Skip to main content
SpringerLink
Log in

Neural Constraints on Attention

  • Chapter
  • First Online:
The Neuropsychology of Attention

  • 5251 Accesses

Abstract

Attention is constrained by processing capacity and resource limitations that differ across people and vary within the individual over time. As discussed previously, the idea that people have a limited attentional capacity had its origins in information theory that became prominent in the early 1950s and was a key assumption of early theories of selective attention. A mechanism whereby information reduction was considered to be enabling selective attention to occur in the context of prevailing capacity limitations. Support for these capacity limitations came from a large number of studies showing that for certain types of selective attention, performance decreased dramatically when information load became excessive.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mesulam, M.-M. (Ed.). (2000). Principles of behavioral neurology (2nd ed.). New York, NY: Oxford University Press.

    Google Scholar 

  2. Kandel, E. R., Schwartz, J. H., & Jessell, T. M. (2000). Principles of neural science (4th ed.). New York, NY: Health Professions Division, McGraw-Hill.

    Google Scholar 

  3. Crick, F. (1989). The recent excitement about neural networks. Nature, 337(6203), 129–132.

    Article  PubMed  Google Scholar 

  4. Crick, F. H. C., & Asanuma, C. (1986). Certain aspects of the anatomy and physiology of the cerebral cortex. In J. L. McClelland & D. E. Rumelhart (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 2). Cambridge, MA: MIT Press.

    Google Scholar 

  5. Zikopoulos, B., & Barbas, H. (2007). Parallel driving and modulatory pathways link the prefrontal cortex and thalamus. PLoS One, 2(9), e848.

    Article  PubMed  Google Scholar 

  6. Dombrowski, S. M., Hilgetag, C. C., & Barbas, H. (2001). Quantitative architecture distinguishes prefrontal cortical systems in the rhesus monkey. Cerebral Cortex, 11(10), 975–988.

    Article  PubMed  Google Scholar 

  7. Barbas, H. (2000). Connections underlying the synthesis of cognition, memory, and emotion in primate prefrontal cortices. Brain Research Bulletin, 52(5), 319–330.

    Article  PubMed  Google Scholar 

  8. Barbas, H., & Rempel-Clower, N. (1997). Cortical structure predicts the pattern of corticocortical connections. Cerebral Cortex, 7(7), 635–646.

    Article  PubMed  Google Scholar 

  9. Barbas, H., & Pandya, D. N. (1987). Architecture and frontal cortical connections of the premotor cortex (area 6) in the rhesus monkey. The Journal of Comparative Neurology, 256(2), 211–228.

    Article  PubMed  Google Scholar 

  10. Barbas, H. (1986). Pattern in the laminar origin of corticocortical connections. The Journal of Comparative Neurology, 252(3), 415–422.

    Article  PubMed  Google Scholar 

  11. Foster, K. H., Gaska, J. P., Nagler, M., & Pollen, D. A. (1985). Spatial and temporal frequency selectivity of neurones in visual cortical areas V1 and V2 of the macaque monkey. The Journal of Physiology, 365, 331–363.

    PubMed  Google Scholar 

  12. Gaska, J. P., Jacobson, L. D., & Pollen, D. A. (1988). Spatial and temporal frequency selectivity of neurons in visual cortical area V3A of the macaque monkey. Vision Research, 28(11), 1179–1191.

    Article  PubMed  Google Scholar 

  13. Gaska, J. P., Pollen, D. A., & Cavanagh, P. (1987). Diversity of complex cell responses to even- and odd-symmetric luminance profiles in the visual cortex of the cat. Experimental Brain Research. Experimentelle Hirnforschung, 68(2), 249–259.

    Google Scholar 

  14. Liu, Z., Gaska, J. P., Jacobson, L. D., & Pollen, D. A. (1992). Interneuronal interaction between members of quadrature phase and anti-phase pairs in the cat’s visual cortex. Vision Research, 32(7), 1193–1198.

    Article  PubMed  Google Scholar 

  15. Pollen, D. A., Gaska, J. P., & Jacobson, L. D. (1990). Physiological constraints on models of visual cortical function. In R. M. J. Cotterill (Ed.), Models of brain function. New York, NY: Cambridge University Press.

    Google Scholar 

  16. Pollen, D. A., Gaska, J. P., & Jacobson, L. D. (1988). Responses of simple and complex cells to compound sine-wave gratings. Vision Research, 28(1), 25–39.

    PubMed  Google Scholar 

  17. Desimone, R., & Gross, C. G. (1979). Visual areas in the temporal cortex of the macaque. Brain Research, 178, 363–380.

    Article  PubMed  Google Scholar 

  18. Desimone, R., Albright, T. D., Gross, C. G., & Bruce, C. J. (1980). Responses of inferior temporal neurons to complex visual stimuli. Society for Neuroscience Abstracts, 6, 581.

    Google Scholar 

  19. Desimone, R. (1998). Visual attention mediated by biased competition in extrastriate visual cortex. Philosophical Transactions of the Royal Society of London, 353(1373), 1245–1255.

    Article  PubMed  Google Scholar 

  20. Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193–222.

    Article  PubMed  Google Scholar 

  21. Rumelhart, D., & McClelland, J. L. (1986). Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 1 & 2). Cambridge, MA: MIT Press.

    Google Scholar 

  22. Gibson, J. J. (1950). The perception of visual world. Boston, MA: Houghton Mifflin.

    Google Scholar 

  23. Gibson, J. J. (1979). Ecological approach to visual perception. Boston, MA: Houghton Mifflin.

    Google Scholar 

  24. Hinton, G. E., Sijnowski, T. J., & Ackley, D. H. (1984). Boltzmann machines: Constraint satisfaction networks that learn. Pittsburgh, PA: Department of Computer Science, Carnegie-Mellon University.

    Google Scholar 

  25. Hinton, G. E., & Sejnowski, T. J. (1986). Learning and relearning in Boltzmann machines in parallel distributed processing: Explorations in the microstructure of cognition. Cambridge, MA: MIT Press.

    Google Scholar 

  26. Grossberg, S. (1976). Adaptive pattern classification and universal recoding, II: Feedback, expectation, olfaction, and illusions. Biological Cybernetics, 23, 187–202.

    Article  PubMed  Google Scholar 

  27. Grossberg, S. (1980). How does the brain build a cognitive code? Psychological Review, 87, 1–51.

    Article  PubMed  Google Scholar 

  28. Grossberg, S., Commons, M. L., Grossberg, S., & Staddon, J. E. R. (1991). A neural network architecture for Pavlovian conditioning: Reinforcement, attention, forgetting, timing. In M. L. Commons, S. Grossberg, & J. E. R. Staddon (Eds.), Neural network models of conditioning and action (pp. 69–122). Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  29. Grossberg S, Kuperstein, M. (1989). Neural dynamics of adaptive sensory-motor control. Expanded ed. New York, NY: Pergamon Press.

    Google Scholar 

  30. Grossberg, S., Mingolla, E., & Ross, W. D. (1994). A neural theory of attentive visual search: Interactions of boundary, surface, spatial, and object representations. Psychological Review, 101(3), 470–489.

    Article  PubMed  Google Scholar 

  31. John, E. (1972). Switchboard versus statistical theories of learning and memory. Science, 11, 850–864.

    Article  Google Scholar 

  32. John, E. R. (1967). Mechanisms of memory. New York, NY: Academic Press.

    Google Scholar 

  33. Sternberg, S. (1969). The discovery of processing stages: Extensions of Donders’ method. Acta Psychologica, 30, 276–315.

    Article  Google Scholar 

  34. Hillary, F. G., Genova, H. M., Medaglia, J. D., et al. (2010). The nature of processing speed deficits in traumatic brain injury: Is less brain more? Brain Imaging and Behavior, 4(2), 141–154.

    Article  PubMed  Google Scholar 

  35. Eckert, M. A., Keren, N. I., Roberts, D. R., Calhoun, V. D., & Harris, K. C. (2010). Age-related changes in processing speed: Unique contributions of cerebellar and prefrontal cortex. Frontiers in Human Neuroscience, 4, 10.

    PubMed  Google Scholar 

  36. Yuan, H., Perdoni, C., & He, B. (2010). Relationship between speed and EEG activity during imagined and executed hand movements. Journal of Neural Engineering, 7(2), 26001.

    Article  PubMed  Google Scholar 

  37. 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.

    Article  PubMed  Google Scholar 

  38. Hoffmann, S., Tittgemeyer, M., & von Cramon, D. Y. (2007). Cognitive impairment in multiple sclerosis. Current Opinion in Neurology, 20(3), 275–280.

    Article  PubMed  Google Scholar 

  39. Anstey, K. J., Mack, H. A., Christensen, H., et al. (2007). Corpus callosum size, reaction time speed and variability in mild cognitive disorders and in a normative sample. Neuropsychologia, 45(8), 1911–1920.

    Article  PubMed  Google Scholar 

  40. Bokeriia, L. A., Golukhova, E. Z., Polunina, A. G., Davydov, D. M., & Begachev, A. V. (2005). Neural correlates of cognitive dysfunction after cardiac surgery. Brain Research. Brain Research Reviews, 50(2), 266–274.

    Article  PubMed  Google Scholar 

  41. Braver, T. S., Reynolds, J. R., & Donaldson, D. I. (2003). Neural mechanisms of transient and sustained cognitive control during task switching. Neuron, 39(4), 713–726.

    Article  PubMed  Google Scholar 

  42. Caplan, D., Waters, G., & Alpert, N. (2003). Effects of age and speed of processing on rCBF correlates of syntactic processing in sentence comprehension. Human Brain Mapping, 19(2), 112–131.

    Article  PubMed  Google Scholar 

  43. Archibald, C. J., & Fisk, J. D. (2000). Information processing efficiency in patients with multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 22(5), 686–701.

    Article  PubMed  Google Scholar 

  44. Thorpe, S., Fize, D., & Marlot, C. (1996). Speed of processing in the human visual system. Nature, 381(6582), 520–522.

    Article  PubMed  Google Scholar 

  45. Carretta, T. R., & Ree, M. J. (1996). U.S. Air Force pilot selection tests: What is measured and what is predictive? Aviation, Space, and Environmental Medicine, 67(3), 279–283.

    PubMed  Google Scholar 

  46. Chiaravalloti, N. D., & DeLuca, J. (2008). Cognitive impairment in multiple sclerosis. Lancet Neurology, 7(12), 1139–1151.

    Article  PubMed  Google Scholar 

  47. DeLuca, J., Johnson, S. K., Beldowicz, D., & Natelson, B. H. (1995). Neuropsychological impairments in chronic fatigue syndrome, multiple sclerosis, and depression. Journal of Neurology, Neurosurgery, & Psychiatry, 58(1), 38–43.

    Article  Google Scholar 

  48. Dineen, R. A., Vilisaar, J., Hlinka, J., et al. (2009). Disconnection as a mechanism for cognitive dysfunction in multiple sclerosis. Brain, 132(Pt 1), 239–249.

    PubMed  Google Scholar 

  49. 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.

    Article  Google Scholar 

  50. Oken, B. S., Flegal, K., Zajdel, D., et al. (2006). Cognition and fatigue in multiple sclerosis: Potential effects of medications with central nervous system activity. Journal of Rehabilitation Research and Development, 43(1), 83–90.

    Article  PubMed  Google Scholar 

  51. 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.

    Article  PubMed  Google Scholar 

  52. Till, C., Ghassemi, R., Aubert-Broche, B., et al. (2011). MRI correlates of cognitive impairment in childhood-onset multiple sclerosis. Neuropsychology, 25(3), 319–332.

    Article  PubMed  Google Scholar 

  53. Lydic, R., Albers, H. E., Tepper, B., & Moore-Ede, M. C. (1982). Three-dimensional structure of the mammalian suprachiasmatic nuclei: A comparative study of five species. The Journal of Comparative Neurology, 204, 225–237.

    Article  PubMed  Google Scholar 

  54. Albers, H. E., Lydic, R., Gander, P. H., & Moore-Ede, M. C. (1984). Role of the suprachiasmatic nuclei in the circadian timing system of the squirrel monkey. I. The generation of rhythmicity. Brain Research, 300, 275–284.

    Article  PubMed  Google Scholar 

  55. Cohen, R. A., & Albers, H. E. (1991). Disruption of human circadian and cognitive regulation following a discrete hypothalamic lesion: A case study. Neurology, 41(5), 726–729.

    Article  PubMed  Google Scholar 

  56. Cohen, R. A., Barnes, H. J., Jenkins, M., & Albers, H. E. (1997). Disruption of short-duration timing associated with damage to the suprachiasmatic region of the hypothalamus. Neurology, 48(6), 1533–1539.

    Article  PubMed  Google Scholar 

  57. Ivry, R. B., Keele, S.W., Diener, H. C. (1988). Dissociation of the lateral and medial cerebellum in movement timing and movement execution. Experimental brain research. Experimentelle Hirnforschung, 73(1):167–180.

    Article  PubMed  Google Scholar 

  58. Meck, W. H. (1984). Attentional bias between modalities: Effect on the internal clock, memory, and decision stages used in animal time discrimination. In J. Gibbon & L. Allan (Eds.), Timing and time perception (Annals of the New York Academy of Sciences) (pp. 528–541). New York, NY: New York Academy of Sciences.

    Google Scholar 

  59. Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes (Vol. 2). New York, NY: Academic Press.

    Google Scholar 

  60. Merzenich, M. M., Kaas, J. H., Wall, J. T., Sur, M., Nelson, R. J., & Feldman, D. J. (1983). Progression of change following median nerve section in the cortical representation of the hand in areas 3b and 1 in adult owl and squirrel monkeys. Neuroscience, 10, 639–665.

    Article  PubMed  Google Scholar 

  61. Bansal, A. K., Truccolo, W., Vargas-Irwin, C. E., & Donoghue, J. P. (2012). Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: Spikes, multiunit activity, and local field potentials. Journal of Neurophysiology, 107(5), 1337–1355.

    Article  PubMed  Google Scholar 

  62. Truccolo, W., Friehs, G. M., Donoghue, J. P., & Hochberg, L. R. (2008). Primary motor cortex tuning to intended movement kinematics in humans with tetraplegia. The Journal of Neuroscience, 28(5), 1163–1178.

    Article  PubMed  Google Scholar 

  63. Rioult-Pedotti, M. S., Donoghue, J. P., & Dunaevsky, A. (2007). Plasticity of the synaptic modification range. Journal of Neurophysiology, 98(6), 3688–3695.

    Article  PubMed  Google Scholar 

  64. Donoghue, J. P., Hochberg, L. R., Nurmikko, A. V., Black, M. J., Simeral, J. D., & Friehs, G. (2007). Neuromotor prosthesis development. Medicine and Health, Rhode Island, 90(1), 12–15.

    PubMed  Google Scholar 

  65. Hochberg, L. R., Serruya, M. D., Friehs, G. M., et al. (2006). Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature, 442(7099), 164–171.

    Article  PubMed  Google Scholar 

  66. Donoghue, J. P., Nurmikko, A., Friehs, G., & Black, M. (2004). Development of neuromotor prostheses for humans. Supplements to Clinical Neurophysiology, 57, 592–606.

    Article  PubMed  Google Scholar 

  67. Hopkins, W. F., & Johnston, D. (1984). Frequency-dependent noradrenergic modulation of long-term potentiation in the hippocampus. Science., 226, 350–352.

    Article  PubMed  Google Scholar 

  68. Grunditz, A., Holbro, N., Tian, L., Zuo, Y., & Oertner, T. G. (2008). Spine neck plasticity controls postsynaptic calcium signals through electrical compartmentalization. The Journal of Neuroscience, 28(50), 13457–13466.

    Article  PubMed  Google Scholar 

  69. Madara, J. C., & Levine, E. S. (2008). Presynaptic and postsynaptic NMDA receptors mediate distinct effects of brain-derived neurotrophic factor on synaptic transmission. Journal of Neurophysiology, 100(6), 3175–3184.

    Article  PubMed  Google Scholar 

  70. Crozier, R. A., Bi, C., Han, Y. R., & Plummer, M. R. (2008). BDNF modulation of NMDA receptors is activity dependent. Journal of Neurophysiology, 100(6), 3264–3274.

    Article  PubMed  Google Scholar 

  71. Bear, M. F., & Malenka, R. C. (1994). Synaptic plasticity: LTP and LTD. Current Opinion in Neurobiology, 4(3), 389–399.

    Article  PubMed  Google Scholar 

  72. Campbell, K. A. (1990). Plasticity in the propagation of hippocampal stimulation-induced activity: A [14C]2-deoxyglucose mapping study. Brain Research, 520(1–2), 199–207.

    Article  PubMed  Google Scholar 

  73. Stelzer, A., Slater, N. T., & ten Bruggencate, G. (1987). Activation of NMDA receptors blocks GABAergic inhibition in an in vitro model of epilepsy. Nature, 326(6114), 698–701.

    Article  PubMed  Google Scholar 

  74. Kaufer, D., Friedman, A., Seidman, S., & Soreq, H. (1998). Acute stress facilitates long-lasting changes in cholinergic gene expression. Nature, 393(6683), 373–377.

    Article  PubMed  Google Scholar 

  75. Ryan, B. K., Vollmayr, B., Klyubin, I., Gass, P., & Rowan, M. J. (2010). Persistent inhibition of hippocampal long-term potentiation in vivo by learned helplessness stress. Hippocampus, 20(6), 758–767.

    Article  PubMed  Google Scholar 

  76. Kasamatsu, T., Pettigrew, J., & Ary, M. (1979). Restoration of visual cortical plasticity by local microperfusion of norepinephrine. The Journal of Comparative Neurology, 185, 163–182.

    Article  PubMed  Google Scholar 

  77. Kasamatsu, T. (1983). Neuronal plasticity maintained by the central norepinephrine system in the cat visual cortex. In A. N. Epstein & M. James (Eds.), Progress in psychobiology and physiological psychology (pp. 1–83). New York, NY: Academic Press.

    Google Scholar 

  78. Sacchetti, B., Scelfo, B., Tempia, F., & Strata, P. (2004). Long-term synaptic changes induced in the cerebellar cortex by fear conditioning. Neuron, 42(6), 973–982.

    Article  PubMed  Google Scholar 

  79. Medina, J. F., Nores, W. L., & Mauk, M. D. (2002). Inhibition of climbing fibres is a signal for the extinction of conditioned eyelid responses. Nature, 416(6878), 330–333.

    Article  PubMed  Google Scholar 

  80. Aumann, T. D., Redman, S. J., & Horne, M. K. (2000). Long-term potentiation across rat cerebello-thalamic synapses in vitro. Neuroscience Letters, 287(2), 151–155.

    Article  PubMed  Google Scholar 

  81. Kreitzer, A. C., & Malenka, R. C. (2008). Striatal plasticity and basal ganglia circuit function. Neuron, 60(4), 543–554.

    Article  PubMed  Google Scholar 

  82. Fino, E., & Venance, L. (2011). Spike-timing dependent plasticity in striatal interneurons. Neuropharmacology, 60(5), 780–788.

    Article  PubMed  Google Scholar 

  83. Fino, E., & Venance, L. (2010). Spike-timing dependent plasticity in the striatum. Frontiers in Synaptic Neuroscience, 2, 6.

    PubMed  Google Scholar 

  84. Worgotter, F., & Porr, B. (2005). Temporal sequence learning, prediction, and control: A review of different models and their relation to biological mechanisms. Neural Computation, 17(2), 245–319.

    Article  PubMed  Google Scholar 

  85. D’Angelo, E., Mazzarello, P., Prestori, F., et al. (2011). The cerebellar network: From structure to function and dynamics. Brain Research Reviews, 66(1–2), 5–15.

    Article  PubMed  Google Scholar 

  86. D’Angelo, E., Koekkoek, S. K., Lombardo, P., et al. (2009). Timing in the cerebellum: Oscillations and resonance in the granular layer. Neuroscience, 162(3), 805–815.

    Article  PubMed  Google Scholar 

  87. Treisman, A. M. (1964). Selective attention in man. British Medical Bulletin, 20, 12–16.

    PubMed  Google Scholar 

  88. McLeod, P., & Posner, M. I. (1984). Privileged loops from perception to act. In H. B. D. Bowhius (Ed.), Attention and performance X. Hillsdale, NJ: Erlbaum.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departments of Neurology, Psychiatry and Aging, Gainesville, FL, USA

    Ronald A. Cohen PhD, ABPP, ABCN (Professor, Director, Adjunct Professor)

  2. Center for Cognitive Aging and Memory, University of Florida College of Medicine, Gainesville, FL, USA

    Ronald A. Cohen PhD, ABPP, ABCN (Professor, Director, Adjunct Professor)

  3. Department of Psychiatry and Human Behavior Warren Alpert School of Medicine, Brown University, Providence, RI, USA

    Ronald A. Cohen PhD, ABPP, ABCN (Professor, Director, Adjunct Professor)

Authors
  1. Ronald A. Cohen PhD, ABPP, ABCN
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

Cohen, R.A. (2014). Neural Constraints on Attention. In: The Neuropsychology of Attention. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-72639-7_22

Download citation

  • DOI: https://doi.org/10.1007/978-0-387-72639-7_22

  • Published:

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-72638-0

  • Online ISBN: 978-0-387-72639-7

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation