Advertisement

Deficitary Nervous Excitability and Subjective Contraction of Time: Time-Dispersive Model

  • Isabel Gonzalo-Fonrodona
  • Miguel A. Porras
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7930)

Abstract

A time-dispersive model of the nervous tissue is used to explain temporal aspects of perception, in relation to the observations and research made by J. Gonzalo [Dinámica Cerebral. Red Temát. Tec. Comput. Natural/Artificial, Univ. Santiago de Compostela, Spain 2010] on human cases with deficitary nervous excitation due to loss of neural mass. In these cases, the cerebral system is less excitable, has a lower reaction time and a slower decay of the cerebral excitation than in a normal case. The model considers the macroscopic excitation response of the neural network involved in the cerebral integration, and contains essential ingredients such as permeability to the excitation of the network and its reaction time. This model accounts for the observed shortening of the perceived duration of a stimulus, the perceived reduction of the lapse between two events, and the lower discrimination between repeated stimuli, when the network of the cerebral system is deficitary. Temporal summation is involved in these effects. The deficit of excitation makes the system more permeable to perception improvement by the presence of other type of stimulus (cross-modal effect due to multisensory integration), or by increasing the intensity of the stimulus, i. e., the cerebral excitation, in which case the subjective contraction of time tends to disappear.

Keywords

time perception temporal discrimination nervous excitation temporal summation chronaxia reaction time 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gonzalo, J.: Dinámica cerebral. La actividad cerebral en función de las condiciones dinámicas de la excitabilidad nerviosa. Consejo Superior de Investigaciones Científicas, Inst. S. Ramón y Cajal, Madrid, vol. I 1945, 1950. Enlarged edition: Gonzalo, J.: Dinámica cerebral. Red Temática en Tecnologías de Computación Natural/Artificial y Universidad de Santiago de Compostela, Spain (2010)Google Scholar
  2. 2.
    Exner, S.: Ein Versuch liber die Netzhaut Peripherie als Organ der Wahrnehmung von Bewegungen. Pflügers Archiv für die Gesamte Physiologie des Menschen und der Tiere 38, 215–218 (1886)Google Scholar
  3. 3.
    Poggel, D.A., Strasburger, H.: Visual perception in space and time. Mapping the visual field of temporal resolution. Acta Neurobiologiae Experimentalis 64, 427–436 (2004)Google Scholar
  4. 4.
    Poggel, D.A., Calmanti, C., Treutwein, B., Strasburger, H.: The Tölz Temporal Topography Study: Mapping the visual field across the life span: Part I. The topography of light detection and temporalinformation processing. Attention, Perception and Psychophysicss 74, 1114–1132 (2012)CrossRefGoogle Scholar
  5. 5.
    Poggel, D.A., Treutwein, B., Calmanti, C., Strasburger, H.: Increasing the temporal grain: Double-pulse resolution is affected by the size of the attention focus. Vision Research 46, 2998–3008 (2006)CrossRefGoogle Scholar
  6. 6.
    Strasburger, H., Rentschler, I., Jüttner, M.: Peripheral vision and pattern recognition: review. Journal of Vision 11(5), 13, 1–82 (2011)Google Scholar
  7. 7.
    Gonzalo, J.: Las funciones cerebrales humanas según nuevos datos y bases fisiológicas: Una introducción a los estudios de Dinámica Cerebral. Trab. Inst. Cajal Invest. Biol. 44, 95–157 (1952); Included as Gonzalo, J.: Dinámica Cerebral, suplem. I. Red Temática en Tecnologías de Computación Natural/Artificial y Universidad de Santiago de Compostela, Spain (2010)Google Scholar
  8. 8.
    Gonzalo, J.: Dinámica Cerebral. Red Temática en Tecnologías de Computación Natural/Artificial y Universidad de Santiago de Compostela, Spain (2010)Google Scholar
  9. 9.
    Bender, M.B., Teuber, H.L.: Neuro-ophthalmology. In: Spiegel III, E.A. (ed.) Progress in Neurology and Psychiatry, ch. 8, pp. 163–182 (1948)Google Scholar
  10. 10.
    Critchley, M.D.: The Parietal lobes, Arnold, London (1953)Google Scholar
  11. 11.
    Delgado, A.E.: Modelos Neurocibernéticos de Dinámica Cerebral. Ph.D.Thesis. E.T.S. de Ingenieros de Telecomunicación. Univ. Politécnica, Madrid (1978)Google Scholar
  12. 12.
    Mira, J., Delgado, A.E., Moreno-Díaz, R.: The fuzzy paradigm for knowledge representation in cerebral dynamics. Fuzzy Sets and Systems 23, 315–330 (1987)CrossRefGoogle Scholar
  13. 13.
    Mira, J., Manjarrés, A., Ros, S., Delgado, A.E., Álvarez, J.R.: Cooperative Organization of Connectivity Patterns and Receptive Fields in the Visual Pathway: Application to Adaptive Thresholdig. In: Sandoval, F., Mira, J. (eds.) IWANN 1995. LNCS, vol. 930, pp. 15–23. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  14. 14.
    Gonzalo, I., Gonzalo, A.: Functional gradients in cerebral dynamics: The J. Gonzalo theories of the sensorial cortex. In: Moreno-Díaz, R., Mira, J. (eds.) Brain Processes, Theories and Models. An Int. Conf. in Honor of W.S. McCulloch 25 Years After his Death, pp. 78–87. MIT Press, Massachusetts (1996)Google Scholar
  15. 15.
    Gonzalo, I.: Allometry in the J. Gonzalo’s model of the sensorial cortex. In: Cabestany, J., Mira, J., Moreno-Díaz, R. (eds.) IWANN 1997. LNCS, vol. 1240, pp. 169–177. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  16. 16.
    Gonzalo, I., Porras, M.A.: Time-dispersive effects in the J. Gonzalo’s research on cerebral dynamics. In: Mira, J., Prieto, A.G. (eds.) IWANN 2001. LNCS, vol. 2084, pp. 150–157. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  17. 17.
    Gonzalo-Fonrodona, I.: Inverted or tilted perception disorder. Revista de Neurología 44, 157–165 (2007)Google Scholar
  18. 18.
    Gonzalo-Fonrodona, I., Porras, M.A.: Physiological Laws of Sensory Visual System in Relation to Scaling Power Laws in Biological Neural Networks. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2007. LNCS, vol. 4527, pp. 96–102. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  19. 19.
    Gonzalo-Fonrodona, I.: Functional gradients through the cortex, multisensory integration and scaling laws in brain dynamics. Neurocomputing 72, 831–838 (2009)CrossRefGoogle Scholar
  20. 20.
    Gonzalo-Fonrodona, I., Porras, M.A.: Scaling effects in crossmodal improvement of visual perception by motor system stimulus. Neurocomputing (2012), doi:10.1016/j.neucom.2012.06.047Google Scholar
  21. 21.
    Pascual-Leone, A., Hamilton, R.: The metamodal organization of the brain. In: Casanova, C., Ptito, M. (eds.) Progress in Brain Research, vol. 134, pp. 1–19. Elsevier, Amsterdam (2001)Google Scholar
  22. 22.
    Pascual-Leone, A., Amedi, A., Fregni, F., Merabet, L.: The plastic human brain cortex. Ann. Rev. Neurosci. 28, 377–401 (2005)CrossRefGoogle Scholar
  23. 23.
    Ghazanfar, A.A., Schroeder, C.E.: Is neocortex essentially multisensory? Trends. Cogn. Sci. 10, 278–285 (2006)CrossRefGoogle Scholar
  24. 24.
    Laurienti, P.J., Burdette, J.H., Maldjian, J.A., Wallace, M.T.: Enhanced multisensory integration in older adults. Neurobiol. Aging 27, 1155–1163 (2006)CrossRefGoogle Scholar
  25. 25.
    Martuzzi, R., Murray, M.M., Michel, C.M., Thiran, J.P., Maeder, P.P., Clarke, S., Meuli, R.A.: Multisensory interactions within human primary cortices revealed by BOLD dynamics. Cereb. Cortex 17, 1672–1679 (2007)CrossRefGoogle Scholar
  26. 26.
    Stein, B.E., Stanford, T.R., Ramachandran, R., Perrault Jr., T.J., Rowland, B.A.: Challenges in quantifying multisensory integration: alternative criteria, models, and inverse effectiveness. Exp. Brain Res. 198, 113–126 (2009)CrossRefGoogle Scholar
  27. 27.
    Hertz, U., Amedi, A.: Disentangling unisensory and multisensory components in audiovisual integration using a novel multifrequency fMRI spectral analysis. NeuroImage 52, 617–632 (2010)CrossRefGoogle Scholar
  28. 28.
    Shams, L., Kim, R.: Crossmodal influences on visual perception. Phys. Life. Rev. 7, 269–284 (2010)CrossRefGoogle Scholar
  29. 29.
    Lakhani, B., Vette, A.H., Mansfield, A., et al.: Electrophysiological Correlates of Changes in Reaction Time Based on Stimulus Intensity. PLoS ONE 7(5), e36407 (2012), doi:10.1371/journal.pone.0036407Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Isabel Gonzalo-Fonrodona
    • 1
  • Miguel A. Porras
    • 2
  1. 1.Departamento de Óptica, Facultad de Ciencias FísicasUniversidad Complutense de MadridMadridSpain
  2. 2.Departamento de Física Aplicada y Grupo de Sistemas ComplejosUniversidad Politécnica de MadridMadridSpain

Personalised recommendations