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Probability of prickliness detection in a model of populations of fiber ends prickling human skin

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Abstract

Although it is relatively easy to judge whether the fabric is prickly, and it is very difficult in quantifying human capability of detection and judgment of fabric-evoked prickliness. As so far, no mathematical relationship is proposed to measure this detectability. On the basis of the neurophysiological property of Aδ-fiber nociceptors responsible for fabricevoked prickliness and the statistical distributions of short-coarse fiber ends protruding above a fabric surface, the present study develops a model to simulate the activation probability of nociceptors, and discusses its sensitivity to the change of the typical features characterizing physical properties of coarse fiber ends and the typical physiological features of Aδ-fiber nociceptors. The results show that the developed model is significantly sensitive to the changing spatial density of Aδ-fiber nociceptors and their mechanothresholds, which reflects differences among person’s capability in detecting and judging fabric-evoked prickliness. In terms of the effect of physical features of coarse fiber ends on the detection of prickliness, the activation probability of populations of nociceptors represents a consistent phenomenon with the subjective evaluation results in literatures. Therefore, the developed model gives a good link between somatosensory physiology and physical cues to fabric-evoked prickliness, and with psychophysical method provides an alternative prediction for the detection of fabricevoked prickliness at the probabilistic level.

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References

  1. P. K. Garnsworthy, R. L. Gully, P. Kenins, and R. A. Westerman, J. Neurophysiol., 59, 1083 (1988).

    CAS  Google Scholar 

  2. G. R. S. Naylor, D. G. Phillips, C. J. Veitch, M. Dolling, and D. J. Marland, Text. Res. J., 67, 288 (1997).

    CAS  Google Scholar 

  3. G. R. S. Naylor, C. J. Veitch, R. J. Mayflcld, and R. Kettlewell, Text. Res. J., 62, 487 (1992).

    Google Scholar 

  4. L. M. Ao and C. W. Yu, J. Donghua University (Natural Science), 33, 756 (2007).

    Google Scholar 

  5. C. J. Veitch and G. R. S. Naylor, Wool Technol. Sheep Breed., 40, 31 (1992).

    Google Scholar 

  6. J. Hu, Y. Li, X. Ding, and J. Y. Hu, Cognitive Neurodynamics, 5, 161 (2011).

    Article  Google Scholar 

  7. J. Hu, Y. Li, X. Ding, and J. Y. Hu, J. Text. Inst., DOI:10.1080/00405000.2010.529280, 2011.

  8. J. Hu, Y. Li, and J. Y. Hu, Fiber. Polym., 11, 790 (2010).

    Article  Google Scholar 

  9. M. Ringkamp and R. A. Meyer in “The Senses: A Comprehensive Reference” (A. I. Basbaum, M. C. Bushnell, D. V. Smith, G. K. Beauchamp, S. J. Firestein, P. Dallos, and D. Oertel Eds.), p.98, Academic Press, 2007.

  10. L.-M. Ao and C.-W. Yu, J. Donghua University (English Edition) 21, 82 (2004).

    Google Scholar 

  11. G. R. S. Naylor, Text. Res. J., 80, 537 (2010).

    Article  CAS  Google Scholar 

  12. H. Adriaensen, J. Gybels, H. O. Handwerker, and J. V. Hees, J. Neurophysiol., 49, 111 (1983).

    CAS  Google Scholar 

  13. J. V. Hees and J. G. Gybels, J. Neurol. Neurosurg. Psychiatry, 44, 600 (1981).

    Article  Google Scholar 

  14. B. Namer and H. O. Handwerker, Exp. Brain Res., 196, 163 (2009).

    Article  CAS  Google Scholar 

  15. Z. Wiesenfeld-Hallin, G. Hallinr, and A. Persson, Brain Res., 311, 375 (1984).

    Article  CAS  Google Scholar 

  16. H. Hensel, J. Neurophysiol., 23, 564 (1960).

    CAS  Google Scholar 

  17. G. Werner and V. B. Mountcastle, J. Neurophysiol., 28, 359 (1965).

    CAS  Google Scholar 

  18. B. Güçlü, Neural Computation, 19, 2638 (2007).

    Article  Google Scholar 

  19. B. Güçlü and S. J. Bolanowski, Neural Computation, 16, 39 (2004).

    Article  Google Scholar 

  20. P. C. Garell, S. L. B. McGillis, and J. D. Greenspan, J. Neurophysiol., 75, 1177 (1996).

    CAS  Google Scholar 

  21. P. R. Burgess and E. R. Perl, J. Physiol. Lond, 1967, 541 (1967).

    Google Scholar 

  22. B. Cooper, M. Ahlquist, R. M. Friedman, B. Loughner, and M. Heft, J. Neurophysiol., 66, 1272 (1991).

    CAS  Google Scholar 

  23. B. Lynn and S. E. Carpenter, Brain Res., 238, 29 (1982).

    Article  CAS  Google Scholar 

  24. Y. Q. Liu, Y. Qi, and W. D. Yu, J. Text. Res., 26, 61 (2005).

    Google Scholar 

  25. P. Kenins, J. Neurophysiol., 59, 1098 (1988).

    CAS  Google Scholar 

  26. M. Koltzenburg, C. L. Stucky, and G. R. Lewin, J. Neurophysiol., 78, 1841 (1997).

    CAS  Google Scholar 

  27. F. Xu, T. Wen, T. J. Lu, and K. A. Seffen, J. Mechan. Phys. Solids, 56, 1852 (2008).

    Article  CAS  Google Scholar 

  28. J. D. Greenspan, M. Thomadaki, and S. L. McGillis, Somatosensory & Motor Res., 14, 107 (1997).

    Article  CAS  Google Scholar 

  29. R. M. Slugg, J. N. Campbell, and R. A. Meyer, J. Neurosci., 24, 4649 (2004).

    Article  CAS  Google Scholar 

  30. M. Dolling, D. Marland, G. R. S. Naylor, and D. G. Phillips, Wool Technol. Sheep Breed., 40, 69 (1992).

    Google Scholar 

  31. G. R. S. Naylor, D. G. Phillips, and C. J. Veitch, Wool Technol. Sheep Breed., 43, 69 (1995).

    Google Scholar 

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Correspondence to Jiyong Hu.

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Hu, J., Yang, X., Ding, X. et al. Probability of prickliness detection in a model of populations of fiber ends prickling human skin. Fibers Polym 13, 79–86 (2012). https://doi.org/10.1007/s12221-012-0079-y

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  • DOI: https://doi.org/10.1007/s12221-012-0079-y

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