BLUE GREEN RED BLACK PINK
5.6 Attention and ageing
5.6 Attention and ageing
Most studies investigating age-related decline in attention rely on alterations in processing speed. Reduced processing speed accounts for most other age-related changes in working memory, episodic memory, reasoning and spatial ability (Figure 5).72
Figure 5: Interrelations among age and five cognitive construct. Speed = processing speed;
PM/WM = primary and working memory; EM = episodic memory; Gf = general fluid intelligence; Reas = reasoning; Space = spatial ability. Numbers correspond to each factor’s weight in the structural equation model. When two numbers are represented, the left one corresponds to adults under the age of 50 years and the right one to those of 50 years and over.
Figure from Verhaeghen & Salthouse (1997).72
However, the relation between speed processing and cognitive ability is not as straightforward as it could appear.73 Age is believed to reduce inhibition, whereas it does not affect automatic processes.74 Given processing speed is affected differently, depending on engaged cognitive functions, there are reasons to believe age mainly affects the way sensory processing is modulated by higher order processes. Older adults have been shown to rely more on “top-down” processes to modulate neural activity in the primary visual cortex than do younger adults.75, 76 This could also explain the lack of consistency in studies on sustained attention. Indeed, for studies that rely on goal-oriented control (i.e. go/no-go task), older adults have even been shown to have improved sustained attention compared to younger adults.77 In the past two decades, studies have concentrated their efforts on understanding effects of age on measures that are most affected: those related to attention shift, dual tasking, managing distractors and noticing changes.78
5.6.1 Attention shift
Switch cost revealed a U-shaped association to age79 with a progressive reduced performance from the age of 18 onwards.80 Older adults require more time than younger ones (266 ms vs. 133 ms) to implement input gain for specific spatial locations.81 This has been supported by results on the trail making test (TMT) and the attention network test (ANT).
- Trail making test -
The trail making test (TMT) is a neuropsychological paper-form test that was initially developed by the US army during the second world war to evaluate overall performance in new recruits.82 The first part (TMT-A) is a visual search task, and the second is a visual search task that includes goal switching (TMT-B). Studies have
neuropsychological tests in predicting driving difficulties.83-85 Those with a TMT-B duration ≥ 187 seconds saw their risk of occasioning a motor vehicle collision increase by 94%.86 Classen et al.87 used an arbitrary cut-off point set at TMT-B>180 sec and found an OR=2.5 of failing an on-road test. For motor-vehicle collisions, Ball et al.88 found an OR of 1.21 and Marottoli et al.89 found an HR of 1.42. Like most neuropsychological tests, this represents a weak association between on-road evaluations and TMT performance values (sensitivity 50%, specificity 88%).84, 90, 91 - Attention network test -
Age-related increase in response time during the attention network test (ANT) have been shown not to be due to differences in controlled conditions related to alertness, orientation or executive attention as described by Posner.92, 93 This is probably due to the fact that spatial localisation is preserved with ageing and that the informative cue on the location of a target is given prior to display. Therefore, the attention shift in the attention neural network task is only affected by age if the stimulus onset asynchrony between the informative cue and the target cue is short enough for the older adult not to be able to process it.94 Older drivers nevertheless required 200 ms more than younger adults to perform the task independently of the condition. The overall mean duration has been shown to be associated with age and driving performance (Figure 6).
Figure 6: Overall mean performance during attention network test and association with age (A) and driving performance (B). ANT = attention network test; R2 = coefficient of determination; MB road test = Manitoba road test. Modified from Weaver et al. (2009).93 These “outstanding” results must be interpreted with caution, as they have never been reproduced.
5.6.3 Dual tasking
When performing two tasks simultaneously, older adults require more time.95 This has been shown to be related to reduced capacities to use similar networks within a short time frame at the response-selection stage.96 Therefore, the time span is increased for response retrieval of secondary tasks compared to prioritised ones.97 This effect seems to be task dependant, as higher demanding tasks show reduced age-related differences.98 A major difference in older adults is also their difficulty to automatise novel tasks and thereby prevent them from rapidly bypassing the bottleneck effect of
PART 5 – Attention and ageing
dual tasking.99 Given sufficient training, older adults will perform just as well as younger ones (Figure 6).100, 101
Figure 7: Dual task performances over multiple sessions. Mean age for older adults was of 63.3 years (n=10), and of 22.7 years for younger adults (n=10). Modified from Strobach et al. (2012).100
Dividing attention has been shown to reduce driving performance.102 Age-related difficulties during dual tasking has been extensively studied in the context of driving through the second subtask of the useful field of view.103, 104
- UFOV -
One of the most widely used visual processing speed tests that investigates dual tasking or “divided attention” is the second subset of the useful field of view test.105,
106 The tasks consist of measuring the threshold duration needed to correctly identify an appearing vehicle in the centre of a screen and the location of a simultaneously appearing vehicle at a 10° eccentric angle from the centre. This task therefore measures the ability to rapidly detect and localise targets. It investigates disengagement from a previous goal and visual search. The first version measuring performances at different angles of eccentricity107-110 was later abandoned for a fixed angle. Using structural equation modelling, Hoffman et al.111 showed that “divided attention” contributed to a latent trait (ρ=0.67) they called “attention deficits”, which in turn was highly correlated to driving impairment on a driving simulator (ρ=0.66).
The association with on-road performance was high for older patients with cognitive impairment (ρ=0.46) but absent for normal older adults (ρ=-0.15).112 So even if the actual version of the useful field of view is widely used in screening procedures, there is little evidence of its ability to correctly identify those with driving difficulties.
5.6.4 Managing distractors78
Managing distractors has been extensively studied in the context of visual search.113 An age-related increase in duration for visual search tasks has been documented since the 60s.114 The age-related loss, however, seems above all to concern complex tasks in which targets with common traits need to be discriminated (inhibition of irrelevant stimuli).115, 116 In consequence, for older adults, increasing the number of targets without redundancy increases the difference of performance compared to younger adults. Adults therefore seem to have more difficulties in distinguishing relevant from irrelevant information.117, 118 Knowing what is to come and where it will appear has older adults perform just as well as younger adults even in the presence of distractors.118 Older adults even perform better than younger adults when being distracted by perceptual interference caused by items flanked on either sides of a
target (filtering tasks).119 This benefit is nevertheless only present when the target location is known in advance.120, 121
Sorting relevant from irrelevant information while driving seems essential. While older drivers might be less distracted by irrelevant distractors, they might also require more time to identify objects that would require them to adapt their behaviour. Age-related decreased ability to inhibit distractors and its relationship to driving performance has mainly been investigated through the third subtask of the useful field of view test.106 “Selective attention” was also shown to be associated with “attention deficit” (ρ=0.72);111 however, its association to on-road performance in healthy older drivers was weak (ρ=0.06).112
5.6.5 Detecting changes
To recognise that a street we regularly drive through has become one-way, or that speed limitations have been changed on a road section or that recent constructions have lowered a bridge we drive under requires us to notice when changes have occurred. Not noticing such changes is called “change blindness”.122, 123
Reduced processing speed has been shown to contribute mostly to difficulties in detecting changes appearing with ageing.124 Age-related differences in detecting changes are however believed to also be due to older adults’ difficulties in forming representations for novel information.125 Visual scanning ability has been shown to be associated with attention deficits that explain driving impairment.111 However, little is nevertheless known on the direct causal relationship between change detection and driving difficulties.
5.7 Conclusion
Attention is not a specific cognitive process and should rather be seen as a combination of different brain processes that are engaged to optimise sensory processing in different contexts.126 Functional networks that modulate sensory input are task specific but nevertheless share common modular processes across a broad range of different demands. As ageing is accompanied by a decreased need to learn,127 observed changes in sensory processing speed can be explained by natural reduced memory search rather than a deficit of “attention”.128 In other words, normal ageing is normally related to normal brain changes that normally make us slower. Therefore, considering reduced “attention” as a sign of decline could be an error. However, the simple fact that we consider it as a sign of regression reveals our society’s difficulty in accepting changes related to ageing. This could contribute to social isolation and stigmatisation of older adults and worsen the problem. Future challenges are, therefore, in finding ways to fully recognise the value of older adults and help them contribute to our society’s wellbeing as much as possible.
5.8 References
1. Moussaïd M, Helbing D, Garnier S, Johansson A, Combe M, Theraulaz G.
Experimental study of the behavioural mechanisms underlying self-‐
organization in human crowds. Proceedings of the Royal Society B:
Biological Sciences. 2009;276(1668):2755-‐62.
PART 5 – Attention and ageing
2. Serences JT, Yantis S. Selective visual attention and perceptual coherence.
Trends in cognitive sciences. 2006;10(1):38-‐45.
3. Pfaff D, Ribeiro A, Matthews J, Kow LM. Concepts and mechanisms of generalized central nervous system arousal. Ann N Y Acad Sci.
2008;1129:11-‐25.
4. Oken BS, Salinsky MC, Elsas SM. Vigilance, alertness, or sustained
attention: physiological basis and measurement. Clinical neurophysiology : official journal of the International Federation of Clinical consciousness. Neural Networks. 2007;20(9):993-‐1003.
8. Kastner S, Pinsk MA. Visual attention as a multilevel selection process.
Cognitive, affective & behavioral neuroscience. 2004;4(4):483-‐500.
9. Anderson B. There is no Such Thing as Attention. Frontiers in psychology.
2011;2:246.
10. Pratte MS, Ling S, Swisher JD, Tong F. How attention extracts objects from noise. J Neurophysiol. 2013;110(6):1346-‐56.
11. Duncan J. EPS Mid-‐Career Award 2004: brain mechanisms of attention.
Quarterly journal of experimental psychology (2006). 2006;59(1):2-‐27.
12. Bloomfield M. Cerberus, the dog of Hades. The Monist. 1904;14(4):523-‐ fluid processing ability in older adults. J Neurosci. 2010;30(27):9253-‐9.
18. Awh E, Belopolsky AV, Theeuwes J. Top-‐down versus bottom-‐up attentional control: a failed theoretical dichotomy. Trends in cognitive sciences. 2012;16(8):437-‐43. contingencies in the human brain. Cortex. 2013;49(6):1733-‐49.
21. Klostermann EC, Braskie MN, Landau SM, O'Neil JP, Jagust WJ. Dopamine and frontostriatal networks in cognitive aging. Neurobiol Aging.
2012;33(3):623.e15-‐24.
22. Klanker M, Feenstra M, Denys D. Dopaminergic control of cognitive flexibility in humans and animals. Frontiers in neuroscience. 2013;7:201.
23. Zhang L, Tong MH, Marks TK, Shan H, Cottrell GW. SUN: A Bayesian framework for saliency using natural statistics. J Vis. 2008;8(7):32 1-‐20.
24. Ghose GM. Attentional modulation of visual responses by flexible input gain. J Neurophysiol. 2009;101(4):2089-‐106.
25. Lee J, Maunsell JH. A normalization model of attentional modulation of
Online decoding of object-‐based attention using real-‐time fMRI. European Journal of Neuroscience. 2014;39(2):319-‐29.
31. Serences JT, Schwarzbach J, Courtney SM, Golay X, Yantis S. Control of object-‐based attention in human cortex. Cereb Cortex. 2004;14(12):1346-‐
57.
32. Geng JJ, Vossel S. Re-‐evaluating the role of TPJ in attentional control:
contextual updating? Neurosci Biobehav Rev. 2013;37(10 Pt 2):2608-‐20.
33. Harel A, Kravitz DJ, Baker CI. Task context impacts visual object review of neuroscience. 2011;34:569-‐99.
36. Mesulam MM. Large-‐scale neurocognitive networks and distributed processing for attention, language, and memory. Ann Neurol.
1990;28(5):597-‐613.
37. Posner MI, Petersen SE. The attention system of the human brain. Annual review of neuroscience. 1990;13:25-‐42.
38. Posner MI, Rothbart MK. Research on attention networks as a model for the integration of psychological science. Annual review of psychology.
2007;58:1-‐23.
39. Fan J, McCandliss BD, Sommer T, Raz A, Posner MI. Testing the efficiency and independence of attentional networks. J Cogn Neurosci.
2002;14(3):340-‐7.
40. Rueda MR, Fan J, McCandliss BD, Halparin JD, Gruber DB, Lercari LP, et al.
Development of attentional networks in childhood. Neuropsychologia.
2004;42(8):1029-‐40.
41. Posner MI. Measuring alertness. Ann N Y Acad Sci. 2008;1129:193-‐9.
PART 5 – Attention and ageing
42. Macleod JW, Lawrence MA, McConnell MM, Eskes GA, Klein RM, Shore DI.
Appraising the ANT: Psychometric and theoretical considerations of the Attention Network Test. Neuropsychology. 2010;24(5):637-‐51.
43. Ishigami Y, Klein RM. 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. 2011;3:17.
44. McConnell MM, Shore DI. Mixing measures: testing an assumption of the Attention Network Test. Atten Percept Psychophys. 2011;73(4):1096-‐
107.
45. Brown HR, Friston KJ. The functional anatomy of attention: a DCM study.
Frontiers in human neuroscience. 2013;7:784.
46. Vossel S, Weidner R, Driver J, Friston KJ, Fink GR. Deconstructing the architecture of dorsal and ventral attention systems with dynamic causal modeling. J Neurosci. 2012;32(31):10637-‐48.
47. Corbetta M, Shulman GL. Control of goal-‐directed and stimulus-‐driven single, integrated representation or a collection of multiple control representations? Frontiers in human neuroscience. 2013;7:524.
51. Friston KJ. Transients, metastability, and neuronal dynamics. search: An ethology. Attention, Perception, & Psychophysics. 2014:1-‐16.
54. Treisman AM, Gelade G. A feature-‐integration theory of attention.
Cognitive Psychology. 1980;12(1):97-‐136.
55. Lee J, Lee JD, Salvucci DD. A Saliency-‐Based Search Model: Application of the Saliency Map for Driver-‐Vehicle Interfaces. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2013;57(1):1933-‐7.
56. Thompson KG, Bichot NP. A visual salience map in the primate frontal eye endogenous and exogenous spatial attention. Behavioural brain research.
2013;237:107-‐23.
59. Ptak R. The frontoparietal attention network of the human brain: action, saliency, and a priority map of the environment. The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.
2012;18(5):502-‐15.
60. Macaluso E, Doricchi F. Attention and predictions: control of spatial attention beyond the endogenous-‐exogenous dichotomy. Frontiers in human neuroscience. 2013;7:685.
61. Kelley TA, Serences JT, Giesbrecht B, Yantis S. Cortical mechanisms for shifting and holding visuospatial attention. Cereb Cortex. 2008;18(1):114-‐
25.
62. Verhaeghen P, Cerella J. Aging, executive control, and attention: a review of meta-‐analyses. Neuroscience & Biobehavioral Reviews.
2002;26(7):849-‐57. attention and short-‐term memory (NTVA). Neuropsychologia.
2011;49(6):1446-‐57.
Dynamic activation of frontal, parietal, and sensory regions underlying anticipatory visual spatial attention. J Neurosci. 2011;31(39):13880-‐9.
68. Mantini D, Corbetta M, Romani GL, Orban GA, Vanduffel W. Evolutionarily novel functional networks in the human brain? J Neurosci.
2013;33(8):3259-‐75.
69. Cohen MR, Maunsell JH. Using neuronal populations to study the mechanisms underlying spatial and feature attention. Neuron.
2011;70(6):1192-‐204.
70. Slagter HA, Giesbrecht B, Kok A, Weissman DH, Kenemans JL, Woldorff MG, et al. fMRI evidence for both generalized and specialized components of attentional control. Brain Res. 2007;1177:90-‐102.
71. Hellyer PJ, Shanahan M, Scott G, Wise RJ, Sharp DJ, Leech R. The control of global brain dynamics: opposing actions of frontoparietal control and default mode networks on attention. J Neurosci. 2014;34(2):451-‐61.
72. Verhaeghen P, Salthouse TA. Meta-‐analyses of age-‐cognition relations in adulthood: estimates of linear and nonlinear age effects and structural models. Psychol Bull. 1997;122(3):231-‐49.
73. Birren JE, Fisher LM. Aging and slowing of behavior: consequences for cognition and survival. Nebraska Symposium on Motivation. Nebraska Symposium on Motivation. 1991;39:1-‐37.
74. Eusop E, Sebban C, Piette F. Vieillissement et ralentissement: l'exemple des processus attentionnels-‐-‐procedures d'evaluation et questions sous-‐
jacentes. Encephale. 2001;27(1):39-‐44.
75. Madden DJ, Spaniol J, Bucur B, Whiting WL. Age-‐related increase in top-‐
down activation of visual features. Quarterly journal of experimental psychology (2006). 2007;60(5):644-‐51.
PART 5 – Attention and ageing
76. Whiting WL, Madden DJ, Babcock KJ. Overriding age differences in attentional capture with top-‐down processing. Psychol Aging.
2007;22(2):223-‐32.
77. Staub B, Doignon-‐Camus N, Despres O, Bonnefond A. Sustained attention in the elderly: what do we know and what does it tell us about cognitive aging? Ageing research reviews. 2013;12(2):459-‐68.
78. Groth KE, Allen PA. Visual attention and aging. Frontiers in bioscience : a journal and virtual library. 2000;5:D284-‐97.
79. Cepeda NJ, Kramer AF, Gonzalez de Sather JC. Changes in executive control across the life span: examination of task-‐switching performance.
Dev Psychol. 2001;37(5):715-‐30.
Washington, DC: War Department, Adjutant General’s Office; 1944.
83. Silva MT, Laks J, Engelhardt E. Neuropsychological tests and driving in dementia: a review of the recent literature. Rev Assoc Med Bras.
2009;55(4):484-‐8.
84. Mathias JL, Lucas LK. Cognitive predictors of unsafe driving in older drivers: a meta-‐analysis. Int Psychogeriatr. 2009;21(4):637-‐53.
85. Martin AJ, Marottoli R, O'Neill D. Driving assessment for maintaining
Can High-‐Risk Older Drivers Be Identified Through Performance-‐Based Measures in a Department of Motor Vehicles Setting? Journal of the American Geriatrics Society. 2006;54(1):77-‐84.
89. Marottoli RA, Richardson ED, Stowe MH, Miller EG, Brass LM, Cooney LM, Academy of Neurology. Neurology. 2010;74(16):1316-‐24.
91. Dobbs BM, Shergill SS. How effective is the Trail Making Test (Parts A and B) in identifying cognitively impaired drivers? Age Ageing.
2013;42(5):577-‐81.
92. Gamboz N, Zamarian S, Cavallero C. Age-‐related differences in the attention network test (ANT). Exp Aging Res. 2010;36(3):287-‐305.
93. Weaver B, Bedard M, McAuliffe J, Parkkari M. Using the Attention Network Test to predict driving test scores. Accid Anal Prev. 2009;41(1):76-‐83.
94. Nissen MJ, Corkin S. Effectiveness of attentional cueing in older and younger adults. J Gerontol. 1985;40(2):185-‐91.
95. Kramer AF, Madden D. Attention. In: Craik FIM, Salthouse TA, eds. The handbook of aging and cognition. Hillsdale: Lawrence Erlbaum;
2008:189–249.
96. Allen PA, Smith AF, Vires-‐Collins H, Sperry S. The psychological refractory period: evidence for age differences in attentional time-‐sharing. Psychol Aging. 1998;13(2):218-‐29.
97. Anderson ND, Craik FI, Naveh-‐Benjamin M. The attentional demands of encoding and retrieval in younger and older adults: 1. Evidence from divided attention costs. Psychol Aging. 1998;13(3):405-‐23.
98. Vaportzis E, Georgiou-‐Karistianis N, Stout JC. Dual task performance in
optimizing dual-‐task performance in younger and older adults. Frontiers in human neuroscience. 2012;6:39.
101. Bherer L, Kramer AF, Peterson MS, Colcombe S, Erickson K, Becic E.
Transfer effects in task-‐set cost and dual-‐task cost after dual-‐task training in older and younger adults: further evidence for cognitive plasticity in attentional control in late adulthood. Exp Aging Res. 2008;34(3):188-‐219.
102. Wood JM, Chaparro A, Lacherez P, Hickson L. Useful field of view predicts driving in the presence of distracters. Optom Vis Sci. 2012;89(4):373-‐81.
103. Wood JM, Owsley C. Useful Field of View Test. Gerontology. 2014.
104. Clay OJ, Wadley VG, Edwards JD, Roth DL, Roenker DL, Ball KK.
Cumulative meta-‐analysis of the relationship between useful field of view and driving performance in older adults: current and future implications. older adults. JAMA. 1998;279(14):1083-‐8.
108. Owsley C, McGwin G, Jr., Ball K. Vision impairment, eye disease, and injurious motor vehicle crashes in the elderly. Ophthalmic epidemiology.
1998;5(2):101-‐13.
109. Cross JM, McGwin G, Jr., Rubin GS, Ball KK, West SK, Roenker DL, et al.
Visual and medical risk factors for motor vehicle collision involvement among older drivers. Br J Ophthalmol. 2009;93(3):400-‐4.
PART 5 – Attention and ageing
110. Rubin GS, Ng ES, Bandeen-‐Roche K, Keyl PM, Freeman EE, West SK. A prospective, population-‐based study of the role of visual impairment in motor vehicle crashes among older drivers: the SEE study. Invest Ophthalmol Vis Sci. 2007;48(4):1483-‐91.
111. Hoffman L, McDowd JM, Atchley P, Dubinsky R. The role of visual
attention in predicting driving impairment in older adults. Psychol Aging.
2005;20(4):610-‐22.
112. Whelihan WM, DiCarlo MA, Paul RH. The relationship of
neuropsychological functioning to driving competence in older persons with early cognitive decline. Arch Clin Neuropsychol. 2005;20(2):217-‐28.
113. Treisman AM. Strategies and models of selective attention. Psychol Rev.
1969;76(3):282-‐99.
114. Rabbitt PM. Set and Age in a Choice-‐Response Task. J Gerontol.
1964;19:301-‐6.
115. Plude DJ, Doussard-‐Roosevelt JA. Aging, selective attention, and feature integration. Psychol Aging. 1989;4(1):98-‐105.
116. Commodari E, Guarnera M. Attention and aging. Aging clinical and experimental research. 2008;20(6):578-‐84.
117. Allen PA, Madden DJ, Groth KE, Crozier LC. Impact of age, redundancy, and perceptual noise on visual search. J Gerontol. 1992;47(2):P69-‐74.
117. Allen PA, Madden DJ, Groth KE, Crozier LC. Impact of age, redundancy, and perceptual noise on visual search. J Gerontol. 1992;47(2):P69-‐74.