & Wall, M. Graphical views of suppression and multicollinearity in multiple linear regression. Am. Stat. 59, 127–136 (2005).

    Article  Google Scholar 

  160. Wurm, L. H. & Fisicaro, S. A. What residualizing predictors in regression analyses does (and what it does not do). J. Mem. Lang. 72, 37–48 (2014).

    Article  Google Scholar 

  161. Gittings, N. S. & Fozard, J. L. Age related changes in visual acuity. Exp. Gerontol. 21, 423–433 (1986).

    Article  CAS  PubMed  Google Scholar 

  162. Costa, A., Hernández, M. & Sebastián-Gallés, N. Bilingualism aids conflict resolution: evidence from the ANT task. Cognition 106, 59–86 (2008).

    Article  PubMed  Google Scholar 

  163. Rey-Mermet, A., Gade, M. & Oberauer, K. Should we stop thinking about inhibition? Searching for individual and age differences in inhibition ability. J. Exp. Psychol. Learn. Mem. Cogn. 44, 501–526 (2018).

    Article  PubMed  Google Scholar 

  164. Sekuler, A. B., Bennett, P. J. & Mamelak, M. Effects of aging on the useful field of view. Exp. Aging Res. 26, 103–120 (2000).

    Article  CAS  PubMed  Google Scholar 

  165. Baayen, R. H. Analyzing Linguistic Data: A Practical Introduction to Statistics Using R (Cambridge Univ. Press, 2008).

  166. Neter, J., Kutner, M., Nachtsheim, C. & Wasserman, W. Applied Linear Statistical Models (Irwin, 1996).

  167. Bosch, S., Veríssimo, J. & Clahsen, H. Inflectional morphology in bilingual language processing: an age-of-acquisition study. Lang. Acquis. 26, 339–360 (2019).

    Article  Google Scholar 

  168. Veríssimo, J., Heyer, V., Jacob, G. & Clahsen, H. Selective effects of age of acquisition on morphological priming: evidence for a sensitive period. Lang. Acquis. 25, 315–326 (2018).

    Article  Google Scholar 

  169. Hassing, L., Wahlin, Å. & Bäckman, L. Minimal influence of age, education, and gender on episodic memory functioning in very old age: a population-based study of nonagenarians. Arch. Gerontol. Geriatr. 27, 75–87 (1998).

    Article  CAS  PubMed  Google Scholar 

  170. Lindenberger, U., Singer, T. & Baltes, P. B. Longitudinal selectivity in aging populations: separating mortality-associated versus experimental components in the Berlin Aging Study (BASE). J. Gerontol. B. 57, P474–P482 (2002).

    Article  Google Scholar 

  171. Singer, T., Verhaeghen, P., Ghisletta, P., Lindenberger, U. & Baltes, P. B. The fate of cognition in very old age: six-year longitudinal findings in the Berlin Aging Study (BASE). Psychol. Aging 18, 318–331 (2003).

    Article  PubMed  Google Scholar 

  172. Chen, Y. et al. Testing a cognitive control model of human intelligence. Sci. Rep. 9, 2898 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  173. Madden, D. J. & Gottlob, L. R. Adult age differences in strategic and dynamic components of focusing visual attention. Aging Neuropsychol. Cogn. 4, 185–210 (1997).

    Article  Google Scholar 

  174. Mathewson, K. J., Dywan, J. & Segalowitz, S. J. Brain bases of error-related ERPs as influenced by age and task. Biol. Psychol. 70, 88–104 (2005).

    Article  PubMed  Google Scholar 

  175. Pettigrew, C. & Martin, R. C. Cognitive declines in healthy aging: evidence from multiple aspects of interference resolution. Psychol. Aging 29, 187–204 (2014).

    Article  PubMed  Google Scholar 

  176. Sullivan, M. P. The functional interaction of visual-perceptual and response mechanisms during selective attention in young adults, young-old adults, and old-old adults. Percept. Psychophys. 61, 810–825 (1999).

    Article  CAS  PubMed  Google Scholar 

  177. Wright, L. L. & Elias, J. W. Age differences in the effects of perceptual noise. J. Gerontol. 34, 704–708 (1979).

    Article  CAS  PubMed  Google Scholar 

  178. Friedman, N. P. & Miyake, A. The relations among inhibition and interference control functions: a latent-variable analysis. J. Exp. Psychol. Gen. 133, 101–135 (2004).

    Article  PubMed  Google Scholar 

  179. Nigg, J. T. On inhibition/disinhibition in developmental psychopathology: views from cognitive and personality psychology and a working inhibition taxonomy. Psychol. Bull. 126, 220–246 (2000).

    Article  CAS  PubMed  Google Scholar 

  180. Rouder, J. N. & Haaf, J. M. A psychometrics of individual differences in experimental tasks. Psychon. Bull. Rev. 26, 452–467 (2019).

    Article  PubMed  Google Scholar 

  181. Stahl, C. et al. Behavioral components of impulsivity. J. Exp. Psychol. Gen. 143, 850–886 (2014).

    Article  PubMed  Google Scholar 

  182. Borella, E., Delaloye, C., Lecerf, T., Renaud, O. & de Ribaupierre, A. Do age differences between young and older adults in inhibitory tasks depend on the degree of activation of information? Eur. J. Cogn. Psychol. 21, 445–472 (2009).

    Article  Google Scholar 

  183. Hsieh, S., Liang, Y.-C. & Tsai, Y.-C. Do age-related changes contribute to the flanker effect? Clin. Neurophysiol. 123, 960–972 (2012).

    Article  PubMed  Google Scholar 

  184. Kawai, N., Kubo-Kawai, N., Kubo, K., Terazawa, T. & Masataka, N. Distinct aging effects for two types of inhibition in older adults: a near-infrared spectroscopy study on the Simon task and the flanker task. NeuroReport 23, 819–824 (2012).

    Article  PubMed  Google Scholar 

  185. Waszak, F., Li, S.-C. & Hommel, B. The development of attentional networks: cross-sectional findings from a life span sample. Dev. Psychol. 46, 337–349 (2010).

    Article  PubMed  Google Scholar 

  186. Wild-Wall, N., Falkenstein, M. & Hohnsbein, J. Flanker interference in young and older participants as reflected in event-related potentials. Brain Res. 1211, 72–84 (2008).

    Article  CAS  PubMed  Google Scholar 

  187. Zeef, E. J. & Kok, A. Age-related differences in the timing of stimulus and response processes during visual selective attention: performance and psychophysiological analyses. Psychophysiology 30, 138–151 (1993).

    Article  CAS  PubMed  Google Scholar 

  188. Lambrechts, A., Karolis, V., Garcia, S., Obende, J. & Cappelletti, M. Age does not count: resilience of quantity processing in healthy ageing. Front. Psychol. 4, 865 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  189. Ioannidis, J. P. A. Issues in comparisons between meta-analyses and large trials. JAMA 279, 1089 (1998).

    Article  CAS  PubMed  Google Scholar 

  190. Slavin, R. & Smith, D. The relationship between sample sizes and effect sizes in systematic reviews in education. Educ. Eval. Policy Anal. 31, 500–506 (2009).

    Article  Google Scholar 

  191. Sterne, J. A. C., Gavaghan, D. & Egger, M. Publication and related bias in meta-analysis. J. Clin. Epidemiol. 53, 1119–1129 (2000).

    Article  CAS  PubMed  Google Scholar 

  192. Vadillo, M. A. Ego depletion may disappear by 2020. Soc. Psychol. 50, 282–291 (2019).

    Article  Google Scholar 

  193. Park, D. C. & McDonough, I. M. The dynamic aging mind: revelations from functional neuroimaging research. Perspect. Psychol. Sci. 8, 62–67 (2013).

    Article  PubMed  Google Scholar 

  194. Reuter-Lorenz, P. A. & Cappell, K. A. Neurocognitive aging and the compensation hypothesis. Curr. Dir. Psychol. Sci. 17, 177–182 (2008).

    Article  Google Scholar 

  195. Staudinger, U. M., Cornelius, S. W. & Baltes, P. B. The aging of intelligence: potential and limits. Ann. Am. Acad. Pol. Soc. Sci. 503, 43–59 (1989).

    Article  Google Scholar 

  196. Salthouse, T. A. Selective review of cognitive aging. J. Int. Neuropsychol. Soc. 16, 754–760 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  197. Hoffman, L., Hofer, S. M. & Sliwinski, M. J. On the confounds among retest gains and age-cohort differences in the estimation of within-person change in longitudinal studies: a simulation study. Psychol. Aging 26, 778–791 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  198. Salthouse, T. A. All data collection and analysis methods have limitations: reply to Rabbitt (2011) and Raz and Lindenberger (2011). Psychol. Bull. 137, 796–799 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  199. Wulff, D. U., De Deyne, S., Jones, M. N., Mata, R. & The Aging Lexicon Consortium. New perspectives on the aging lexicon. Trends Cogn. Sci. https://doi.org/10/gf4x6z (2019).

  200. Cornman, J. C. et al. Cohort profile: the Social Environment and Biomarkers of Aging Study (SEBAS) in Taiwan. Int. J. Epidemiol. 45, 54–63 (2016).

    Article  PubMed  Google Scholar 

  201. Weinstein, M. et al. Social Environment and Biomarkers of Aging Study (SEBAS) in Taiwan, 2000 and 2006 (Inter-university Consortium for Political and Social Research, 2014).

  202. Haaf, J. M. & Rouder, J. N. Some do and some don’t? Accounting for variability of individual difference structures. Psychon. Bull. Rev. 26, 772–789 (2019).

    Article  PubMed  Google Scholar 

  203. Rouder, J. N., Kumar, A. & Haaf, J. M. Why most studies of individual differences with inhibition tasks are bound to fail. Preprint at https://doi.org/10.31234/osf.io/3cjr5 (2019).

  204. Ratcliff, R. Methods for dealing with reaction time outliers. Psychol. Bull. 114, 510–532 (1993).

    Article  CAS  PubMed  Google Scholar 

  205. Baayen, R. H. & Milin, P. Analyzing reaction times. Int. J. Psychol. Res. 3, 12–28 (2010).

    Article  Google Scholar 

  206. Kliegl, R., Masson, M. E. J. & Richter, E. M. A linear mixed model analysis of masked repetition priming. Vis. Cogn. 18, 655–681 (2010).

    Article  Google Scholar 

  207. Cerella, J., Poon, L. W. & Williams, D. M. in Aging in the 1980s: Psychological Issues (ed. Poon, L. W.) 332–340 (American Psychological Association, 1980).

  208. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article  Google Scholar 

  209. Schad, D. J., Vasishth, S., Hohenstein, S. & Kliegl, R. How to capitalize on a priori contrasts in linear (mixed) models: a tutorial. J. Mem. Lang. 110, 104038 (2020).

    Article  Google Scholar 

  210. Barr, D. J., Levy, R., Scheepers, C. & Tily, H. J. Random effects structure for confirmatory hypothesis testing: keep it maximal. J. Mem. Lang. 68, 255–278 (2013).

    Article  Google Scholar 

  211. Matuschek, H., Kliegl, R., Vasishth, S., Baayen, R. H. & Bates, D. Balancing type I error and power in linear mixed models. J. Mem. Lang. 94, 305–315 (2017).

    Article  Google Scholar 

  212. Fox, J. Applied Regression Analysis and Generalized Linear Models (SAGE, 2015).

  213. Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).

  214. Bäckman, L., Nyberg, L., Lindenberger, U., Li, S.-C. & Farde, L. The correlative triad among aging, dopamine, and cognition: current status and future prospects. Neurosci. Biobehav. Rev. 30, 791–807 (2006).

    Article  PubMed  Google Scholar 

  215. Mann, D. M. A. The locus coeruleus and its possible role in ageing and degenerative disease of the human central nervous system. Mech. Ageing Dev. 23, 73–94 (1983).

    Article  CAS  PubMed  Google Scholar 

  216. Mather, M. in The Cognitive Neurosciences (eds Poeppel, D. et al.) 91–104 (MIT Press, 2019).

  217. Schliebs, R. & Arendt, T. The cholinergic system in aging and neuronal degeneration. Behav. Brain Res. 221, 555–563 (2011).

    Article  CAS  PubMed  Google Scholar 

  218. Walhovd, K. B. et al. Consistent neuroanatomical age-related volume differences across multiple samples. Neurobiol. Aging 32, 916–932 (2011).

    Article  PubMed  Google Scholar 

  219. Braskie, M. N. et al. Relationship of striatal dopamine synthesis capacity to age and cognition. J. Neurosci. 28, 14320–14328 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  220. Karrer, T. M., Josef, A. K., Mata, R., Morris, E. D. & Samanez-Larkin, G. R. Reduced dopamine receptors and transporters but not synthesis capacity in normal aging adults: a meta-analysis. Neurobiol. Aging 57, 36–46 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  221. Kelley, T. A. & Yantis, S. Learning to attend: effects of practice on information selection. J. Vis. 9(7), 16 (2009).

    Article  PubMed  Google Scholar 

  222. Rueda, M. R., Rothbart, M. K., McCandliss, B. D., Saccomanno, L. & Posner, M. I. Training, maturation, and genetic influences on the development of executive attention. Proc. Natl Acad. Sci. USA 102, 14931–14936 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  223. Rueda, M. R., Checa, P. & Cómbita, L. M. Enhanced efficiency of the executive attention network after training in preschool children: immediate changes and effects after two months. Dev. Cogn. Neurosci. 2, S192–S204 (2012).

    Article  PubMed  Google Scholar 

  224. Bialystok, E., Craik, F. I. M., Klein, R. & Viswanathan, M. Bilingualism, aging, and cognitive control: evidence from the Simon task. Psychol. Aging 19, 290–303 (2004).

    Article  PubMed  Google Scholar 

  225. Bialystok, E., Martin, M. M. & Viswanathan, M. Bilingualism across the lifespan: the rise and fall of inhibitory control. Int. J. Biling. 9, 103–119 (2005).

    Article  Google Scholar 

  226. Bialystok, E., Craik, F. I. M. & Luk, G. Bilingualism: consequences for mind and brain. Trends Cogn. Sci. 16, 240–250 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  227. Costa, A., Hernández, M., Costa-Faidella, J. & Sebastián-Gallés, N. On the bilingual advantage in conflict processing: now you see it, now you don’t. Cognition 113, 135–149 (2009).

    Article  PubMed  Google Scholar 

  228. Hayakawa, S. & Marian, V. Consequences of multilingualism for neural architecture. Behav. Brain Funct. 15, 6 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  229. Pelham, S. D. & Abrams, L. Cognitive advantages and disadvantages in early and late bilinguals. J. Exp. Psychol. Learn. Mem. Cogn. 40, 313–325 (2014).

    Article  PubMed  Google Scholar 

  230. Schroeder, S. R., Marian, V., Shook, A. & Bartolotti, J. Bilingualism and musicianship enhance cognitive control. Neural Plast. 2016, 4058620 (2016).

    Article  PubMed  Google Scholar 

  231. Woumans, E., Ceuleers, E., Van der Linden, L., Szmalec, A. & Duyck, W. Verbal and nonverbal cognitive control in bilinguals and interpreters. J. Exp. Psychol. Learn. Mem. Cogn. 41, 1579–1586 (2015).

    Article  PubMed  Google Scholar 

  232. Todd, M. et al. Apolipoprotein E, cognitive function, and cognitive decline among older Taiwanese adults. PLoS ONE 13, e0206118 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  233. Chung, Y., Rabe-Hesketh, S., Dorie, V., Gelman, A. & Liu, J. A nondegenerate penalized likelihood estimator for variance parameters in multilevel models. Psychometrika 78, 685–709 (2013).

    Article  PubMed  Google Scholar 

  234. Baayen, R. H., Davidson, D. J. & Bates, D. M. Mixed-effects modeling with crossed random effects for subjects and items. J. Mem. Lang. 59, 390–412 (2008).

    Article  Google Scholar 

  235. Callejas, A., Lupiáñez, J. & Tudela, P. The three attentional networks: on their independence and interactions. Brain Cogn. 54, 225–227 (2004).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the Deutsche Forschungsgemeinschaft (German Research Foundation) Project ID 317633480 (SFB 1287, Project Q) (University of Potsdam, to J.V.); NIH R01 AG016790 (Princeton University, to N.G.), with a subcontract to M.T.U. at Georgetown University; NIH R01 AG016661 (Georgetown University, to M.W.); NSF BCS 1940980 (Georgetown University, to M.T.U.); and the Graduate School of Arts and Sciences, Georgetown University (to M.W.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank the Health Promotion Administration at the Ministry of Health in Taiwan for their support of this project; M. Pullman and L. Babcock for task preparation and testing; and M. Posner, M. Riesenhuber, M. Rugg, D. Fernandez-Duque and especially D. Glei for helpful comments.

Author information

Authors and Affiliations

  1. Center of Linguistics, School of Arts and Humanities, University of Lisbon, Lisbon, Portugal

    João Veríssimo

  2. Department of Linguistics, University of Potsdam, Potsdam, Germany

    João Veríssimo

  3. School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA

    Paul Verhaeghen

  4. Office of Population Research, Princeton University, Princeton, NJ, USA

    Noreen Goldman

  5. Center for Population and Health, Georgetown University, Washington, DC, USA

    Maxine Weinstein

  6. Department of Neuroscience, Georgetown University, Washington, DC, USA

    Michael T. Ullman

Authors
  1. João Veríssimo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paul Verhaeghen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Noreen Goldman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maxine Weinstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael T. Ullman
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The study was conceived by M.T.U. and M.W. and designed by M.T.U., as part of the larger SEBAS project led by N.G. and M.W. J.V. performed the data preparation and analysis. J.V. and M.T.U. wrote the paper, with contributions from P.V. as well as N.G. and M.W. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to João Veríssimo or Michael T. Ullman.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Human Behaviour thanks Gregory Samanez-Larkin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Demographic and cognitive information for participants, presented in 5-year age brackets.

This table is for informational purposes only; we remind readers that all analyses were performed with age as a continuous variable. For each age bracket, the columns show first of all the sample size (total, with number of females in parentheses) and the mean (and SD) of years of education. Thus, these two columns display the age distributions of sex and education, the two variables covaried out in our analyses; for discussion of these distributions see refs. 73,109. The subsequent columns display means (and SDs) of the previously published cognitive measures of working memory (n-back task; mean d’ over 1-back and 2-back109) and declarative memory (recognition memory task; mean d’ over real and novel objects73) for this sample. Note that the sample sizes in each 5-year age bracket are slightly different for the working memory scores (Supplementary Table 1 in ref. 109) and the declarative memory scores (Table 1 in Ref. 73) than for the data in the present paper (for example, due to slightly different subsets of participants having valid performance measures in the respective tasks). For a more general cognitive measure obtained in this sample, see Ref. 232. N: number of participants; NA: not available; SD: standard deviation.

Extended Data Fig. 2 Results from the mixed-effects logistic regression model on the accuracy of responses.

We examined accuracy using generalized linear mixed-effects regression with a logit link function. We analysed correct/incorrect responses produced prior to the timeout period (number of data points: 49,980), for the same 702 participants as in the main analysis on RTs. The same fixed-effect predictors were included as in the main analysis. Models included a random by-participant intercept only, because models with random slopes did not converge. Model convergence was reached only by applying a weakly informative Bayesian prior on the effects233 (priors on fixed effects were normal distributions with mean=0 and SD = 3 for the intercept, and mean=0 and SD = 0.4 for slopes). Effect sizes are reported as unstandardized estimates (b-values) in the logit scale with 95% confidence intervals, together with z-values; p-values are reported as two-tailed, with exact values to three digits. The significant interaction between age and the executive effect indicated that age was associated with increasing executive efficiency, parallel to the finding of increasing efficiency in the main analysis on RTs. Follow-up analyses indicated that there was an interference cost on accuracy for incongruent flankers at the minimum age of 58 years (back-transformed accuracy in percent correct for congruent, 99.70% vs. incongruent, 99.38%; b = -0.7302 [-0.9756, -0.4847], z = -5.83, p < .001), but no significant difference between incongruent and congruent flankers at the maximum age of 98 years (back-transformed accuracy for congruent, 98.10% vs. incongruent, 97.94%; b = -0.0825 [-0.4693, 0.3043], z = -0.42, p = .676). Likewise, there was no significant executive effect at age 90 (b = -0.2127 [-0.5150, 0.0897], z = -1.38, p = .168). The executive effect was almost nine times larger at the minimum than maximum age, as revealed by the regression coefficients in the logit scale (b values: 58 years: -0.7302 vs. 98 years: -0.0825), and twice as large in back-transformed accuracy (0.32% vs. 0.16%). Education effect: higher education was associated with higher accuracy (across all ages and all cues and flankers).

Extended Data Fig. 3 Results from the linear mixed-effects regression model on log RTs.

P-values were obtained from t-tests with 49,163 degrees of freedom, calculated as the number of data points (that is, 49,176) minus the number of fixed effect estimates (that is, 13)234. Here and elsewhere, effect sizes of linear mixed-effects models are reported as unstandardized estimates (b-values) with 95% confidence intervals, together with t-values; p-values are reported as two-tailed, with exact values to three digits. Education effect: higher education was associated with faster responses (across all ages and all cues and flankers). Trial effect: later trials were associated with faster responses. Follow-up analyses to the two network interactions (Alerting X Executive, Orienting X Executive) were performed. Note that both of these interactions, and the general patterns found in their follow-up analyses, are commonly reported for the ANT in both younger and older adults122,138,140,144,145,146,152,235. First, the alerting effect was significant in trials with congruent flankers (b = 0.0127 [0.0070, 0.0184], t = 4.37, p < .001), but not in trials with incongruent flankers (b = -0.0032 [-0.0089, 0.0026], t = -1.08, p = .279). Second, the orienting effect was larger in trials with incongruent flankers (b = 0.0227 [0.0169, 0.0284], t = 7.76, p < .001) than in those with congruent flankers (b = 0.0094 [0.0037, 0.0151], t = 3.24, p = .001), but was significant in both. We also followed up on both interactions by examining the executive effect in the different cue types involved in alerting and orienting. The executive effect was larger in trials with a central cue (b = 0.0777 [0.0713, 0.0841], t = 23.79, p < .001) than in trials with no cue (b = 0.0618 [0.0554, 0.0682], t = 18.95, p < .001) (Alerting X Executive). The executive effect was also larger in trials with a central cue (see just above) than in trials with a spatial cue (b = 0.0644 [0.0580, 0.0708], t = 19.75, p < .001) (Orienting X Executive). CI: confidence interval.

Extended Data Fig. 4 Mean untransformed RTs for each condition, and mean attentional network effects, in milliseconds, presented in 5-year age brackets.

This table displays mean untransformed RTs, by flanker and cue condition (with SDs in parentheses), together with mean attentional network effects (computed as differences between the mean untransformed RTs in each pair of relevant conditions for example, between the central and no cue conditions for alerting), in 5-year age brackets. This table is for informational purposes only; we remind readers that all analyses were performed on log-transformed RTs with age as a continuous variable. Congruent and incongruent flanker types are computed over all three cue types, and each cue type is computed over congruent and incongruent flankers. RTs: response times; N: number of participants; SDs: standard deviations.

Extended Data Fig. 5 Linear effects of age on the efficiencies of the three attentional networks, showing network effects for each of the 702 participants.

The linear age effects are displayed for (a) the alerting network, (b) the orienting network, and (c) the executive network. For each network, each data point reflects the difference between mean log-transformed RTs in the two relevant conditions (for example, between no cue and central cue for alerting) for each participant. The y-axis ranges (maximum minus minimum) of the three panels are identical, while their numerical values differ; specifically, because the executive effect is larger than the other two attentional effects, the numerical values for the y-axis in panel (c) are shifted upwards.

Extended Data Fig. 6 Age effects on RTs shown separately for each of the cue and flanker conditions.

Though age-related slowdowns were observed for all conditions, the RT increases differed among the relevant conditions, that is, among the cue or flanker types. These interactions yielded the observed age effects on efficiency for the three attentional networks. First (panel a), the RT increase with aging was greater for trials preceded by a central cue (solid line; b = 0.0087 [0.0072, 0.0103], t = 11.12, p < .001) than for those with no cue (line with short dashes; b = 0.0080 [0.0064, 0.0095], t = 10.15, p < .001), leading to the observed decrease of alerting efficiency with aging. (The reasons for the detrimental effect of the central cue relative to no cue at later ages remain to be determined; perhaps once the alerting benefit has decreased past a certain point, processing costs associated with the presentation of the cue become predominant.) Second (also in panel a), the RT increase with age was smaller for trials preceded by spatial cues (line with long dashes; b = 0.0081 [0.0065, 0.0096], t = 10.29, p < .001) than for those preceded by a central cue (see just above), yielding the reported increase in orienting efficiency with age (that is, older participants benefited particularly from spatial cues as compared to central cues). Third (panel b), the age-related RT increase in incongruent flanker trials (solid line; b = 0.0077 [0.0061, 0.0092], t = 9.79, p < .001) was smaller than for congruent trials (dashed line; b = 0.0089 [0.0073, 0.0104], t = 11.29, p < .001), leading to the observed increase in executive efficiency with aging.

Extended Data Fig. 7 Density plots for the distributions of untransformed RTs of correct trials.

The plots show these RTs (from the trials analysed in the main regression model) for incongruent flankers (solid lines) and congruent flankers (dashed lines), for younger participants (n = 592; panel a) and older participants (n = 110; panel b), split at the midpoint of the age range (age 78). As can be seen, very few responses for either the incongruent or congruent trials approached the timeout of 1,700ms for either group of participants, arguing against a ‘timeout’ alternative explanation for the age-related increase in efficiency of the executive network (see ‘Linear effects of age: sensitivity analyses’, in Results).

Extended Data Fig. 8 Discovery of the optimal breakpoint model.

This was achieved by comparing model goodness-of-fit (AIC) for regression-with-breakpoints models with breakpoints at successive ages. The AIC of the optimal model (with a breakpoint at age 76) is indicated with the gray arrow.

Extended Data Fig. 9 The nonlinear effect of age on the efficiency of the executive network, showing the executive effect for each of the 702 participants.

The nonlinear age effect is displayed for (a) the model with a quadratic term for age, and (b) the breakpoint model with the optimal breakpoint (age 76). Each data point reflects the difference between the mean log-RTs for incongruent and congruent flankers, for each participant.

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2 and Fig. 1.

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Veríssimo, J., Verhaeghen, P., Goldman, N. et al. Evidence that ageing yields improvements as well as declines across attention and executive functions. Nat Hum Behav 6, 97–110 (2022). https://doi.org/10.1038/s41562-021-01169-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41562-021-01169-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing