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36 - Far Transfer and Cognitive Training: Examination of Two Hypotheses on Mechanisms

from Part V - Later Life and Interventions

Published online by Cambridge University Press:  28 May 2020

Ayanna K. Thomas
Affiliation:
Tufts University, Massachusetts
Angela Gutchess
Affiliation:
Brandeis University, Massachusetts
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Summary

Even healthy older people undergo some cognitive decline with real-world consequences, although the neural plasticity persisting in older brains indicates substrates for interventions. Yet there is no consensus on cognitive interventions. The literature on cognitive training is equivocal regarding the factors important in far transfer of training to untrained abilities. That there have been few hypotheses on mechanisms underlying far transfer of training is an obstacle to the design of cognitive interventions. We evaluate two hypotheses: (1) updating and (2) distraction suppression. (1) The updating hypothesis argues that updating and monitoring of working memory representations is an important mechanism of far transfer of training. Two meta-analyses of n-back training tasks found small, but significant, effect sizes in favor of transfer to fluid intelligence (Gf) in young and older people. However, direct tests of the updating hypothesis supported only narrow transfer effects. (2) The distraction suppression hypothesis argues that suppression of irrelevant events has a central role in cognitive processing. Perceptual discrimination training improved distraction suppression, enhanced neural activity associated with task-relevant targets, suppressed neural activity associated with task-irrelevant distractions, improved brain-stem evoked potential firing patterns and “speech-in-noise” perception, transferred to working memory, and reduced risk of dementia in a large-scale study. The evidence supports the conclusion that the strongest far transfer of cognitive training would be achieved by combined updating and distraction suppression training. Even small effect sizes of transfer to Gf can be beneficial to older people, consistent with the growing evidence for the role of lifestyle factors, including educational attainment, in risk of Alzheimer’s disease.

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The Cambridge Handbook of Cognitive Aging
A Life Course Perspective
, pp. 666 - 684
Publisher: Cambridge University Press
Print publication year: 2020

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References

Agarwal, S., Driscoll, J. C., Gabaix, X., & Laibson, D. (2009). The age of reason: Financial decisions over the lifecycle with implications for regulation. Brookings Papers on Economic Activity, 2, 51117.Google Scholar
Amer, T., Anderson, J. A. E., Campbell, K. L., Hasher, L., & Grady, C. L. (2016). Age differences in the neural correlates of distraction regulation: A network interaction approach. NeuroImage, 139, 231239. doi: 10.1016/j.neuroimage.2016.06.036Google Scholar
Anderson, S., White-Schwoch, T., Choi, H. J., & Kraus, N. (2014). Partial maintenance of auditory-based cognitive training benefits in older adults. Neuropsychologia, 62, 286296. doi: 10.1016/j.neuropsychologia.2014.07.034Google Scholar
Anderson, S., White-Schwoch, T., Parbery-Clark, A., & Kraus, N. (2013). Reversal of age-related neural timing delays with training. Proceedings of the National Academy of Sciences USA, 110(11), 43574362. doi: 10.1073/pnas.1213555110Google Scholar
Andrews-Hanna, J. R., Snyder, A. Z., Vincent, J. L., et al. (2007). Disruption of large-scale brain systems in advanced aging. Neuron, 56(5), 924935. doi: 10.1016/j.neuron.2007.10.038Google Scholar
Anguera, J. A., Boccanfuso, J., Rintoul, J. L., et al. (2013). Video game training enhances cognitive control in older adults. Nature, 501(7465), 97101. doi: 10.1038/nature12486Google Scholar
Au, J., Sheehan, E., Tsai, N., et al. (2015). Improving fluid intelligence with training on working memory: A meta-analysis. Psychonomic Bulletin and Review, 22(2), 366377. doi: 10.3758/s13423-014-0699-xGoogle Scholar
Ball, K., Berch, D. B., Helmers, K. F., et al. (2002). Effects of cognitive training interventions with older adults: A randomized controlled trial. JAMA, 288(18), 22712281. doi: 10.1001/jama.288.18.2271Google Scholar
Berry, A. S., Zanto, T. P., Clapp, W. C., et al. (2010). The influence of perceptual training on working memory in older adults. PLoS One, 5(7), e11537. doi: 10.1371/journal.pone.0011537Google Scholar
Berry, A. S., Zanto, T. P., Rutman, A. M., Clapp, W. C., & Gazzaley, A. (2009). Practice-related improvement in working memory is modulated by changes in processing external interference. Journal of Neurophysiology, 102, 17791789. doi: 10.1152/jn.00179.2009Google Scholar
Blacker, K. J., Negoita, S., Ewen, J. B., & Courtney, S. M. (2017). N-back versus complex span working memory training. Journal of Cognitive Enhancement, 1(4), 434454. doi: 10.1007/s41465-017-0044-1Google Scholar
Boldrini, M., Fulmore, C. A., Tartt, A. N., et al. (2018). Human hippocampal neurogenesis persists throughout aging. Cell Stem Cell, 22(4), 589599 e5. doi: 10.1016/j.stem.2018.03.015Google Scholar
Bower, J. D., & Andersen, G. J. (2012). Aging, perceptual learning, and changes in efficiency of motion processing. Vision Research, 61, 144156. doi: 10.1016/j.visres.2011.07.016Google Scholar
Bower, J. D., Watanabe, T., & Andersen, G. J. (2013). Perceptual learning and aging: Improved performance for low-contrast motion discrimination. Frontiers in Psychology, 4, 66. doi: 10.3389/fpsyg.2013.00066Google Scholar
Boyke, J., Driemeyer, J., Gaser, C., Buchel, C., & May, A. (2008). Training-induced brain structure changes in the elderly. Journal of Neuroscience, 28(28), 70317035. doi: 10.1523/JNEUROSCI.0742-08.2008Google Scholar
Burgess, G. C., Gray, J. R., Conway, A. R., & Braver, T. S. (2011). Neural mechanisms of interference control underlie the relationship between fluid intelligence and working memory span. Journal of Experimental Psychology: General, 140(4), 674692. doi: 10.1037/a0024695Google Scholar
Cashdollar, N., Fukuda, K., Bocklage, A., et al. (2013). Prolonged disengagement from attentional capture in normal aging. Psychology of Aging, 28(1), 7786. doi: 10.1037/a0029899Google Scholar
Chan, M. Y., Park, D. C., Savalia, N. K., Petersen, S. E., & Wig, G. S. (2014). Decreased segregation of brain systems across the healthy adult lifespan. Proceedings of the National Academy of Sciences USA, 111(46), E4997E5006. doi: 10.1073/pnas.1415122111Google Scholar
Chein, J. M., & Schneider, W. (2005). Neuroimaging studies of practice-related change: fMRI and meta-analytic evidence of a domain-general control network for learning. Brain Research: Cognitive Brain Research, 25(3), 607623. doi: 10.1016/j.cogbrainres.2005.08.013Google Scholar
Chelazzi, L., Miller, E. K., Duncan, J., & Desimone, R. (2001). Responses of neurons in macaque area V4 during memory-guided visual search. Cerebral Cortex, 11(8), 761772. doi: 10.1093/cercor/11.8.761Google Scholar
Clapp, W. C., Rubens, M. T., & Gazzaley, A. (2010). Mechanisms of working memory disruption by external interference. Cerebral Cortex, 20(4), 859872. doi: 10.1093/cercor/bhp150Google Scholar
Cole, M. W., Yarkoni, T., Repovs, G., Anticevic, A., & Braver, T. S. (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. Journal of Neuroscience, 32(26), 89888999. doi: 10.1523/JNEUROSCI.0536-12.2012Google Scholar
Coley, N., Ngandu, T., Lehtisalo, J., et al. (2019). Adherence to multidomain interventions for dementia prevention: Data from the FINGER and MAPT trials. Alzheimer’s and Dementia, 15(6), 729741. doi: 10.1016/j.jalz.2019.03.005Google Scholar
Daffner, K. R., Zhuravleva, T. Y., Sun, X., et al. (2012). Does modulation of selective attention to features reflect enhancement or suppression of neural activity? Biological Psychology, 89(2), 398407. doi: 10.1016/j.biopsycho.2011.12.002Google Scholar
Dahlin, E., Neely, A. S., Larsson, A., Bäckman, L., & Nyberg, L. (2008). Transfer of learning after updating training mediated by the striatum. Science, 320(5882), 15101512. doi: 10.1126/science.1155466Google Scholar
Daneman, M., & Hannon, B. (2007). What do working memory span tasks really measure? In Logie, R. H., Osaka, N., & D’Esposito, M. (Eds.), The cognitive neuroscience of working memory (pp. 2142). Oxford: Oxford University Press.Google Scholar
DeLoss, D. J., Watanabe, T., & Andersen, G. J. (2015). Improving vision among older adults: Behavioral training to improve sight. Psychological Science, 26(4), 456466. doi: 10.1177/0956797614567510Google Scholar
Edwards, J. D., Fausto, B. A., Tetlow, A. M., Corona, R. T., & Valdes, E. G. (2018). Systematic review and meta-analyses of useful field of view cognitive training. Neuroscience and Biobehavioral Reviews, 84, 7291. doi: 10.1016/j.neubiorev.2017.11.004Google Scholar
Edwards, J. D., Ross, L. A., Ackerman, M. L., et al. (2008). Longitudinal predictors of driving cessation among older adults from the ACTIVE clinical trial. Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 63(1), P6P12. doi: 10.1093/geronb/63.1.P6Google Scholar
Edwards, J. D., Xu, H., Clark, D. O., et al. (2017). Speed of processing training results in lower risk of dementia. Alzheimer’s and Dementia (NY), 3(4), 603611. doi: 10.1016/j.trci.2017.09.002Google Scholar
Engvig, A., Fjell, A. M., Westlye, L. T., et al. (2010). Effects of memory training on cortical thickness in the elderly. NeuroImage, 52(4), 16671676. doi: 10.1016/j.neuroimage.2010.05.041Google Scholar
Ferreira, L. K., Regina, A. C., Kovacevic, N., et al. (2016). Aging effects on whole-brain functional connectivity in adults free of cognitive and psychiatric disorders. Cerebral Cortex, 26(9), 38513865. doi: 10.1093/cercor/bhv190Google Scholar
Fornito, A., Harrison, B. J., Zalesky, A., & Simons, J. S. (2012). Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection. Proceedings of the National Academy of Sciences USA, 109(31), 1278812793. doi: 10.1073/pnas.1204185109Google Scholar
Foroughi, C. K., Monfort, S. S., Paczynski, M., McKnight, P. E., & Greenwood, P. M. (2016). Placebo effects in cognitive training. Proceedings of the National Academy of Sciences USA, 113(27), 74707474. doi: 10.1073/pnas.1601243113Google Scholar
Fox, M. D., Snyder, A. Z., Vincent, J. L., et al. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences USA, 102(27), 96739678. doi: 10.1073/pnas.0504136102Google Scholar
Fukuda, K., Vogel, E., Mayr, U., & Awh, E. (2010). Quantity, not quality: The relationship between fluid intelligence and working memory capacity. Psychonomic Bulletin and Review, 17(5), 673679. doi: 10.3758/17.5.673Google Scholar
Gazzaley, A., Clapp, W., Kelley, J., et al. (2008). Age-related top-down suppression deficit in the early stages of cortical visual memory processing. Proceedings of the National Academy of Sciences USA, 105(35), 1312213126. doi: 10.1073/pnas.0806074105Google Scholar
Gazzaley, A., Cooney, J. W., Rissman, J., & D’Esposito, M. (2005). Top-down suppression deficit underlies working memory impairment in normal aging. Nature Neuroscience, 8(10), 12981300. doi: 10.1038/nn1543Google Scholar
Geerligs, L., Renken, R. J., Saliasi, E., Maurits, N. M., & Lorist, M. M. (2015). A brain-wide study of age-related changes in functional connectivity. Cerebral Cortex, 25(7), 19871999. doi: 10.1093/cercor/bhu012Google Scholar
Gilbert, C. D., & Sigman, M. (2007). Brain states: Top-down influences in sensory processing. Neuron, 54(5), 677696. doi: 10.1016/j.neuron.2007.05.019Google Scholar
Goh, J. O., An, Y., & Resnick, S. M. (2012). Differential trajectories of age-related changes in components of executive and memory processes. Psychology and Aging, 27(3), 707719. doi: 10.1037/a0026715Google Scholar
Golland, Y., Golland, P., Bentin, S., & Malach, R. (2008). Data-driven clustering reveals a fundamental subdivision of the human cortex into two global systems. Neuropsychologia, 46(2), 540553. doi: 10.1016/j.neuropsychologia.2007.10.003Google Scholar
Gottfredson, L. S. (1997). Why g matters: The complexity of everyday life. Intelligence, 24(1), 79132. doi: 10.1016/S0160-2896(97)90014-3Google Scholar
Grady, C. L., & Craik, F. I. (2000). Changes in memory processing with age. Current Opinion in Neurobiology, 10(2), 224231. doi: 10.1016/S0959-4388(00)00073-8Google Scholar
Grady, C. L., Maisog, J. M., Horwitz, B., et al. (1994). Age-related changes in cortical blood flow activation during visual processing of faces and location. Journal of Neuroscience, 14, 14501462. doi: 10.1523/JNEUROSCI.14-03-01450.1994Google Scholar
Grady, C. L., Sarraf, S., Saverino, C., & Campbell, K. (2016). Age differences in the functional interactions among the default, frontoparietal control, and dorsal attention networks. Neurobiology of Aging, 41, 159172. doi: 10.1016/j.neurobiolaging.2016.02.020Google Scholar
Greenwood, P. M. (2007). Functional plasticity in cognitive aging: Review and hypothesis. Neuropsychology, 21(6), 657673. doi: 10.1037/0894-4105.21.6.657Google Scholar
Greenwood, P. M., & Parasuraman, R. (1999). Scale of attentional focus in visual search. Perception and Psychophysics, 61, 837859. doi: 10.3758/BF03206901Google Scholar
Greenwood, P. M., & Parasuraman, R. (2004). The scaling of spatial attention in visual search and its modification in healthy aging. Perception and Psychophysics, 66, 322. doi: 10.3758/BF03194857Google Scholar
Greenwood, P. M., & Parasuraman, R. (2016). The mechanisms of far transfer from cognitive training: Review and hypothesis. Neuropsychology, 30(6), 742755. doi: 10.1037/neu0000235Google Scholar
Greenwood, P. M., Parasuraman, R., & Alexander, G. E. (1997). Controlling the focus of spatial attention during visual search: Effects of advanced aging and Alzheimer disease. Neuropsychology, 11(1), 312. doi.org/10.1037/0894-4105.11.1.3Google Scholar
Hasher, L., Stoltzfus, E. R., Zacks, R. T., & Rypma, B. (1991). Age and inhibition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17(1), 163169. doi: 10.1037//0278-7393.17.1.163Google Scholar
Hasher, L., & Zacks, R. T. (1979). Automatic and effortful processes in memory. Journal of Experimental Psychology: General, 108, 356388. doi: 10.1037/0096-3445.108.3.356Google Scholar
Hillyard, S. A., & Anllo-Vento, L. (1998). Event-related brain potentials in the study of visual selective attention. Proceedings of the National Academy of Sciences USA, 95, 781788. doi: 10.1073/pnas.95.3.781Google Scholar
Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences USA, 105(19), 68296833. doi: 10.1073/pnas.0801268105Google Scholar
Jobe, J. B., Smith, D. M., Ball, K., et al. (2001). ACTIVE: A cognitive intervention trial to promote independence in older adults. Controlled Clinical Trials, 22(4), 453479. doi: 10.1016/S0197-2456(01)00139-8Google Scholar
Karbach, J., & Verhaeghen, P. (2014). Making working memory work: A meta-analysis of executive-control and working memory training in older adults. Psychological Science, 25(11), 20272037. doi: 10.1177/0956797614548725Google Scholar
Kelly, A. M., & Garavan, H. (2005). Human functional neuroimaging of brain changes associated with practice. Cerebral Cortex, 15(8), 10891102. doi: 10.1093/cercor/bhi005Google Scholar
Kelly, M. E., Loughrey, D., Lawlor, B. A., et al. (2014). The impact of cognitive training and mental stimulation on cognitive and everyday functioning of healthy older adults: A systematic review and meta-analysis. Ageing Research Reviews, 15, 2843. doi: 10.1016/j.arr.2014.02.004Google Scholar
Kraus, N., Bradlow, A. R., Cheatham, M. A., et al. (2000). Consequences of neural asynchrony: A case of auditory neuropathy. Journal of the Association for Research in Otolaryngology, 1(1), 3345. doi: 10.1007/s101620010004Google Scholar
Kristjansson, A., & Nakayama, K. (2002). The attentional blink in space and time. Vision Research, 42(17), 20392050. doi: 10.1016/S0042-6989(02)00129-3Google Scholar
Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2004). Academic performance, career potential, creativity, and job performance: Can one construct predict them all? Journal of Personality and Social Psychology, 86(1), 148161. doi: 10.1037/0022-3514.86.1.148Google Scholar
Lawton, M. P., & Brody, E. M. (1969). Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist, 9(3), 179186. doi: 10.1093/geront/9.3_Part_1.179Google Scholar
Lewis, C. M., Baldassarre, A., Committeri, G., Romani, G. L., & Corbetta, M. (2009). Learning sculpts the spontaneous activity of the resting human brain. Proceedings of the National Academy of Sciences USA, 106(41), 1755817563. doi: 10.1073/pnas.0902455106Google Scholar
Li, X., Allen, P. A., Lien, M. C., & Yamamoto, N. (2017). Practice makes it better: A psychophysical study of visual perceptual learning and its transfer effects on aging. Psychology and Aging, 32(1), 1627. doi: 10.1037/pag0000145Google Scholar
Livingston, G., Sommerlad, A., Orgeta, V., et al. (2017). Dementia prevention, intervention, and care. Lancet, 390(10113), 26732734. doi: 10.1016/S0140-6736(17)31363-6Google Scholar
Luck, S. J., Hillyard, S. A., Mouloua, M., et al. (1994). Effects of spatial cuing on luminance detectability: Psychophysical and electrophysiological evidence for early selection. Journal of Experimental Psychology: Human Perception and Performance, 20, 887904. doi: 10.1037//0096-1523.20.4.887Google Scholar
Madden, D. J., Whiting, W. L., Cabeza, R., & Huettel, S. A. (2004). Age-related preservation of top-down attentional guidance during visual search. Psychology and Aging, 19(2), 304309. doi: 10.1037/0882-7974.19.2.304Google Scholar
Mahncke, H. W., Connor, B. B., Appelman, J., et al. (2006). Memory enhancement in healthy older adults using a brain plasticity-based training program: A randomized, controlled study. Proceedings of the National Academy of Sciences USA, 103(33), 1252312528. doi: 10.1073/pnas.0605194103Google Scholar
McNab, F., Varrone, A., Farde, L., et al. (2009). Changes in cortical dopamine D1 receptor binding associated with cognitive training. Science, 323(5915), 800802. 10.1126/science.1166102Google Scholar
Melby-Lervag, M., & Hulme, C. (2013). Is working memory training effective? A meta-analytic review. Developmental Psychology, 49(2), 270291. doi: 10.1037/a0028228Google Scholar
MetLife (2011). The MetLife Study of Elder Financial Abuse: Crimes of Occasion, Desperation, and Predation against America’s Elders. National Committee for the Prevention of Elder Abuse, and Virginia Tech. ltcombudsman.org/uploads/files/issues/mmi-elder-financial-abuse.pdfGoogle Scholar
Mishra, J., de Villers-Sidani, E., Merzenich, M., & Gazzaley, A. (2014). Adaptive training diminishes distractibility in aging across species. Neuron, 84(5), 10911103. doi: 10.1016/j.neuron.2014.10.034Google Scholar
Miyake, A., Friedman, N. P., Emerson, M. J., et al. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41(1), 49100. doi: 10.1006/cogp.1999.0734Google Scholar
Mukai, I., Kim, D., Fukunaga, M., et al. (2007). Activations in visual and attention-related areas predict and correlate with the degree of perceptual learning. Journal of Neuroscience, 27(42), 1140111411. doi: 10.1523/JNEUROSCI.3002-07.2007Google Scholar
Ngandu, T., Lehtisalo, J., Solomon, A., et al. (2015). A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): A randomised controlled trial. Lancet, 385(9984), 22552263. doi: 10.1016/S0140-6736(15)60461-5Google Scholar
Nigbur, R., Ivanova, G., & Sturmer, B. (2011). Theta power as a marker for cognitive interference. Clinical Neurophysiology, 122(11), 21852194. doi: 10.1016/j.clinph.2011.03.030Google Scholar
NIH US (2018). US Study to Protect Brain Health through Lifestyle Intervention to Reduce Risk (POINTER). https://clinicaltrials.gov/ct2/show/NCT03688126Google Scholar
Norton, S., Matthews, F. E., Barnes, D. E., Yaffe, K., & Brayne, C. (2014). Potential for primary prevention of Alzheimer’s disease: An analysis of population-based data. Lancet Neurology, 13(8), 788794. doi: 10.1016/S1474-4422(14)70136-XGoogle Scholar
Sadaghiani, S., Poline, J. B., Kleinschmidt, A., & D’Esposito, M. (2015). Ongoing dynamics in large-scale functional connectivity predict perception. Proceedings of the National Academy of Sciences USA, 112(27), 84638468. doi: 10.1073/pnas.1420687112Google Scholar
Salminen, T., Kuhn, S., Frensch, P. A., & Schubert, T. (2016). Transfer after dual n-back training depends on striatal activation change. Journal of Neuroscience, 36(39), 1019810213. doi: 10.1523/JNEUROSCI.2305-15.2016Google Scholar
Sawaki, R., & Luck, S. J. (2013). Active suppression after involuntary capture of attention. Psychonomic Bulletin and Review, 20(2), 296301. doi: 10.3758/s13423-012-0353-4Google Scholar
Schoups, A., Vogels, R., Qian, N., & Orban, G. (2001). Practising orientation identification improves orientation coding in V1 neurons. Nature, 412(6846), 549553. doi: 10.1038/35087601Google Scholar
Smith, G. E., Housen, P., Yaffe, K., et al. (2009). A cognitive training program based on principles of brain plasticity: Results from the Improvement in Memory with Plasticity-Based Adaptive Cognitive Training (IMPACT) study. Journal of the American Geriatrics Society, 57(4), 594603. doi: 10.1111/j.1532-5415.2008.02167.xGoogle Scholar
Söderqvist, S., & Bergman Nutley, S. (2017). Are measures of transfer effects missing the target? Journal of Cognitive Enhancement, 1(4), 508512. doi: 10.1007/s41465-017-0048-xGoogle Scholar
Soveri, A., Antfolk, J., Karlsson, L., Salo, B., & Laine, M. (2017). Working memory training revisited: A multi-level meta-analysis of n-back training studies. Psychonomic Bulletin and Review, 24(4), 10771096. doi: 10.3758/s13423-016-1217-0Google Scholar
Stine-Morrow, E. A., Parisi, J. M., Morrow, D. G., & Park, D. C. (2008). The effects of an engaged lifestyle on cognitive vitality: A field experiment. Psychology and Aging, 23(4), 778786. doi: 10.1037/a0014341Google Scholar
Strenziok, M., Parasuraman, R., Clarke, E., et al. (2014). Neurocognitive enhancement in older adults: Comparison of three cognitive training tasks to test a hypothesis of training transfer in brain connectivity. NeuroImage, 85, 10271039. doi: 10.1016/j.neuroimage.2013.07.069Google Scholar
Suzuki, M., & Gottlieb, J. (2013). Distinct neural mechanisms of distractor suppression in the frontal and parietal lobe. Nature Neuroscience, 16(1), 98104. doi: 10.1038/nn.3282Google Scholar
Theeuwes, J. (1994). Stimulus-driven capture and attentional set: Selective search for color and visual abrupt onsets. Journal of Experimental Psychology: Human Perception and Performance, 20(4), 799806. doi: 10.1037//0096-1523.20.4.799Google Scholar
Tomasi, D., & Volkow, N. D. (2012). Aging and functional brain networks. Molecular Psychiatry, 17(5), 471, 549–458. doi: 10.1038/mp.2011.81Google Scholar
Vogel, E. K., McCollough, A. W., & Machizawa, M. G. (2005). Neural measures reveal individual differences in controlling access to working memory. Nature, 438(7067), 500503. doi: 10.1038/nature04171Google Scholar
Vossel, S., Geng, J. J., & Fink, G. R. (2014). Dorsal and ventral attention systems: Distinct neural circuits but collaborative roles. Neuroscientist, 20(2), 150159. doi: 10.1177/1073858413494269Google Scholar
Waris, O., Soveri, A., & Laine, M. (2015). Transfer after working memory updating training. PLoS One, 10(9), e0138734. doi: 10.1371/journal.pone.0138734Google Scholar
Whitton, J. P., Hancock, K. E., Shannon, J. M., & Polley, D. B. (2017). Audiomotor perceptual training enhances speech intelligibility in background noise. Current Biology, 27(21), 32373247.e6. doi: 10.1016/j.cub.2017.09.014Google Scholar
Wiley, J., Jarosz, A. F., Cushen, P. J., & Colflesh, G. J. (2011). New rule use drives the relation between working memory capacity and Raven’s Advanced Progressive Matrices. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37(1), 256263. doi: 10.1037/a0021613Google Scholar

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