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
Subscribe to Nature+
Get immediate online access to Nature and 55 other Nature journal
Subscribe to Journal
Get full journal access for 1 year
only $9.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
The anonymized data (with accompanying documentation) have been uploaded to the Open Science Framework. They can be found at https://osf.io/59er2/.
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/.
Nyberg, L., Lövdén, M., Riklund, K., Lindenberger, U. & Bäckman, L. Memory aging and brain maintenance. Trends Cogn. Sci. 16, 292–305 (2012).
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).
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).
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).
Wierenga, C. E. et al. Age-related changes in word retrieval: role of bilateral frontal and subcortical networks. Neurobiol. Aging 29, 436–451 (2008).
Greve, A., Cooper, E. & Henson, R. N. No evidence that ‘fast-mapping’ benefits novel learning in healthy older adults. Neuropsychologia 60, 52–59 (2014).
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).
Howard, D. V. et al. Implicit sequence learning: effects of level of structure, adult age, and extended practice. Psychol. Aging 19, 79–92 (2004).
Howard, J. H. & Howard, D. V. Aging mind and brain: is implicit learning spared in healthy aging?. Front. Psychol. 4, 817 (2013).
Shea, C. H., Park, J.-H. & Braden, H. W. Age-related effects in sequential motor learning. Phys. Ther. 86, 478–488 (2006).
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).
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).
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).
Naveh-Benjamin, M. & Kilb, A. Age-related differences in associative memory: the role of sensory decline. Psychol. Aging 29, 672–683 (2014).
Salthouse, T. A. The processing-speed theory of adult age differences in cognition. Psychol. Rev. 103, 403–428 (1996).
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).
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).
Park, D. C. et al. Mediators of long-term memory performance across the life span. Psychol. Aging 11, 621–637 (1996).
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).
Cabeza, R., Nyberg, L. & Park, D. C. Cognitive Neuroscience of Aging: Linking Cognitive and Cerebral Aging (Oxford Univ. Press, 2016).
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).
Schaie, K. W. & Willis, S. L. The Seattle Longitudinal Study of adult cognitive development. ISSBD Bull. 2010(1), 24–29 (2010).
Anderson, N. D. & Craik, F. I. M. 50 years of cognitive aging theory. J. Gerontol. B 72, 1–6 (2017).
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).
Hedden, T. & Gabrieli, J. D. E. Insights into the ageing mind: a view from cognitive neuroscience. Nat. Rev. Neurosci. 5, 87–96 (2004).
Park, D. C. et al. Models of visuospatial and verbal memory across the adult life span. Psychol. Aging 17, 299–320 (2002).
Piolino, P., Desgranges, B., Benali, K. & Eustache, F. Episodic and semantic remote autobiographical memory in ageing. Memory 10, 239–257 (2002).
Schaie, K. W. Intellectual Development in Adulthood: The Seattle Longitudinal Study (Cambridge Univ. Press, 1996).
Cansino, S. et al. The decline of verbal and visuospatial working memory across the adult life span. Age (Dordr.) 35, 2283–2302 (2013).
Ikier, S., Yang, L. & Hasher, L. Implicit proactive interference, age, and automatic versus controlled retrieval strategies. Psychol. Sci. 19, 456–461 (2008).
Jacoby, L. L. Ironic effects of repetition: measuring age-related differences in memory. J. Exp. Psychol. Learn. Mem. Cogn. 25, 3–22 (1999).
Laver, G. D. Adult aging effects on semantic and episodic priming in word recognition. Psychol. Aging 24, 28–39 (2009).
Campbell, K. L. et al. Robust resilience of the frontotemporal syntax system to aging. J. Neurosci. 36, 5214–5227 (2016).
Cohen-Shikora, E. R. & Balota, D. A. Visual word recognition across the adult lifespan. Psychol. Aging 31, 488–502 (2016).
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).
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).
Shafto, M. A. & Tyler, L. K. Language in the aging brain: the network dynamics of cognitive decline and preservation. Science 346, 583–587 (2014).
Cabeza, R. et al. Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing. Nat. Rev. Neurosci. 19, 701–710 (2018).
Horton, S., Baker, J. & Schorer, J. Expertise and aging: maintaining skills through the lifespan. Eur. Rev. Aging Phys. Act. 5, 89–96 (2008).
Horn, J. L. in Aging and Cognitive Processes (eds Craik, F. I. M. & Trehub, S.) 237–278 (Plenum, 1982).
Horn, J. L. & Cattell, R. B. Age differences in fluid and crystallized intelligence. Acta Psychol. (Amst.) 26, 107–129 (1967).
Li, S.-C. in International Encyclopedia of the Social and Behavioral Sciences (eds Smelser, N. J. & Baltes, P. B.) 310–317 (Elsevier, 2001).
Baltes, P. B. & Kliegl, R. in Neurology (eds. Poeck, K. et al.) 1–17 (Springer, 1986).
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).
Cattell, R. B. Abilities: Their Structure, Growth, and Action (Houghton Mifflin, 1971).
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).
Horn, J. L. & Donaldson, G. On the myth of intellectual decline in adulthood. Am. Psychol. 31, 701–719 (1976).
Spreng, R. N. & Turner, G. R. The shifting architecture of cognition and brain function in older adulthood. Perspect. Psychol. Sci. 14, 523–542 (2019).
Verhaeghen, P. Aging and vocabulary score: a meta-analysis. Psychol. Aging 18, 332–339 (2003).
Baltes, P. B. & Smith, J. The fascination of wisdom: its nature, ontogeny, and function. Perspect. Psychol. Sci. 3, 56–64 (2008).
Lim, K. T. K. & Yu, R. Aging and wisdom: age-related changes in economic and social decision making. Front. Aging Neurosci. 7, 120 (2015).
Happé, F. G. E., Winner, E. & Brownell, H. The getting of wisdom: theory of mind in old age. Dev. Psychol. 34, 358–362 (1998).
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).
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).
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).
Urry, H. L. & Gross, J. J. Emotion regulation in older age. Curr. Dir. Psychol. Sci. 19, 352–357 (2010).
Li, Y., Baldassi, M., Johnson, E. J. & Weber, E. U. Complementary cognitive capabilities, economic decision making, and aging. Psychol. Aging 28, 595–613 (2013).
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).
Strough, J., Schlosnagle, L. & DiDonato, L. Understanding decisions about sunk costs from older and younger adults’ perspectives. J. Gerontol. B 66B, 681–686 (2011).
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).
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).
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).
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).
Ibanez, A. et al. Empathy, sex and fluid intelligence as predictors of theory of mind. Pers. Individ. Differ. 54, 616–621 (2013).
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).
Kanfer, R. & Ackerman, P. L. Aging, adult development, and work motivation. Acad. Manage. Rev. 29, 440–458 (2004).
Lövdén, M., Ghisletta, P. & Lindenberger, U. Cognition in the Berlin Aging Study (BASE): the first 10 years. Aging Neuropsychol. Cogn. 11, 104–133 (2004).
Ng, T. W. H. & Feldman, D. C. The relationship of age to ten dimensions of job performance. J. Appl. Psychol. 93, 392–423 (2008).
Ardelt, M. Wisdom as expert knowledge system: a critical review of a contemporary operationalization of an ancient concept. Hum. Dev. 47, 257–285 (2004).
Buchler, N. E. G. & Reder, L. M. Modeling age-related memory deficits: a two-parameter solution. Psychol. Aging 22, 104–121 (2007).
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).
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).
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).
Stine-Morrow, E. A. L. The Dumbledore hypothesis of cognitive aging. Curr. Dir. Psychol. Sci. 16, 295–299 (2007).
Umanath, S. & Marsh, E. J. Understanding how prior knowledge influences memory in older adults. Perspect. Psychol. Sci. 9, 408–426 (2014).
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).
Fernandez-Duque, D. & Posner, M. I. Relating the mechanisms of orienting and alerting. Neuropsychologia 35, 477–486 (1997).
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).
Zanto, T. P. & Gazzaley, A. in The Oxford Handbook of Attention (eds Nobre, A. C. & Kastner, S.) 927–971 (Oxford Univ. Press, 2014).
Kinsella, G., Storey, E. & Crawford, J. R. in Neurology and Clinical Neuroscience (ed. Schapira, A. H. V.) 83–95 (Elsevier, 1998).
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).
Del Missier, F., Mäntylä, T. & Bruine de Bruin, W. Executive functions in decision making: an individual differences approach. Think. Reason. 16, 69–97 (2010).
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).
Martin, R. & Allen, C. A disorder of executive function and its role in language processing. Semin. Speech Lang. 29, 201–210 (2008).
Mazuka, R., Jincho, N. & Oishi, H. Development of executive control and language processing. Lang. Linguist. Compass 3, 59–89 (2009).
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).
Braver, T. S. & Barch, D. M. A theory of cognitive control, aging cognition, and neuromodulation. Neurosci. Biobehav. Rev. 26, 809–817 (2002).
Buckner, R. L. Memory and executive function in aging and AD. Neuron 44, 195–208 (2004).
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).
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).
Hasher, L. & Zacks, R. T. in The Psychology of Learning and Motivation (ed. Bower, G. H.) Vol. 22, 193–225 (Academic, 1988).
Hasher, L., Stoltzfus, E. R., Zacks, R. T. & Rypma, B. Age and inhibition. J. Exp. Psychol. Learn. Mem. Cogn. 17, 163–169 (1991).
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).
Posner, M. I., Rothbart, M. K. & Ghassemzadeh, H. Restoring attention networks. Yale J. Biol. Med. 92, 139–143 (2019).
West, R. L. An application of prefrontal cortex function theory to cognitive aging. Psychol. Bull. 120, 272–292 (1996).
West, R. L. In defense of the frontal lobe hypothesis of cognitive aging. J. Int. Neuropsychol. Soc. 6, 727–729 (2000).
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).
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).
Mather, M. & Harley, C. W. The locus coeruleus: essential for maintaining cognitive function and the aging brain. Trends Cogn. Sci. 20, 214–226 (2016).
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).
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).
Raz, N. & Rodrigue, K. M. Differential aging of the brain: patterns, cognitive correlates and modifiers. Neurosci. Biobehav. Rev. 30, 730–748 (2006).
Bopp, K. L. & Verhaeghen, P. Aging and verbal memory span: a meta-analysis. J. Gerontol. B. 60, P223–P233 (2005).
Bopp, K. L. & Verhaeghen, P. Aging and n-back performance: a meta-analysis. J. Gerontol. B https://doi.org/10/ggjwwt (2020)
Burke, D. M. & Osborne, G. in Inhibition in Cognition (eds Gorfein, D. S. & MacLeod, C. M.) 163–183 (American Psychological Association, 2007).
Glisky, E. L. in Brain Aging: Models, Methods, and Mechanisms (ed. Riddle, D. R.) Ch. 1 (Taylor & Francis, 2007).
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).
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).
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).
Rey-Mermet, A. & Gade, M. Inhibition in aging: what is preserved? What declines? A meta-analysis. Psychon. Bull. Rev. 25, 1695–1716 (2018).
Verhaeghen, P. Aging and executive control: reports of a demise greatly exaggerated. Curr. Dir. Psychol. Sci. 20, 174–180 (2011).
Verhaeghen, P. The Elements of Cognitive Aging: Meta-analyses of Age-Related Differences in Processing Speed and Their Consequences (Oxford Univ. Press, 2013).
Verhaeghen, P. & Cerella, J. Aging, executive control, and attention: a review of meta-analyses. Neurosci. Biobehav. Rev. 26, 849–857 (2002).
Friedman, N. P. & Miyake, A. Unity and diversity of executive functions: individual differences as a window on cognitive structure. Cortex 86, 186–204 (2017).
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).
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).
Petersen, S. E. & Posner, M. I. The attention system of the human brain: 20 years after. Annu. Rev. Neurosci. 35, 73–89 (2012).
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).
Uttl, B. & Graf, P. Color–Word Stroop test performance across the adult life span. J. Clin. Exp. Neuropsychol. 19, 405–420 (1997).
Posner, M. I. & Petersen, S. E. The attention system of the human brain. Annu. Rev. Neurosci. 13, 25–42 (1990).
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).
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).
Fan, J. et al. Testing the behavioral interaction and integration of attentional networks. Brain Cogn. 70, 209–220 (2009).
Posner, M. I., Sheese, B. E., Odludaş, Y. & Tang, Y. Analyzing and shaping human attentional networks. Neural Netw. 19, 1422–1429 (2006).
Wang, H., Fan, J. & Johnson, T. R. A symbolic model of human attentional networks. Cogn. Syst. Res. 5, 119–134 (2004).
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).
Fan, J., Mccandliss, B., Fossella, J., Flombaum, J. & Posner, M. I. The activation of attentional networks. NeuroImage 26, 471–479 (2005).
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).
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).
Beane, M. & Marrocco, R. T. in Cognitive Neuroscience of Attention (ed. Posner, M. I.) 313–325 (Guilford, 2004).
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).
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).
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).
Fossella, J. et al. Assessing the molecular genetics of attention networks. BMC Neurosci. 3, 14 (2002).
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).
Posner, M. I. Orienting of attention. Q. J. Exp. Psychol. 32, 3–25 (1980).
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).
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).
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).
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).
MacLeod, J. W. et al. Appraising the ANT: psychometric and theoretical considerations of the Attention Network Test. Neuropsychology 24, 637–651 (2010).
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).
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).
Fernandez-Duque, D. & Black, S. E. Attentional networks in normal aging and Alzheimer’s disease. Neuropsychology 20, 133–143 (2006).
Gamboz, N., Zamarian, S. & Cavallero, C. Age-related differences in the Attention Network Test (ANT). Exp. Aging Res. 36, 287–305 (2010).
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).
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).
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).
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).
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).
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).
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).
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).
Baayen, R. H. A real experiment is a factorial experiment? Ment. Lex. 5, 149–157 (2010).
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).
Kamkar, N. H. & Morton, J. B. CanDiD: a framework for linking executive function and education. Front. Psychol. 8, 1187 (2017).
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).
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).
Friedman, L. & Wall, M. Graphical views of suppression and multicollinearity in multiple linear regression. Am. Stat. 59, 127–136 (2005).
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).
Gittings, N. S. & Fozard, J. L. Age related changes in visual acuity. Exp. Gerontol. 21, 423–433 (1986).
Costa, A., Hernández, M. & Sebastián-Gallés, N. Bilingualism aids conflict resolution: evidence from the ANT task. Cognition 106, 59–86 (2008).
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).
Sekuler, A. B., Bennett, P. J. & Mamelak, M. Effects of aging on the useful field of view. Exp. Aging Res. 26, 103–120 (2000).
Baayen, R. H. Analyzing Linguistic Data: A Practical Introduction to Statistics Using R (Cambridge Univ. Press, 2008).
Neter, J., Kutner, M., Nachtsheim, C. & Wasserman, W. Applied Linear Statistical Models (Irwin, 1996).
Bosch, S., Veríssimo, J. & Clahsen, H. Inflectional morphology in bilingual language processing: an age-of-acquisition study. Lang. Acquis. 26, 339–360 (2019).
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).
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).
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).
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).
Chen, Y. et al. Testing a cognitive control model of human intelligence. Sci. Rep. 9, 2898 (2019).
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).
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).
Pettigrew, C. & Martin, R. C. Cognitive declines in healthy aging: evidence from multiple aspects of interference resolution. Psychol. Aging 29, 187–204 (2014).
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).
Wright, L. L. & Elias, J. W. Age differences in the effects of perceptual noise. J. Gerontol. 34, 704–708 (1979).
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).
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).
Rouder, J. N. & Haaf, J. M. A psychometrics of individual differences in experimental tasks. Psychon. Bull. Rev. 26, 452–467 (2019).
Stahl, C. et al. Behavioral components of impulsivity. J. Exp. Psychol. Gen. 143, 850–886 (2014).
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).
Hsieh, S., Liang, Y.-C. & Tsai, Y.-C. Do age-related changes contribute to the flanker effect? Clin. Neurophysiol. 123, 960–972 (2012).
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).
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).
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).
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).
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).
Ioannidis, J. P. A. Issues in comparisons between meta-analyses and large trials. JAMA 279, 1089 (1998).
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).
Sterne, J. A. C., Gavaghan, D. & Egger, M. Publication and related bias in meta-analysis. J. Clin. Epidemiol. 53, 1119–1129 (2000).
Vadillo, M. A. Ego depletion may disappear by 2020. Soc. Psychol. 50, 282–291 (2019).
Park, D. C. & McDonough, I. M. The dynamic aging mind: revelations from functional neuroimaging research. Perspect. Psychol. Sci. 8, 62–67 (2013).
Reuter-Lorenz, P. A. & Cappell, K. A. Neurocognitive aging and the compensation hypothesis. Curr. Dir. Psychol. Sci. 17, 177–182 (2008).
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).
Salthouse, T. A. Selective review of cognitive aging. J. Int. Neuropsychol. Soc. 16, 754–760 (2010).
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).
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).
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).
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).
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).
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).
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).
Ratcliff, R. Methods for dealing with reaction time outliers. Psychol. Bull. 114, 510–532 (1993).
Baayen, R. H. & Milin, P. Analyzing reaction times. Int. J. Psychol. Res. 3, 12–28 (2010).
Kliegl, R., Masson, M. E. J. & Richter, E. M. A linear mixed model analysis of masked repetition priming. Vis. Cogn. 18, 655–681 (2010).
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).
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
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).
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).
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).
Fox, J. Applied Regression Analysis and Generalized Linear Models (SAGE, 2015).
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).
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).
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).
Mather, M. in The Cognitive Neurosciences (eds Poeppel, D. et al.) 91–104 (MIT Press, 2019).
Schliebs, R. & Arendt, T. The cholinergic system in aging and neuronal degeneration. Behav. Brain Res. 221, 555–563 (2011).
Walhovd, K. B. et al. Consistent neuroanatomical age-related volume differences across multiple samples. Neurobiol. Aging 32, 916–932 (2011).
Braskie, M. N. et al. Relationship of striatal dopamine synthesis capacity to age and cognition. J. Neurosci. 28, 14320–14328 (2008).
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).
Kelley, T. A. & Yantis, S. Learning to attend: effects of practice on information selection. J. Vis. 9(7), 16 (2009).
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).
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).
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).
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).
Bialystok, E., Craik, F. I. M. & Luk, G. Bilingualism: consequences for mind and brain. Trends Cogn. Sci. 16, 240–250 (2012).
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).
Hayakawa, S. & Marian, V. Consequences of multilingualism for neural architecture. Behav. Brain Funct. 15, 6 (2019).
Pelham, S. D. & Abrams, L. Cognitive advantages and disadvantages in early and late bilinguals. J. Exp. Psychol. Learn. Mem. Cogn. 40, 313–325 (2014).
Schroeder, S. R., Marian, V., Shook, A. & Bartolotti, J. Bilingualism and musicianship enhance cognitive control. Neural Plast. 2016, 4058620 (2016).
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).
Todd, M. et al. Apolipoprotein E, cognitive function, and cognitive decline among older Taiwanese adults. PLoS ONE 13, e0206118 (2018).
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).
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).
Callejas, A., Lupiáñez, J. & Tudela, P. The three attentional networks: on their independence and interactions. Brain Cogn. 54, 225–227 (2004).
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.
The authors declare no competing interests.
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 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).
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.
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).
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.
About this article
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