Life-span and Life-course Studies; Developmental and Educational Psychology; Experimental and Cognitive Psychology
Abstract :
[en] Cognitive fatigue arises after a long-lasting task, as attested by increases in reaction times (RTs). However, most studies have focused on young adults. Therefore, we investigated cognitive fatigue through changes in RT distributions in three age groups-young, middle-aged, and older adults-during a 160-min Stroop task. Task duration was divided into four blocks and the ex-Gaussian parameters (μ, σ, τ) were extracted from individual RT distributions in each time block for each item type. The results showed a significant Group effect on μ. Young adults had smaller μ values than the other two groups, meaning that middle-aged and older people performed the whole task slower than young adults. By contrast, τ showed no Group effect but increased with Time-on-Task in middle-aged people. Older adults did not show τ increase with Time-on-Task, which echoes studies showing some resistance to task monotony in this population. Globally, our results showed dissociated age and Time-on-Task effect on the ex-Gaussian parameters, confirming the relevance of this approach in the cognitive fatigue domain. We proposed here that cognitive fatigue affects only the decision component of response production, and that midlife may be a life stage with high sensitivity to cognitive fatigue.
Research center :
GIGA CRC In vivo Imaging-Cognitive Neurosciences - ULiège
Disciplines :
Theoretical & cognitive psychology Neurology
Author, co-author :
Gilsoul, Jessica ; Université de Liège - ULiège > Département de Psychologie > Neuropsychologie de l'adulte
Libertiaux, Vincent ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Neuroimaging, data acquisition and processing
Depierreux, Frédérique ; Centre Hospitalier Universitaire de Liège - CHU > > Service de neurologie
Collette, Fabienne ; Université de Liège - ULiège > Département de Psychologie
Language :
English
Title :
Cognitive Fatigue in Young, Middle-Aged, and Older Adults: A Response Time Distribution Approach
Akerstedt, T., & Gillberg, M. (1990). Subjective and objective sleepiness in the active individual. The International Journal of Neuroscience, 52(1–2), 29–37. 10.3109/00207459008994241 DOI: 10.3109/00207459008994241
Aldwin, C. M., & Levenson, M. R. (2001). Stress, coping, and health at midlife: A developmental perspective. Handbook of midlife development (pp. 188–214). John Wiley & Sons Inc.
Almeida, D. M., & Horn, M. C. (2004). Is daily life more stressful during middle adulthood? In O. G. Brim, C. D. Ryff, & R. C. Kessler (Eds.), How healthy are we? A national study of well-being at midlife (pp. 425–451). University of Chicago Press.
Arnau, S., Möckel, T., Rinkenauer, G., & Wascher, E. (2017). The interconnection of mental fatigue and aging: An EEG study. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology, 117, 17–25. 10.1016/j.ijpsycho.2017.04.003 DOI: 10.1016/j.ijpsycho.2017.04.003
Balota, D. A., Yap, M. J., Cortese, M. J., & Watson, J. M. (2008). Beyond mean response latency: Response time distributional analyses of semantic priming. Journal of Memory and Language, 59(4), 495–523. 10.1016/j.jml.2007.10.004 DOI: 10.1016/j.jml.2007.10.004
Bielak, A., Cherbuin, N., Bunce, D., & Anstey, K. (2013). Intraindividual variability is a fundamental phenomenon of aging: Evidence from an 8-year longitudinal study across young, middle, and older adulthood. Developmental Psychology. 10.1037/a0032650 DOI: 10.1037/a0032650
Boksem, M. A. S., Meijman, T. F., & Lorist, M. M. (2005). Effects of mental fatigue on attention: An ERP study. Cognitive Brain Research, 25(1), 107–116. 10.1016/j.cogbrainres.2005.04.011 DOI: 10.1016/j.cogbrainres.2005.04.011
Boksem, M. A. S., Meijman, T. F., & Lorist, M. M. (2006). Mental fatigue, motivation and action monitoring. Biological Psychology, 72(2), 123–132. 10.1016/j.biopsycho.2005.08.007 DOI: 10.1016/j.biopsycho.2005.08.007
Boksem, M. A. S., & Tops, M. (2008). Mental fatigue: Costs and benefits. Brain Research Reviews, 59(1), 125–139. 10.1016/j.brainresrev.2008.07.001 DOI: 10.1016/j.brainresrev.2008.07.001
Borragán, G., Slama, H., Destrebecqz, A., & Peigneux, P. (2016). Cognitive fatigue facilitates procedural sequence learning. Frontiers in Human Neuroscience. 10.3389/fnhum.2016.00086 DOI: 10.3389/fnhum.2016.00086
Brewer, G. A. (2011). Analyzing response time distributions: Methodological and theoretical suggestions for prospective memory researchers. Zeitschrift Für Psychologie/journal of Psychology, 219(2), 117–124. 10.1027/2151-2604/a000056 DOI: 10.1027/2151-2604/a000056
Brewer, G. A., Lau, K. K. H., Wingert, K. M., Ball, B. H., & Blais, C. (2017). Examining depletion theories under conditions of within-task transfer. Journal of Experimental Psychology: General, 146(7), 988–1008. 10.1037/xge0000290 DOI: 10.1037/xge0000290
Burbeck, S. L., & Luce, R. D. (1982). Evidence from auditory simple reaction times for both change and level detectors. Perception & Psychophysics, 32(2), 117–133. 10.3758/BF03204271 DOI: 10.3758/BF03204271
Burke, S. E., Samuel, I. B. H., Zhao, Q., Cagle, J., Cohen, R. A., Kluger, B., & Ding, M. (2018). Task-based cognitive fatigability for older adults and validation of mental fatigability subscore of Pittsburgh fatigability scale. Frontiers in Aging Neuroscience, 10(327), 1–7. 10.3389/fnagi.2018.00327 DOI: 10.3389/fnagi.2018.00327
Collette, F., & Salmon, E. (2014). Les modifications du fonctionnement exécutif dans le vieillissement normal. Psychologie Française, 59(1), 41–58. 10.1016/j.psfr.2013.03.006 DOI: 10.1016/j.psfr.2013.03.006
Crawford, J. R., Bryan, J., Luszcz, M. A., Obonsawin, M. C., & Stewart, L. (2000). The executive decline hypothesis of cognitive aging: Do executive deficits qualify as differential deficits and do they mediate age-related memory decline? Aging, Neuropsychology, and Cognition, 7(1), 9–31. 10.1076/anec.7.1.9.806 DOI: 10.1076/anec.7.1.9.806
Dawson, M. R. W. (1988). Fitting the ex-Gaussian equation to reaction time distributions. Behavior Research Methods, Instruments, & Computers, 20(1), 54–57. 10.3758/BF03202603 DOI: 10.3758/BF03202603
de Jong, M., Jolij, J., Pimenta, A., & Lorist, M. M. (2018). Age modulates the effects of mental fatigue on typewriting. Frontiers in Psychology. 10.3389/fpsyg.2018.01113 DOI: 10.3389/fpsyg.2018.01113
Dinges, D. F. (1995). An overview of sleepiness and accidents. Journal of Sleep Research, 4(2), 2–14. 10.1111/j.1365-2869.1995.tb00220.x DOI: 10.1111/j.1365-2869.1995.tb00220.x
Echouffo-Tcheugui, J. B., Conner, S. C., Himali, J. J., Maillard, P., DeCarli, C. S., Beiser, A. S., Vasan, R. S., & Seshadri, S. (2018). Circulating cortisol and cognitive and structural brain measures: The Framingham Heart Study. Neurology, 91(21), 1961–1970. 10.1212/WNL.0000000000006549 DOI: 10.1212/WNL.0000000000006549
Esposito, F., Otto, T., Zijlstra, F. R. H., & Goebel, R. (2014). Spatially distributed effects of mental exhaustion on resting-state FMRI networks. PLoS ONE, 9(4), e94222. 10.1371/journal.pone.0094222 DOI: 10.1371/journal.pone.0094222
Falkenstein, M., Hoormann, J., & Hohnsbein, J. (2002). Inhibition-related ERP components: Variation with modality, age, and time-on-task. Journal of Psychophysiology, 16(3), 167–175. 10.1027//0269-8803.16.3.167 DOI: 10.1027//0269-8803.16.3.167
Falkenstein, M., Yordanova, J., & Kolev, V. (2006). Effects of aging on slowing of motor-response generation. International Journal of Psychophysiology, 59(1), 22–29. 10.1016/j.ijpsycho.2005.08.004 DOI: 10.1016/j.ijpsycho.2005.08.004
Farnsworth, D. (1947). The Farnsworth dichotomous test for color blindness, Panel D-15: Manual. The Psychological Corp.
Gilsoul, J., Libertiaux, V., & Collette, F. (2022). Cognitive fatigue in young, middle-aged, and older: Breaks as a way to recover. Applied Psychology, 71(4), 1565–1597. 10.1111/apps.12358 DOI: 10.1111/apps.12358
Gui, D., Xu, S., Zhu, S., Fang, Z., Spaeth, A. M., Xin, Y., Feng, T., & Rao, H. (2015). Resting spontaneous activity in the default mode network predicts performance decline during prolonged attention workload. NeuroImage, 120, 323–330. 10.1016/j.neuroimage.2015.07.030 DOI: 10.1016/j.neuroimage.2015.07.030
Hasher, L., & Zacks, R. T. (1988). Working memory, comprehension, and aging: A review and a new view. In G. H. Bower (Ed.), The psychology of learning and motivation (pp. 193–225). Academic Press.
Heathcote, A., Popiel, S. J., & Mewhort, D. J. (1991). Analysis of response time distributions: An example using the Stroop task. Psychological Bulletin, 109(2), 340–347. 10.1037/0033-2909.109.2.340 DOI: 10.1037/0033-2909.109.2.340
Hoffmann, S., & Falkenstein, M. (2011). Aging and error processing: Age related increase in the variability of the error-negativity is not accompanied by increase in response variability. PLoS ONE, 6(2), Article e17482. 10.1371/journal.pone.0017482 DOI: 10.1371/journal.pone.0017482
Hohle, R. H. (1965). Inferred components of reaction times as functions of foreperiod duration. Journal of Experimental Psychology, 69, 382–386. 10.1037/h0021740 DOI: 10.1037/h0021740
Hopstaken, J. F., van der Linden, D., Bakker, A. B., & Kompier, M. A. J. (2015a). A multifaceted investigation of the link between mental fatigue and task disengagement. Psychophysiology, 52(3), 305–315. 10.1111/psyp.12339 DOI: 10.1111/psyp.12339
Hopstaken, J. F., van der Linden, D., Bakker, A. B., & Kompier, M. A. J. (2015b). The window of my eyes: Task disengagement and mental fatigue covary with pupil dynamics. Biological Psychology, 110, 100–106. 10.1016/j.biopsycho.2015.06.013 DOI: 10.1016/j.biopsycho.2015.06.013
Hopstaken, J. F., van der Linden, D., Bakker, A. B., Kompier, M. A. J., & Leung, Y. K. (2016). Shifts in attention during mental fatigue: Evidence from subjective, behavioral, physiological, and eye-tracking data. Journal of Experimental Psychology: Human Perception and Performance, 42(6), 878–889. 10.1037/xhp0000189 DOI: 10.1037/xhp0000189
Horne, J. A., Ostberg, O. (1976). A self assessment questionnaire to determine morningness eveningness in human circadian rhythms. International Journal of Chronobiology, 4, 97–110. Retrieved from https://www.researchgate.net/publication/22126774_A_Self_Assessment_Questionnaire_to_Determine_Morningness_Eveningness_in_Human_Circadian_Rhythms
Hu, Z., Yi, C., Hao, J., Qiao, X., & Guo, X. (2018). Comparative study on the effects of lighting on cognitive ergonomics in single and multi-working modes. NeuroQuantology. 10.14704/nq.2018.16.5.1290 DOI: 10.14704/nq.2018.16.5.1290
Kaida, K., Takahashi, M., Akerstedt, T., Nakata, A., Otsuka, Y., Haratani, T., & Fukasawa, K. (2006). Validation of the Karolinska sleepiness scale against performance and EEG variables. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 117(7), 1574–1581. 10.1016/j.clinph.2006.03.011 DOI: 10.1016/j.clinph.2006.03.011
Klaassen, E. B., Evers, E. A. T., de Groot, R. H. M., Backes, W. H., Veltman, D. J., & Jolles, J. (2014). Working memory in middle-aged males: Age-related brain activation changes and cognitive fatigue effects. Biological Psychology, 96, 134–143. 10.1016/j.biopsycho.2013.11.008 DOI: 10.1016/j.biopsycho.2013.11.008
Klaassen, E. B., Plukaard, S., Evers, E. A. T., de Groot, R. H. M., Backes, W. H., Veltman, D. J., & Jolles, J. (2016). Young and middle-aged schoolteachers differ in the neural correlates of memory encoding and cognitive fatigue: A functional MRI study. Frontiers in Human Neuroscience, 10(148), 1–12. 10.3389/fnhum.2016.00148 DOI: 10.3389/fnhum.2016.00148
Kluger, B. M., Krupp, L. B., & Enoka, R. M. (2013). Fatigue and fatigability in neurologic illnesses: Proposal for a unified taxonomy. Neurology, 80(4), 409–416. 10.1212/WNL.0b013e31827f07be DOI: 10.1212/WNL.0b013e31827f07be
Kokubun, K., Nemoto, K., Oka, H., Fukuda, H., Yamakawa, Y., & Watanabe, Y. (2018). Association of fatigue and stress with gray matter volume. Frontiers in Behavioral Neuroscience, 12(154), 1–6. 10.3389/fnbeh.2018.00154 DOI: 10.3389/fnbeh.2018.00154
Lachman, M. E. (2004). Development in midlife. Annual Review of Psychology, 55(1), 305–331. 10.1146/annurev.psych.55.090902.141521 DOI: 10.1146/annurev.psych.55.090902.141521
Lacouture, Y., & Cousineau, D. (2008). How to use MATLAB to fit the ex-Gaussian and other probability functions to response times. Tutorials in Quantitative Methods for Psychology, 4(1), 35–45. 10.20982/tqmp.04.1.p035 DOI: 10.20982/tqmp.04.1.p035
Lenth, R. (2018). lsmeans: Least-squares means (version 2.30-0) [Computer software]. Retrieved from https://CRAN.R-project.org/package=lsmeans
Lim, J., Teng, J., Wong, K. F., & Chee, M. W. L. (2016). Modulating rest-break length induces differential recruitment of automatic and controlled attentional processes upon task reengagement. NeuroImage, 134, 64–73. 10.1016/j.neuroimage.2016.03.077 DOI: 10.1016/j.neuroimage.2016.03.077
Lorist, M. M. (2008). Impact of top-down control during mental fatigue. Brain Research, 1232, 113–123. 10.1016/j.brainres.2008.07.053 DOI: 10.1016/j.brainres.2008.07.053
Lorist, M. M., Bezdan, E., ten Caat, M., Span, M. M., Roerdink, J. B. T. M., & Maurits, N. M. (2009). The influence of mental fatigue and motivation on neural network dynamics; An EEG coherence study. Brain Research, 1270, 95–106. 10.1016/j.brainres.2009.03.015 DOI: 10.1016/j.brainres.2009.03.015
Lorist, M. M., Klein, M., Nieuwenhuis, S., Jong, R., Mulder, G., & Meijman, T. F. (2000). Mental fatigue and task control: Planning and preparation. Psychophysiology, 37, 614–625. 10.1017/S004857720099005X DOI: 10.1017/S004857720099005X
Luce, R. D. (1986). Response times. Oxford University Press.
MacLeod, C. M., & MacDonald, P. A. (2000). Interdimensional interference in the Stroop effect: Uncovering the cognitive and neural anatomy of attention. Trends in Cognitive Sciences, 4(10), 383–391. 10.1016/S1364-6613(00)01530-8 DOI: 10.1016/S1364-6613(00)01530-8
Mattis, S. (1976). Dementia rating scale. In R. Bellack & B. Keraso (Eds.), Geriatric psychiatry X (pp. 77–121). Grune and Stratton.
May, J. F., & Baldwin, C. L. (2009). Driver fatigue: The importance of identifying causal factors of fatigue when considering detection and countermeasure technologies. Transportation Research Part F: Traffic Psychology and Behaviour, 12(3), 218–224. 10.1016/j.trf.2008.11.005 DOI: 10.1016/j.trf.2008.11.005
McAuley, T., Yap, M., Christ, S. E., & White, D. A. (2006). Revisiting inhibitory control across the life span: Insights from the ex-Gaussian distribution. Developmental Neuropsychology, 29(3), 447–458. 10.1207/s15326942dn2903_4 DOI: 10.1207/s15326942dn2903_4
Monsch, A. U., Bondi, M. W., Salmon, D. P., Butters, N., Thal, L. J., Hansen, L. A., Wiederholt, W. C., Cahn, D. A., & Klauber, M. R. (1995). Clinical validity of the Mattis Dementia Rating Scale in detecting dementia of the Alzheimer type: A double cross-validation and application to a community-dwelling sample. Archives of Neurology, 52(9), 899–904. 10.1001/archneur.1995.00540330081018 DOI: 10.1001/archneur.1995.00540330081018
Moret-Tatay, C., Lemus-Zúñiga, L.-G., Tortosa, D. A., Gamermann, D., Vázquez-Martínez, A., Navarro-Pardo, E., & Conejero, J. A. (2017). Age slowing down in detection and visual discrimination under varying presentation times. Scandinavian Journal of Psychology, 58(4), 304–311. 10.1111/sjop.12372 DOI: 10.1111/sjop.12372
Myerson, J., Robertson, S., & Hale, S. (2007). Aging and intraindividual variability in performance: Analyses of response time distributions. Journal of the Experimental Analysis of Behavior, 88(3), 319–337. 10.1901/jeab.2007.88-319 DOI: 10.1901/jeab.2007.88-319
Nakagawa, S., Sugiura, M., Akitsuki, Y., Hosseini, S. M. H., Kotozaki, Y., Miyauchi, C. M., Yomogida, Y., Yokoyama, R., Takeuchi, H., & Kawashima, R. (2013). Compensatory effort parallels midbrain deactivation during mental fatigue: An fMRI study. PLoS ONE, 8(2), e56606. 10.1371/journal.pone.0056606 DOI: 10.1371/journal.pone.0056606
Nelder, J., & Mead, R. (1965). A simplex method for function minimization. Computer Journal, 7(4), 308–313. 10.1093/comjnl/7.4.308 DOI: 10.1093/comjnl/7.4.308
Office for National Statistics. (2016). Measuring national well-being. Retrieved from https://webcache.googleusercontent.com/search?q=cache:5PS5NpPe250J:https://www.ons.gov.uk/peoplepopulationandcommunity/wellbeing/articles/measuringnationalwellbeing/atwhatageispersonalwellbeingthehighest/pdf+&cd=2&hl=fr&ct=clnk&gl=be
Park, D. C., & Reuter-Lorenz, P. (2009). The adaptive brain: Aging and neurocognitive scaffolding. Annual Review of Psychology, 60, 173–196. 10.1146/annurev.psych.59.103006.093656 DOI: 10.1146/annurev.psych.59.103006.093656
Park, H., Kennedy, K. M., Rodrigue, K. M., Hebrank, A., & Park, D. C. (2013). An fMRI study of episodic encoding across the lifespan: Changes in subsequent memory effects are evident by middle-age. Neuropsychologia, 51(3), 448–456. 10.1016/j.neuropsychologia.2012.11.025 DOI: 10.1016/j.neuropsychologia.2012.11.025
Persson, J., Larsson, A., & Reuter-Lorenz, P. A. (2013). Imaging fatigue of interference control reveals the neural basis of executive resource depletion. Journal of Cognitive Neuroscience, 25(3), 338–351. 10.1162/jocn_a_00321 DOI: 10.1162/jocn_a_00321
Philip, P., Taillard, J., Quera-Salva, M. A., Bioulac, B., & Akerstedt, T. (1999). Simple reaction time, duration of driving and sleep deprivation in young versus old automobile drivers. Journal of Sleep Research, 8(1), 9–14. 10.1046/j.1365-2869.1999.00127.x DOI: 10.1046/j.1365-2869.1999.00127.x
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., EISPACK authors, Heisterkamp, S., Van Willigen, B., & R-core (2020). nlme: Linear and Nonlinear Mixed Effects Models (Version 3.1–145) [Computer software]. Retrieved from https://CRAN.R-project.org/package=nlme
Radloff, L. S. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401. 10.1177/014662167700100306 DOI: 10.1177/014662167700100306
Reuter-Lorenz, P. A., & Cappell, K. A. (2008). Neurocognitive aging and the compensation hypothesis. Current Directions in Psychological Science, 17(3), 177–182. 10.1111/j.1467-8721.2008.00570.x DOI: 10.1111/j.1467-8721.2008.00570.x
Rey-Mermet, A., & Gade, M. (2018). Inhibition in aging: What is preserved? What declines? A meta-analysis. Psychonomic Bulletin & Review, 25(5), 1695–1716. 10.3758/s13423-017-1384-7 DOI: 10.3758/s13423-017-1384-7
Roggeveen, A. B., Prime, D. J., & Ward, L. M. (2007). Lateralized readiness potentials reveal motor slowing in the aging brain. The Journals of Gerontology: Series B, 62(2), P78–P84. 10.1093/geronb/62.2.P78 DOI: 10.1093/geronb/62.2.P78
Salthouse, T. (1979). Adult age and the speed-accuracy trade-off. Ergonomics, 22, 811–821. 10.1080/00140137908924659 DOI: 10.1080/00140137908924659
Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103(3), 403–428. 10.1037/0033-295X.103.3.403 DOI: 10.1037/0033-295X.103.3.403
Salthouse, T. A. (2000). Aging and measures of processing speed. Biological Psychology, 54, 35–54. 10.1016/S0301-0511(00)00052-1 DOI: 10.1016/S0301-0511(00)00052-1
Schmiedek, F., Oberauer, K., Wilhelm, O., Süß, H.-M., & Wittmann, W. W. (2007). Individual differences in components of reaction time distributions and their relations to working memory and intelligence. Journal of Experimental Psychology: General, 136(3), 414–429. 10.1037/0096-3445.136.3.414 DOI: 10.1037/0096-3445.136.3.414
Staub, B., Doignon-Camus, N., Bacon, E., & Bonnefond, A. (2014). Age-related differences in the recruitment of proactive and reactive control in a situation of sustained attention. Biological Psychology, 103, 38–47. 10.1016/j.biopsycho.2014.08.007 DOI: 10.1016/j.biopsycho.2014.08.007
Stern, Y., Zarahn, E., Hilton, H., Flynn, J., Delapaz, R., & Rakitin, B. (2003). Exploring the neural basis of cognitive reserve. Journal of Clinical and Experimental Neuropsychology, 25, 691–701. 10.1076/jcen.25.5.691.14573 DOI: 10.1076/jcen.25.5.691.14573
Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643–662. 10.1037/h0054651 DOI: 10.1037/h0054651
Tam, A., Luedke, A. C., Walsh, J. J., Fernandez-Ruiz, J., & Garcia, A. (2015). Effects of reaction time variability and age on brain activity during Stroop task performance. Brain Imaging and Behavior, 9(3), 609–618. 10.1007/s11682-014-9323-y DOI: 10.1007/s11682-014-9323-y
Terentjeviene, A., Maciuleviciene, E., Vadopalas, K., Mickeviciene, D., Karanauskiene, D., Valanciene, D., Solianik, R., Emeljanovas, A., Kamandulis, S., & Skurvydas, A. (2018). Prefrontal cortex activity predicts mental fatigue in young and elderly men during a 2 h “Go/NoGo” task. Frontiers in Neuroscience, 12(620), 1–12. 10.3389/fnins.2018.00620 DOI: 10.3389/fnins.2018.00620
Tipper, S. P. (1985). The negative priming effect: Inhibitory priming by ignored objects. The Quarterly Journal of Experimental Psychology Section A, 37(4), 571–590. 10.1080/14640748508400920 DOI: 10.1080/14640748508400920
Ulloa, B., Møller, V., & Sousa-Poza, A. (2013). How does subjective well-being evolve with age? A literature review. Journal of Population Ageing. 10.1007/s12062-013-9085-0 DOI: 10.1007/s12062-013-9085-0
Unsworth, N., Redick, T. S., Lakey, C. E., & Young, D. L. (2010). Lapses in sustained attention and their relation to executive control and fluid abilities: An individual differences investigation. Intelligence, 38(1), 111–122. 10.1016/j.intell.2009.08.002 DOI: 10.1016/j.intell.2009.08.002
Van der Elst, W., Van Boxtel, M. P. J., Van Breukelen, G. J. P., & Jolles, J. (2006). The Stroop color-word test: Influence of age, sex, and education; and normative data for a large sample across the adult age range. Assessment, 13(1), 62–79. 10.1177/1073191105283427 DOI: 10.1177/1073191105283427
Vasquez, B. P., Binns, M. A., & Anderson, N. D. (2018). Response time consistency is an indicator of executive control rather than global cognitive ability. Journal of the International Neuropsychological Society: JINS, 24(5), 456–465. 10.1017/S1355617717001266 DOI: 10.1017/S1355617717001266
Verhaeghen, P., Cerella, J., Basak, C., & Bopp, K. L. (2005). Aging and varieties of cognitive control: A review of meta-analyses on resistance to interference, coordination, and task switching, and an experimental exploration of age-sensitivity in the newly identified process of focus switching. In R. Engle, G. Sedek, U. Von Hecker, & D. McIntosh (Eds.), Cognitive limitations in aging and psychopathology (pp. 160–189). Cambridge: Cambridge University Press. DOI: 10.1017/CBO9780511720413.008
Wang, C., Ding, M., & Kluger, B. M. (2014). Change in intraindividual variability over time as a key metric for defining performance-based cognitive fatigability. Brain and Cognition, 85, 251–258. 10.1016/j.bandc.2014.01.004 DOI: 10.1016/j.bandc.2014.01.004
Wascher, E., Heppner, H., Kobald, S. O., Arnau, S., Getzmann, S., & Möckel, T. (2016). Age-sensitive effects of enduring work with alternating cognitive and physical load: A study applying mobile EEG in a real life working scenario. Frontiers in Human Neuroscience, 9(711), 1–14. 10.3389/fnhum.2015.00711 DOI: 10.3389/fnhum.2015.00711
West, R. L., Murphy, K. J., Armilio, M. L., Craik, F. I. M., & Stuss, D. T. (2002). Lapses of intention and performance variability reveal age-related increases in fluctuations of executive control. Brain and Cognition, 49(3), 402–419. 10.1006/brcg.2001.1507 DOI: 10.1006/brcg.2001.1507
Wolkorte, R., Kamphuis, J., & Zijdewind, I. (2014). Increased reaction times and reduced response preparation already starts at middle age. Frontiers in Aging Neuroscience, 6(79), 1–12. 10.3389/fnagi.2014.00079 DOI: 10.3389/fnagi.2014.00079
Woods, D. L., Wyma, J. M., Yund, E. W., Herron, T. J., & Reed, B. (2015). Age-related slowing of response selection and production in a visual choice reaction time task. Frontiers in Human Neuroscience. 10.3389/fnhum.2015.00193 DOI: 10.3389/fnhum.2015.00193