Reverse correlation analysis of chromatic contrast perception based on chromatic mechanism models

Abstract

We applied the classification image (CI) method to examine the effects of heterochromatic noise on color perception. Moreover, rather than the typical CI analysis procedure, we analyzed the CI data based on chromatic mechanism models. The stimulus was a superposition image of two uniformly colored squares (signal images) and a multicolored texture (a noise image), whose colors were chosen from an isoluminant plane of the Derrington-Krauskopf-Lennie color space. The observers judged the relative chromatic contrasts of the two signal squares on the different noise textures. The CI showed strong color modulations, whose color directions differed from the signal. Additionally, the model analysis demonstrated that the model with four mechanisms and the cardinal mechanisms model were not inferior to the model with more mechanisms with regard to explaining our experiment data; the cardinal model’s fit with the observer’s behaviors was improved by simply adjusting the relative sensitivities of the four mechanisms.

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References

  1. 1)

    V. C. Smith and J. Pokorny: Vision Res. 15 (1975) 161.

    Article  Google Scholar 

  2. 2)

    A. M. Derrington, J. Krauskopf, and P. Lennie: J. Physiol. 357 (1984) 241.

    Article  Google Scholar 

  3. 3)

    V. C. Smith, B. B. Lee, J. Pokorny, P. R. Martin, and A. Valberg: J. Physiol. 458 (1992) 191.

    Article  Google Scholar 

  4. 4)

    J. Krauskopf, D. R. Williams, and D. W. Heeley: Vision Res. 22 (1982) 1123.

    Article  Google Scholar 

  5. 5)

    R. M. Boynton and N. Kambe: Color Res. Appl. 5 (1980) 13.

    Article  Google Scholar 

  6. 6)

    P. Lennie, J. Krauskopf, and G. Sclar: J. Neurosci. 10 (1990) 649.

    Google Scholar 

  7. 7)

    G. J. Brouwer and D. J. Heeger: J. Neurosci. 29 (2009) 13992.

    Article  Google Scholar 

  8. 8)

    E. Goddard, D. J. Mannion, J. S. McDonald, S. G. Solomon, and C. W. G. Clifford: J. Vision 10 (2010) 25.

    Article  Google Scholar 

  9. 9)

    M. A. Webster and J. D. Mollon: Nature 349 (1991) 235.

    Article  ADS  Google Scholar 

  10. 10)

    M. A. Webster and J. D. Mollon: Vision Res. 34 (1994) 1993.

    Article  Google Scholar 

  11. 11)

    I. Kuriki: J. Opt. Soc. Am. A 24 (2007) 1858.

    Article  ADS  Google Scholar 

  12. 12)

    K. R. Gegenfurtner and D. C. Kiper: J. Opt. Soc. Am. A 9 (1992) 1880.

    Article  ADS  Google Scholar 

  13. 13)

    A. Li and P. Lennie: Vision Res. 37 (1997) 83.

    Article  Google Scholar 

  14. 14)

    M. D’Zmura and K. Knoblauch: Vision Res. 38 (1998) 3117.

    Article  Google Scholar 

  15. 15)

    N. Goda and M. Fujii: Vision Res. 41 (2001) 2475.

    Article  Google Scholar 

  16. 16)

    T. Hansen and K. R. Gegenfurtner: J. Vision 6 (2006) 5.

    Google Scholar 

  17. 17)

    M. J. Sankeralli and K. T. Mullen: J. Opt. Soc. Am. A 14 (1997) 2633.

    Article  ADS  Google Scholar 

  18. 18)

    F. Giulianini and R. T. Eskew, Jr.: Vision Res. 38 (1998) 3913.

    Article  Google Scholar 

  19. 19)

    J. Krauskopf and K. Gegenfurtner: Vision Res. 32 (1992) 2165.

    Article  Google Scholar 

  20. 20)

    M. J. Sankeralli and K. T. Mullen: J. Opt. Soc. Am. A 16 (1999) 2625.

    Article  ADS  Google Scholar 

  21. 21)

    T. Hansen, M. Giesel, and K. R. Gegenfurtner: J. Vision 8 (2008) 2.

    Article  Google Scholar 

  22. 22)

    M. Giesel, T. Hansen, and K. R. Gegenfurtner: J. Vision 9 (2009) 11.

    Article  Google Scholar 

  23. 23)

    B. L. Beard and A. J. Ahumada, Jr.: Proc. SPIE 3299 (1998) 79.

    Article  ADS  MATH  Google Scholar 

  24. 24)

    C. K. Abbey and M. P. Eckstein: J. Vision 2 (2002) 5.

    Article  Google Scholar 

  25. 25)

    J. M. Gold, R. F. Murray, P. J. Bennett, and A. B. Sekuler: Curr. Biol. 10 (2000) 663.

    Article  Google Scholar 

  26. 26)

    L. L. Kontsevich and C. W. Tyler: Vision Res. 44 (2004) 1493.

    Article  Google Scholar 

  27. 27)

    R. Bouet and K. Knoblauch: Visual Neurosci. 21 (2004) 283.

    Article  Google Scholar 

  28. 28)

    T. Hansen and K. R. Gegenfurtner: J. Opt. Soc. Am. A 22 (2005) 2081.

    Article  ADS  Google Scholar 

  29. 29)

    E. Brenner, J. J. M. Granzier, and J. B. J. Smeets: Vision Res. 47 (2007) 2557.

    Article  Google Scholar 

  30. 30)

    R. T. Eskew, Jr.: Vision Res. 49 (2009) 2686.

    Article  Google Scholar 

  31. 31)

    R. F. Murray: J. Vision 11 (2011) 2.

    Article  Google Scholar 

  32. 32)

    D. H. Brainard: Spatial Vision 10 (1997) 433.

    Article  Google Scholar 

  33. 33)

    D. G. Pelli: Spatial Vision 10 (1997) 437.

    Article  Google Scholar 

  34. 34)

    D. I. MacLeod and R. M. Boynton: J. Opt. Soc. Am. A 69 (1979) 1183.

    Article  ADS  Google Scholar 

  35. 35)

    K. Knoblauch and L. T. Maloney: J. Vision 8 (2008) 10.

    Article  Google Scholar 

  36. 36)

    T. Hansen and K. Gegenfurtner: J. Vision 13 (2013) 1010.

    Article  Google Scholar 

  37. 37)

    B. R. Conway: J. Neurosci. 21 (2001) 2768.

    Google Scholar 

  38. 38)

    J. R. Newton and R. T. Eskew: Visual Neurosci. 20 (2003) 511.

    Article  Google Scholar 

  39. 39)

    K. T. Mullen: Vision Res. 31 (1991) 119.

    Article  Google Scholar 

  40. 40)

    K. T. Mullen and F. A. A. Kingdom: Vision Res. 36 (1996) 1995.

    Article  Google Scholar 

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Correspondence to Takehiro Nagai.

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Sato, T., Nagai, T. & Nakauchi, S. Reverse correlation analysis of chromatic contrast perception based on chromatic mechanism models. OPT REV 21, 526–540 (2014). https://doi.org/10.1007/s10043-014-0083-0

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Keywords

  • color vision
  • higher order chromatic mechanisms
  • classification images