Advertisement

Processing Speed and Attentional Resources

  • Ronald A. Cohen
Chapter

Abstract

The brain’s transmission rate and speed of information processing influence the quantity of information that can be evaluated and consequently constrain attentional capacity. Processing speed, as measured by reaction time, was studied intermittently as an indicator of individual differences in mental function in the late nineteenth century, but this line of investigation was essentially abandoned until the 1970s [1].

Keywords

Processing Speed Attentional Blink Cognitive Resource Choice Reaction Time Intellectual Performance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Vernon, P. A. (1987). Speed of information-processing and intelligence. Norwood, NJ: Ablex.Google Scholar
  2. 2.
    Hirst, W. (1986). The psychology of attention. In J. LeDoux & W. Hirst (Eds.), Mind and brain: Dialogues in cognitive neuroscience (pp. 105–141). New York: Cambridge University.Google Scholar
  3. 3.
    Schneider, W., & Shiffrin, R. M. (1977). Controlled and automatic human information processing: I. Detection, search, and attention. Psychological Review, 84, 1–66.Google Scholar
  4. 4.
    Kahneman, D. (1973). Attention and effort. Englewood Cliffs: Prentice-Hall.Google Scholar
  5. 5.
    Fracker, M. L., & Wickens, C. D. (1989). Resources, confusions, and compatibility in dual-axis tracking: Displays, controls, and dynamics. Journal of Experimental Psychology. Human Perception and Performance, 15(1), 80–96.PubMedGoogle Scholar
  6. 6.
    Wickens, C. D., & Liu, Y. (1988). Codes and modalities in multiple resources: A success and a qualification. Human Factors, 30(5), 599–616.PubMedGoogle Scholar
  7. 7.
    Wickens, C. D., Mountford, S. J., & Schreiner, W. (1981). Multiple resources, task-hemispheric integrity, and individual differences in time-sharing. Human Factors, 23(2), 211–229.PubMedGoogle Scholar
  8. 8.
    Wickens, C. D., & Kessel, C. (1980). Processing resource demands of failure detection in dynamic systems. Journal of Experimental Psychology. Human Perception and Performance, 6(3), 564–577.PubMedGoogle Scholar
  9. 9.
    Stern, Y. (2009). Cognitive reserve. Neuropsychologia, 47(10), 2015–2028.PubMedGoogle Scholar
  10. 10.
    Stern, Y., Zarahn, E., Habeck, C., et al. (2008). A common neural network for cognitive reserve in verbal and object working memory in young but not old. Cerebral Cortex, 18(4), 959–967.PubMedGoogle Scholar
  11. 11.
    Stern, Y. (2006). Cognitive reserve and Alzheimer disease. Alzheimer Disease and Associated Disorders, 20(2), 112–117.PubMedGoogle Scholar
  12. 12.
    Strauss, M. E., & Fritsch, T. (2004). Factor structure of the CERAD neuropsychological battery. Journal of International Neuropsychological Society, 10(4), 559–565.Google Scholar
  13. 13.
    Scarmeas, N., Zarahn, E., Anderson, K. E., et al. (2004). Cognitive reserve-mediated modulation of positron emission tomographic activations during memory tasks in Alzheimer disease. Archives of Neurology, 61(1), 73–78.PubMedGoogle Scholar
  14. 14.
    Kaplan, R. F., Cohen, R. A., Moscufo, N., et al. (2009). Demographic and biological influences on cognitive reserve. Journal of Clinical and Experimental Neuropsychology, 31(7), 868–876.PubMedGoogle Scholar
  15. 15.
    Stern, R. A., Silva, S. G., Chaisson, N., & Evans, D. L. (1996). Influence of cognitive reserve on neuropsychological functioning in asymptomatic human immunodeficiency virus-1 infection. Archives of Neurology, 53(2), 148–153.PubMedGoogle Scholar
  16. 16.
    Roldan-Tapia, L., Garcia, J., Canovas, R., & Leon, I. (2012). Cognitive reserve, age, and their relation to attentional and executive functions. Applied Neuropsychology, 19(1), 2–8.PubMedGoogle Scholar
  17. 17.
    Alosco, M. L., Spitznagel, M. B., Raz, N., et al. (2012). Cognitive reserve moderates the association between heart failure and cognitive impairment. Journal of Clinical and Experimental Neuropsychology, 34(1), 1–10.PubMedGoogle Scholar
  18. 18.
    Shannon, C. E. (1997). The mathematical theory of communication. 1963. MD Computing, 14(4), 306–317.PubMedGoogle Scholar
  19. 19.
    Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. Urbana: University of Illinois Press.Google Scholar
  20. 20.
    Broadbent, D. E. (1958). Perception and communication (p. 1958). London: Pergamon Press.Google Scholar
  21. 21.
    Eysenck, H. J., & Berger, M. (1982). A model for intelligence. Berlin, New York: Springer.Google Scholar
  22. 22.
    Eysenck, H. J., & Fulker, D. W. (2007). The structure & measurement of intelligence. New Brunswick, NJ: Transaction Publishers.Google Scholar
  23. 23.
    Jensen, A. R. (1993). Spearman’s g: Links between psychometrics and biology. Annals of the New York Academy of Sciences, 702, 103–129.PubMedGoogle Scholar
  24. 24.
    Jenson, A. R. (1982). Reaction time and psychometric g. In H. J. Eysenk (Ed.), A model for intelligence. New York: Springer.Google Scholar
  25. 25.
    Jenson, A. R., & Vernon, P. A. (1986). Jensen’s reaction time studies: A reply to Longstreth. Intelligence, 10, 153–179.Google Scholar
  26. 26.
    Detterman, D. K. (1987). What does reaction time tell us about intelligence? In P. A. Vernon (Ed.), Speed of information processing and intelligence (pp. 177–200). Norwood, NJ: Ablex.Google Scholar
  27. 27.
    van der Meer, E., Beyer, R., Horn, J., et al. (2010). Resource allocation and fluid intelligence: Insights from pupillometry. Psychophysiology, 47(1), 158–169.PubMedGoogle Scholar
  28. 28.
    Ryan, J. J., Sattler, J. M., & Lopez, S. J. (2000). Age effects on Wechsler Adult Intelligence Scale-III subtests. Archives of Clinical Neuropsychology, 15(4), 311–317.PubMedGoogle Scholar
  29. 29.
    Gunther, V. K., Schafer, P., Holzner, B. J., & Kemmler, G. W. (2003). Long-term improvements in cognitive performance through computer-assisted cognitive training: A pilot study in a residential home for older people. Aging & Mental Health, 7(3), 200–206.Google Scholar
  30. 30.
    Zimprich, D., & Martin, M. (2002). Can longitudinal changes in processing speed explain longitudinal age changes in fluid intelligence? Psychology and Aging, 17(4), 690–695.PubMedGoogle Scholar
  31. 31.
    Borella, E., Carretti, B., Riboldi, F., & De Beni, R. (2010). Working memory training in older adults: Evidence of transfer and maintenance effects. Psychology and Aging, 25(4), 767–778.PubMedGoogle Scholar
  32. 32.
    Chen, T., & Li, D. (2007). The roles of working memory updating and processing speed in mediating age-related differences in fluid intelligence. Neuropsychology, Development, and Cognition. Section B, Aging, Neuropsychology and Cognition, 14(6), 631–646.PubMedGoogle Scholar
  33. 33.
    Clay, O. J., Edwards, J. D., Ross, L. A., et al. (2009). Visual function and cognitive speed of processing mediate age-related decline in memory span and fluid intelligence. Journal of Aging and Health, 21(4), 547–566.PubMedGoogle Scholar
  34. 34.
    Colzato, L. S., Spape, M., Pannebakker, M. M., & Hommel, B. (2007). Working memory and the attentional blink: Blink size is predicted by individual differences in operation span. Psychonomic Bulletin & Review, 14(6), 1051–1057.Google Scholar
  35. 35.
    Finkel, D., Reynolds, C. A., McArdle, J. J., & Pedersen, N. L. (2007). Age changes in processing speed as a leading indicator of cognitive aging. Psychology and Aging, 22(3), 558–568.PubMedGoogle Scholar
  36. 36.
    Mogle, J. A., Lovett, B. J., Stawski, R. S., & Sliwinski, M. J. (2008). What’s so special about working memory? An examination of the relationships among working memory, secondary memory, and fluid intelligence. Psychological Science, 19(11), 1071–1077.PubMedGoogle Scholar
  37. 37.
    Perrotin, A., Tournelle, L., & Isingrini, M. (2008). Executive functioning and memory as potential mediators of the episodic feeling-of-knowing accuracy. Brain and Cognition, 67(1), 76–87.PubMedGoogle Scholar
  38. 38.
    Salthouse, T. A., Fristoe, N., McGuthry, K. E., & Hambrick, D. Z. (1998). Relation of task switching to speed, age, and fluid intelligence. Psychology and Aging, 13(3), 445–461.PubMedGoogle Scholar
  39. 39.
    Schretlen, D., Pearlson, G. D., Anthony, J. C., et al. (2000). Elucidating the contributions of processing speed, executive ability, and frontal lobe volume to normal age-related differences in fluid intelligence. Journal of International Neuropsychological Society, 6(1), 52–61.Google Scholar
  40. 40.
    Shelton, J. T., Elliott, E. M., Matthews, R. A., Hill, B. D., & Gouvier, W. D. (2010). The relationships of working memory, secondary memory, and general fluid intelligence: Working memory is special. Journal of Experimental Psychology. Learning, Memory, and Cognition, 36(3), 813–820.PubMedGoogle Scholar
  41. 41.
    Swanson, H. L. (2004). Working memory and phonological processing as predictors of children’s mathematical problem solving at different ages. Memory & Cognition, 32(4), 648–661.Google Scholar
  42. 42.
    Norman, D., & Bobrow, D. A. (1975). On data-limited and resource-limited processes. Cognitive Psychology, 7, 44–64.Google Scholar
  43. 43.
    Davranche, K., Nazarian, B., Vidal, F., & Coull, J. (2011). Orienting attention in time activates left intraparietal sulcus for both perceptual and motor task goals. Journal of Cognitive Neuroscience, 23(11), 3318–3330.PubMedGoogle Scholar
  44. 44.
    Mulder, H., Pitchford, N. J., & Marlow, N. (2011). Processing speed mediates executive function difficulties in very preterm children in middle childhood. Journal of International Neuropsychological Society, 28, 1–10.Google Scholar
  45. 45.
    Channon, S., Mockler, C., & Lee, P. (2005). Executive functioning and speed of processing in phenylketonuria. Neuropsychology, 19(5), 679–686.PubMedGoogle Scholar
  46. 46.
    Channon, S., German, E., Cassina, C., & Lee, P. (2004). Executive functioning, memory, and learning in phenylketonuria. Neuropsychology, 18(4), 613–620.PubMedGoogle Scholar
  47. 47.
    Ruff, R. M., Niemann, H., Allen, C. C., Farrow, C. E., & Wylie, T. (1992). The Ruff 2 and 7 selective attention test: A neuropsychological application. Perceptual and Motor Skills, 75(3 Pt 2), 1311–1319.PubMedGoogle Scholar
  48. 48.
    Corbetta, M., Miezin, F. M., Dobmeyer, S., Shulman, G. L., & Petersen, S. E. (1991). Selective and divided attention during visual discriminations of shape, color, and speed: Functional anatomy by positron emission tomography. Journal of Neuroscience, 11(8), 2383–2402.PubMedGoogle Scholar
  49. 49.
    Walsh, D. A. (1988). Aging and visual information processing: Potential implications for everyday seeing. Journal of the American Optometric Association, 59(4 Pt 1), 301–306.PubMedGoogle Scholar
  50. 50.
    Wickens, C. D., Braune, R., & Stokes, A. (1987). Age differences in the speed and capacity of information processing: 1. A dual-task approach. Psychology and Aging, 2(1), 70–78.PubMedGoogle Scholar
  51. 51.
    Visser, T. A., & Ohan, J. L. (2012). How does information processing speed relate to the attentional blink? PloS One, 7(3), e33265.PubMedGoogle Scholar
  52. 52.
    Swearer, J. M., & Kane, K. J. (1996). Behavioral slowing with age: Boundary conditions of the generalized slowing model. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 51(4), P189–P200.PubMedGoogle Scholar
  53. 53.
    Salthouse, T. A. (1982). Adult cognition: An experimental psychology of human aging. New York: Springer.Google Scholar
  54. 54.
    Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103(3), 403–428.PubMedGoogle Scholar
  55. 55.
    Salthouse, T. A. (2009). Decomposing age correlations on neuropsychological and cognitive variables. Journal of International Neuropsychological Society, 15(5), 650–661.Google Scholar
  56. 56.
    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), 118–127.PubMedGoogle Scholar
  57. 57.
    Salthouse, T. A., Pink, J. E., & Tucker-Drob, E. M. (2008). Contextual analysis of fluid intelligence. Intelligence, 36(5), 464–486.PubMedGoogle Scholar
  58. 58.
    Cassel, W., Stephan, S., Ploch, T., & Peter, J. H. (1989). [Psychological aspects of sleep related disorders of respiratory control]. Pneumologie, 43(Suppl 1), 625–629.PubMedGoogle Scholar
  59. 59.
    Kirasic, K. C., Allen, G. L., Dobson, S. H., & Binder, K. S. (1996). Aging, cognitive resources, and declarative learning. Psychology and Aging, 11(4), 658–670.PubMedGoogle Scholar
  60. 60.
    Marshall, P. S., Forstot, M., Callies, A., Peterson, P. K., & Schenck, C. H. (1997). Cognitive slowing and working memory difficulties in chronic fatigue syndrome. Psychosomatic Medicine, 59(1), 58–66.PubMedGoogle Scholar
  61. 61.
    Byrne, M. D. (1998). Taking a computational approach to aging: The SPAN theory of working memory. Psychology and Aging, 13(2), 309–322.PubMedGoogle Scholar
  62. 62.
    Anderson, V. A., Godber, T., Smibert, E., Weiskop, S., & Ekert, H. (2004). Impairments of attention following treatment with cranial irradiation and chemotherapy in children. Journal of Clinical and Experimental Neuropsychology, 26(5), 684–697.PubMedGoogle Scholar
  63. 63.
    Andersson, S., Lovdahl, H., & Malt, U. F. (2010). Neuropsychological function in unmedicated recurrent brief depression. Journal of Affective Disorders, 125(1–3), 155–164.PubMedGoogle Scholar
  64. 64.
    Ballard, C., Stephens, S., Kenny, R., Kalaria, R., Tovee, M., & O’Brien, J. (2003). Profile of neuropsychological deficits in older stroke survivors without dementia. Dementia and Geriatric Cognitive Disorders, 16(1), 52–56.PubMedGoogle Scholar
  65. 65.
    Rao, S. M. (1995). Neuropsychology of multiple sclerosis. Current Opinion in Neurology, 8(3), 216–220.PubMedGoogle Scholar
  66. 66.
    Rohlf, H., Jucksch, V., Gawrilow, C., et al. (2012). Set shifting and working memory in adults with attention-deficit/hyperactivity disorder. Journal of Neural Transmission, 119(1), 95–106.PubMedGoogle Scholar
  67. 67.
    Stenneken, P., Egetemeir, J., Schulte-Korne, G., Muller, H. J., Schneider, W. X., & Finke, K. (2011). Slow perceptual processing at the core of developmental dyslexia: A parameter-based assessment of visual attention. Neuropsychologia, 49(12), 3454–3465.PubMedGoogle Scholar
  68. 68.
    Strang-Karlsson, S., Andersson, S., Paile-Hyvarinen, M., et al. (2010). Slower reaction times and impaired learning in young adults with birth weight <1500 g. Pediatrics, 125(1), e74–e82.PubMedGoogle Scholar
  69. 69.
    Stroup, S., Appelbaum, P., Swartz, M., et al. (2005). Decision-making capacity for research participation among individuals in the CATIE schizophrenia trial. Schizophrenia Research, 80(1), 1–8.PubMedGoogle Scholar
  70. 70.
    Birren, J. E., Woods, A. M., & Williams, M. V. (1980). Behavioral slowing with age: Causes, organization and consequences. In L. W. Poon (Ed.), Aging in the 1980’s. Washington: American Psychological Association.Google Scholar
  71. 71.
    Batista, S., Zivadinov, R., Hoogs, M., et al. (2012). Basal ganglia, thalamus and neocortical atrophy predicting slowed cognitive processing in multiple sclerosis. Journal of Neurology, 259(1), 139–146.PubMedGoogle Scholar
  72. 72.
    De Sonneville, L. M., Boringa, J. B., Reuling, I. E., Lazeron, R. H., Ader, H. J., & Polman, C. H. (2002). Information processing characteristics in subtypes of multiple sclerosis. Neuropsychologia, 40(11), 1751–1765.PubMedGoogle Scholar
  73. 73.
    Lazeron, R. H., de Sonneville, L. M., Scheltens, P., Polman, C. H., & Barkhof, F. (2006). Cognitive slowing in multiple sclerosis is strongly associated with brain volume reduction. Multiple Sclerosis (Houndmills, Basingstoke, England), 12(6), 760–768.Google Scholar
  74. 74.
    Rao, S. M., St Aubin-Faubert, P., & Leo, G. J. (1989). Information processing speed in patients with multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 11(4), 471–477.PubMedGoogle Scholar
  75. 75.
    Urbanek, C., Weinges-Evers, N., Bellmann-Strobl, J., et al. (2010). Attention Network Test reveals alerting network dysfunction in multiple sclerosis. Multiple Sclerosis (Houndmills, Basingstoke, England), 16(1), 93–99.Google Scholar
  76. 76.
    Arrondo, G., Alegre, M., Sepulcre, J., Iriarte, J., Artieda, J., & Villoslada, P. (2009). Abnormalities in brain synchronization are correlated with cognitive impairment in multiple sclerosis. Multiple Sclerosis (Houndmills, Basingstoke, England), 15(4), 509–516.Google Scholar
  77. 77.
    Scherer, P., Bauer, H., & Baum, K. (1997). Alternate finger tapping test in patients with migraine. Acta Neurologica Scandinavica, 96(6), 392–396.PubMedGoogle Scholar
  78. 78.
    Paul, R. H., Beatty, W. W., Schneider, R., Blanco, C., & Hames, K. (1998). Impairments of attention in individuals with multiple sclerosis. Multiple Sclerosis (Houndmills, Basingstoke, England), 4(5), 433–439.Google Scholar
  79. 79.
    Sweet, L. H., Vanderhill, S. D., Jerskey, B. A., Gordon, N. M., Paul, R. H., & Cohen, R. A. (2010). Subvocal articulatory rehearsal during verbal working memory in multiple sclerosis. Neurocase, 16(5), 418–425.PubMedGoogle Scholar
  80. 80.
    Chiaravalloti, N. D., & DeLuca, J. (2008). Cognitive impairment in multiple sclerosis. Lancet Neurology, 7(12), 1139–1151.PubMedGoogle Scholar
  81. 81.
    DeLuca, J., Genova, H. M., Hillary, F. G., & Wylie, G. (2008). Neural correlates of cognitive fatigue in multiple sclerosis using functional MRI. Journal of the Neurological Sciences, 270(1–2), 28–39.PubMedGoogle Scholar
  82. 82.
    DeLuca, J., Johnson, S. K., & Natelson, B. H. (1993). Information processing efficiency in chronic fatigue syndrome and multiple sclerosis. Archives of Neurology, 50(3), 301–304.PubMedGoogle Scholar
  83. 83.
    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 International Neuropsychological Society, 15(3), 383–393.Google Scholar
  84. 84.
    Paul, R. H., Ernst, T., Brickman, A. M., et al. (2008). Relative sensitivity of magnetic resonance spectroscopy and quantitative magnetic resonance imaging to cognitive function among nondemented individuals infected with HIV. Journal of International Neuropsychological Society, 14(5), 725–733.Google Scholar
  85. 85.
    Paul, R. H., Yiannoutsos, C. T., Miller, E. N., et al. (2007). Proton MRS and neuropsychological correlates in AIDS dementia complex: Evidence of subcortical specificity. The Journal of Neuropsychiatry and Clinical Neurosciences, 19(3), 283–292.PubMedGoogle Scholar
  86. 86.
    Paul, R., Cohen, R., Navia, B., & Tashima, K. (2002). Relationships between cognition and structural neuroimaging findings in adults with human immunodeficiency virus type-1. Neuroscience and Biobehavioral Reviews, 26(3), 353–359.PubMedGoogle Scholar
  87. 87.
    Cohen, R. A., Boland, R., Paul, R., et al. (2001). Neurocognitive performance enhanced by highly active antiretroviral therapy in HIV-infected women. AIDS (London, England), 15(3), 341–345.Google Scholar
  88. 88.
    Harezlak, J., Buchthal, S., Taylor, M., et al. (2011). Persistence of HIV-associated cognitive impairment, inflammation, and neuronal injury in era of highly active antiretroviral treatment. AIDS (London, England), 25(5), 625–633.Google Scholar
  89. 89.
    Gongvatana, A., Cohen, R. A., Correia, S., et al. (2011). Clinical contributors to cerebral white matter integrity in HIV-infected individuals. Journal of Neurovirology, 17(5), 477–486.PubMedGoogle Scholar
  90. 90.
    Cohen, R. A., de la Monte, S., Gongvatana, A., et al. (2011). Plasma cytokine concentrations associated with HIV/hepatitis C coinfection are related to attention, executive and psychomotor functioning. Journal of Neuroimmunology, 233(1–2), 204–210.PubMedGoogle Scholar
  91. 91.
    Gongvatana, A., Schweinsburg, B. C., Taylor, M. J., et al. (2009). White matter tract injury and cognitive impairment in human immunodeficiency virus-infected individuals. Journal of Neurovirology, 15(2), 187–195.PubMedGoogle Scholar
  92. 92.
    Cohen, R. A., Poppas, A., Forman, D. E., et al. (2009). Vascular and cognitive functions associated with cardiovascular disease in the elderly. Journal of Clinical and Experimental Neuropsychology, 31(1), 96–110.PubMedGoogle Scholar
  93. 93.
    Haley, A. P., Sweet, L. H., Gunstad, J., et al. (2007). Verbal working memory and atherosclerosis in patients with cardiovascular disease: An fMRI study. Journal of Neuroimaging, 17(3), 227–233.PubMedGoogle Scholar
  94. 94.
    Jefferson, A. L., Tate, D. F., Poppas, A., et al. (2007). Lower cardiac output is associated with greater white matter hyperintensities in older adults with cardiovascular disease. Journal of American Geriatrics Society, 55(7), 1044–1048.Google Scholar
  95. 95.
    Hoth, K. F., Tate, D. F., Poppas, A., et al. (2007). Endothelial function and white matter hyperintensities in older adults with cardiovascular disease. Stroke; a journal of cerebral circulation, 38(2), 308–312.PubMedGoogle Scholar
  96. 96.
    Haley, A. P., Forman, D. E., Poppas, A., et al. (2007). Carotid artery intima-media thickness and cognition in cardiovascular disease. International Journal of Cardiology, 121(2), 148–154.PubMedGoogle Scholar
  97. 97.
    Jefferson, A. L., Paul, R. H., Ozonoff, A., & Cohen, R. A. (2006). Evaluating elements of executive functioning as predictors of instrumental activities of daily living (IADLs). Archives of Clinical Neuropsychology, 21(4), 311–320.PubMedGoogle Scholar
  98. 98.
    Jefferson, A. L., Poppas, A., Paul, R. H., & Cohen, R. A. (2007). Systemic hypoperfusion is associated with executive dysfunction in geriatric cardiac patients. Neurobiology of Aging, 28(3), 477–483.PubMedGoogle Scholar
  99. 99.
    Paul, R. H., Haque, O., Gunstad, J., et al. (2005). Subcortical hyperintensities impact cognitive function among a select subset of healthy elderly. Archives of Clinical Neuropsychology, 20(6), 697–704.PubMedGoogle Scholar
  100. 100.
    Paul, R., Garrett, K., & Cohen, R. (2003). Vascular dementia: A diagnostic conundrum for the clinical neuropsychologist. Applied Neuropsychology, 10(3), 129–136.PubMedGoogle Scholar
  101. 101.
    Cohen, R. A., Paul, R. H., Ott, B. R., et al. (2002). The relationship of subcortical MRI hyperintensities and brain volume to cognitive function in vascular dementia. Journal of International Neuropsychological Society, 8(6), 743–752.Google Scholar
  102. 102.
    Moser, D. J., Cohen, R. A., Paul, R. H., et al. (2001). Executive function and magnetic resonance imaging subcortical hyperintensities in vascular dementia. Neuropsychiatry, Neuropsychology, and Behavioral Neurology, 14(2), 89–92.PubMedGoogle Scholar
  103. 103.
    Cohen, R. A., O’Donnell, B. F., Meadows, M. E., Moonis, M., Stone, W. F., & Drachman, D. A. (1995). ERP indices and neuropsychological performance as predictors of functional outcome in dementia. Journal of Geriatric Psychiatry and Neurology, 8(4), 217–225.PubMedGoogle Scholar
  104. 104.
    Goodin, D. S., Squires, K. C., & Starr, A. (1978). Long latency event-related components of the auditory evoked potential in dementia. Brain, 101, 635–648.PubMedGoogle Scholar
  105. 105.
    Goodin, D. S., Starr, A., Chippendale, T., & Squires, K. C. (1983). Sequential changes in the P3 component of the auditory evoked potential in confusional states and dementing illnesses. Neurology, 33(9), 1215–1218.PubMedGoogle Scholar
  106. 106.
    Pfefferbaum, A., Ford, J. M., Roth, W. T., & Kopell, B. S. (1980). Age differences in P3-reaction time associations. Electroencephalography and Clinical Neurophysiology, 49, 257–265.PubMedGoogle Scholar
  107. 107.
    Pfefferbaum, A., Ford, J. M., White, P. M., & Roth, W. T. (1989). P3 in schizophrenia is affected by stimulus modality, response requirements, medication status, and negative symptoms. Archives of General Psychiatry, 46(11), 1035–1044.PubMedGoogle Scholar
  108. 108.
    Polich, J., Ladish, C., & Bloom, F. E. (1990). P300 assessment of early Alzheimer’s disease. Electroencephalography and Clinical Neurophysiology, 77(3), 179–189.PubMedGoogle Scholar
  109. 109.
    Polich, J., Moore, A. P., & Wiederhold, M. D. (1995). P300 assessment of chronic fatigue syndrome. Journal of Clinical Neurophysiology, 12(2), 186–191.PubMedGoogle Scholar
  110. 110.
    Polich, J., Pollock, V. E., & Bloom, F. E. (1994). Meta-analysis of P300 amplitude from males at risk for ­alcoholism. Psychological Bulletin, 115(1), 55–73.PubMedGoogle Scholar
  111. 111.
    Syndulko, K., Hansch, E. C., Cohen, S. N., et al. (1982). Long-latency event-related potentials in normal aging and dementia. Advances in Neurology, 32, 279–285.PubMedGoogle Scholar
  112. 112.
    Hansch, E. C., Syndulko, K., Cohen, S. N., Goldberg, Z. I., Potvin, A. R., & Tourtellotte, W. W. (1982). Cognition in Parkinson disease: An event-related potential perspective. Annals of Neurology, 11(6), 599–607.PubMedGoogle Scholar
  113. 113.
    O’Donnell, B. F., Squires, N. K., Martz, M. J., Chen, J. R., & Phay, A. J. (1987). Evoked potential changes and neuropsychological performance in Parkinson’s disease. Biological Psychology, 24(1), 23–37.PubMedGoogle Scholar
  114. 114.
    Homberg, V., Hefter, H., Granseyer, G., et al. (1986). Event-related potentials in patients with Huntington’s disease and relative at-risk in relation to detailed psychometry. Electroencephalography and Clinical Neurophysiology, 63, 552–569.PubMedGoogle Scholar
  115. 115.
    Goodin, D. S., Aminoff, M. J., Kutukcu, Y., & Marks, W. J., Jr. (1999). Order effects in response times of parkinsonian patients and normal controls. Muscle & Nerve, 22(5), 567–572.Google Scholar
  116. 116.
    Kutukcu, Y., Marks, W. J., Jr., Goodin, D. S., & Aminoff, M. J. (1999). Simple and choice reaction time in Parkinson’s disease. Brain Research, 815(2), 367–372.PubMedGoogle Scholar
  117. 117.
    Kutukcu, Y., Marks, W. J., Jr., Goodin, D. S., & Aminoff, M. J. (1998). Cerebral accompaniments to simple and choice reaction tasks in Parkinson’s disease. Brain Research, 799(1), 1–5.PubMedGoogle Scholar
  118. 118.
    Goodin, D. S., & Aminoff, M. J. (1987). The distinction between different types of dementia using evoked potentials. Electroencephalography and Clinical Neurophysiology. Supplement, 40, 695–698.PubMedGoogle Scholar
  119. 119.
    Goodin, D. S., & Aminoff, M. J. (1986). Electrophysiological differences between subtypes of dementia. Brain, 109(Pt 6), 1103–1113.PubMedGoogle Scholar
  120. 120.
    O’Donnell, B. F., Cohen, R. A., Hokama, H., et al. (1993). Electrical source analysis of auditory ERPs in medial temporal lobe amnestic syndrome. Electroencephalography and Clinical Neurophysiology, 87(6), 394–402.PubMedGoogle Scholar
  121. 121.
    Kraiuhin, C., Gordon, E., Meares, R., & Howson, A. (1986). Psychometrics and event-related potentials in the diagnosis of dementia. Journal of Gerontology, 41(2), 154–162.PubMedGoogle Scholar
  122. 122.
    Gordon, E., Kraiuhin, C., Stanfield, P., Meares, R., & Howson, A. (1986). The prediction of normal P3 latency and the diagnosis of dementia. Neuropsychologia, 24(6), 823–830.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Ronald A. Cohen
    • 1
    • 2
    • 3
  1. 1.Departments of Neurology, Psychiatry and AgingGainesvilleUSA
  2. 2.Center for Cognitive Aging and MemoryUniversity of Florida College of MedicineGainesvilleUSA
  3. 3.Department of Psychiatry and Human Behavior Warren Alpert School of MedicineBrown UniversityProvidenceUSA

Personalised recommendations