Skip to main content Accessibility help
×
Home
Hostname: page-component-568f69f84b-h2zp4 Total loading time: 0.279 Render date: 2021-09-18T16:39:50.862Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": true, "newCiteModal": false, "newCitedByModal": true, "newEcommerce": true, "newUsageEvents": true }

The impact of brain size on pilot performance varies with aviation training and years of education

Published online by Cambridge University Press:  02 March 2010

MAHEEN M. ADAMSON*
Affiliation:
Department of Veterans Affairs and Sierra-Pacific MIRECC, Palo Alto, California Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
VIKTORIYA SAMARINA
Affiliation:
Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
XU XIANGYAN
Affiliation:
Department of Veterans Affairs and Sierra-Pacific MIRECC, Palo Alto, California
VIRGINIA HUYNH
Affiliation:
Department of Veterans Affairs, San Francisco, California University of California, San Francisco, California
QUINN KENNEDY
Affiliation:
Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
MICHAEL WEINER
Affiliation:
Department of Veterans Affairs, San Francisco, California University of California, San Francisco, California
JEROME YESAVAGE
Affiliation:
Department of Veterans Affairs and Sierra-Pacific MIRECC, Palo Alto, California Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
JOY L. TAYLOR
Affiliation:
Department of Veterans Affairs and Sierra-Pacific MIRECC, Palo Alto, California Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
*Corresponding
*Correspondence and reprint requests to: Maheen M. Adamson, Stanford/VA Aging Clinical Research Center, 3801 Miranda Avenue (151Y), Palo Alto, CA, 94304. E-mail: madamson@stanford.edu

Abstract

Previous studies have consistently reported age-related changes in cognitive abilities and brain structure. Previous studies also suggest compensatory roles for specialized training, skill, and years of education in the age-related decline of cognitive function. The Stanford/VA Aviation Study examines the influence of specialized training and skill level (expertise) on age-related changes in cognition and brain structure. This preliminary report examines the effect of aviation expertise, years of education, age, and brain size on flight simulator performance in pilots aged 45–68 years. Fifty-one pilots were studied with structural magnetic resonance imaging, flight simulator, and processing speed tasks. There were significant main effects of age (p < .01) and expertise (p < .01), but not of whole brain size (p > .1) or education (p > .1), on flight simulator performance. However, even though age and brain size were correlated (r = −0.41), age differences in flight simulator performance were not explained by brain size. Both aviation expertise and education were involved in an interaction with brain size in predicting flight simulator performance (p < .05). These results point to the importance of examining measures of expertise and their interactions to assess age-related cognitive changes. (JINS, 2010, 16, 412–423.)

Type
Research Articles
Copyright
Copyright © The International Neuropsychological Society 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Artola, A., von Frijtag, J. C., Fermont, P. C., Gispen, W. H., Schrama, L. H., Kamal, A., et al. (2006). Long-lasting modulation of the induction of LTD and LTP in rat hippocampal CA1 by behavioural stress and environmental enrichment. European Journal of Neuroscience, 23, 261–272.CrossRefGoogle ScholarPubMed
Baddeley, A.D. (1986). Working memory, Oxford: Oxford University Press.Google Scholar
Baltes, P.B., & Kliegl, R. (1992). Further testing of limits of cognitive plasticity: Negative age differences in a mnemonic skill are robust. Developmental Psychology, 28, 121–125.CrossRefGoogle Scholar
Baron, R.M., & Kenny, D.A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182.CrossRefGoogle ScholarPubMed
Bartzokis, G. (2004). Age-related myelin breakdown: A developmental model of cognitive decline and Alzheimer’s disease. Neurobiol Aging, 25, 5–18; author reply 49–62.CrossRefGoogle ScholarPubMed
Bengtsson, S.L., Nagy, Z., Skare, S., Forsman, L., Forssberg, H., & Ullen, F. (2005). Extensive piano practicing has regionally specific effects on white matter development. Nature Neuroscience, 8, 1148–1150.CrossRefGoogle ScholarPubMed
Bennett, D.A., Wilson, R.S., Schneider, J.A., Evans, D.A., Aggarwal, N.T., Arnold, S.E., et al. (2003). Apolipoprotein E epsilon4 allele, AD pathology, and the clinical expression of Alzheimer’s disease. Neurology, 60, 246–252.CrossRefGoogle ScholarPubMed
Bruandet, A., Richard, F., Bombois, S., Maurage, C.A., Masse, I., Amouyel, P., et al. (2008). Cognitive decline and survival in Alzheimer’s disease according to education level. Dementia and Geriatric Cognitive Disorders, 25, 74–80.CrossRefGoogle ScholarPubMed
Cabeza, R., Ciaramelli, E., Olson, I.R., & Moscovitch, M. (2008). The parietal cortex and episodic memory: An attentional account. Nature Reviews. Neuroscience, 9, 613–625.CrossRefGoogle Scholar
Charness, N., & Campbell, J.I.D. (1988). Acquiring skill at mental calculation in adulthood: A task decomposition. Journal of Experimental Psychology: General, 117, 115–129.CrossRefGoogle Scholar
Colcombe, S.J., Erickson, K.I., Scalf, P.E., Kim, J.S., Prakash, R., McAuley, E., et al. (2006). Aerobic exercise training increases brain volume in aging humans. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 61, 1166–1170.CrossRefGoogle ScholarPubMed
Collins, D.L., Holmes, C.J., Peters, T.M., & Evans, A.C. (1995). Automatic 3-D model-based neuroanatomical segmentation. Hum Brain Mapping, 3, 190–208.CrossRefGoogle Scholar
Dahlin, E., Neely, A.S., Larsson, A., Backman, L., & Nyberg, L. (2008). Transfer of learning after updating training mediated by the striatum. Science, 320, 1510–1512.CrossRefGoogle ScholarPubMed
Dawant, B.M., Hartmann, S.L., Thirion, J.P., Maes, F., Vandermeulen, D., & Demaerel, P. (1999). Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations: Part I, Methodology and validation on normal subjects. IEEE Transactions on Medical Imaging, 18, 909–916.CrossRefGoogle ScholarPubMed
Driemeyer, J., Boyke, J., Gaser, C., Buchel, C., & May, A. (2008). Changes in gray matter induced by learning–revisited. PLoS One, 3, e2669.CrossRefGoogle ScholarPubMed
Dufouil, C., Alperovitch, A., & Tzourio, C. (2003). Influence of education on the relationship between white matter lesions and cognition. Neurology, 60, 831–836.CrossRefGoogle ScholarPubMed
Ericsson, K.A., & Lehmann, A.C. (1996). Expert and exceptional performance: Evidence of maximal adaptation to task constraints. Annual Review of Psychology, 47, 273–305.CrossRefGoogle ScholarPubMed
Gee, J.C., Reivich, M., & Bajcsy, R. (1993). Elastically deforming 3D atlas to match anatomical brain images. Journal of Computer Assisted Tomography, 17, 225–236.CrossRefGoogle ScholarPubMed
Grady, C.L., & Craik, F.I. (2000). Changes in memory processing with age. Current Opinion in Neurobiology, 10, 224–231.CrossRefGoogle ScholarPubMed
Green, C.S., & Bavelier, D. (2008). Exercising your brain: A review of human brain plasticity and training-induced learning. Psychology and Aging, 23, 692–701.CrossRefGoogle ScholarPubMed
Greenwood, P.M. (2007). Functional plasticity in cognitive aging: Review and hypothesis. Neuropsychology, 21, 657–673.CrossRefGoogle ScholarPubMed
Hertzog, C., Kramer, A.F., Wilson, R.S., & Lindenberger, U. (2009). Enrichment effects on adult cognitive development: Can the functional capacity of older adults be preserved and enhanced?. Psychological Science in the Public Interest, 9, 1–65.CrossRefGoogle Scholar
Iosifescu, D.V., Shenton, M.E., Warfield, S.K., Kikinis, R., Dengler, J., Jolesz, F. A., et al. (1997). An automated registration algorithm for measuring MRI subcortical brain structures. Neuroimage, 6, 13–25.CrossRefGoogle ScholarPubMed
Jastrzembski, T.S., Charness, N., & Vasyukova, C. (2006). Expertise and age effects on knowledge activation in chess. Psychology and Aging, 21, 401–405.CrossRefGoogle ScholarPubMed
Kay, G. (1995). Cogscreen aeromedical edition professional manual. Odessa, FL: Psychological Assessment resources, Inc.Google Scholar
Kraemer, H.K., Kiernan, M., Essex, M., & Kupfer, D.J. (2008). How and why criteria defining moderators and mediators differ between the Baron & Kenny and MacArthur approaches. Health Psychology, 27, S101–S108.CrossRefGoogle ScholarPubMed
Le Carret, N., Lafont, S., Mayo, W., & Fabrigoule, C. (2003). The effect of education on cognitive performances and its implication for the constitution of the cognitive reserve. Developmental Neuropsychology, 23, 317–337.CrossRefGoogle ScholarPubMed
Lindenberger, U., Scherer, H., & Baltes, P.B. (2001). The strong connection between sensory and cognitive performance in old age: Not due to sensory acuity reductions operating during cognitive assessment. Psychology and Aging, 16, 196–205.CrossRefGoogle ScholarPubMed
Luszcz, M.A., Bryan, J., & Kent, P. (1997). Predicting episodic memory performance of very old men and women: Contributions from age, depression, activity, cognitive ability, and speed. Psychology and Aging, 12, 340–351.CrossRefGoogle ScholarPubMed
Lycke, C., Specht, K., Ersland, L., & Hugdahl, K. (2008). An fMRI study of phonological and spatial working memory using identical stimuli. Scandinavian Journal of Psychology, 49, 393–301.CrossRefGoogle ScholarPubMed
MacLullich, A.M., Ferguson, K.J., Deary, I.J., Seckl, J.R., Starr, J.M., & Wardlaw, J.M. (2002). Intracranial capacity and brain volumes are associated with cognition in healthy elderly men. Neurology, 59, 169–174.CrossRefGoogle ScholarPubMed
Manly, J.J., Schupf, N., Tang, M.X., & Stern, Y. (2005). Cognitive decline and literacy among ethnically diverse elders. Journal of Geriatric Psychiatry and Neurology, 18, 213–217.CrossRefGoogle ScholarPubMed
Mori, E., Hirono, N., Yamashita, H., Imamura, T., Ikejiri, Y., Ikeda, M., et al. (1997). Premorbid brain size as a determinant of reserve capacity against intellectual decline in Alzheimer’s disease. American Journal of Psychiatry, 154, 18–24.Google ScholarPubMed
Morrow, D.G., Menard, W.E., Stine-Morrow, E.A., Teller, T., & Bryant, D. (2001). The influence of expertise and task factors on age differences in pilot communication. Psychology and Aging, 16, 31–46.CrossRefGoogle ScholarPubMed
Morrow, D.G., Miller, L.M., Ridolfo, H.E., Menard, W., Stine-Morrow, E.A., & Magnor, C. (2005). Environmental support for older and younger pilots’ comprehension of air traffic control information. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 60, P11–P18.CrossRefGoogle ScholarPubMed
Moscovitch, M., & Winocur, G. (1992). The neuropsychology of memory and aging. In Craik, F.I.M., & Salthouse, T.A. (Eds.), The handbook of aging and cognition. Hillsdale, NJ: L. Erlbaum Associates; pp. 315–371.Google Scholar
Olesen, P.J., Westerberg, H., & Klingberg, T. (2004). Increased prefrontal and parietal activity after training of working memory. Nature Neuroscience, 7, 75–79.CrossRefGoogle ScholarPubMed
Raz, N., Lindenberger, U., Rodrigue, K.M., Kennedy, K.M., Head, D., Williamson, A., et al. (2005). Regional brain changes in aging healthy adults: General trends, individual differences and modifiers. Cerebral Cortex, 15, 1676–1689.CrossRefGoogle ScholarPubMed
Richards, M., & Deary, I.J. (2005). A life course approach to cognitive reserve: A model for cognitive aging and development? Annals of Neurology, 58, 617–622.CrossRefGoogle ScholarPubMed
Salthouse, T.A. (1992). Influence of processing speed on adult age differences in working memory. Acta Psychologica, 79, 155–170.CrossRefGoogle ScholarPubMed
Salthouse, T.A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103, 403–428.CrossRefGoogle ScholarPubMed
Schaie, K.W. (1989). Perceptual speed in adulthood: Cross-sectional and longitudinal studies. Psychology and Aging, 4, 443–453.CrossRefGoogle ScholarPubMed
Sole-Padulles, C., Bartres-Faz, D., Junque, C., Vendrell, P., Rami, L., Clemente, I.C., et al. (2009). Brain structure and function related to cognitive reserve variables in normal aging, mild cognitive impairment and Alzheimer’s disease. Neurobiology of Aging, 30, 1114–1124CrossRefGoogle ScholarPubMed
Spinks, R., McKirgan, L.W., Arndt, S., Caspers, K., Yucuis, R., & Pfalzgraf, C.J. (2009). IQ estimate smackdown: Comparing IQ proxy measures to the WAIS-III. Journal of the International Neuropsychology Society, 15, 590–596.CrossRefGoogle ScholarPubMed
Springer, M.V., McIntosh, A.R., Winocur, G., & Grady, C.L. (2005). The relation between brain activity during memory tasks and years of education in young and older adults. Neuropsychology, 19, 181–192.CrossRefGoogle ScholarPubMed
Stern, Y. (2006). Cognitive reserve and Alzheimer disease. Alzheimer Disease and Associated Disorders, 20(Suppl. 2), S69–S74.CrossRefGoogle ScholarPubMed
Stern, Y. (2009). Cognitive reserve. Neuropsychologia, 47, 2015–2028.CrossRefGoogle ScholarPubMed
Studholme, C., Cardenas, V., Maudsley, A., & Weiner, M. (2003). An intensity consistent filtering approach to the analysis of deformation tensor derived maps of brain shape. Neuroimage, 19, 1638–1649.CrossRefGoogle ScholarPubMed
Studholme, C., Novotny, E., Zubal, I.G., & Duncan, J.S. (2001). Estimating tissue deformation between functional images induced by intracranial electrode implantation using anatomical MRI. Neuroimage, 13, 561–576.CrossRefGoogle ScholarPubMed
Sullivan, E.V., Rohlfing, T., & Pfefferbaum, A. (2010). Quantitative fiber tracking of lateral and interhemispheric white matter systems in normal aging: Relations to timed performance. Neurobiology of Aging, 31, 464–481.CrossRefGoogle ScholarPubMed
Taylor, J.L., Kennedy, Q., Noda, A., & Yesavage, J.A. (2007). Pilot age and expertise predict flight simulator performance: A 3-year longitudinal study. Neurology, 68, 648–654.CrossRefGoogle ScholarPubMed
Taylor, J.L., O’Hara, R., Mumenthaler, M.S., & Yesavage, J.A. (2000). Relationship of CogScreen-AE to flight simulator performance and pilot age. Aviation, Space, and Environmental Medicine, 71, 373–380.Google ScholarPubMed
Valenzuela, M.J., Jones, M., Wen, W., Rae, C., Graham, S., Shnier, R., et al. (2003). Memory training alters hippocampal neurochemistry in healthy elderly. Neuroreport, 14, 1333–1337.CrossRefGoogle ScholarPubMed
Van Leemput, K., Maes, F., Vandermeulen, D., & Suetens, P. (1999). Automated model-based tissue classification of MR images of the brain. IEEE Transactions on Medical Imaging, 18, 897–908.CrossRefGoogle Scholar
Wagner, A.D., Shannon, B.J., Kahn, I., & Buckner, R.L. (2005). Parietal lobe contributions to episodic memory retrieval. Trends in Cognitive Science, 9, 445–453.CrossRefGoogle ScholarPubMed
West, R.L. (1996). An application of prefrontal cortex function theory to cognitive aging. Psychology Bulletin, 120, 272–292.CrossRefGoogle ScholarPubMed
Yesavage, J.A., Mumenthaler, M.S., Taylor, J.L., Friedman, L., O’Hara, R., Sheikh, J., et al. (2002). Donepezil and flight simulator performance: Effects on retention of complex skills. Neurology, 59, 123–125.CrossRefGoogle ScholarPubMed
Yesavage, J.A., Taylor, J.L., Mumenthaler, M.S., Noda, A., & O’Hara, R. (1999). Relationship of age and simulated flight performance. Journal of the American Geriatric Society, 47, 819–823.CrossRefGoogle ScholarPubMed
9
Cited by

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

The impact of brain size on pilot performance varies with aviation training and years of education
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

The impact of brain size on pilot performance varies with aviation training and years of education
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

The impact of brain size on pilot performance varies with aviation training and years of education
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *