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NIH Toolbox Cognitive Battery (NIHTB-CB): The NIHTB Pattern Comparison Processing Speed Test

Published online by Cambridge University Press:  24 June 2014

Noelle E. Carlozzi*
Affiliation:
Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, Michigan
David S. Tulsky
Affiliation:
Rusk Institute/Department of Rehabilitation Medicine, Department of Orthopedic Surgery, Department of General Medicine, New York University, New York, New York Spinal Cord Injury Laboratory, Neuropsychology and Neuroscience Laboratory, Kessler Foundation, New Jersey
Nancy D. Chiaravalloti
Affiliation:
Neuropsychology and Neuroscience Laboratory, Traumatic Brain Injury Laboratory, Kessler Foundation, West Orange, New Jersey
Jennifer L. Beaumont
Affiliation:
Department of Medical Social Sciences, Northwestern University, Chicago, Illinois
Sandra Weintraub
Affiliation:
Department of Psychiatry and Cognitive Neurology and Alzheimer’s Disease Center, Northwestern, University, Chicago, Illinois
Kevin Conway
Affiliation:
National Institute on Drug Abuse, Washington, District of Columbia
Richard C. Gershon
Affiliation:
Department of Medical Social Sciences, Northwestern University, Chicago, Illinois
*
Correspondence and reprint requests to: Noelle E. Carlozzi, Department of Physical Medicine and Rehabilitation, University of Michigan, North Campus Research Complex, Building 14, 2800 Plymouth Road, Ann Arbor, Michigan 48109-2800. E-mail: carlozzi@med.umich.edu.

Abstract

The NIH Toolbox (NIHTB) Pattern Comparison Processing Speed Test was developed to assess processing speed within the NIHTB for the Assessment of Neurological Behavior and Function Cognition Battery (NIHTB-CB). This study highlights validation data collected in adults ages 18–85 on this measure and reports descriptive data, test–retest reliability, construct validity, and preliminary work creating a composite index of processing speed. Results indicated good test–retest reliability. There was also evidence for both convergent and discriminant validity; the Pattern Comparison Processing Speed Test demonstrated moderate significant correlations with other processing speed tests (i.e., WAIS-IV Coding, Symbol Search and Processing Speed Index), small significant correlations with measures of working memory (i.e., WAIS-IV Letter-Number Sequencing and PASAT), and non-significant correlations with a test of vocabulary comprehension (i.e., PPVT-IV). Finally, analyses comparing and combining scores on the NIHTB Pattern Comparison Processing Speed Test with other measures of simple reaction time from the NIHTB-CB indicated that a Processing Speed Composite score performed better than any test examined in isolation. The NIHTB Pattern Comparison Processing Speed Test exhibits several strengths: it is appropriate for use across the lifespan (ages, 3–85 years), it is short and easy to administer, and it has high construct validity. (JINS, 2014, 20, 1–12)

Type
Special Series
Copyright
Copyright © The International Neuropsychological Society 2014 

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References

Disclosures

Dr. Carlozzi is funded by NIH grants R03NS065194, R01NR013658, R01NS077946, U01NS056975. She was previously funded by contracts H133B090024, B6237R, H133G070138, H133A070037-08A and a grant from the NJ Department of Health and Senior Services.Google Scholar
Dr. Tulsky is funded by NIH contracts H133B090024, H133N060022, H133G070138, B6237R, cooperative agreement U01AR057929, and grant, R01HD054659. He has received consultant fees from the Institute for Rehabilitation and Research, Frazier Rehabilitation Institute/Jewish Hospital, Craig Hospital, and Casa Colina Centers for Rehabilitation.Google Scholar
Dr. Chiaravalloti is funded by the National Multiple Sclerosis Society (NMSS; PP1952), the National Institutes of Health (NIH; R01NR013658; R01AG032088), the National Institute on Disability and Rehabilitation Research (NIDRR: H133A120030) the New Jersey Commission on Spinal Cord Injury Research (CSCR13IRG018) and the New Jersey Commission on Brain Injury Research (CBIR12IRG004 to support research unrelated to this project.Google Scholar
Ms. Beaumont served as a consultant for NorthShore University HealthSystem, FACIT.org, and Georgia Gastroenterology Group PC. She received funding for travel as an invited speaker at the North American Neuroendocrine Tumor Symposium.Google Scholar
Dr. Weintraub is funded by NIH grants # R01DC008552, P30AG013854, and the Ken and Ruth Davee Foundation and conducts clinical neuropsychological evaluations (35% effort) for which her academic-based practice clinic bills. She serves on the editorial board of Dementia & Neuropsychologia and advisory boards of the Turkish Journal of Neurology and Alzheimer’s and Dementia.Google Scholar
Dr. Conway reports no disclosures.Google Scholar
Dr. Gershon has received personal compensation for activities as a speaker and consultant with Sylvan Learning, Rockman, and the American Board of Podiatric Surgery. He has several grants awarded by NIH: N01-AG-6-0007, 1U5AR057943-01, HHSN260200600007, 1U01DK082342-01, AG-260-06-01, HD05469, NINDS: U01 NS 056 975 02, NHLBI K23: K23HL085766 NIA; 1RC2AG036498-01; NIDRR: H133B090024, OppNet: N01-AG-6-0007.Google Scholar
Disclaimer: The views and opinions expressed in this report are those of the authors and should not be construed to represent the views of NIH or any of the sponsoring organizations, agencies, or the U.S. government.Google Scholar

REFERENCES

Ball, K., & Vance, D.E. (2008). Everyday life applications and rehabilitation of processing speed deficits: Aging as a model for clinical populations. In J. DeLuca & J.H. Kalmar (Eds.), Information processing speed in clinical populations. New York: Taylor and Francis.Google Scholar
Barker-Collo, S.L. (2006). Quality of life in multiple sclerosis: Does information-processing speed have an independent effect? Archives of Clinical Neuropsychology, 21(2), 167174.Google Scholar
Bell, N.L., Lassiter, K.S., Matthews, T.D., & Hutchinson, M.B. (2001). Comparison of the Peabody Picture Vocabulary Test—Third Edition and Wechsler Adult Intelligence Scale—Third Edition with university students. Journal of Clinical Psychology, 57(3), 417422.Google Scholar
Camarata, S., & Woodcock, R. (2006). Sex differences in processing speed: Developmental effects in males and females. Intelligence, 34(3), 231252. doi:10.1016/j.intell.2005.12.001Google Scholar
Campbell, D.T., & Fiske, D.W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81105.CrossRefGoogle ScholarPubMed
Carlozzi, N.E., Tulsky, D.S., Kail, R.V., & Beaumont, J.L. (2013). Chapter VI. NIH Toolbox Cognition Battery (CB): Measuring processing speed. Monographs of the Society for Research in Child Development, 78(4), 88102.Google Scholar
Cerella, J., & Hale, S. (1994). The rise and fall in information-processing rates over the life span. Acta Psychologica, 86(2–3), 109197.CrossRefGoogle ScholarPubMed
Chaytor, N., & Schmitter-Edgecombe, M. (2004). Working memory and aging: A cross-sectional and longitudinal analysis using a self-ordered pointing task. Journal of the International Neuropsychological Society, 10(4), 489503.CrossRefGoogle ScholarPubMed
Chiaravalloti, N.D., Christodoulou, C., Demaree, H.A., & DeLuca, J. (2003). Differentiating simple versus complex processing speed: Influence on new learning and memory performance. Journal of Clinical and Experimental Neuropsychology, 25(4), 489501.CrossRefGoogle ScholarPubMed
Coyle, T.R., Pillow, D.R., Snyder, A.C., & Kochunov, P. (2011). Processing speed mediates the development of general intelligence (g) in adolescence. Psychological Science, 22(10), 12651269.Google Scholar
Crowe, S.F. (2000). Does the letter number sequencing task measure anything more than digit span? Assessment, 7(2), 113117.Google Scholar
DeLuca, J. (2008). How fast, how slow and how come. In J. DeLuca & J.H. Kalmar (Eds.), Information processing speed in clinical populations. New York: Taylor and Francis.Google Scholar
DeLuca, J., Barbieri-Berger, S., & Johnson, S.K. (1994). The nature of memory impairments in multiple sclerosis: Acquisition versus retrieval. Journal of Clinical and Experimental Neuropsychology, 16, 183189.Google Scholar
DeLuca, J., Chelune, G.J., Tulsky, D.S., Lengenfelder, J., & Chiaravalloti, N.D. (2004). Is speed of processing or working memory the primary information processing deficit in multiple sclerosis? Journal of Clinical and Experimental Neuropsychology, 26(4), 550562.Google Scholar
DeLuca, J., Christodoulou, C., Diamond, B.J., Rosenstein, E.D., Kramer, N., & Natelson, B.H. (2004). Working memory deficits in chronic fatigue syndrome: Differentiating between speed and accuracy of information processing. Journal of the International Neuropsychological Society, 10(1), 101109.Google Scholar
DeLuca, J., Gaudino, E.A., Diamond, B.J., Christodoulou, C., & Engel, R.A. (1998). Acquisition and storage deficits in multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 20(3), 376390.CrossRefGoogle ScholarPubMed
Demaree, H.A., DeLuca, J., Gaudino, E.A., & Diamond, B.J. (1999). Speed of information processing as a key deficit in multiple sclerosis: Implications for rehabilitation. Journal of Neurology, Neurosurgery, & Psychiatry, 67(5), 661663.Google Scholar
Dempster, F.N. (1981). Memory span - Sources of individual and developmental differences. Psychological Bulletin, 89(1), 63100.Google Scholar
Denegar, C.R., & Ball, D.W. (1993). Assessing reliability and precision of measurement: An introduction to intraclass correlation and standard error of measurement. Journal of Sport Rehabilitation, 2(1), 3542.CrossRefGoogle Scholar
Donders, J., Tulsky, D.S., & Zhu, J. (2001). Criterion validity of new WAIS-II subtest scores after traumatic brain injury. Journal of the International Neuropsychological Society, 7(7), 892898.Google Scholar
Dunn, L.M., & Dunn, D.M. (2007). Peabody Picture Vocabulary Test - Fourth Edition. Minneapolis, MN. NCS Pearson.Google Scholar
Finkel, S.I., Mintzer, J.E., Dysken, M., Krishnan, K.R., Burt, T., & McRae, T. (2004). A randomized, placebo-controlled study of the efficacy and safety of sertraline in the treatment of the behavioral manifestations of Alzheimer’s disease in outpatients treated with donepezil. International Journal of Geriatric Psychiatry, 19(1), 918.Google Scholar
Flavell, J.H. (1992). Cognitive development: Past, present, and future. Developmental Psychology, 28, 9981005.Google Scholar
Forn, C., Ripolles, P., Cruz-Gomez, A.J., Belenguer, A., Gonzalez-Torre, J.A., & Avila, C. (2013). Task-load manipulation in the Symbol Digit Modalities Test: An alternative measure of information processing speed. Brain and Cognition, 82(2), 152160.Google Scholar
Gaudino, E.A., Chiaravalloti, N.D., DeLuca, J., & Diamond, B.J. (2001). A comparison of memory performance in relapsing-remitting, primary progressive and secondary progressive, multiple sclerosis. Neuropsychiatry, Neuropsychology, and Behavioral Neurology, 14(1), 3244.Google Scholar
Genova, H.M., Hillary, F.G., Wylie, G., Rypma, B., & Deluca, J. (2009). Examination of processing speed deficits in multiple sclerosis using functional magnetic resonance imaging. Journal of the International Neuropsychological Society, 15(3), 383393.CrossRefGoogle ScholarPubMed
Gershon, R.C., Cella, D., Fox, N.A., Havlik, R.J., Hendrie, H.C., & Wagster, M.V. (2010). Assessment of neurological and behavioural function: The NIH toolbox. Lancet Neurology, 9(2), 138139.Google Scholar
Gold, J.M., Carpenter, C., Randolph, C., Goldberg, T.E., & Weinberger, D.R. (1997). Auditory working memory and Wisconsin Card Sorting Test performance in schizophrenia. Archives of General Psychiatry, 54(2), 159165.CrossRefGoogle ScholarPubMed
Gronwall, D. (1977). Paced auditory serial-addition task - Measure of recovery from concussion. Perceptual and Motor Skills, 44(2), 367373.Google Scholar
Gronwall, D., & Wrightson, P. (1981). Memory and information processing capacity after closed head injury. Journal of Neurology, Neurosurgery, and Psychiatry, 44(10), 889895.Google Scholar
Hale, S. (1990). A global developmental trend in cognitive processing speed. Child Development, 61(3), 653663.CrossRefGoogle ScholarPubMed
Han, S.D., Arfanakis, K., Fleischman, D.A., Leurgans, S.E., Tuminello, E.R., Edmonds, E.C., & Bennett, D.A. (2012). Functional connectivity variations in mild cognitive impairment: Associations with cognitive function. Journal of the International Neuropsychological Society, 18(1), 3948.Google Scholar
Haut, M.W., Kuwabara, H., Leach, S., & Arias, R.G. (2000). Neural activation during performance of number-letter sequencing. Applied Neuropsychology, 7(4), 237242.Google Scholar
Hawkins, K.A. (1998). Indicators of brain dysfunction derived from graphic representations of the WAIS-III/WMS-III Technical Manual clinical samples data: A preliminary approach to clinical utility. The Clinical Neuropsychologist, 12(4), 535551.Google Scholar
Hinkley, L.B., Marco, E.J., Findlay, A.M., Honma, S., Jeremy, R.J., Strominger, Z., … Sherr, E.H. (2012). The role of corpus callosum development in functional connectivity and cognitive processing. Public Library of Science One, 7(8), e39804.Google Scholar
Huttenlocher, P.R. (1979). Synaptic density in human frontal cortex - developmental Developmental changes and effects of aging. Brain Research, 163(2), 195205.Google Scholar
Jensen, A.R. (1982). Reaction times and psychometric g. In H. Eysenck (Ed.), A model for intelligence. Berlin: Springer-Verlag.Google Scholar
Jensen, A.R. (1993). Why is reaction time correlated with psychometric g. Current Directions in Psychological Science, 2, 5356.Google Scholar
Kail, R. (1991). Processing time declines exponentially during childhood and adolescence. Developmental Psychology, 27(2), 259266.Google Scholar
Kail, R. (2008). Speed of processing in childhood and adolescence: Nature, consequences, and implications for understanding atypical development. In J. DeLuca & J.H. Kalmar (Eds.), Information processing speed in clinical populations. New York: Taylor and Francis.Google Scholar
Kalmar, J.H., & Chiaravalloti, N.D. (2008). Information processing speed in multiple sclerosis: A primary deficit? In P.D. John DeLuca & P.D. Jessica H. Kalmar (Eds.), Information processing speed in clinical populations. New York: Taylor and Francis.Google Scholar
Kochunov, P., Coyle, T., Lancaster, J., Robin, D.A., Hardies, J., Kochunov, V., … Fox, P.T. (2010). Processing speed is correlated with cerebral health markers in the frontal lobes as quantified by neuroimaging. Neuroimage, 49(2), 11901199.Google Scholar
Leavitt, V.M., Wylie, G., Genova, H., Chiaravalloti, N., & Deluca, J. (2012). Altered effective connectivity during performance of an information processing speed task in multiple sclerosis. Multiple Sclerosis, 18(4), 409417.CrossRefGoogle ScholarPubMed
Lengenfelder, J., Bryant, D., Diamond, B.J., Kalmar, J.H., Moore, N.B., & DeLuca, J. (2006). Processing speed interacts with working memory efficiency in multiple sclerosis. Archives of Clinical Neuropsychology, 21(3), 229238.Google Scholar
Lezak, M.D. (1995). Neuropsychological assessment (3rd ed.). New York: Oxford University Press.Google Scholar
Llorente, A.M., Miller, E.N., D’EliaD’Elia, L.F., Selnes, O.A., Wesch, J., Becker, J.T., & Satz, P. (1998). Slowed information processing in HIV-1 disease. The Multicenter AIDS Cohort Study (MACS). Journal of Clinical and Experimental Neuropsychology, 20(1), 6072.Google Scholar
Mabbott, D.J., Laughlin, S.N., Rockel, M., & Bouffet, E. (2005). Age related changes in DTI measures of white matter and processing speed. Paper presented at the Organization for Human Brain Mapping, Toronto.Google Scholar
Madigan, N.K., DeLuca, J., Diamond, B.J., Tramontano, G., & Averill, A. (2000). Speed of information processing in traumatic brain injury: Modality-specific factors. Journal of Head Trauma Rehabilitation, 15(3), 943956.Google Scholar
Majeres, R.L. (1997). Sex differences in phonetic processing: Speed of identification of alphabetical sequences. Perceptual and Motor Skills, 85(3 Pt 2), 12431251.CrossRefGoogle ScholarPubMed
Majeres, R.L. (1999). Sex differences in phonological processes: Speeded matching and word reading. Memory and Cognition, 27(2), 246253.Google Scholar
Martin, T.A., Donders, J., & Thompson, E. (2000). Potential of and problems with new measures of psychometric intelligence after traumatic brain injury. Rehabilitation Psychology, 45(4), 402408.Google Scholar
O’Brien, A.R., & Tulsky, D. (2008). The history of processing speed and its relationship to intelligence. In J. DeLuca & J.H. Kalmar (Eds.), Information processing speed in clinical populations. New York: Taylor and Francis.Google Scholar
Posthuma, D., & de Geus, E. (2008). The genetics of information processing speed in humans. In J. DeLuca & J.H. Kalmar (Eds.), Information processing speed in clinical populations. New York: Taylor and Francis.Google Scholar
Ready, R.E., Baran, B., Chaudhry, M., Schatz, K., Gordon, J., & Spencer, R.M. (2011). Apolipoprotein E-e4, processing speed, and white matter volume in a genetically enriched sample of midlife adults. American Journal of Alzheimer’s Disease and Other Dementias, 26(6), 463468.Google Scholar
Salthouse, T.A. (1985). Speed of behavior and its implications for cognition. In J.E. Birren & K.W. Schaie (Eds.), Handbook of the psychology of aging (2nd ed., pp. 400426). New York: Van Nostrand Reinhold.Google Scholar
Salthouse, T.A. (1990). Cognitive competence and expertise in aging. In J.E. Birren & K.W. Schaie (Eds.), Handbook of the psychology of aging (3rd ed., pp. 310319). San Diego: Academic Press.Google Scholar
Salthouse, T.A. (1993). Speed mediation of adult age-differences in cognition. Developmental Psychology, 29(4), 722738.Google Scholar
Salthouse, T.A., Babcock, R.L., & Shaw, R.J. (1991). Effects of adult age on structural and operational capacities in working memory. Psychology and Aging, 6(1), 118127.Google Scholar
Salthouse, T.A., & Coon, V.E. (1993). Influence of task-specific processing speed on age differences in memory. Journal of Gerontology, 48(5), P245P255.Google Scholar
Sasson, E., Doniger, G.M., Pasternak, O., Tarrasch, R., & Assaf, Y. (2012). Structural correlates of cognitive domains in normal aging with diffusion tensor imaging. Brain Structure and Function, 217(2), 503515.Google Scholar
Sawamoto, N., Honda, M., Hanakawa, T., Fukuyama, H., & Shibasaki, H. (2002). Cognitive slowing in Parkinson's Parkinson’s disease: A behavioral evaluation independent of motor slowing. The Journal of Neuroscience, 22(12), 51985203.Google Scholar
Schaie, K.W. (1989). Perceptual speed in adulthood: Cross-sectional and longitudinal studies. Psychology and Aging, 4(4), 443453.Google Scholar
Schaie, K.W. (1994). The course of adult intellectual development. American Psychologist, 49(4), 304313.Google Scholar
Sherman, E.M.S., Strauss, E., & Spellacy, F. (1997). Validity of the paced auditory serial addition test (PASAT) in adults referred for neuropsychological assessment after head injury. The Clinical Neuropsychologist, 11(1), 3445.Google Scholar
Siegel, L.S. (1994). Working memory and reading: A life-span perspective. International Journal of Behavioral Development, 17, 109124.Google Scholar
Sliwinski, M., & Buschke, H. (1997). Processing speed and memory in aging and dementia. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 52(6), 308318.Google Scholar
Smith, A.M., Walker, L.A., Freedman, M.S., Berrigan, L.I. St, Pierre, J., Hogan, M.J., & Cameron, I. (2012). Activation patterns in multiple sclerosis on the Computerized Tests of Information Processing. Journal of Neurological Science, 312(1–2), 131137.Google Scholar
Sternberg, S. (1966). High-speed scanning in human memory. Science, 153(3736), 652654.Google Scholar
Strauss, E., Sherman, E.M.S., & Spreen, O. (2006). A compendium of neuropsychological tests: Administration, norms, and commentary (3rd ed.). New York: Oxford University Press.Google Scholar
Takeuchi, H., Taki, Y., Hashizume, H., Sassa, Y., Nagase, T., Nouchi, R., & Kawashima, R. (2011). Effects of training of processing speed on neural systems. The Journal of Neuroscience, 31(34), 1213912148.Google Scholar
Tiersky, L.A., Johnson, S.K., Lange, G., Natelson, B.H., & DeLuca, J. (1998). Neuropsychology of chronic fatigue syndrome: A critical review. Journal of Clinical and Experimental Neuropsychology, 19, 560586.Google Scholar
Tulsky, D.S., Saklofske, D.H., & Zhu, J. (2003). Revising a standard: An evaluation of the origin and development of the WAIS-III. In D.S. Tulsky, D.H. Saklofske, G.J. Chelune, R.K. Heaton, R.J. Ivnik, R. Bornstein, A. Prifitera & M.F. Ledbetter (Eds.), Clinical interpretation of the WAIS-III and WMS-III. San Diego, CA: Academic Press.Google Scholar
van Duinkerken, E., Klein, M., Schoonenboom, N.S., Hoogma, R.P., Moll, A.C., Snoek, F.J., … Diamant, M. (2009). Functional brain connectivity and neurocognitive functioning in patients with long-standing type 1 diabetes with and without microvascular complications: A magnetoencephalography study. Diabetes, 58(10), 23352343.CrossRefGoogle ScholarPubMed
van Duinkerken, E., Schoonheim, M.M., Sanz-Arigita, E.J., IJzerman, R.G., Moll, A.C., Snoek, F.J., … Barkhof, F. (2012). Resting-state brain networks in type 1 diabetic patients with and without microangiopathy and their relation to cognitive functions and disease variables. Diabetes, 61(7), 18141821.Google Scholar
Vernon, P.A. (1983). Speed of information processing and general intelligence. Intelligence, 7, 5370.Google Scholar
Vernon, P.A. (1987). Speed of information-processing and intelligence. Norwood, NJ: Ablex.Google Scholar
Vichinsky, E.P., Neumayr, L.D., Gold, J.I., Weiner, M.W., Rule, R.R., Truran, D., … Neuropsychological Dysfunction and Neuroimaging Adult Sickle Cell Anemia Study Group. (2010). Neuropsychological dysfunction and neuroimaging abnormalities in neurologically intact adults with sickle cell anemia. The Journal of the American Medical Association, 303(18), 18231831.Google Scholar
Wechsler, D. (2008). Wechsler Adult Intelligence Scale IV. San Antonio: Harcourt Assessment Inc.Google Scholar
Weintraub, S., Dikmen, S., Heaton, R., Tulsky, D.S., Zelazo, P.D., Slotkin, J., … Gershon, R. (Under Review). The cognition battery of the NIH toolbox for assessment of neurological and behavioral function: Validation in an adult sample. Journal of the International Neuropsychological Society.Google Scholar
Yakovlev, P.I., & Lecours, A.R. (1967). The myelogenetic cycles of regional maturation of the brain. In A. Minkowski (Ed.), Regional development of the brain in early life. Oxford: Blackwell.Google Scholar
Ystad, M., Hodneland, E., Adolfsdottir, S., Haasz, J., Lundervold, A.J., Eichele, T., & Lundervold, A. (2011). Cortico-striatal connectivity and cognition in normal aging: A combined DTI and resting state fMRI study. Neuroimage, 55(1), 2431.Google Scholar
Zelazo, P.D., Anderson, J.E., Richler, J., Wallner-Allen, K., Beaumont, J.L., Conway, K., & Weintraub, S. (In Press). NIH toolbox cognition battery (CB): Measuring executive function and attention. Journal of the International Neuropsychological Society.Google Scholar
Zimprich, D., & Martin, M. (2002). Can longitudinal changes in processing speed explain longitudinal age changes in fluid intelligence? Psychology and Aging, 17(4), 690695.CrossRefGoogle ScholarPubMed